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:

1. Develop the right data skills
2. Learn data science fundamentals
3. Learn key programming languages for data science
4. Work on data science projects to develop your practical data skills
5. Develop visualizations and practice presenting them
6. Develop a portfolio to showcase your data science skills
7. Raise your online profile
8. Apply to relevant Data Scientist jobs

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.

Data Scientist Related Skills

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.

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.

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.

Key Data Science Programming Languages and Tools

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.

Depending on their objectives, Data Scientists might choose from a wide range of tools including h2o.ai, TensorFlow, Apache Mahout, and Accord.Net.

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 behavior 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’ll 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 modeling, and machine learning to predict future outcomes or even generate recommendations.

• Use Excel and SQL to manage and query databases
• Use Python and R to analyze data using statistical methods
• Build data models that analyze behaviors and yield new insights
• Use statistical analysis to predict unknowns

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.

Popular Data Visualization Programs

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.

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 communication skills and demonstrate that you can do more than just crunch the numbers.

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.

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.

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 an online data science network like Kaggle 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.

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!

Because the work Data Scientists do touches so many different industries and disciplines, the roles Data Scientists can fill go by many different names, including:

There are many other variations out there, and these will continue to evolve as data science becomes ever more prevalent.

But while the list of job titles in data science may seem to be never-ending, there are four main categories that describe the different roles Data Scientists most commonly fill:

Data Engineer

Data Engineers collect, store, and organize data. Job ads for Data Engineers will typically list a range of responsibilities, including the ability to source external data, build data warehouses, and design data models – three tasks that also build a foundation for data analytics and machine learning.

Data Engineer is a relatively advanced professional position, and so typically requires a background in computer science, math, or engineering, as well as knowledge of SQL, Python, Java, or Ruby, and the ability to manage and design databases.

Data Analyst

Data Analysts use the data organized and made accessible by the work of a Data Engineer, turning it into insights that can solve problems, optimize products, and help make evidence-based decisions.

Data Analysts can take complex information and turn it into stats that business execs can use to inform strategy and planning, often in the form of easy-to-understand data visualizations like charts and graphs.

Related job titles include Operations Research Analysts and Business Intelligence Analysts. SQL is the foundation for a career in data analytics, as well, alongside knowledge of Python or R, and the ability to create data visualizations using software like Tableau.

Data Scientist

Depending on the company, people with the job title of “Data Scientist” might be expected to do the work of a Data Engineer and Data Analyst (collect, organize, and analyze data), as well as more strategic data work.

Where the Data Scientist role differs from the Data Analyst and Engineer’s role is in the Data Scientist’s ability to lead a company’s big data strategy by asking the right questions and developing new ideas, products, and services.

Here, knowledge of Python, SQL, and Tableau are key, alongside other programming languages, an understanding of how databases are built and maintained, strong communication skills, and business acumen.

Machine Learning Engineer

Machine Learning Engineers design software that can uncover insights and learn from results as more and more data is gathered.

There’s quite a bit of overlap between Data Scientists and Machine Learning Engineers; both work with data to produce insights. The difference is that Data Scientists uncover insights to present to people (for example, CEOs and other business leaders), while Machine Learning Engineers design the tools that can discover insights and generate results.

Machine Learning Engineers depend on advanced math skills, programming skills (in Python, R, and Java), knowledge of Hadoop, data modeling experience, and experience working in an Agile environment.

Yes, the data science field is one of the fastest-growing in technology, with more than 2.7 million new jobs in data forecast to be created.

This growth also looks set to continue when you factor in the increased importance of data skills. According to the 2020 Digital Skills Survey, 89 percent of professionals believe that improved data skills will improve success at their organization, and 78 percent believe that AI is the technology that will have the greatest impact in the coming years.

In 2020, Glassdoor reported the average Data Scientist salary is $84,000 a year in Canada and over $113,000 in the U.S.

No, you do not need a specific degree to be a Data Scientist, but you do need the right hard and soft skills to be considered for a role in data.

Keep in mind that Data Scientists have traditionally been very highly educated; at last count, 88 percent have at least a Master’s degree, and 46 percent have a PhD.

Some of the most common educational paths to a career in data science begin with a Bachelor’s degree in computer science, mathematics, or statistics.

But that level of education is not a hard requirement. A strong portfolio and a resume replete with all the requisite skills can go a very long way—especially as the demand for Data Scientists continues to outpace the rate at which universities can produce them. Ultimately, the surest way to become a Data Science is simply to begin doing data science.

Even if you have no job experience in data, it’s still possible to become a Data Scientist. But before you begin exploring the specializations within the field of data science, you’ll need to develop a broad base of knowledge in a related field. That could be mathematics, engineering, statistics, data analysis, programming, or IT – some Data Scientists have even started out in finance and baseball scouting.

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.

To move from a data science-adjacent field into data science itself, you’ll need to acquire a specific set of skills, and the most effective way to do this is by enrolling in a data science course or bootcamp with a structured learning program.

Expect to learn data science essentials like data collection and analysis, data modeling, data visualization and the data visualization tools most commonly used by Data Scientists. By the end of your data science course, you should know how to use Python, R, and Hadoop, and how to build models that analyze behavior, predict unknowns, and be able to repackage data into user-friendly forms.

With skills training and a strong portfolio, you can begin working on establishing your public profile as a Data Scientist.

A well-executed project that you pull off on your own is a great way to do just that. Pick a subject you’re really interested in, ask a question about it, and try to answer that question with data. Then, publish your work on GitHub to present your process, work, and findings to highlight your technical skills and creativity in a compelling narrative.

• Develop a base knowledge in a related field, such as mathematics, engineering, statistics, data analysis, programming, or IT
• Master the data science fundamentals: Python, SQL, Excel, R, and Hadoop
• Enroll in a data science course or bootcamp
• Establish your public data science profile through a strong portfolio and projects posted on platforms such as Github

You can learn the skills needed to become a Data Scientist in as little as 12 weeks, which is why it has become increasingly common for neophyte Data Scientists to attend data science bootcamps, which allow for more hands-on learning and targeted skills development.

The general consensus, however, is that given the complexity and seniority of the role, it may take years of experience before you can become a good Data Scientist.