Data Category Archives - General Assembly Blog

Data Literacy for Leaders

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For years, the importance of data has been echoed in boardroom discussions and listed on company roadmaps. Now, with 99% of businesses reporting active investment in big data and AI, it’s clear that all businesses are beginning to recognize the power of data to transform our world of work.

While all leaders recognize the needs and benefits of becoming data-driven, only 24% have successfully created a data-driven organization. That is because transformation is not considered holistically and instead leaders focus on business, tools and technology and talent in silos. Usually leaving skill acquisition amongst leaders and the broader organization for last. It’s no wonder that 67% of leaders say they are not comfortable accessing or using data.

We’ve worked with businesses, such as Bloomberg, to help them gain the skills they need to successfully leverage data within their organizations & we haven’t left leaders out of the conversation. In fact, we know that leaders are crucial to the success of data transformation efforts & just like their teams, they need to be equipped with the skills to understand and communicate with data.

Why Should I Train My Leaders on Data?

When embarking on a data transformation, we always recommend that leaders be trained as the first step in company-wide skill acquisition. We recommend this approach for a few reasons:

  • Leaders Need to Understand Their Role in Data Transformation:  Analytics can’t be something data team members do in a silo. They need to be fully incorporated into the business, rather than an afterthought. However, businesses will struggle to make that change if every leader does not understand his or her responsibility in data transformation.
  • Leadership Training Shows a Commitment to Change: According to New Vantage Partners, 92% of data transformation failures are attributed to the inability of leaders to form a data-driven culture. In order for your employees to truly become data-driven, they have to be able to see a real commitment from leaders to organizational goals and operational change. Training your leaders first sends that message that data is here to stay. 
  • Leaders Need to Be Prepared to Work With Data-Driven Teams: Increasingly, leaders are expected to make data-driven decisions that impact the success of the organization. Without literacy, leaders will continue to feel uncomfortable communicating with and using data to make decisions. This discomfort will trickle down to employees and real change will never be felt. 

Just like your broader organization, leaders cannot be expected to understand the role they play or the importance of data transformation without proper training. 

What Does Data Literacy For Leaders Look Like? 

Leaders need to be able to readily identify opportunities to use data effectively. In order to get there leaders need to:

Build a Data-Driven Mindset:

While every leader brings a wealth of experience to your org, many leaders are not data natives, and it can be a big leap to make this shift in thinking. Training leaders all at once gives you the opportunity to get your leaders on the same page and build a shared understanding and vocabulary.

So what does building a data-driven mindset look like in practice? To truly have a data-driven mindset leaders must be aware of the data landscape, as well as the opportunity of data, be mindful of biases inherent in data with an eye towards overcoming that bias, as well as being curious about how data can influence our decisions.

Leaders should walk away from training with a baseline understanding of key data concepts, a shared vocabulary, knowing how data flows through an organization and be able to pinpoint where data can have an impact in the org.

Understand the Data Life Cycle

Leaders are responsible for having oversight of every phase of the data life cycle and must be able to help teams weed out bias at any point. Without this foundation, leaders will have a hard time knowing where to invest in a data transformation and how to lead projects and teams.

All leaders should be equipped to think about and ask questions about each phase of the life cycle. For example:

  • Data Identification: What data do we have, and what form is it in? 
  • Data Generation: Where will the data come from and how reliable is the source? 
  • Data Acquisition: How will the data get from the source to us? 

It is not the role of the leader to know where all the data comes from or what gaps exist, but being able to understand what questions to ask, is important to acquire the necessary insights to inform a sound business strategy.

Get to Know the Role of Data Within the Org

In an organization that’s undergoing a data transformation, there’s no shortage of projects that could command a leader’s attention and investment. Leaders must be equipped to understand where to invest to put their plans into action.

Based on existing structure, leaders need to understand the key data roles, such as data analysts or machine learning engineers, why they are important and how they differ. Once a leader has the knowledge of the data teams, they will be able to identify the opportunity of data within their team and role.

Make Better Data-Driven Decisions

Leaders who rely on intuition alone run the huge risk of being left behind by competitors that use data-driven insights. With more and more companies adjusting to this new world order, it’s imperative that leaders become more data literate in order to make important business-sustaining decisions moving forward. 

Leaders should walk away from training with a baseline understanding of key data concepts, a shared vocabulary, knowing how data flows through an organization and be able to pinpoint where data can have an impact in the org.

Getting Started With Leadership Training 

Including data training specifically for your leaders in your data transformation efforts is crucial. While leaders are busy tackling other important business initiatives, they, just like the rest of your organization must be set up with the right skills to successfully meet the future of work. Investment in data skills for leaders will help you to forge a truly data-driven culture and business.

To learn more about how GA equips leaders and organizations to take on data transformation get in touch with us here.

Five Ways to Build Organizational Data Literacy

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Data is everywhere and in every part of your business; however, data is often left for technical teams to figure out. In recent years, data has been prioritized in digital transformation efforts, with an increasing amount of businesses striving to be data-first. Hoping to leverage new tools, technologies and hiring data analysts and scientists are often overlooking one essential fact: data is for everyone, and every employee can benefit from acquiring data skills.

Businesses who leave skills out of the equation in their data transformation efforts are further widening their skill gaps. In fact, according to Accenture, 74% of employees report feeling overwhelmed when working with data. According to Deloitte, contributors aren’t the only ones; 67% of leaders say they are not comfortable accessing or using data. It’s time to change all of this.

Perhaps this anxiety and discomfort stem from businesses misunderstanding the role every employee has in leveraging data: 

  • Leaders set the vision and use data to ensure that they are making the right business decisions. 
  • Data practitioners solve complex problems with a blend of technical ability in analytics and data science. 
  • The broader organization uses data to understand impact, communicate results, and make decisions. 

All roles can benefit from upskilling to shift mindsets, gain fluency, and build efficiencies across the business, with building literacy across the broader organization being the most urgent priority.

What does data literacy look like?

Data literacy is the ability to create, read, and analyze data, and then communicate that information and use it effectively. To do this, people must understand how data is collected, where it comes from, what it shows, how it can be used, and why it’s important. 

Being data-literate means understanding:

  • Data Culture
    • Literacy Goal:  Understanding the data lifecycle, data roles and responsibilities, and how data flows through an organization. 
  • Data Ethics & Privacy
    • Literacy Goal:   Explain why ethics and privacy are essential and understand the role each employee has to play. 
  • Data Visualizations
    • Literacy Goal:  Learn why common types of visualizations are chosen to promote certain comparisons and interpret the information. 
  • Statistics
    • Literacy Goal:  Describe data and spot trends in visualizations. 
  • Artificial Intelligence (AI)
    • Literacy Goal:  Identify opportunities to integrate AI and data science tools within your workflow.

Giving data skills to all employees will help businesses meet their loftiest data transformation goals. Training all employees comes with many benefits, such as higher decision quality and improved cross-functional communication. According to Deloitte, in companies where all employees train on analytics, 88% exceeded their business goals.

Five Ways to Build a Data-Literate Organization

1. Understand How Data is Being Used in Your Business

Shifting mindsets at the top of the org chart is essential to becoming a data-literate org. Being a role model for your employees helps build trust with your new skills — they will help you form a data-driven agenda. With the right skills, you’ll be able to prioritize projects with the most business impact.  Data literacy also helps you effectively communicate with data practitioners within your organization and help focus your contributors on the data points that truly matter.

2. Define Preferred Data Usage in Your Business 

Data is plentiful, so narrowing that data down to only the most essential points is imperative to success. Understand what data you wish to collect and track, how that data will be used, and what tools and skills are needed to leverage that data successfully. 

3. Get Leadership Buy-in Across the Business

Getting buy-in from leaders across  the business is essential to establishing a data-first culture. Any strategic initiative starts at the top, and leaders that understand the power of a strong data culture will be willing to make the tools, training, and people investments necessary to build one. 

4. Create a Training Plan

Once you know what data you wish to use, consider which skills would be the most beneficial. Remember, everyone can benefit from training. We recommend building literacy skills where there are definite gaps among leaders and across the broader organization.

5. Put New Skills Into Practice

Your plan is in place! Now, give your teams learning opportunities and explain why these skills will matter to the business’s success.After training, provide team members opportunities to practice their new skills by giving them goals directly related to using, communicating with, and becoming more data-proficient.

Continue to offer learning opportunities for those employees who wish to advance past literacy and into hard skills. Consider upskilling your data practitioners to become more efficient.

In an era of increased digitization, many businesses still don’t know how to use data to gain  critical insights and information on goals and objectives. From the intern to the C-suite, it’s more important than ever for all business members to create, read, analyze, and communicate data pertaining to these objectives. Data literacy at all levels can and should be encouraged to future proof the organization and support overall business goals. Investing in upskilling to ensure that everyone is comfortable bringing data to the table has ROIs well beyond cost. 

Thinking about building your teams’ data literacy? Learn more about how our data curriculum can help your business make this powerful pivot.

15 Data Science Projects to get you Started

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When it comes to getting a job in data science, aspiring data scientists need to act like artists. Yes, that’s correct, and what I mean by that is those looking to enter this field need to have a data science portfolio of previously completed data science projects. What better way to prove to your future data science team that you’re capable of being a data scientist than proving you can do the work?

A common problem for data science entrants is that employers want candidates with experience; but how do you get experience without having experience? If you’re looking to get that first foot in the door, it would behoove you to undertake a couple of data science projects to show future employers you’ve got what it takes to use big data to identify opportunities and succeed in the field.

The good news is that we live in a time of open and abundant data. Websites like Kaggle offer a treasure trove of free data on everything from crime statistics to Pokemon to Bitcoin and more. However, the wealth of easily accessible data can be overwhelming, which is why we’ve taken it upon ourselves to present 15 data science projects you can execute in Python to showcase and improve your skills. Our diverse collection of project ideas covers a variety of topics from Spotify songs to fake news to fraud detection and techniques such as clustering, regression, and natural language processing.

Before you dive in, be sure to adhere to these four guidelines no matter which data science projects you choose:

1. Articulate the Problem and/or Scenario

It’s not enough to do a project where you use “X” to predict “Y”; you need to add some context to your work because data science does not occur in a vacuum. Tell us what you’re trying to solve and how data science can address that. Employers want to know if you can turn a problem into a question and a question into a solution. A good place to start is to depict a real-world scenario in which your project would be useful.

2. Publish and Explain Your Work

Create a GitHub repository where you can upload your Jupyter Notebooks and data. Write a blog post in which you narrate your project from start to finish, talk about the problem or question at the heart of the project, explain your decision to clean the data in a certain way or why you decided to use a certain algorithm. Potential employers need to understand your methodology.

3. Use Domain Expertise

If you’re trying to break into a specific field such as finance, health, or sports, use your knowledge of this area to enhance your project. This could mean deriving a useful question to a pressing problem or articulating a well-thought-out interpretation of your project’s results. For example, if you’re looking to become a data scientist in the finance sector, then it would be worthwhile to show how your methods can generate a return on investment.

4. Be Creative and Different

Anyone can copy and paste code that trains a machine learning algorithm. If you want to stand out, review existing data science projects that use the same data and fill in the gaps left by them. If you’re working on a prediction project, try coming up with an unexpected variable that you think would be beneficial.

Data Science Projects

1. Titanic Data

Working on the Titanic dataset is a rite of passage in data science. It’s a useful dataset that beginners can work with to improve their feature engineering and classification skills. Try using a decision tree so you can visualize the relationships between the features and the probability of surviving the Titanic.

2. Spotify Data

Spotify has an amazing API that provides access to rich data on their entire catalog of songs. You can grab cool attributes such as a song’s acousticness, danceability, and energy. The great thing about this data source is that the project possibilities are almost endless. You can use these features to try to predict genre or popularity. One fun idea would be to try to better understand your own music, training a machine learning classifier on two sets of songs; songs you like and songs you do not.

3. Personality Data Clustering

You’ve probably heard the phrase, “There are X types of people.” Well, now you can actually find out how many types of people there really are. Using this dataset of almost 20k responses to the Big Five Personality Test, you can actually answer this question. Throw this data into a clustering algorithm such as KMeans and sort this into K number of groups. Once you decide on the optimal number of clusters, it’s incumbent on you to define each cluster. Come up with labels that add meaning to each group and don’t be afraid to use plenty of charts and graphs to support your interpretation.

4. Fake News

If you have an interest in natural language processing, building a classifier to differentiate between fake and real news is a great way to demonstrate that. Fake news is a problem that social media platforms have been struggling with for the past several years and a project that tackles this problem is a great way to show you care about solving real-world problems. Use your classifier to identify interesting insights about the patterns in fake versus real news; for example, tell us which words or phrases are most associated with fake news articles.

5. COVID-19 Dataset

There probably isn’t a more relevant use of data science than a project analyzing COVID-19. This dataset provides a wealth of information related to the pandemic. It provides a great opportunity to show off your exploratory data analysis chops. Take a deep dive into this data and through the use of data visualization unearth patterns about the rate of Covid infection by county, state, and by country.

6. Telco Customer Churn

If you’re looking for a straightforward project that is extremely applicable to the business world, then this one’s for you. Use this dataset to train a classifier that predicts customer churn. If you can show employers you know how to prevent customers from leaving their business you’ll most definitely grab their attention. Pro tip: this is a great projection to show your understanding of classification metrics besides accuracy such as precision and recall.

7. Lending Club Loans

Like the Telco project, the Lending Club loan dataset is extremely relevant to the business world. Here you can train a classifier that predicts whether or not a Lending Club loanee will pay back a loan using a wealth of information such as credit score, loan amount, and loan purpose. There are a lot of variables at your disposal, so I’d recommend starting with a handful of features and working your way up from there. See how far you can get with just the basics.

Also, this is a fairly untidy dataset that will require extensive cleaning and feature engineering, which is a good thing because that is often the case with real-world data. Be sure to explain your methodology behind preparing your dataset for the machine learning algorithm — this informs the audience of your domain expertise.

8. Breast Cancer Detection

This dataset provides a simpler classification scenario in which you can use health-related variables to predict instances of breast cancer. If you’re looking to apply your data science skills to the medical field, this is certainly worth a shot.

9. Housing Regression

If classification isn’t your thing, then might I recommend this ready-made regression project in which you can predict home prices using variables like square footage, number of bedrooms, and year built. A project such as this can help you understand the factors driving home sales and let you get creative in your feature engineering. Try to involve outside data that can serve as proxies for quality of life, education, and other things that might influence home prices. And if you want to show off your scraping skills, then you can always create your own dataset by scraping Zillow.

10. Seeds Clustering

The seeds dataset from UCI provides a simple opportunity to use clustering. Use the seven attributes to sort the 210 seeds into K number of groups. If you’re looking to go beyond KMeans, try using hierarchical clustering, which can be useful for this dataset because the low number of samples can be easily visualized with a dendrogram.

11. Credit Card Fraud Detection

Another project idea for those of you intent on using business world data is to train a classifier to predict instances of credit card fraud. The value of this project to you comes from the fact that it’s an imbalanced dataset, meaning that one class vastly outweighs the other (in this case, non-fraudulent transactions versus fraudulent). Training a model that is 99% accurate is essentially useless so it’s up to you to use non-accuracy metrics to demonstrate the success of your model.

12. AutoMPG

This is a great beginner regression project in which you can use car features to predict their fuel efficiency. Given that this data is from the past, an interesting idea you can use is to see how well this model does on data from recent cars, as a way to show how car fuel efficiency has evolved over the years.

13. World Happiness

Using data science to unlock what’s behind happiness? Maybe you can with this dataset on world happiness rankings. You can go a number of ways with this project; you can use regression to predict happiness score, cluster countries based on socio-economic characteristics, or visualize the change in happiness throughout the world from the years 2015 to 2019.

14. Political Identity

The Nationscape Data Set is an absolute goldmine of data on the demographics and political identities of Americans. If you’re a politics junkie it’ll be sure to satisfy your fix. Their most recent round of data features over 300,000 instances of data collected from extensive surveys of Americans. If you’re interested in using demographic information for political ideology or party identification this is the dataset for you. This is an especially great project to flex your domain expertise in study design, research, and conclusion. Political analysis is replete with shoddy interpretations that lack empirical data analysis and you could use this dataset to either confirm or dispel them. But be warned that this data will require plenty of cleaning, which is something you’ll need to get used to, given that’s the majority of the job.

15. Box Office Prediction

If you’re a movie buff, then we’ve got you covered with the TMDB dataset. See if you can build a workable box office revenue prediction model trained on 5000 movies worth of data. Does genre actually correlate with box office success? Can we use runtime and language to help explain the variation in the revenue? Find out the answers to those questions and more with this project.

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Why Should You Become a Data Scientist?

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Data is everywhere

The amount of data captured and recorded in 2020 is approximately 50 zettabytes, i.e., 50 followed by 21 zeros(!) and it’s constantly growing. Other than data captured from social media platforms, as individuals, we are constantly using devices that measure our health by tracking the number of footsteps, heart rate, sleep, and other physiological signals more regularly. Data analytics has helped greatly to discover patterns in our day-to-day activities and gently nudge us towards better health via everyday exercise and improving our quality of sleep. Just like how we track our health, internet sensors are used on everyday devices such as refrigerators, washing machines, internet routers, lights etc., to not only operate them remotely but also to monitor their functional health and provide analytics that help with troubleshooting in case of failure. 

Organizations are capturing data to better understand their products and help their consumers. Industrial plants today are installed with a variety of sensors (accelerometers, thermistors, pressure gauges) that constantly monitor high-valued equipment in order to track their performance and better predict downtime.  As internet users, we’ve experienced the convenience that results from capturing our browsing data — better search results on search engines, personalized recommendation on ecommerce websites, structured and organized inboxes, etc. Each of these features is an outcome of data science techniques of information retrieval and machine learning applied on big data. 

On the enterprise side, digital transformation such as digital payments and ubiquitous use of software and apps has propelled data generation. With a smart computer in every palm and a plethora of sensors both on commercial and industrial scale, the amount of data generated and captured will continue to explode. This constant generation of data drives new and innovative possibilities for organizations and their consumers through approaches and toolsets rooted in data science. 

Data science drives new possibilities

Data science is the study of data aimed towards making informed decisions.

On the one hand, monitoring health data and data analytics is guiding individuals to make better decisions towards their health goals. On the other hand, aggregation of health data at the community level in a convenient and accessible way sets the stage to conduct interdisciplinary research towards answering questions like, Does the amount of physical activity relate to our heart health? Can changes in heart rate over a period of time help predict heart disorders? Is weight loss connected with the quality of our sleep? In the past it was unimaginable to support such research with significant data points. However, today, a decade worth of such big data enables us to drive research on the parameters connected to different aspects of our health. It’s significant that this research is not restricted to laboratories and academic institutions but are instead driven by collaborative efforts between industry and academia.

Due to the infusion of such data, many traditional industries like insurance are getting disrupted. Previously, insurance premiums were calculated based on age and a single medical test that was performed at sign up. Now, there are efforts taken by life insurance providers to lower premiums through regular monitoring of their customers fitness trackers. With access to this big data, insurance providers are trying to understand and quantify health risks. The research efforts described above would drive quantifiable ways to measure overall health risk by fusing a variety of health metrics. All these new products will heavily rely on the use of advanced analytics that uses artificial intelligence and machine learning (AI/ML) techniques to develop models that predict personalized premiums. In order to drive these new possibilities for insights, the application of data science toolsets approaches goes through a rigorous process.

Data science is an interdisciplinary process

A data science process typically starts up with a business problem. Data required to solve the problem can come from multiple sources. Social media data such as text and images from social media platforms like Facebook and Instagram would be compartmentalized from enterprise data such as customer info and their transactions. However, depending on the problem to be solved, all relevant data are collected and can be fused across social media and enterprise domains to gain unique insights to solve the business problem.

A data science generalist works on different data formats and systematically analyses the data to extract insights from it. Data science can be subdivided into several specialized areas based on data format used to extract insights: (1) computer vision, i.e., field of study of image data, (2) natural language processing, i.e. analysis of textual data, (3) time-series processing, i.e. analysis of data varying in time such as stock market, sensor data, etc. 

A data scientist specialist is capable of applying advanced machine learning techniques, to convert unstructured data to structured format by extracting the relevant attributes of an entity from unstructured data with great accuracy. No other area has seen the impact of the data science generalist or the specialist more than in the product development lifecycle, across a gamut of organizations of all sizes.

Data scientist as a unifier in the product development lifecycle

The role of a data scientist spans across multiple stages of the product development process. Typically, a product development goes through the stages of envisioning, choosing different features to build and finally, designing those specific features. A data scientist is a unifier across all of these stages in the modern world. Even during the envisioning part, data analysis on the marketing data enables the decision on what features need to be built in terms of the need from the maximal number of customers and from a competitive standpoint. 

Once the feature list has been decided, the next step is designing those specific features. Typically, such design activities have been in the realm of designers and to a lesser extent developers. Traditionally, the designer designs features and then makes a judgment call based on user experience studies with a small sample size. However, what might be a good design for 10 users might not be a good design for 90 other users. In such situations, the designers’ judgment cannot necessarily address the entire user base. 

Organizations run different experiments to gather systematic data to audit the progress of the product. With data science toolsets, deriving the ground truth no longer needs to be constrained by such traditional design approaches. Based on the nature of the feature design, data from A/B experiment testing can provide input to both developers and designers alike on design options and product decisions that are optimal for the user base. 

Data science is the future

The spectrum of the data scientist’s role and contribution is vast. On one end, the data scientist can drive new possibilities through data-backed insights in areas like healthcare, suggest personalization options for users based on their needs, etc. On the other end, the data scientist can drive a cost-based discussion on which feature to design or what optimal option to choose. Data scientists are now the voices of customers throughout the product development process, and the unifiers through an interdisciplinary approach.

Just like making a presentation, editing documents and composing emails have become ubiquitous skills today, data science skills will pervasively be used across different functional roles to make business decisions. With the explosion in the amount of data, the demand for data scientists, data analysts, and big data engineers in the job market will only rise. Organizations are constantly looking for data professionals who can convert data into insights to make better decisions. A career in data science is simulating — the dynamic and ever-evolving nature of the field tied closely with current research keeps one young!

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How to Get a Job in Data Science Fast

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You want to get a data science job fast. Obviously, no one wants one to get a job slowly. But the time it takes to find a job is relative to you and your situation. When I was seeking my first data science job, I had normal just Kevin bills and things to budget for, plus a growing family who was hoping I’d get a job fast. This was different from some of my classmates, while others had their own versions of why they needed a job fast, too. I believe that when writing a how-to guide on getting a data science job quickly, we should really acknowledge that we’re talking about getting you, the reader, a job faster. Throughout this article, we’ll discuss how to get a job as a data scientist faster than you might otherwise, all things considered.

Getting a job faster is not an easy task in any industry, and getting a job faster as a data scientist has additional encumbrances. Some jobs, extremely well-paying jobs, require a nebulous skill set that most adults could acquire after several years in the professional working world. Data science is not one of those jobs. For all the talk about what a data scientist actually does, there’s a definite understanding that the set of skills necessary to successfully execute any version of the job are markedly technical, a bit esoteric, and specialized. This has pros and cons, which we’ll discuss. The community of people who aspire to join this field, as well as people already in the field, is fairly narrow which also has pros and cons.

Throughout this article, we’ll cover two main ways to speed up the time it takes to get a data science job: becoming aware of the wealth of opportunities, and increasing the likelihood that you could be considered employable.

Becoming Aware of the Wealth of Opportunities

Data science is a growing, in-demand field. See for yourself in Camm, Bowers, and Davenport’s article, “The Recession’s Impact on Analytics and Data Science” and “Why data scientist is the most promising job of 2019” by Alison DeNisco Rayome. It’s no secret however that these reports often only consider formal data science job board posts. You may have heard or already know that there exists a hidden job market. It stands to reason that if this hidden job market exists, there may also be a number of companies who have not identified their need for a data scientist yet, but likely need some portion of data science work. Here’s your action plan, assuming you already have the requisite skills to be a data scientist:

1. Find a company local to your region. This is easier if you know someone at that company, but if you don’t know anyone, just think through the industries that you’d like to build a career in. Search for several companies in those fields and consider a list of problems that might be faced by that organization, or even those industries at large.

2. Do some data work. Try to keep the scope of the project limited to something you could accomplish in one to two weekends. The idea here is not to create a thesis on some topic, but rather to add to your list of projects you can comfortably talk about in a future interview. This also does not have to be groundbreaking, bleeding edge work. Planning, setting up, and executing a hypothesis test for a company who is considering two discount rates for an upcoming sale will give you a ton more fodder for interviews over a half-baked computer vision model with no clear deliverable or impact on a business.

3. You have now done data science work. If you didn’t charge money for your services on the first run, shame on you. Charge more next time.

4. Repeat this process. The nice thing about these mini projects is that you can queue up your next potential projects while you execute the work for your current project at the same time.

Alternatively, you could consider jobs that are what I call the “yeah but there’s this thing…” type jobs. For example, let’s say you’re setting up a database for a non-profit and really that’s all they need. The thing is… it’s really your friend’s non-profit, all they need is their website to log some info into a database, and they can’t pay you. Of course you should not do things that compromise your morals or leave you feeling as though you’ve lowered your self worth in any way. Of course you’d help out your friend. Of course you would love some experience setting up a database, even if you don’t get to play with big data. Does that mean that you need to explain all of those in your next job interview? Of course not! Take the job and continue to interview for others. Do work as a data engineer. Almost everyone’s jobs have a “yeah but” element to them; it’s about whether the role will help increase your likelihood of being considered employable in the future.

Increasing the Likelihood That You Could Be Considered Employable

Thought experiment: a CTO comes to you with a vague list of Python libraries, deep learning frameworks, and several models which seem relevant to some problems your company is facing and tasks you with finding someone who can help solve those issues. Who would you turn to if you had to pick a partner in this scenario? I’ll give you a hint — you picked the person who satisfied three, maybe four criteria on what you and that team are capable of.

Recruiting in the real world is no different. Recruiters are mitigating their risk of hiring someone that won’t be able to perform the duties of the position. The way they execute is by figuring out the skills (usually indicated by demonstrated use of a particular library) necessary for the position, then finding the person who seems like they can execute on the highest number of the listed skills. In other words, a recruiter is looking to check a lot of boxes that limit the risk of you as a candidate. As a candidate, the mindset shift you need to come to terms with is that they want and need to hire someone. The recruiter is trying to find the lowest risk person, because the CTO likely has some sort of bearing on that recruiter’s position. You need to basically become the least risky hire, which makes you the best hire, amongst a pool of candidates.

There are several ways to check these boxes if you’re the recruiter. The first is obvious: find out where a group of people who successfully complete the functions of the job were trained, and then hire them. In data science, we see many candidates with training from a bootcamp, a master’s program, or PhDs. Does that mean that you need these degrees to successfully perform the function of the job? I’d argue no — it just means that people who are capable of attaining those relevant degrees are less risky to hire. Attending General Assembly is a fantastic way to show that you have acquired the relevant skills for the job.

Instead of having your resume alone speak to your skill, you can have someone in your network speak to your skills. Building a community of people who recognize your value in the field is incredibly powerful. While joining other pre-built networks is great, and opens doors to new opportunities, I’ve personally found that the communities I co-created are the strongest for me when it comes to finding a job as a data scientist. These have taken two forms: natural communities (making friends), and curated communities. Natural communities are your coworkers, friends, and fellow classmates. They become your community who can eventually speak up and advocate for you when you’re checking off those boxes. Curated communities might be a Meetup group that gathers once a month to talk about machine learning, or an email newsletter of interesting papers on Arxiv, or a Slack group you start with former classmates and data scientists you meet in the industry. In my opinion, the channel matters less, as long as your community is in a similar space as you.

Once you have the community, you can rely on them to pass things your way and you can do the same. Another benefit of General Assembly is its focus on turning thinkers into a community of creators. It’s almost guaranteed that someone in your cohort, or at a workshop or event has a similar interest as you. I’ve made contacts that passed alongside gig opportunities, and I’ve met my cofounder inside the walls of General Assembly! It’s all there, just waiting for you to act.

Regardless of what your job hunt looks like, it’s important to remember that it’s your job hunt. You might be looking for a side gig to last while you live nomadically, a job that’s a stepping stone, or a new career as a data scientist. You might approach the job hunt with a six-pack of post-graduate degrees; you might be switching from a dead end role or industry, or you might be trying out a machine learning bootcamp after finishing your PhD. Regardless of your unique situation, you’ll get a job in data science fast as long as you acknowledge where you’re currently at, and work ridiculously hard to move forward.

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What is Data Science?

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It’s been anointed “the sexiest job of the 21st century”, companies are rushing to invest billions of dollars into it, and it’s going to change the world — but what do people mean when they mention “data science”? There’s been a lot of hype about data science and deservedly so, but the excitement has helped obfuscate the fundamental identity of the field. Anyone looking to involve themselves in data science needs to understand what it actually is and is not.

In this article, we’ll lay out a deep definition of the field, complete descriptions of the data science workflow, and data science tasks used in the real world. We hope that any would-be entrants into this line of work will come away reading this article with a nuanced understanding of data science that can help them decide to enter and navigate this exciting line of work.

So What Actually is Data Science?

A quick definition of data science might be articulated as an interdisciplinary field that primarily uses statistics and computer programming to derive insights from and base decisions from a collection of information represented as numerical figures. The “science” part in data science is quite apt because data science very much follows a scientific process that involves formulating a hypothesis and using a specific toolset to confirm or dispel that hypothesis. At the end of the day, data science is about turning a problem into a question and a question into an answer and/or solution.

Tackling the meaning of data science also means interrogating the meaning of data. Data can be easily described as “information encoded as numbers” but that doesn’t tell us why it’s important. The value of data stems from the notion that data is a tangible manifestation of the intangible. Data provides solid support to aid our interpretations of the world. For example, a weather app can tell you it’s cold outside but telling you that the temperature is 38 degrees fahrenheit provides you with a stronger and specific understanding of the weather.

Data comes in two forms: qualitative and quantitative.

Qualitative data is categorical data that does not naturally come in the form of numbers, such as demographic labels that you can select on a census form to indicate gender, state, and ethnicity.

Quantitative data is numerical data that can be processed through mathematical functions; for example stock prices, sports stats, and biometric information.

Quantitative can be subdivided into smaller categories such as ordinal, discrete, and continuous.

Ordinal: A sort of qualitative and quantitative hybrid variable in which the values have a hierarchical ranking. Any sort of star rating system of reviews is a perfect example of this; we know that a four-star review is greater than a three-star review, but can’t say for sure that a four- star review is twice as good as a two-star review.

Discrete: These are countable and finite values that often appear in the form of integers. Examples include number of franchises owned by a company and number of votes cast in an election. It’s important to remember discrete variables have a finite range of numbers and can never be negative.

Continuous: Unlike discrete variables, continuous can appear in decimal form and have an infinite range of possibilities. Things like company profit, temperature, and weight can all be described as continuous. 

What Does Data Science Look Like?

Now that we’ve established a base understanding of data science, it’s time to delve into what data science actually looks like. To answer this question, we need to go over the data science workflow, which encapsulates what a data science project looks like from start to finish. We’ll touch on typical questions at the heart of data science projects and then examine an example data science workflow to see how data science was used to achieve success.

The Data Science Checklist

A good data science project is one that satisfies the following criteria:

Specificity: Derive a hypothesis and/or question that’s specific and to the point. Having a vague approach can often lead to a waste of time with no end product.

Attainability: Can your questions be answered? Do you have access to the required data? It’s easy to come up with an interesting question but if it can’t be answered then it has no value. The same goes for data, which is only useful if you can get your hands on it.

Measurability: Can what you’re applying data science to be quantified? Can the problem you’re addressing be represented in numerical form? Are there quantifiable benchmarks for success? 

As previously mentioned, a core aspect of data science is the process of deriving a question, especially one that is specific and achievable. Typical data science questions ask things like, does X predict Y and what are the distinct groups in our data? To get a sense of data science questions, let’s take a look at some business-world-appropriate ones:

  • What is the likelihood that a customer will buy this product?
  • Did we observe an increase in sales after implementing a new policy?
  • Is this a good or bad review?
  • How much demand will there be for my service tomorrow?
  • Is this the cheapest way to deliver our goods?
  • Is there a better way to segment our marketing strategies?
  • What groups of products are customers purchasing together?
  • Can we automate this simple yes/no decision?

All eight of these questions are excellent examples of how businesses use data science to advance themselves. Each question addresses a problem or issue in a way that can be answered using data science.

The Data Science Workflow

Once we’ve established our hypothesis and questions, we can now move onto what I like to call the data science workflow, a step-by-step description of a typical data science project process.

After asking a question, the next steps are:

  1. Get and Understand the Data. We obviously need to acquire data for our project, but sometimes that can be more difficult than expected if you need to scrape for it or if privacy issues are involved. Make sure you understand how the data was sampled and the population it represents. This will be crucial in the interpretation of your results.
  1. Data Cleaning and Exploration. The dirty secret of data science is that data is often quite dirty so you can expect to do significant cleaning which often involves constructing your variables in a way that makes your project doable. Get to know your data through exploratory data analysis. Establish a base understanding of the patterns in your dataset through charts and graphs.
  1. Modeling. This represents the main course of the data science process; it’s where you get to use the fancy powerful tools. In this part, you build a model that can help you answer a question such as can we predict future sales of a product from your dataset.
  1. Presentation. Now it’s time to present the results of your findings. Did you confirm or dispel your hypothesis? What are the answers to the questions you started off with? How do your results advance our understanding of the issue at hand? Articulate your project in a clear and concise manner that makes it digestible for your audience, which could be another team in your company or your company’s executives.

Data Science Workflow Example: Predicting Neonatal Infection

Now let’s parse out an example of how data science can affect meaningful real-world impact, taken from the book Big Data: A Revolution That Will Transform How We Live, Work, and Think.

We start with a problem: Children born prematurely are at high risk of developing infections, many of which are not detected until after a child is sick.

Then we turn that problem into a question: Can we detect patterns in the data that accurately predict infection before it occurs?

Next, we gather relevant data: variables such as heart rate, respiration rate, blood pressure, and more.

Then we decide on the appropriate tool: a machine learning model that uses past data to predict future outcomes.

Finally, what impact do our methods have? The model is able to predict the onset of infection before symptoms appear, thus allowing doctors to administer treatment earlier in the infection process and increasing the chances of survival for patients.

This is a fantastic example of data science in action because every step in the process has a clear and easily understandable function towards a beneficial outcome.

Data Science Tasks

Data scientists are basically Swiss Army knives, in that they possess a wide range of abilities — it’s why they’re so valuable. Let’s go over the specific tasks that data scientists typically perform on the job.

Data acquisition: For data scientists, this usually involves querying databases set up by their companies to provide easy access to reams of data. Data scientists frequently write SQL queries to retrieve data. Outside of querying databases, data scientists can use APIs or web scraping to acquire data.

Data cleaning: We touched on this before, but it can’t be emphasized enough that data cleaning will take up the vast majority of your time. Cleaning oftens means dealing with null values, dropping irrelevant variables, and feature engineering which means transforming data in a way so that it can be processed by a model.

Data visualization: Crafting and presenting visually appealing and understandable charts is a hugely valuable skill. Visualization has an uncanny ability to communicate important bits of information from a mass of data. Good data scientists will use data visualization to help themselves and their audiences better understand what’s going on.

Statistical analysis: Statistical tests are used to confirm and/or dispel a data scientist’s hypothesis. A t-test or chi-square are used to evaluate the existence of certain relationships. A/B testing is a popular use case of statistical analysis; if a team wants to know which of two website designs leads to more clicks, then an A/B test is the right solution.

Machine learning: This is where data scientists use models that make predictions based on past observations. If a bank wants to know which customers are likely to pay back loans, then they can use a machine learning model trained on past loans to answer that question.

Computer science: Data scientists need adequate computer programming skills because many of the tasks they undertake involve writing code. In addition, some data science roles require data scientists to function as software engineers because data scientists have to implement their methodologies into their company’s backend servers.

Communication: You can be a math and computer whiz, but if you can’t explain your work to a novice audience, your talents might as well be useless. A great data scientist can distill digestible insights from complex analyses for a non-technical audience, translating how a p-value or correlation score is relevant to a part of the company’s business. If your company is going to make a potentially costly or lucrative decision based on your data science work, then it’s incumbent on you to make sure they understand your process and results as much as possible.

Conclusion

We hope this article helped to demystify this exciting and increasingly important line of work. It’s pertinent to anyone who’s curious about data science — whether it’s a college student or an executive thinking about hiring a data science team — that they understand what this field is about and what it can and cannot do.

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5 High-Paying Careers That Require Data Analysis Skills

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Data-Driven-UX-Design

The term “big data” is everywhere these days, and with good reason. More products than ever before are connected to the Internet: phones, music players, DVRs, TVs, watches, video cameras…you name it. Almost every new electronic device created today is connected to the Internet in some way for some purpose.

The result of all those things connected to the Internet is data. Big, big data. What’s that mean for you? Simply put, it means if you can quickly, accurately, and intelligently sift through data and find trends, you are extremely valuable in today’s tech job market. More specifically, here are five job titles that require data analytics skills and expertise to get ahead. 

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Computer Science vs. Data Science: What is the Difference?

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Maybe you want to learn more about data science since you’ve heard it’s “the sexiest job of the 21st century.” Or maybe your software engineer friend is trying to talk you into learning computer science. Either way, both data science and computer science skills are in demand. In this article, we will cover the major differences between data science and computer science to clarify the distinction between these two fields.

Before we dive into the differences, let’s define these two sciences:

Data Science vs. Computer Science

Data science is an interdisciplinary field that uses data to extract insights and inform decisions. It’s often referred to as a combination of statistics, business acumen, and computer science. Data scientists clean, explore, analyze, and model with data using programming languages such as Python and R along with techniques such as statistical models, machine learning, and deep learning.

While it’s one part of data science, computer science is its own broader field of study involving a range of both theoretical and practical topics like data structures and algorithms, hardware and software, and information processing. It has many applications in fields like machine learning, software engineering, and mathematics.

History

While many of the topics used in data science have been around for a while, data science as a field is in its infancy. In 1974, Peter Naur defined the term “data science” in his work, Concise Survey of Computer Methods. However, even Naur couldn’t have predicted the vast amount of data that our modern world would generate on a daily basis only a few decades later. It wasn’t until the early 2000s that data science was recognized as its own field. It gained popularity in the early 2010s, leading to the field as we know it today — a blend of statistics and computer science to drive insights and make data-driven business decisions. “Data science,” “big data,” “artificial intelligence,” “machine learning,” and “deep learning” have all become buzzwords in today’s world. These are all components of data science and while trendy, they can provide practical benefits to companies. Historically, we did not have the storage capacity to hold the amount of data that we are able to collect and store today. This is one reason that data science has become a popular field only recently. The emergence of big data and the advancements in technology have paved the way for individuals and businesses to harness the power of data. While many of the tools that data scientists use have been around for many years, we have not had the software or hardware requirements to make use of these tools until recently.

Computer science, on the other hand, has been a field of study for centuries. This is one of the main differences between it and data science. Ada Lovelace is known for pioneering the field of computer science as the person who wrote the first computer algorithm in the 1840s. However, computing devices such as the abacus date back thousands of years. Computer science is a topic that has been formally researched for much longer than data science, and companies have been using computer science tools for decades. It’s an umbrella field that has numerous subdomains and applications. 

Applications

The applications of each of these fields in the industry differs as well. Computer science skills are used in many different jobs including that of a data scientist. However, common roles involving computer science skills include software engineers, computer engineers, software developers, and web developers. Two roles that use computer science, front end engineer and Java developer, ranked first and second respectively on Glassdoor’s 50 Best Jobs in America for 2020 list. While these roles do not formally require degrees, many people in these jobs hold a degree or come from a background in computer science. 

Common computer science job tasks include writing, testing, and debugging code, developing software, and designing applications. Individuals that use computer science in their roles often create new software and web applications. They need to have excellent problem solving skills and be able to write code in programming languages such as Python, Ruby, JavaScript, Java, or C#. They also need to have a fundamental understanding of how these languages work, and be well-versed in object oriented programming.

Data science is applied in job titles such as data scientist, data analyst, machine learning engineer, and data engineer. Data scientist and data engineer ranked third and sixth respectively on Glassdoor’s 50 Best Jobs in America for 2020. Individuals in these roles come from a variety of backgrounds including computer science, statistics, and mathematics. 

Common data science job tasks include cleaning and exploring data, extracting insights from data, and building and optimizing models. Data scientists analyze and reach conclusions based on data. They need to be well versed in statistics and mathematics topics including linear algebra and calculus as well as programming languages such as Python, R, and SQL. They also need to have excellent communication skills as they are often presenting insights, data visualizations, and recommendations to stakeholders.

Since computer science is one component of data science, there is often crossover in these roles and responsibilities. For example, computer science tasks like programming and debugging are used in both computer science jobs and data science jobs. Both of these fields are highly technical and require knowledge of data structures and algorithms. However, the depth of this knowledge required for computer science vs. data science varies. It’s often said that data scientists know more about statistics than a computer scientist but more about computer science than a statistician. This reinforces the interdisciplinary nature of data science.

The Use of Data

Data, or information such as numbers, text, and images, has applications in both computer science and data science. The study and use of data structures is a topic in computer science. Data structures are ways to organize, manage, and store data in ways that it can be used efficiently; a sub-domain of computer science, it allows us to store and access data in our computer’s memory. Data science benefits from data structures to access data, but the main goal of data science is to analyze and make decisions based on the data, often using statistics and machine learning.

The Future of Computer Science and Data Science

Today, all companies and industries can benefit from both of these fields. Computer scientists drive business value by developing software and tools while data scientists drive business value by answering questions and making decisions based on data. As software continues to integrate with our lives and daily routines, computer science skills will continue to be critical and in demand. As we continue to create and store vast amounts of data on a daily basis, data science skills will also continue to be critical and in demand. Both fields are constantly evolving as technology advances and both computer scientists and data scientists need to stay current with the latest tools, methods, and technologies.

The field of data science would not exist without computer science. Today, the two fields complement each other to further applications of artificial intelligence, machine learning, and personalized recommendations. Many of the luxuries that we have today — a favorite streaming service that recommends new movies, the ability to unlock our phones with facial recognition technology, or virtual home assistants that let us play our favorite music just by speaking — are made possible by computer science and made better by data science. As long as bright, motivated individuals continue to learn data science and computer science, these two fields will continue to advance technology and improve the quality of our lives.

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How is Python Used in Data Science?

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Python is a popular programming language used by both developers and data scientists. But what makes it so popular and why are so many data scientists choosing Python over other programming languages? In this article, we’ll explore the advantages of Python programming and why it’s useful for data science.

What is Python?

No, we’re not talking about the giant, tropical snake. Python is a general-purpose, high-level programming language. It supports object oriented, structured, and functional programming paradigms.

Python was created in the late 1980s by the Dutch programmer Guido van Rossum who wanted a project to fill his time over the holiday break. His goal was to create a programming language that was a descendant of the ABC programming language but would appeal to Unix/C hackers. Van Rossum writes that he chose the name Python for this language, “being in a slightly irreverent mood (and a big fan of Monty Python’s Flying Circus).”

Python went through many updates and iterations and by the year 2008, Python 3.0 was released. This was designed to fix many of the design flaws in the language, with an emphasis on removing redundant features. While this update had some growing pains as it was not backwards compatible, the new updates made way for Python as we know it today. It continues to be well-maintained and supported as a popular, open source programming language.

In “The Zen of Python,” developer Tim Peters summarizes van Rossum’s guiding principles for writing code in Python:

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one– and preferably only one –obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea — let’s do more of those!

These principles touch on some of the advantages of Python in data science. Python is designed to be readable, simple, explicit, and explainable. Even the first principle states that Python code should be beautiful. In general, Python is a great programming language for many tasks and is becoming increasingly popular for developers. But now you may be wondering, why learn Python for data science?

Why Python for Data Science?

The first of many benefits of Python in data science is its simplicity. While some data scientists come from a computer science background or know other programming languages, many come from backgrounds in statistics, mathematics, or other technical fields and may not have as much coding experience when they enter the field of data science. Python syntax is easy to follow and write, which makes it a simple programming language to get started with and learn quickly. 

In addition, there are plenty of free resources available online to learn Python and get help if you get stuck. Python is an open source language, meaning the language is open to the public and freely available. This is beneficial for data scientists looking to learn a new language because there is no up-front cost to start learning Python. This also means that there are a lot of data scientists already using Python, so there is a strong community of both developers and data scientists who use and love Python.

The Python community is large, thriving, and welcoming. Python is the fourth most popular language among all developers based on a 2020 Stack Overflow survey of nearly 65,000 developers. Python is especially popular among data scientists. According to SlashData, there are 8.2 million active Python users with “a whopping 69% of machine learning developers and data scientists now us[ing] Python (compared to 24% of them using R).”4 A large community brings a wealth of available resources to Python users. Not only are there numerous books and tutorials available, there are also conferences such as PyCon where Python users across the world can come together to share knowledge and connect. Python has created a supportive and welcoming community of data scientists willing to share new ideas and help one another. 

If the sheer number of people using Python doesn’t convince you of the importance of Python for data science, maybe the libraries available to make your data science coding easier will. A library in Python is a collection of modules with pre-built code to help with common tasks. They essentially allow us to benefit from and build on top of the work of others. In other languages, some data science tasks would be cumbersome and time consuming to code from scratch. There are countless libraries like NumPy, Pandas, and Matplotlib available in Python to make data cleaning, data analysis, data visualization, and machine learning tasks easier. Some of the most popular libraries include:

  • NumPy: NumPy is a Python library that provides support for many mathematical tasks on large, multidimensional arrays and matrices.
  • Pandas: The Pandas library is one of the most popular and easy-to-use libraries available. It allows for easy manipulation of tabular data for data cleaning and data analysis.
  • Matplotlib: This library provides simple ways to create static or interactive boxplots, scatterplots, line graphs, and bar charts. It’s useful for simplifying your data visualization tasks.
  • Seaborn: Seaborn is another data visualization library built on top of Matplotlib that allows for visually appealing statistical graphs. It allows you to easily visualize beautiful confidence intervals, distributions, and other graphs.
  • Statsmodels: This statistical modeling library builds all of your statistical models and statistical tests including linear regression, generalized linear models, and time series analysis models.
  • Scipy: Scipy is a library used for scientific computing that helps with linear algebra, optimization, and statistical tasks.
  • Requests: This is a useful library for scraping data from websites. It provides a user-friendly and responsive way to configure HTTP requests.

In addition to all of the general data manipulation libraries available in Python, a major advantage of Python in data science is the availability of powerful machine learning libraries. These machine learning libraries make data scientists’ lives easier by providing robust, open source libraries for any machine learning algorithm desired. These libraries offer simplicity without sacrificing performance. You can easily build a powerful and accurate neural network using these frameworks. Some of the most popular machine learning and deep learning libraries in Python include:

  • Scikit-learn: This popular machine learning library is a one-stop-shop for all of your machine learning needs with support for both supervised and unsupervised tasks. Some of the machine learning algorithms available are logistic regression, k-nearest neighbors, support vector machine, random forest, gradient boosting, k-means, DBSCAN, and principal component analysis.
  • Tensorflow: Tensorflow is a high-level library for building neural networks. Since it was mostly written in C++, this library provides us with the simplicity of Python without sacrificing power and performance. However, working with raw Tensorflow is not suited for beginners.
  • Keras: Keras is a popular high-level API that acts as an interface for the Tensorflow library. It’s a tool for building neural networks using a Tensorflow backend that’s extremely user friendly and easy to get started with.
  • Pytorch: Pytorch is another framework for deep learning created by Facebook’s AI research group. It provides more flexibility and speed than Keras, but since it has a low-level API, it is more complex and may be a little bit less beginner friendly than Keras. 

What Other Programming Languages are Used for Data Science?

Python is the most popular programming language for data science. If you’re looking for a new job as a data scientist, you’ll find that Python is also required in most job postings for data science roles. Jeff Hale, a General Assembly data science instructor, scraped job postings from popular job posting sites to see what was required for jobs with the title of “Data Scientist.” Hale found that Python appears in nearly 75% of all job postings. Python libraries including Tensorflow, Scikit-learn, Pandas, Keras, Pytorch, and Numpy also appear in many data science job postings.

Image source: The Most In-Demand Tech Skills for Data Scientists by Jeff Hale

R, another popular programming language for data science, appeared in roughly 55% of the job postings. While R is a useful tool for data science and has many benefits including data cleaning, data visualization, and statistical analysis, Python continues to become more popular and preferred among data scientists for a majority of tasks. In fact, the average percentage of job postings requiring R dropped by about 7% between 2018 and 2019, while Python increased in the percentage of job postings requiring the language. This isn’t to say that learning R is a waste of time; data scientists that know both of these languages can benefit from the strengths of both languages for different purposes. However, since Python is becoming increasingly popular, there’s a high chance that your team uses Python, and it’s important to use the language that your team is comfortable with and prefers.

What is the Future of Python for Data Science?

As Python continues to grow in popularity and as the number of data scientists continues to increase, the use of Python for data science will inevitably continue to grow. As we advance machine learning, deep learning, and other data science tasks, we’ll likely see these advancements available for our use as libraries in Python. Python has been well-maintained and continuously growing in popularity for years, and many of the top companies use Python today. With its continued popularity and growing support, Python will be used in the industry for years to come.

Whether you’ve been a data scientist for years or you are just beginning your data science journey, you can benefit from learning Python for data science. The simplicity, readability, support, community, and popularity of the language — as well as the libraries available for data cleaning, visualization, and machine learning — all set Python apart from other programming languages. If you aren’t already using Python for your work, give it a try and see how it can simplify your data science workflow.

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Understanding the Difference Between Data Analytics and Data Science

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Data analytics and data science are two key terms thrown around in the tech and business world. What do they mean, and what’s the difference between the two? Data analytics is concerned with performing statistical analysis on existing datasets to solve problems and find answers to current issues we don’t know the answers to. Data science focuses on creating actionable insights and predictions from raw and structured data, often in large quantities.

This article will discuss the critical differences between data analytics and data science. First, we’ll explain what big data is, followed by a little more information on each role: data analyst and data scientist.

What is Big Data?

Big data can often be challenging to comprehend. Big data is usually more extensive and more complex than other datasets and may contain multiple sources. Put simply, big data is too large to process and understand using traditional data processing methods. This is where data analysts and data scientists come in — their job is to interpret this data and present it to their company or organization.

The original definition of big data, prefaced by Gartner (2001) is as follows: “Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

The Three V’s of Big Data

To better understand big data, whether as a data analyst, scientist, or curious individual, we must apply the three V’s: Volume, Velocity, and Variety.

Volume

When it comes to an understanding of big data, the volume of this data matters significantly. Big data requires you to process an increased volume of unstructured data, e.g. Twitter feeds, sensor-enabled equipment, forum responses, or comments and reviews on webpages or mobile apps. This data can be difficult to comprehend; however, it’s crucial that there’s a lot of it in order to make valid claims. The volume of big data depends on the organization’s size and the questions that are being asked.

Velocity

In regards to big data, velocity is the speed at which data is received and then interpreted. Some pieces of software can do this automatically, depending on the complexity and structure of the data. However, this is not always possible, making the velocity much slower as it’s done manually by a data analyst or data scientist.

Variety

Finally, we have a variety. This refers to the different types of data that are available, both structured and unstructured. For example, data types may include audio, text, video, comments on forums, reviews, and other metadata. In the last few years, we’ve seen a rise in unstructured data (such as interviews, which then need to be transcribed), audio recordings, and video interviews.

Value & Veracity

Although the three V’s mentioned above are the go-to for big data, more recently, two new V’s have been introduced: value and veracity. For example, all data contains an intrinsic value, but this value cannot be understood until the data is understandable. Some data contains more intrinsic value than others, and this is determined by the data source and the truthfulness of the data, e.g. can you rely on the data source?

Big data is becoming more and more mainstream, especially for large tech companies (and others that deal in large quantities of data) to better understand their users and their products. For instance, companies such as Apple use big data to understand and map user experience and intentions, and to help create new products that customers will actually be interested in — solutions to problems that others don’t yet recognize as obstacles.

Data Analytics vs. Data Science

As mentioned previously, both data analytics and data science are somewhat similar and often confused. To eliminate this confusion and to better help you understand the difference, we’ve provided a brief description of each role below.

What does a data analyst do?

A data analyst’s job consists of sorting through data to provide visual and written reports to uncover insights in a dataset. These datasets could be on any topic, whether a crime, government funding, or within the sports performance industry. Often, many data scientists practice first as a data analyst, learning the ropes and better understanding data as a whole.

What does a data scientist do?

A data scientist’s role is to collect and analyze data to gather valuable insights, later sharing these with their organization or company. Similar to a data analyst, the role of a data scientist exists across many different industries.

Unlike data analysts who provide insights via representations of data, data scientists are more significantly involved by creating their own experiments, cleaning data, finding patterns, building algorithms, and finally, sharing their data and newly found insights with their team in an easy to understand process.

What is the difference between data analytics and data science?

This next section will explain several key differences between data analytics and data science to help you better understand each role in more detail.

1.   Data science is multidisciplinary

One of the main differences between data analytics and data science is that data science incorporates numerous disciplines, including data analytics, data engineering, machine learning, and software engineering, to name a few. In particular, data science relies heavily on machine learning and data analytics. Without traditional data analytics, whether performed by an analyst or a data scientist, it would be difficult and nearly impossible to understand big data.

Ultimately, a data scientist’s role is to understand and re-structure big data, identify patterns, and educate business leaders and decision-makers on their findings to adjust current practices for better, more effective results.

2.   The unknown vs. the known

A data scientist’s role is to predict future events or further data by analyzing past data patterns. On the other hand, a data analyst looks at current data and perspectives to better understand current events. This fundamental difference is paramount, and a critical distinction between the two sets of expertise. Essentially, data scientists focus on the future, and data analysts center their attention on the now.

3.   Hands-on machine learning experience

Data analysts are not expected or required to have hands-on machine learning experience. Similarly, those within this role are not likely to build statistical models or conduct advanced experiments to better understand big data.

Data scientists, on the other hand, are expected to have hands-on machine learning experience and are required to build their own statistical models and conduct their own experiments. As you can see, the roles are somewhat similar, but a data scientist’s role is more advanced and a step up from a data analyst. This is why many data scientists start out as data analysts.

4.   Addressing vs. formulating questions

Generally, data analysts are given questions to address by their business or organization. The request usually has to do with understanding a specific dataset to better benefit the business and their regular operations, e.g. cutting costs, increasing footfall, or understanding sales trends of distinct products or services.

Conversely, data scientists formulate these questions and provide solutions that will benefit the business. Usually, these questions are about events that haven’t happened yet; with greater focus on predicting the future as opposed to understanding current data and events.

5.   Multiple sources vs. single sources

Data analysts typically use and interpret data from a single source, such as a CRM system, while data scientists collect and gain insights from multiple data sources — sources that are often disconnected and more complex to understand. This is why processes such as machine learning and statistical models are used to better understand this big data.

6.   Visualization skills

Data analysts are not always required to possess business acumen or exceptional data visualization skills. Instead, their role is to interpret the data in an easy-to-understand fashion, not to implement changes to a business setting or real-world scenario. By comparison, data scientists are required to show business acumen and advanced data visualization skills, putting newly understood data to work in a business setting and contextualizing potential impacts on a business and its current decisions and processes.

Frequently Asked Questions

Can a data analyst become a data scientist?

Yes, data analysts can become data scientists. Many data scientists often start as data analysts, learning the big data world’s ropes and the various methods involved in interpreting and making sense of data. With this being said, an advanced degree is not necessary but may support you during the transition process.

Which is better for business: analytics or data science?

Business analytics is concerned with the analysis of data to make key business decisions, while data science uses statistics and various other methods to complement and inform business decisions. While there’s no correct answer, if you think you’d like to be more involved in a business decision, then a business analyst role is probably for you.

Data analyst vs. data scientist salary — which is better?

According to Glassdoor, the average salary for a data analyst ranges from $83,000 to $115,000, while data scientists earn, on average, upwards of $168,000 a year.

To conclude

Data analytics and data science have different roles within the same industry; however, they’re somewhat similar. As we’ve discussed, data analysts focus on sorting through current datasets to provide insights and visualizations in response to a business or organization’s question or current problem. On the other hand, data scientists formulate their questions as well as the subsequent answers and solutions that will benefit the business, focusing typically on events that have not yet happened.

Many data scientists often become data analysts first, helping them to better understand big data and the many processes involved in its analysis. Think of a data scientist as a more advanced data analyst — they ask questions, use machine-learning, build statistical models, and conduct experiments. However, both roles share the critical goal of a better understanding of big data.

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