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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Designing a Dashboard in Tableau for Business Intelligence

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Tableau is a data visualization platform that focuses on business intelligence. It has become very popular in recent years because of its flexibility and beauty. Clients love the way Tableau presents data and how easy it makes performing analyses. It is one of my favorite analytical tools to work with.

A simple way to define a Tableau dashboard is as a glance view of a company’s key performance indicators, or KPIs. There are different kinds of dashboards available — it all depends on the business questions being asked and the end-user. Is this for an operational team (like one at a distribution center) that needs to see the number of orders by hour and if sales goals are achieved? Or, is this for a CEO who would like to measure the productivity of different departments and products against forecast? The first case will require the data to be updated every 10 minutes, almost in real-time. The second doesn’t require the same cadence, and once a day will be enough to track the company performance.

Over the past few years, I’ve built many dashboards for different types of users, including department heads, business analysts, and directors, and helped many mid-level managers with data analysis. If you are looking for Tableau dashboard examples, you have come to the right place. Here are some best practices for creating Tableau dashboards I’ve learned throughout my career.

Why Use a Data Visualization?

A data visualizations tool is one of the most effective ways to analyze data from any business process (sales, returns, purchase orders, warehouse operation, customer shopping behavior, etc.).

Below we have a grid report and bar chart that contain the same data source information. Which is easier to interpret?

Grid report

Bar Chart
Grid report vs. bar chart.

That’s right — it’s quicker to identify the category with the lowest sales, Tops, using the chart.

Many companies previously used grid reports to operate and make decisions, and many departments still do today, especially in retail. I once went to a trading meeting on a Monday morning where team members printed pages of Excel reports with rows and rows of sales and stock data by product and took them to a meeting room with a ruler and a highlighter to analyze sales trends. Some of these reports took at least two hours to prepare and required combining data from different data sources with VLOOKUPs — a function that allows users to search through columns in Excel. After the meeting, they threw the papers away (a waste of paper and ink), and then the following Monday it all started again.

Wouldn’t it be better to have an effective dashboard and reporting tool in which the company’s KPIs were updated daily and presented in an interactive dashboard that could be viewed on tablets/laptops and digitally sliced and diced? That’s where tools like Tableau server dashboards come in. You can drill down into details and answer questions raised in the meeting in real-time when creating a Tableau project – something you couldn’t do with paper copies.

How to Design a Dashboard in Tableau SERVER

Step 1: Identify who will use the dashboard and with what frequency.

Tableau dashboards can be used for many different purposes, such as measuring different KPIs, and therefore will be designed differently for each circumstance. This means that, before you can begin designing a new dashboard, you need to know who is going to use it and how often.

Step 2: Define your topic.

The stakeholder (i.e., director, sales manager, CEO, business analyst, buyer) should be able to tell you what kind of business questions need to be answered and the decisions that will be made based on the dashboard.

Here, I am going to use the dataset for my Tableau dashboard example from a fictional retail company to report on monthly sales.

The commercial director would like to know 1) the countries to which the company’s products have been shipped, 2) which categories are performing well, and 3) sales by product. The option of browsing products is a plus, so the tableau dashboard should include as much detail as possible.

Step 3: Initially, make sure you have all of the necessary data available to answer the questions specified in your new dashboard.

Clarify how often you will get the data, the format in which you will receive the data (inside a database or in loose files), the cleanliness of the data, and if there are any data quality issues. You need to evaluate all of this before you promise a delivery date.

Step 4: Create your dashboard.

When it comes to dashboard design, it’s best-practice to present data from top to bottom when in presentation mode. The story should go from left to right, like a comic book, where you start at the top left and finish at the bottom right.

Let’s start by adding the data set to Tableau. For this demo, the data is contained in an Excel file generated by software I developed myself. It’s all dummy data.

To connect to an Excel file from Tableau, select “Excel” from the Connect menu. The tables are on separate Excel sheets, so we’re going to use Tableau to join them, as shown in the image below. Once the tables are joined, go to the bottom and select Sheet 1 to create your first visualization.

Excel Sheet in Tableau
Joining Excel sheet in Tableau.

We have two columns in the Order Details table: Quantity and Unit Price. The sales amount is Quantity x Unit Price, so we’re going to create the new metric, “Sales Amount.” Right-click on the measures and select Create > Calculated Field.

Creating a Map in Tableau

We can use maps to visualize data with a geographical component and compare values across geographical regions. To answer our first question — “Which countries the company’s products have been shipped to?” — we’ll create a map view of sales by country.

1. Add Ship Country to the rows and Sales Amount to the columns.

2. Change the view to a map.

Map
Visualizing data across geographical regions.

3. Add Sales Amount to the color pane. Darker colors mean higher sales amounts aggregated by country.

4. You can choose to make the size of the bubbles proportional to the Sales Amount. To do this, drag the Sales Amount measure to the Size area.

5. Finally, rename the sheet “Sales by Country.”

Creating a Bar Chart in Tableau

Now, let’s visualize the second request, “Which categories are performing well?” We’ll need to create a second sheet. The best way to analyze this data is with bar charts, as they are to compare data across categories. Pie charts work in a similar way, but in this case we have too many categories (more than four) so they wouldn’t be effective.

1. To create a bar chart, add Category Name to the rows and Sales Amount to the columns.

2. Change the visualization to a bar chart.

3. Switch columns and rows, sort it by descending order, and show the values so users can see the exact value that the size of the rectangle represents.

4. Drag the category name to “Color.”

5. Now, rename the sheet to “Sales by Category.”

Sales category bar chart
Our Sales by Category breakdown.

Assembling a Dashboard in Tableau

Finally, the commercial director would like to see the details of the products sold by each category.

Our last page will be the product detail page. Add Product Name and Image to the rows and Sales Amount to the columns. Rename the sheet as “Products.”

We are now ready to create our first dashboard! Rearrange the chart on the dashboard so that it appears similar to the example below. To display the images, drag the Web Page object next to the Products grid.

Dashboard Assembly
Assembling our dashboard.

Additional Actions in Tableau

Now, we’re going to add some actions on the dashboard such that when we click on a country, we’ll see both the categories of products and a list of individual products sold.

1. Go to Dashboard > Actions.

2. Add Action > Filter.

3. Our “Sales by Country” chart is going to filter Sales by Category and Products.

4. Add a second action: Sales by Category will filter Products.

5. Add a third action, this time selecting URL.

6. Select Products, <Image> on URL, and click on the Test Link to test the image’s URL.

What we have now is an interactive dashboard with a worldwide sales view. To analyze a specific country, we click on the corresponding bubble on the map and Sales by Category will be filtered to what was sold in that country.

When we select a category, we can see the list of products sold for that category. And, when we hover on a product, we can see an image of it.

In just a few steps, we have created a simple dashboard from which any department head would benefit.

Dashboard
The final product.

Dashboards in Tableau at General Assembly

In GA’s Data Analytics course, students get hands-on training with the versatile Tableau platform. Students will learn the ins and outs of the data visualization tool and create dashboards to solve real-world problems in 1-week, accelerated or 10-week, part-time course formats — on campus and online. You can also get a taste in our interactive tableau training with these classes and workshops.

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Meet Our Expert

Samanta Dal Pont is a business intelligence and data analytics expert in retail, eCommerce, and online media. With an educational background in software engineer and statistics, her great passion is transforming businesses to make the most of their data. Responsible for the analytics, reporting, and visualization in a global organization, Samanta has been an instructor for Data Analytics courses and SQL bootcamps at General Assembly London since 2016.

Samanta Dal Pont, Data Analytics Instructor, General Assembly London

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. 

 

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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Data at Work: 3 Real-World Problems Solved by Data Science

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At first glance, data science seems to be just another business buzzword — something abstract and ill-defined. While data can, in fact, be both of these things, it’s anything but a buzzword. Data science and its applications have been steadily changing the way we do business and live our day-to-day lives — and considering that 90% of all of the world’s data has been created in the past few years, there’s a lot of growth ahead of this exciting field.

While traditional statistics and data analysis have always focused on using data to explain and predict, data science takes this further. It uses data to learn — constructing algorithms and programs that collect from various sources and apply hybrids of mathematical and computer science methods to derive deeper actionable insights. Whereas traditional analysis uses structured data sets, data science dares to ask further questions, looking at unstructured “big data” derived from millions of sources and nontraditional mediums such as text, video, and images. This allows companies to make better decisions based on its customer data.

So how is this all manifesting in the market? Here, we look at three real-world examples of how data science drives business innovation across various industries and solves complex problems.

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How to Quickly get an Internship in Data Science

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After studying statistics, probability, programming, algorithms, and data structures for long hours, putting all the knowledge in action is essential. An internship at a great company is a great way to practice your skills, but at the same time is one of the most difficult jobs. Especially with such vast competition.  

Nowadays, many other opportunities are branded as “internship experiences” but they’re not actually internships. A key distinction is as follows: if you’re asked to pay for an internship, then it’s not an internship. An internship is a free opportunity to work in a specific industry for a short period of time, usually shadowing an existing employee or team.

This article will provide you with five tips to help you secure your first data science internship. However, first we’ll discuss what exactly data science is and what the job entails.

What is data science?

Data science focuses on obtaining actionable insights from data, both raw and unstructured, often in large quantities. This big data is analyzed by data analysts as it’s so complex it cannot be understood by existing software or machines.

Ultimately, data science is concerned with providing solutions to problems we don’t yet know are problems or concerns. It’s essentially about looking into the future and finding fixes for things that may happen or might be implemented. On the other hand, a data analyst’s role is to investigate current data and how this impacts the now.

What is the role of a data scientist?

As a data science intern, you will be responsible for collecting, cleaning, and analyzing various datasets to gather valuable insights. Later, with the help of other data scientists, these insights will be shared with the company in an effort to contribute to business strategies or product development. Within the role of a data scientist, you will be expected to be independent in your work collecting and cleaning data, finding patterns, building algorithms, and even conducting your own experiments and sharing these with your team.

5 Tips to Finding Your First Data Science Internship

Now that you know what data science is and what a data science analyst does, you may be wondering how to get a data science internship. Here are five actionable tips to land your first data science internship, beginning with a more obvious one: acquiring the right skills.

1.   Acquire the right skills

As a data scientist, you’re expected to possess a variety of complex skills. Therefore, you should begin learning these now to set yourself aside from your competition and increase the likelihood of landing a data science internship.

In fact, regardless of your internship role, you should be actively learning new skills all of the time, preferably skills that are related to your industry (e.g. data science). There’s no set formula to acquire skills; there are numerous ways to get started, such as online data science courses (some of which are free), additional University modules, or conducting some data science work yourself, perhaps in your free time.

The more relevant data science skills you have, the more appealing you’ll be to employees looking for a data science intern. So, start learning now and distinguish yourself from your competition; you won’t regret it.

2.   Customize each data science application

A common problem many graduate students make when applying for internships online is bulk-applying and using the same CV and cover letter for each application. This is a lengthy and tedious process, and rarely pays dividends.

Instead, students should customize each data science application to each company or organization that they’re applying for. Not all data science jobs are the same — their requirements are somewhat different, both in the industry and the company’s goals and beliefs. To increase your likelihood of landing a data science internship, you need to be genuinely interested in the company you are applying for, and show this in your application. Be sure to read through their website, look at their previous work, initiatives, goals, and beliefs. And finally, make sure that the companies you are applying for are places you actually want to work at, or else the sincerity of your application may be cast in a negative light, even if you don’t realize this.

3.   Create a portfolio

To stand out in such a saturated market, it’s essential to create your very own portfolio. Ideally, your portfolio should consist of one or several of your own projects where you collect your own data. It’s good to indicate you have the experience on paper, but showing this to potential employers first-hand shows that you’re willing to go above and beyond, and that you truly do understand datasets and other data scientist tasks.

Your portfolio project(s) should be demonstrable, covering all typical steps of machine learning and general data science tasks such as collecting and cleaning data, looking for outliers, building models, evaluating models, and drawing conclusions based on your data and findings.  Furthermore, go ahead and create a short brief to explain your project(s), to include as a preface to your portfolio.

4.   Practicing for interviews is crucial

While your application may land you an interview, your interview is the penultimate deciding factor as to whether or not you get the data science internship. Therefore, it’s essential to prepare the best you can. 

There are several things you can do to prepare:

●  Research what to expect in the interview.

●  Know your project and portfolio like the back of your hand.

●  Research common interview questions and company information.

●  Practice interview questions and scenarios with a friend or family member. 

Let’s break down each of these points further.

Research what to expect in the interview.

Every interview is different, but you can research roughly what to expect. For example, you could educate yourself on the company’s latest policies and events, ongoing initiatives, or their plans for the coming months. Taking the time to research the company will come through in your interview and show the interviewer that you’re dedicated and willing to do the work.

Know your project and portfolio like the back of your hand.

To show your competence and expertise, it’s essential to have a deep and thorough understanding of your project and portfolio. You’ll need to be able to answer any questions your interviewer asks, and provide detailed and knowledgeable answers.

Prior to the interview, familiarize yourself with your project, revisiting past data, experiments, and conclusions. The more you know, the better equipped you’ll be.

Research common interview questions and company information.

Most data science internship interviews follow a similar series of questions. Before your interview, research these, create a list of the most popular and difficult questions, and prepare your answers for each question. Even if these exact questions may not come up, similar ones are likely to. Preparing thoughtful answers in advance provides you with the best opportunity to express professional and knowledgeable answers that are sure to impress your interviewers.

This leads us to our next point: practicing these questions.

Practice interview questions and scenarios with a friend or family member.

Once you’ve researched a variety of different questions, try answering these with a friend or family member, ideally in a similar environment as the interview. Practicing your answers to these questions will help you be more confident and less nervous. 

Be sure to go over the more difficult questions, just in case they come up in your actual data science internship interview.

Ask whomever is interviewing you (the friend or family member, for example) to ask some of their own questions, too, catching you off guard and forcing you to think on your feet. This too helps you get ready for the interview, since this is likely to happen regardless of how well you prepare.

5.   Don’t be afraid to ask for feedback

You’re not going to get every data science internship you apply for. Even if you did, you wouldn’t be able to take them all. Therefore, we recommend asking for feedback on your interview and application in general.

If you didn’t land the internship the first time, you can use this feedback and perhaps re-apply at a future date. Most organizations and companies will be happy to offer feedback unless they have policies in place preventing them. With clear feedback, you’ll be able to work on potential weaknesses in your application and interview and identify areas of improvement for next time.

Over time, after embracing and implementing this feedback, you’ll become more confident and better suited to the interview environment — a skill that will undoubtedly help you out later in life.

Frequently Asked Questions

What do data analyst interns do?

Data analyst interns are responsible for collecting and analyzing data and creating visualizations of this data, such as written reports, graphs, and presentations.

How do I get a data science job with no experience?

Getting a data science job with no experience will be very difficult. Therefore, we recommend obtaining a degree in a relevant subject (e.g. computer science) if possible and creating your own portfolio to showcase your expertise to potential employers.

What does a data science intern do?

Data science interns perform very similar roles and tasks to full-time data scientists. However, the main difference here is that interns often shadow or work with another data scientist, not alone. As an intern, you can expect to collect and clean data, create experiments, find patterns in data, build algorithms, and more.

To Conclude

Data science internships are few and far between, and landing one can be difficult. But it’s not impossible and the demand for these roles is slowly increasing as the field becomes more popular.

The role of a data scientist intern includes analyzing data, creating experiments, building algorithms, and utilizing machine learning, amongst a variety of other tasks. To successfully get a data science internship, you should begin acquiring the right skills now, customize each application, create your very own portfolio and project, practice for interviews, and don’t be afraid to ask for feedback on unsuccessful applications.

Best of luck to all those applying, and remember: preparation is key.

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User Experience Jobs: 7 Options & How to Choose a UX Career You Love

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If you have ever done a quick job search for “user experience design,” chances are you’ve seen a number of titles and descriptions that aren’t always as simple as “UX designer.”

User experience has a variety of specializations, and as a job seeker and practitioner, you should know the skills and applications that come with each. Understanding these differences will help you decide your UX career path and and help you find the appropriate job to fit your interests and skill set.

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Best Resources for Learning Digital Marketing in 2021

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Whether you’re looking to learn to do your own digital marketing for your business, get into the life of a digital marketer, or add new skills to your existing arsenal, there are plenty of free and affordable resources out there to help you learn your CPCs from your CPMs, and how to master the tools of the trade.

Below is a list of our favorite resources and certifications to help you learn digital marketing in 2020!

Guides + Blogs

Moz

Search Engine Optimization is key to any digital marketing strategy, and Moz is the go-to free resource for all things SEO. It’s got everything you need whether you’re a complete newbie to keyword research and optimization, or an experienced digital marketer looking to refresh your skill set.

Best for: SEO

Our pick: The One Hour Guide to SEO is a quickfire lesson in 6 easy-to-digest videos, covering all the need-to-know SEO essentials in just one hour.

Content Marketing Institute

Explore blogs, resources, and guides on all things content marketing with the Content Marketing Institute. They also have a killer daily newsletter that you should definitely sign up for to keep on top of all the latest trends in content marketing.

Best for: Content marketing

Our pick: Getting Started in Content Marketing is a “back to basics” series designed to get you started, offering content marketing essentials, processes to implement, and helpful templates.

Ahrefs 

Ahrefs is one of the best hubs full of tutorials, case studies, and opinion pieces from some of the best in the industry. Check out some of their great free tools for when you’ve mastered your SEO skills!

Best for: SEO

Our pick: Once you’ve learned the SEO basics, one of the best free tools out there is the Ahrefs SEO toolbar, a chrome extension that allows you to do top level SEO audits of any website with the click of a button.

Neil Patel

An icon in the digital world, Neil Patel hosts an amazing comprehensive suite of educational content on anything and everything you need to learn digital marketing.

Best for: SEO, content marketing, email marketing, social media, e-commerce, and search.

Our pick: Instagram Unlocked is part of the free digital marketing training series, and offers a free two-week training module to help you learn social media marketing strategies specifically for Instagram growth — something everybody wants.

AdEspresso Academy 

AdEspresso Academy includes step-by-step guides to learn both Facebook and Google Ads that are easy to understand, as well as regular webinars, blogs, and downloadable ebooks full of great free content.

Best for: Facebook Ads and Google Ads

Our pick: On the Academy page, there’s a great list of 6 easy steps to getting on top of Facebook Ads; start with an eight part guide that covers everything from setting up an account, all the way through to reporting and optimisation.

Social Media Examiner

With guides, studies, webinars, and a great podcast to help you keep up to date with the world of social, Social Media Examiner is your hub for social media knowledge. 

Best for: Social media marketing

Our pick: While we typically hear a lot about Facebook and Instagram, it’s not often people talk about the power of social media marketing on LinkedIn — a no brainer for B2B companies. This guide to LinkedIn ads is a great starting point for anyone new to LinkedIn ads, and provides a huge number of helpful Linkedin articles and strategy templates.

Search Engine Land

What started as a major resource for all things search-related, Search Engine Land has now branched into email, social, and retail. It offers free webinars, how-to guides, handy resources, and tools for auditing to help you understand almost all aspects of digital marketing.

Best for: Search, Email, Social and Retail

Our pick: Google Ads can be confusing (don’t worry, we get it!) but this beginner’s guide to paid search is incredibly easy to follow and understand, with things like glossaries for common terms and how to do keyword research — a must read for those who are new to paid search!

Unbounce

UnBounce is a landing page building platform, but also has a very good resource and learning centre to help you understand everything you need to know about landing pages, conversion optimisation, and where landing pages sit within the wider digital marketing landscape. 

Best for: Landing pages and conversion rate optimisation

Our pick: Never given landing pages a thought until now? This 8 module introduction is a great way to understand the fundamentals of landing pages, why they matter, and how to use them.

Certifications

While there’s plenty of free guides, resources and blogs out there, a certification can help you stand out from the crowd when looking for a job as a digital marketer, or give you an easy to follow holistic overview of a topic, coming out with the confidence to action your learnings. Here’s our picks for the best online certifications out there:

Google Analytics Academy + Google Digital Garage

Get certified in Google Ads, Google Analytics, and Google My Business while also completing non-certification short courses in more niche areas, or explore courses on topics like Google Shopping and YouTube.

Cost: Free

Facebook Blueprint

After utilising the library of free resources Facebook offers through their learning centre (there are over 90 courses!), you can apply your knowledge of social media marketing and beyond to their Blueprint Exams and obtain a Facebook certification in a few key areas. The best part? They’ll guide you through exactly what you need to learn for each course.  

Cost: $150 USD

Hubspot Academy

With both short courses and certifications, HubSpot Academy is globally recognised, and has many different digital marketing courses to help you learn digital marketing essentials, covering almost all areas including social media marketing, SEO, and business analytics.

Cost: Free

Hootsuite Academy

Hootsuite Academy offers socially focused certifications and courses with an exam at the end of each certification. As a leading social media platform, the Hootsuite brand is very well respected within the industry, and their certifications are too.

Cost: $99–$999 USD

And lucky last, we can’t go past one of the best resources for learning digital marketing — General Assembly! GA offers part-time and full-time digital marketing courses, as well as short hands-on workshops across all areas of digital marketing, and is one of the industry’s most respected education providers. Want to know more? Get in touch!

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Top 15 Skills Every Digital Marketer Needs to Master

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Tech is booming, and the recent COVID-19 pandemic has accelerated the need for organizations of all sizes to move away from traditional marketing and establish a competitive online presence as swiftly as possible. This need fuels the demand for skilled digital marketing professionals worldwide.

In fact, at this very moment, there are over 150K digital marketing jobs available on LinkedIn alone, which makes digital marketing a perfect opportunity for young professionals and career changers to enter the tech industry and future-proof their job prospects for the years to come. But what is digital marketing? What are the digital marketing skills needed to get started? Is digital marketing a technical or creative skill?

First of all, digital marketing is not a monolith, but rather a collection of skills and competencies. As a senior digital marketer, you need to combine creative analytical and technical skills to communicate the right message at the right time to the right audience. At the same time, you need to understand the nuances of how various digital channels work to be able to track, analyse, and optimise your marketing plans.

Digital marketers come in many shapes and sizes. As a digital marketer, you will be required to wear many hats and work on a diverse range of projects and challenges during your career. In principal, there are two broad types of digital marketing:

  1. Performance Marketing
  2. Content Marketing

Each type requires digital professionals with a different set of skills to succeed. However, there are some digital marketing skills that both types need to “check” as prerequisites for the role. Here’s a digital marketing skills list that every digital marketer needs to master:

Digital Marketing Foundational Skills

Required for performance, content, and social media marketing roles.

1. Search Engine Marketing and SEO

Understanding how search engines index websites and rank pages will not only enable you to grasp one of the most sought-after digital marketing skills in the market, but also provide you with all the foundational knowledge required to project manage web development and content marketing projects. Moreover, SEO skills are essential for optimising product pages within e-commerce ecosystems such as Amazon, Lazada, and eBay.

2. Copywriting

Copywriting is an absolute essential skill for every digital marketing professional. Digital marketing is all about communicating the right message to the right audience at the right time. The art of crafting compelling messages is at the heart of everything a digital professional does. Whether it’s for social media advertising, building landing pages, developing banner ads, or crafting paid search ads, there is always an element of copywriting involved.

3. Data Analytics and Visualization

Data-driven marketing is not only a recent buzzword but an essential digital marketing skill. Every digital marketing activity comes with data, so at minimum, digital marketing professionals ought to know how to work with and visualize data using tools like Excel or Google Analytics. In today’s digital marketing industry, every role comes with a wealth of data to be collected and analysed. For example, a social media marketer will need to report on the effectiveness of social media campaigns, the same way a pay-per-click (PPC) executive is required to report on paid media performance.

4. Basics of Business and Finance

Understanding the basics of business and finance is an absolute must-have to succeed in the digital marketing industry. The end objective of digital marketing is to generate profit for the business. Upon entering the digital marketing space, you will be bombarded with jargon such as CPA (cost per acquisition), CPL (cost per lead), CPM (cost per 1,000 impressions) and more! The ability to understand these metrics and connect them with the “big picture” is one of the very first skills you will need to master.

Performance Marketing Skills

Required for media buying and analytical roles.

1. Pay-per-Click Fundamentals

Pay-per-click or PPC covers the most popular kinds of digital advertising such as Paid Search, Facebook Advertising, Amazon advertising, etc. Every digital marketer needs to understand the PPC advertising auction logic as well as some platform fundamentals to be able to set up and optimise PPC campaigns successfully on various digital marketing channels.

2. Media Planning and Buying

Media planning and buying are some of the oldest advertising skills that are still relevant in the market. Understanding how to purchase media inventory directly or via programmatic advertising, the targeting options, as well as the pros and cons of each approach, is essential for every marketer who wants to build a career in the numerical side of digital marketing. Lastly, being able to deliver a complete media plan is an absolute must for both agency and in-house digital marketing roles.

3. Digital Tracking and Analytics

Performance marketers need to be experts in digital tracking — meaning they should be able to put together and implement a digital measurement plan. Moreover, they should understand how to set up conversion tracking on various platforms, make use of UTM tags or various tracking codes effectively, and how to take advantage of third-party tracking tools if necessary.

Content Marketing Skills

Required for content marketing and social media roles.

1. Social Media Marketing Know-How

Social media has become an integral part of our lives. At the same time, the social media marketing landscape is constantly expanding and evolving. Every content marketing professional should understand the basics of how social media algorithms operate to be able to conceptualise and develop impactful, relevant, and attention-grabbing social media content. Moreover, as a social media professional you should be the first to embrace and explore new social media channels and tactics.

2. Intermediate Design Skills

In an ever-expanding digital marketing ecosystem, the need for marketing visuals is greater than ever. The ability to ideate, develop, and modify marketing assets and collateral on the fly is a must-have skill for every content marketing professional. Experience with tools like Photoshop and online platforms such as Canva or equivalent will give you a competitive advantage in the digital recruitment market.

3. Endless Creativity

Marketing and creativity go hand in hand! As a digital content marketer, you should be able to conceptualise, project manage, and implement creative digital marketing campaigns as needed. Furthermore, you should familiarise yourself with concepts such as marketing seasonality and campaign-thinking, as well as being able to deliver click-worthy creatives for various advertising purposes. Experience with video production and editing will be a huge plus in the years to come.

How can I improve my digital marketing skills?

Digital marketing is evolving fast! No matter how senior you may become, always remember that every digital marketer needs to upskill and reskill on a yearly basis to stay relevant in an ever-changing industry. On this note, it’s worth pointing out the skills required to improve your digital marketing know-how for future trends:

1. Project Management and Collaboration

Digital marketing is a fast-paced and multi-faceted job. You’ll need to be on top of various projects, channels, and marketing initiatives at the same time. Moreover, you’ll have to communicate effectively with a diverse range of internal and external stakeholders. Consider actively investing in and growing “soft skills” such as teamwork, empathy, adaptability, and problem solving.

2. Customer Relationship Management (CRM)

As mentioned, data is at the heart of every digital marketing initiative. The ever-growing data protectionism and the rise of marketing automation means that the internet will be a safer place for all of us, but it also fuels the need for customer relationship management (CRM) as a key skill within the digital marketing space. Understanding how to work with first-party data, the media opportunities they open, and the fundamentals of marketing automation, is an essential skill for all senior digital marketers.

3. Email Marketing

Email is still the number one most effective digital marketing channel. Why? There is a lot more than meets the eye to strategizing and implementing an effective email marketing campaign. Crafting an intriguing subject line, writing an engaging click-worthy email, and leveraging marketing automation in the context of email marketing are extremely valuable skills in the digital marketing industry.

4. User Experience Design (UX)

UX or user experience design is a relatively new entry in the long list of digital marketing skills to master. UX is the area of digital marketing or product design that ensures intuitive, meaningful, and positive interactions throughout a customer’s journey. Think of UXers as the architects of the digital space. Understanding how to best structure a website or mobile app, the empathetic design thinking involved, and what a good user experience entails is a very practical must-have skill for any senior digital marketer, product manager, or project manager.

5. Presentation and Communication Skills

Last but not least, whether you end up working in-house, within an agency environment, or running your own business, you will always have to present your ideas to various stakeholders, teammates, clients, or investors. The ability to deliver clean, clear, and impactful presentation documents, as well as being able to communicate with confidence, are key skills you should aim to master.

Conclusion

Digital marketing includes a diverse collection of skills and competencies you should aim to develop depending on which part of the industry you’d like to build your career on. Assuming you are a beginner in the space, the safest way to land your dream digital marketing role is to invest in a structured course, launch your own side-hustle to gain practical experience in the above areas, or both! As an experienced digital marketer, you should aim to regularly upskill yourself through credible workshops, seminars, and industry-specific events.

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What Is an Income Share Agreement? (ISA)

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Financing education is a huge decision at any point of life—even more so in such uncertain times. That’s where an income share agreement (ISA) might be a great option to invest in yourself. In professional education, an ISA is not a loan, but rather a financial structure where tuition is repaid as a percentage of your monthly income for a fixed number of years.

How do Income Share Agreements Work?

At General Assembly, our ISA, Catalyst, allows students to learn in-demand tech skills in our full-time immersive courses and land a job with the help of our career services team. Repayment begins only once you secure a role earning at least $40,000 per year. After you’ve reached the minimum income threshold, you’ll start paying back 10% percent of your monthly earned income over 48 months.

Learn More About Other Financial Aid Options

While ISAs offer repayment flexibility, if you’re still wondering how to afford General Assembly we reccomend checking out our various financial aid options. Our Admissions team can guide you through scholarships, grants, and more.

Get Started Today

With our ISA, you can confidently invest in tech skills without immediate financial strain. Our full-time bootcamp and short courses offer varied financing options to make starting this educational journey accessible. Connect with the admissions team to learn more about General Assembly’s financing options.

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