How to Become a Data Scientist

IDSA Professional Certification in Data Sciences prepares students to expand their career prospects or change career paths by developing foundational data science skills. Candidates for the Professional Certification, a non-degree, part-time program, are required to complete a minimum of four required courses: Algorithms for Data Science, Probability and Statistics for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization.

How to Become a Data Scientist

There are many ways to become a Data Scientist, but because it is generally a high-level position, Data Scientists have traditionally been well educated, with degrees in mathematics, statistics, and computer science, among others. This, however, has started to change.


How to become a Data Scientist in eight steps:

  • Develop the right data skills
  • Learn data science fundamentals
  • Learn key programming languages for data science
  • Work on data science projects to develop your practical data skills
  • Develop visualizations and practice presenting them
  • Develop a portfolio to showcase your data science skills
  • Raise your online profile
  • Apply to relevant Data Scientist jobs

1. Develop the Right Data Skills

If you do not have any work experience in data, you can still become a Data Scientist, but you will have to develop the right background to work toward a data science career. Data Scientist is a high-level position; before you reach that degree of specialization, you’ll want to develop a broad base of knowledge in an associated field. That could be mathematics, engineering, statistics, data analysis, programming, or IT—some Data Scientists have even started out in finance and baseball scouting.

But whatever field you begin with, it should include the fundamentals: Python, SQL, and Excel. These skills will be essential to working with and organizing raw data. It doesn’t hurt to be familiar with Tableau as well, a tool you’ll use often to create visualizations. Keep an eye out for opportunities to help you start thinking like a Data Scientist; the more this background lets you work with data, the more it will help you with the next step.

2. Learn Data Science Fundamentals

A data science course or bootcamp can be an ideal way to acquire or build on data science fundamentals. Expect to learn essentials like how to collect and store data, analyze and model data, and visualize and present data using every tool in the data science toolkit, including specialized applications like visualization programs Tableau and PowerBI—among others.

By the end of your training, you should be able to use Python and R to build models that analyze behavior and predict unknowns, and be able to repackage data into user-friendly forms.

Many job postings list advanced degrees as a requirement for Data Science positions. Sometimes, that’s non-negotiable, but as demand outstrips supply the proof is increasingly in the pudding. That is, evidence of the requisite skills often outweighs mere credentialism. What’s most important to hiring managers is an ability to demonstrate mastery of the subject in some way, and it’s increasingly understood that this demonstration doesn’t have to follow traditional channels.

3. Learn Key Programming Languages for Data Science

Data Scientists rely on a number of specialized tools and programs developed specifically for data cleaning, analysis, and modeling. In addition to general-purpose Excel, Data Scientists need to be familiar with a statistical programming language like Python, R, or Hive, and query languages like SQL.

One of a Data Scientist’s most important tools is RStudio Server, which supports a development environment for working with R on a server. Open-source Jupyter Notebook is another popular application, comprising statistical modeling, data viz, machine learning functions, and more.

Data science increasingly involves machine learning as well – tools that apply artificial intelligence to give systems the ability to learn and become more accurate without being explicitly programmed. The tools used for machine learning depend to a large extent on the application – that is, whether you’re training the computer to identify images, for example, or extract trends from social media posts. Depending on their objectives, Data Scientists might choose from a wide range of tools including, TensorFlow, Apache Mahout, and Accord.Net.

4. Work on Data Science Projects to Develop Your Practical Data Skills

Once you’ve learned the basics of the programming languages and digital tools Data Scientists use, you can begin putting them to use, practicing your newly acquired skills and building them out even more. Try to take on projects that draw on a wide range of skills – using Excel and SQL to manage and query databases, and Python and R to analyze data using statistical methods, build models that analyze behaviour and yield new insights, and use statistical analysis to predict unknowns.

As you practice, try to touch on different stages in the process, beginning with the initial research of a company or market sector, then defining and collecting the right data for the task at hand, cleaning and testing that data to optimize its utility. Finally, you can create and apply your own algorithms to analyze and model that data, ultimately packaging it into easy-to-read visuals or dashboards that allow users to interact with and query your data in a straightforward way. You might even practice presenting your findings to others to improve your communication skills.

You will also want to practice working with different types of data – text, structured data, images, audio, and even video. Every industry uses its own types of data to help leadership make better, more informed decisions. As a working Data Scientist, you’ll likely be specialized in just one or two – but as a beginner building out your skillset, you’ll want to get to know the fundamentals of as many types as possible.

Tackling more complex projects will give you the opportunity to explore all the ways data can be used. Once you’ve mastered using descriptive analytics to examine data for patterns, you’ll be in a stronger position to attempt using more complicated statistical techniques like data mining, predictive modelling and machine learning to predict future outcomes or even generate recommendations.

5. Develop Visualizations and Practice Presenting Them

Using programs like Tableau, PowerBI, Bokeh, Plotly, or Infogram, practice building your own visualizations from scratch, finding the best way to let the data speak for itself. Excel comes into play even during this step: although the basic premise behind spreadsheets is straightforward – making calculations or graphs by correlating the information in their cells – Excel remains incredibly useful after more than 30 years and is virtually unavoidable in the field of data science.

But creating beautiful visualizations is just the beginning. As a Data Scientist, you’ll also need to be able to use these visualizations to present your findings to a live audience. These communication skills may come naturally to you, but if not, rest assured that anyone can improve with practice. Start small, if necessary – delivering presentations to a single friend, or even your pet – before moving on to a group setting.

6. Build a Portfolio to Showcase Your Data Science Skills

Once you’ve done your preliminary research, gotten the training, and practiced your new skills by building out an impressive range of projects, your next step is to demonstrate those skills by developing the polished portfolio that will land you your dream job. In fact, your portfolio may be the most important contributor to your job hunt. BrainStation’s Data Science Bootcamp, for example, is designed to offer a project-based experience that helps students build out an impressive portfolio of completed real-world projects. It is one of the best ways to stand out in the job market.

When applying for a Data Scientist position, consider displaying your work with GitHub in addition to (or instead of) your own website. GitHub easily shows your process, work, and results while simultaneously boosting your profile in a public network. But don’t stop there. Your portfolio is your chance to show your communications skills and demonstrate that you can do more than just crunch the numbers. It’s helpful to showcase a range of different techniques, since data science is a pretty broad field – meaning there are many ways to approach a problem, and a variety of approaches you can bring to the table.

Accompany your data with a compelling narrative and demonstrate the problems you’re working to solve so the employer understands your merit. GitHub allows you to show your code within a larger context, rather than in isolation, making your contributions easier to understand.

When you’re applying for a specific job, don’t include your whole body of work. Highlight just a few pieces that relate most closely to the position you’re applying to, and that will best showcase your range of skills throughout the whole data science process – starting with a basic data set, defining a problem, doing a cleanup, building a model, and ultimately finding a solution.

7. Raise Your Profile

A well-executed project that you pull off on your own can be a great way to demonstrate your abilities and impress potential hiring managers. Pick something that you’re really interested in, ask a question about it, and try to answer that question with data. As mentioned above, you should also consider displaying your work on GitHub.

Document your journey and present your findings—beautifully visualized—with a clear explanation of your process, highlighting your technical skills and creativity. Your data should be accompanied by a compelling narrative that demonstrates the problems you’ve solved—highlighting your process and the creative steps you’ve taken—to ensure an employer understands your merit.

Becoming a member of a data science network is another great way to show that you’re engaged with the community, show off your chops as an aspiring Data Scientist, and continue to grow both your expertise and your outreach.

8. Apply to Relevant Data Scientist Jobs

There are many roles within the data science field. After picking up the essential skills, people often go on to specialize in various subfields, such as Data Engineers, Data Analysts, or Machine Learning Engineers, among many others. Find out what a company prioritizes, what they’re working on, and confirm that it suits your strengths, goals, and what you see yourself doing down the line. And be sure to look beyond Silicon Valley: cities like Boston, Chicago, and New York are experiencing a scarcity of technical talent, so opportunities abound!