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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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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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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8 Simple Ways to Turn Your Skills into a Profitable Side Business

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In the US alone, there are over 28 million small businesses. Of those, an estimated 22 million consist of a single operating member—solopreneurs as I like to call them.

Many of these small business owners started their businesses as nothing more than the intersection of passion and skills that combined to create a business idea with the ability to earn extra money and scale into something truly sustainable.

As someone who’s successfully launched four profitable side businesses over the past four years, I’ve learned a lot about how to turn your skills into a healthy side income. From building physical products to selling my consultative services, and building my own suite of digital products, I’ve been able to generate thousands in extra income each month.

If you’re ready to build a foundation for one day becoming gainfully self-employed, here are my top eight ways to get started with a profitable side business today.

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Improving Diversity, Equity, & Inclusion Within Your Organization

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Systemic racism has been a critical problem for generations, and the Black Lives Matter (BLM) movement has brought centuries of injustice to the spotlight. Over the last six months, following the deaths of Ahmaud Arbery, Breonna Taylor, George Floyd, and so many others, individuals worldwide have taken a stand to fight oppression and discrimination against Black, Indigenous, and People of Color (BIPOC).

It’s an inflamed and sensitive time that calls for radical change. Diverse companies not only outperform their less diverse peers, but they also forge stronger connections with their customers. 77% of U.S. consumers said it was “deeply important that companies respond to racial injustice to earn or keep their trust.” As consumer bases diversify and consumers change their spending habits, companies need to ensure that their content, messaging, product, design, and data align with these shifts. While organizations know they need stronger commitments to diversity, equity, and inclusion (DEI), many don’t know where to start — individual companies often take action but lack coordinated guidance. 

Our Standards Boards were established to increase the clarity of and access to careers in marketing, AI & data science, product management, and UX design. To date, the Boards have primarily focused on providing clarity on the skills needed within specific fields by publishing career frameworks and certifications. Now, it’s time to connect to the access portion of their work. Together, the Standards Boards have crafted DEI principles that guide organizations on how to provide equitable access to skills and career paths for their employees. 

Improving Diversity, Equity, & Inclusion: A Practical Guide

To create a meaningful guide to DEI, our Standards Board Members reflected on what diversity, equity, and inclusion meant as individuals, employees, and leaders of organizations. With this in mind, we focused on improving the current DEI practices each member saw being used and creating a practical playbook that could be applied across companies and disciplines. Ultimately, we hope this playbook serves as a starting point for conversations around DEI that lead to career paths for diverse talent and helps leaders create work environments in which all can succeed. 

Our Standards Board DEI task force drafted a playbook of seven overarching principles that have been refined through feedback from colleagues, DEI experts, GA instructors and staff, plus more. These principles were designed to guide any organization’s DEI strategy, regardless of function, industry, geography, or company size.

These principles were created by leaders in various industries who have a real conviction for driving change. Below, you’ll find a few principles our board members stand behind; they hope you’ll use these to drive conversations and assess how your organization is implementing DEI.

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Marla Kaplowitz, president and CEO at 4A’s noted: 

“We all recognize a critical need to address systemic issues with diversity, equity, and inclusion through actions — not just words. These principles were created to support action plans for every company to ensure a culture of belonging for all employees, at all levels throughout the organization.” 

We hope these principles spark conversations at your organizations that lead to tactical activities such as revisiting policies, analyzing pay equity, and tracking diversity data. While some of these principles are being implemented across board member organizations, some aren’t. Our intention is to enable organizations to implement DEI policies across every level of an organization through actions, not just words.

The Actions We Are Taking 

It’s essential that these aforementioned principles are put into action. Across the Standards Boards, we’ll be incorporating DEI into career frameworks, assessments, and products. We’ll also be actively recruiting more board members in 2021 to ensure our boards are representative of the talent in their industries.

Within GA, we’re also committed to aligning these principles with our work. We’re actively promoting equity and justice by using our platform to discuss why we should all be angry, and we’re making real commitments to ensure we’re not idle in the face of systemic racism. We’re cultivating conversations about our diversity story and creating a culture of dissent through creating an Inclusion Committee as well as a Fireside Chat series that brings employees and executives together for candid conversations on D&I (both started in 2019). 

We’re cultivating our future employee base by updating our policies to require a diverse slate of interview candidates for all leadership-level positions, revisiting internal promotion criteria, and launching a mentorship program (Code Grow) so our Black, Indigenous, and People of Color (BIPOC) staff has formal avenues to develop their careers. To attract diverse talent, we are utilizing outlier career-search platforms like AngelList, Underdog.io, Vettery, c0ffe3, Black Creatives, and more.

We’re transparent about the areas of difference we’re cultivating by reformalizing our Employee Resource Groups (ERGs) with dedicated executive sponsors. And we’re tying outcomes to actions by measuring all our people metrics and making plans to improve the experiences of underrepresented groups in our organization. We’re also ensuring DEI is central in our product development.

The principles set forward by the Standards Boards are essential to capturing many voices across multiple sectors because they encapsulate what has been learned on our individual and collective journeys. We look to evolving and integrating these principles into GA’s courses, continue the hard work and commitment to DEI at GA, and further develop organizational behaviors, along with the willingness of our Standards Board partners to do the same. 

The list below notes the leaders that have signed on. If you’re a leader who is ready to join us and adopt these principles, you can sign on here.

Participating Leaders:

Shri Bhupathi, Founder and Technical Fellow, MILL5
Gideon Bullock
Andrea Chesleigh
Chad Evans, SVP, Product and Platform, NBA
Stephen Gates
Benjamin Harrell, Chief Marketing Officer, Priceline
Marla Kaplowitz, President and CEO, 4A’s
Willy Lai
Louis Lecat
Kevin Lyons, SVP of Technology, Nielsen
Francisco Martin, Head of Business Development, Thrive Global
Marilyn McDonald, SVP of B2B Experiences, Mastercard
Kristof Neirynck, CMO of Global Brands, Walgreens Boots Alliance
Gretchen O’Hara, VP of AI & Sustainability, Strategy & Partnership, Microsoft
Michelle Onvural, CEO, Bonobos
Seth Rogin, CEO, Magnolia Media Partners
Nick Perugini
Adam Powers
Professor Andrew Stephen, Associate Dean of Research & L’Oréal Professor of Marketing
Linda Tong, General Manager, AppDynamics (a Cisco Company) 
Sang Valte, UX Director, Jellyfish

It’s a new world that calls for moral bravery and clear actions. We welcome all feedback on these principles and look forward to hearing how your organization implements these and other DEI initiatives. 

 

5 High-Paying Careers That Require Data Analysis Skills

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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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The Difference Between a Startup and a Small Business

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If you work in the technology industry, or live in a tech hub such as Silicon Valley, Hong Kong, or New York —it’s likely that you or someone you know is in the process of conceptualizing or even launching his or her own startup venture.

A startup venture is often misunderstood for simply a small new business. The truth is, there is a significant difference between a startup and a small business.

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UX, Visual, or Graphic: Which Type of Design Is Right for You?

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  • CC Image Courtesy of Thomas Brasington on Flickr

You can be pardoned for sometimes feeling confused about all the terminology and job titles floating around in the design world. What is the difference between graphic design, visual design, and user experience design? Do each of the three roles provide a different service? For visual and graphic designers, the difference may lie mainly in the job title and salary expectations. However, a user experience designer has very different end goals and responsibilities from a visual or graphic designer. Below is a breakdown of what each of these designers does within the design industry, to help you decide what type of design is right for you. Continue reading

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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