9 Factors that Affect How Long It Takes to Become a Data Scientist

Becoming a data scientist isn’t easy, but it can certainly be worth the effort for the right person.

Not only is it a naturally innovative field, but data scientists are in high demand at companies across nearly every industry. That’s why the Bureau of Labor Statistics expects operations research jobs like data science to grow by 25% over the next 10 years.

Companies need qualified data scientists to help them make sense of Big Data for generating insights and driving important decisions. From marketing and sales to supply chains and business operations, data scientists play a role in every department’s activities.

Despite the high demand, data science is still an emerging industry, so the current job market can’t meet current demands.

This is good news for anyone considering data science as a career because the field can provide incredible job security for the right candidates.

Do you think you’re ready to become a data scientist? Learn how long it takes depending on your skill set, education, and experience as well as the top paths for entering the field.

How Long Does It Take to Become a Data Scientist?

It generally takes five to 10 years to become a data scientist depending on your current skill set and level of knowledge in statistics.

Data science requires you to master two separate disciplines: data analysis/statistics and programming. Building the appropriate level of expertise in both fields takes time and commitment.

Knowledge and skill aren’t enough either. You also need to prove your experience to employers through a diverse portfolio of projects that reflect the industry’s job demands.

Here’s a snapshot of how long it might take to become a data scientist based on your current education level and skill.

If you already have the right level of programming experience and data analysis knowledge, you can become a data scientist in just 12 weeks through NYC Data Science Bootcamp.

9 Factors That Influence How Long It Takes to Become a Data Scientist

Since data science requires a unique combination of expertise, it’s hard to say how long it takes everyone to become a data scientist. The process depends more on where you currently stand relative to the skills and experience data science jobs require.

Use these factors to get a general idea of how long it might take based on your experience.

1. Highest Level of Education

Most data scientists – 75% – hold either a master’s degree or PhD. If you have at least a master’s degree, you’re in the best position to become a data scientist. In fact, data science boats the highest concentration of PhDs of any field.

Another 20% have a bachelor’s degree.

In a technical field like data science, graduate degrees aren’t arbitrary. The field demands advanced calculus, algebra, and statistics knowledge along with other skills. Plus, most jobs require degrees.

2. Type of Degree

Due to the specialty nature of data science, your degree’s concentration can dictate how long it takes you to become a data scientist. Some universities offer graduate programs in data science specifically, but computer science graduates can also streamline their data science careers.

The chart below outlines other useful degree concentrations for entering data science quickly.

3. Python and R Programming Experience

Python and R are the must-have programming skills for data scientists. Not only that, but data scientists must also understand Python at a master level. This can take anywhere from a few months to several years depending on where your Python skills currently stand.

About 50% of data scientists on Kaggle say they have between one and five years of experience working with Python, but many have several more:

Kaggle

4. Mathematics and Statistics Knowledge

Data scientists apply their programming skills to data sets to transform and manipulate them into useful insights through various tools. That’s why programming is only half of the equation.

If you already have advanced experience in Python, you’ll still need to familiarize yourself with the relevant math and statistics. Specifically, these areas:

• Basic calculus principles
• Linear algebra for performing functions
• Probability and statistics
• Discrete math for finite positions
• Graph theory for solving certain problems

Your data science career path will typically only require general principles of calculus. However, probability, statistics, and linear algebra will come up daily for most data scientists.

5. Data Analysis and Visualization Experience

Do you have experience as a data analyst working with visualizations? You’re in a great position to blend in some programming and become a data scientist.

Data scientists need to manage and manipulate data into useful insights. To do that, you’ll have to present reports, visualizations, and explanations to other people at a company who aren’t data scientists.

Anyone with SQL experience in business operations applications could also move swiftly through data science bootcamp if they have the other skills covered.

6. Professional Background

Your current career might provide some leverage for breaking into data science if it matches the field’s often required skills. Software engineers with bachelor’s degrees or master’s degrees could make the most seamless transition to data science if they have a strong background in Python.

Software programmers, data analysts, and data engineers can also become data scientists in less time than those entering the field from other industries.

7. Familiarity with Data Science Tools

Beyond programming and data analysis, you’ll need to apply these skills within the appropriate data science tools. You’ll need to learn the commonly used data science tools in business environments.

These tools typically fall into four main categories:

• Data grabbing, storage, and cleaning
• Data manipulation and transformation
• Data modeling
• Data visualizations and presentations

8. Portfolio of Project Experience

Even with the perfect degree and skill qualifications, companies need to see a diverse portfolio of relevant data science projects before offering you a job. If you already have a background in programming, you might be off to a great start.

Otherwise, you’ll have to find other routes to build your portfolio with projects. Make sure to choose projects that relate directly to the industry where you’re applying for jobs.

• Internships at companies in the industry
• Hackathons, bounties, or ethical hacking
• Live projects from relevant companies
• Coding challenges and group projects
• PhD research projects

9. Completion of Data Science Bootcamp

A bootcamp can provide tremendous leverage for becoming a data scientist in just 12 weeks. If you have a degree and are well versed in Python and statistics, NYC Data Science Bootcamp can help you blend the two disciplines.

You’ll learn how to apply Python and R programming to data analytics and visualizations, machine learning, deep learning, Big Data, and more, along with all the relevant tools. Students also complete hands-on projects for their portfolios.

Plus, NYC Data Science Bootcamp also offers career assistance like mock interviews, resume guidance, networking events, and a 2,000+ alumni network. That’s why it consistently ranks among the top data science bootcamps on SwitchUp.

How to Become a Data Scientist with a Degree

As mentioned earlier, 95% of data scientists hold at least a bachelor’s degree but 75% have a master’s degree or higher.

A master’s in computer science improves your chances of becoming a data scientist quickly through bootcamp. However, those with bachelor’s degrees or degrees in other fields can still get there with the right preparation.

Evaluate Your Level of Programming Experience

Anyone thinking of becoming a data scientist who already holds a degree will first want to examine their level of programming experience in Python and R.

To prepare for a data science bootcamp, you’ll need to master:

• Data wrangling
• String manipulation
• Control structures
• Generating reports
• Creating visualizations
• Documenting code

For data manipulation and visualization modules, you’ll want to know how to use NumPy, SciPy, pandas, matplotlib, and seaborn within the IPython notebook.

Consider Your Level of Data Analysis and Statistics

Next, you’ll need to evaluate whether your knowledge of statistics meets the basic demands for data science theory. Brush up on linear algebra, calculus principles, and probability.

SQL databases and commands can help prepare you for applying statistics and data analysis to data science as well.

Enroll in Data Science Bootcamp Prep

When you think your Python, R programming, and data analysis skills have reached their peak, sign up for the NYC Data Science Bootcamp Prep.

You’ll learn the foundations of Python, R, and data visualization for building applications in R Shiny and using all the practical tools in the main data science bootcamp.

Build a Project Portfolio to Prove Your Skill

Both during and after bootcamp, you’ll need to bulk up your experience with projects you can add to a portfolio. Your portfolio should mimic tasks and solve problems commonly found throughout businesses in the industry you plan to work.

By enrolling in the main NYC Data Science Bootcamp, you’ll complete a capstone project designed to meet modern business demands, which you can add to your portfolio. You can also have the opportunity to work on live projects from leading companies.

How to Become a Data Scientist Without a Degree

While challenging and rare, 5% of data scientists don’t have a bachelor’s degree. This means becoming a data scientist without a degree isn’t impossible for the right people.

Start from a Career with Overlapping Data Science Skills

It’s easiest to become a data scientist without a degree if you transition from a similar field. Software engineering, data engineering, data analysis, and software programming are your best bets.

Keep in mind that even if you have the right level of experience and skill, many companies may not hire you for data science roles without a degree.

Build the Relevant Statistics and Data Analysis Knowledge

Data analysis and statistics are some of the toughest skills to learn outside of a college setting. Advanced calculus isn’t necessary, but you will need to use other types of math daily like:

• Linear regression
• Probability
• Linear algebra
• Discrete math
• Graph theory

Learn the Right Skills in Python and R Programming

Of all the mandatory skills for becoming a data scientist, Python tops the list. Nearly every job requires data scientists to apply Python and R programming to data sets for manipulation, transformation, cleaning, and more.

Make sure to learn Python and R as they relate to data analysis and visualizations with modules like NumPy, SciPy, pandas, matplotlib, and seaborn.

See If You’re Ready with Data Science Bootcamp Prep

NYC Data Science Bootcamp Prep takes you through introductory Python, R programming, data analysis, and visualizations to prepare students for the full intensity of bootcamp.

You’ll learn how to use all the relevant tools for manipulating and wrangling data, generating reports, applying Python codes, and more. It can also help you figure out if data science is the right career path for you.

How to Become a Data Scientist in 12 Weeks

NYC Data Science Bootcamp teaches students the skills to become data scientists in just 12 weeks. While intensive, students complete the bootcamp armed with the experience they need to apply for jobs and tools to succeed in the field.

Bootcamp is only appropriate if you already have the right level of knowledge in Python, R programming, and data analysis. Those that do will learn things like:

• Data analytics with R for loading, saving, and transforming data, dynamic reports, and dynamic data in Shiny.
• Data analytics with Python for visualization packages like NumPy, SciPy, pandas, matplotlib, and seaborn.
• Machine learning with R for algorithms, mining data, building linear models, and so much more.
• Machine learning with Python for supervised and unsupervised learning algorithms, clustering, other models, and more.
• Advanced topics like Big Data or Deep Learning, depending on the student’s planned career path.
• Hadoop, Spark, Hive, TensorFlow, Natural Language Processing

NYC Data Science Bootcamp students also complete a capstone project designed to apply the skills to a practical business operations scenario, along with other hands-on projects throughout the course.

As part of the NYC Data Science alumni, you’d also unlock access to useful career assistance thanks to the school’s corporate partnerships and a large network of over 2,000 students. Instead of entering the job market unprepared, you can take advantage of things like:

• Personalized resume reviews and mock interviews
• Mock coding challenges to prepare for interview processes
• Networking events with industry partners
• Opportunities to work on live projects for leading companies

NYC Data Science alumni already work at companies like Google, Verizon, Deutsche Bank, and more.

Apply for the Upcoming NYC Data Science Bootcamp

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