How to Become a Data Scientist from Scratch

How to Become a Data Scientist from Scratch

Posted by Vivian Zhang

Updated: Sep 15, 2021

Data science work is continuously advancing, making it an exciting field to tap into. It is also highly versatile. As a data scientist, you can work in virtually any industry, including retail, healthcare, manufacturing, finance, technology, and business management. Learning data science will give you a solid foundation for more advanced technical skills, while also positioning you to grow into high-level leadership roles.

To become a data scientist, you must familiarize yourself with essential programming languages, understand statistics, and learn data analysis, SQL, and machine learning algorithms. After gaining the proper knowledge, it is vital to gain hands-on experience before scoring your dream job.

Data science is a hot field to enter in the modern age. Currently, there are more open data science positions than qualified professionals to fill them. According to Glassdoor, it ranks #2 in the “50 Best Jobs in America for 2021.”

Our data science roadmap step-by-step guide will provide a complete guide to the tools, resources, and steps you need to follow to go from zero (having no experience in the field) to becoming a successful and knowledgeable data scientist.

What is a Data Scientist?

Data scientists are data experts who can solve complex technical problems for organizations. Using various techniques, math, and science, they analyze large data sets (structured and unstructured) to extract essential insights.

Data science involves identifying trends and managing information to solve business challenges. You can think of a data scientist as a hybrid role that wraps math, computer science, curiosity, and creativity all into one.

How to Become a Data Scientist from Scratch

Keep in mind that there are no guarantees of success, but this guide will help you take the essential steps necessary to build a strong foundation the fastest way possible. Here’s the best process for anyone who wants to learn data science from scratch.

1. Get an Undergraduate or Graduate Degree

To become a data scientist, the first step is earning a degree in engineering, computer science, or statistics. If you already have an undergraduate or graduate degree in one of these fields, you are off to a great start. While you may get into the field without an official degree, it can be very challenging. Only 5% make it with just an associate’s degree or high school diploma.

Data Science Programs

In school, you will learn the essential skills needed to apply for internships and jobs in data science. You can gain vital SQL, Python, and R skills. To be competitive in this market, you should build a solid technical background in software engineering, computer science, math, and statistics.

2. Learn Python and R Programming in Depth

Next, it is time to learn programming languages at a more advanced level. Python is the most common type of coding language. It is what most data scientists use because of its simplicity and versatility. It is a robust, open-source language that supports various libraries.

R programming is another important language for many data scientists to know today. It is an intuitive language explicitly designed for statistical analysis. You can use it to create reports, develop software packages, and more.

If you are hoping to work for a top tech firm someday (like Google or Facebook), you will most likely use R programming. Airbnb, Uber, the New York Times, IBM, and Microsoft all use R programming, as do many financial institutions, academic institutions, and research labs (Dataquest).

3. Review Algebra, Calculus, and Data Science Statistics

Before applying to data scientist positions, you may need to brush up on your math and statistics skills. To decipher data effectively, you must understand linear algebra, statistics, and calculus. Statistics and algorithms will become your bread and butter. Knowing these skills inside and out will help you stand out from others vying for the same role.

4. Complete Bootcamp Prep

Before enrolling in a data science program, we highly recommend taking a few preliminary courses. Our Data Science Bootcamp Prep program will prepare you for our immersive Data Science Bootcamp. Take classes in Python, data analysis, R programming, and visualization to build a stronger foundation and prepare yourself to build applications on your own in the future. You will learn practical tools you can carry throughout your entire career.

The program includes three courses:

  • Introductory Python
  • Data Science with Python: Data Analysis and Visualization
  • Data Science with R: Data Analysis and Visualization

5. Enroll in Data Science Bootcamp

Next, you will be ready to enroll in our comprehensive online program – Data Science Bootcamp – to gain relevant industry project experience. We offer in-person (on-campus) and remote live instruction as well as interactive distance learning. You will learn how to apply programming skills to data analysis and visualizations through accelerated learning. The program will equip you with the skills and tools you need to succeed as a data scientist at top companies.

Our Bootcamp is the best in the U.S., regularly updated with advice and knowledge from leading industry experts. We offer students an engaging community across various industries, along with lifelong career support. Choose from a 12- or 16-week full-time schedule or a 24-week, part-time commitment (20-30 hours per week).

The Bootcamp is 400 hours total and includes eight modules, listed below:

  • Data Science Toolkit
  • Data Analytics with Python
  • Data Analytics with R
  • Business Cases in Data Science
  • Machine Learning I
  • Machine Learning II
  • Data Science: Advanced Topics
  • Capstone Project

6. Understand How to Use Industry Data Science Tools

Learning the right software is essential. Most tools fall into four main categories: transformation, modeling, visualization, and storage and cleaning. Basic tools every data scientist should understand front to back include:

  • Amazon Cloud
  • Tableau
  • Hadoop
  • Spark
  • Docker

Tableau is a powerful, industry-leading data visualization tool to help users simplify raw data and analyze it quickly. You can deploy the platform on-premises, in the cloud, or natively by integrating it with Salesforce.

TensorFlow is a more advanced, intuitive platform. Its end-to-end open-source AI library uses data flow graphs to build machine learning models. It also has a flexible and comprehensive toolkit for cutting-edge ML.

7. Learn Processes for Basic Machine Learning Applications

Machine learning is a branch of artificial intelligence. It is a form of data analysis – studying computer algorithms that can learn (improve) by using data. By identifying patterns, systems can make logical decisions automatically – without human interaction.

Learning critical processes for machine learning is another essential step for aspiring data scientists. Experts predict the ML market to grow from USD 15.5 billion in 2021 to USD 152.24 billion by 2028 at a 38.6% CAGR.

To remain competitive in the data science market, you will need to understand how to implement ML algorithms and build and implement ML models.

8. Apply Data Science Skills to Relevant Industry Projects

To gain the skills you need to work for leading organizations, you need experience working on real projects. You can achieve this in several ways: through volunteering, coursework and workshops, internships, and more (covered later in the article).

Here are some examples of hands-on projects to add to your portfolio. These will look good to potential employers and help you stand out from other candidates:

  • Machine learning
  • Data cleaning using R or Python
  • Exploratory data analysis (EDA) using Python or SQL
  • Interactive data visualizations (using dashboard software like Tableau or TensorFlow)

9. Build Your Portfolio

Add the projects to your portfolio that you have completed successfully to demonstrate your experience to potential employers. Having a strong data science portfolio will help you stand out and get hired. Your portfolio should demonstrate the specific skills, languages, and experience employers are seeking. Here are some of our top tips for building a successful portfolio:

  • Showcase practical and relatable projects that you can apply in everyday life.
  • Illustrate a balanced set of technical skills.
  • Demonstrate good coding practices, business sense, and presentation skills.
  • Use actual data and real technologies.
  • Create Easy-to-Understand Data Visualizations to Demonstrate Your Data Storytelling Expertise

10. Find Opportunities

Finding a relevant internship, mentorship, live project, or entry-level position can give you the experience you need to grow into your dream role. Try to find data science positions in your desired industry.

As a career-oriented data scientist, you may choose to start as a data analyst or junior data scientist and work your way toward a higher-level position over time. Building projects before you are hired can demonstrate initiative and passion, making your resume and portfolio appealing to hiring managers.

11. Determine Your Long-Term Career Goals

What are your long-term goals? When searching for the right path to begin your career in data science, as a data scientist, make sure you understand your options. Read job descriptions carefully and ensure you have the skillsets to match. In addition to working under the title of data scientist, here is a list of viable roles you can fill with your experience and background:

  • Data scientist
  • Data engineer
  • Data analyst
  • Data and analytics manager
  • Data architect
  • Data administrator
  • Business analyst
  • Statistician
  • Machine learning engineer
  • Machine learning scientist

12. Perfect Your Resume and Transferable Skills

Design your resume to reflect your desired data scientist role. Your resume should speak to the specific opportunity you are applying for, so you may need to tweak it for each application. Make sure the language you use is suitable to the organization, matching its tone and level of professionalism.

Before applying to any organization, ensure the job description requires skills unique to data science, such as experience in Python, SQL, R, statistical modeling, and business intelligence tools. This will help you avoid non-legitimate job postings. Be sure and research the company in depth to know that it is well established and has an optimistic market outlook.

Here are several other quick tips to help you prepare your resume and application for a data scientist position:

  • Keep your resume brief, and update it for every application.
  • Focus on business outcomes to show hiring managers you understand your role on a team and in a company.
  • Highlight relevant projects.
  • List skills the particular job description includes.
  • Include soft skills in addition to your technical skills.

13. Build a Professional Network

To find the best jobs in data science at leading companies, you may need to tap into professional networks. Many companies prefer hiring candidates from top schools or by referral rather than posting to job boards. Joining a professional organization may give you access to highly sought-after jobs.

Attend data science events and conferences, connect with other data scientists on LinkedIn, and stay in touch with past colleagues and classmates. Reaching out to your alumni network and introducing yourself to second-tier connections may also open new doors that connect you to employers. Participating in contests, sponsored projects, and internships can also help you expand your network.

14. Land Your Dream Role

To find your dream company, you first need to know what to look for. While you may focus on what you can bring to the company, you also need to vet the employer to decide if the position would be a good fit. Keep in mind that industries with excellent growth figures are often in the finance, technology, and healthcare sectors.

Since employers tend to pay data scientists well, criminals commonly use fake job postings to collect personal information for identity theft. Therefore, you should always conduct in-depth research on the company before applying – to ensure they’re legitimate. Also inquire about a company’s benefits, the size of the team and department, and how promotions work. Ask who you would report to and work with directly.

Your job search may take months. Starting with a well-honed strategy can help you pinpoint the type of organization you want to work for and the role you wish to fulfill. Your strategy should include what you’re looking for in a company, your dream position, and the resources you need to get there. Throughout the process, revisit your vision and desired outcomes. To find the right position, you may need to enhance your resume. Or, you might just need to be patient with the process.

15. Continually Learn and Grow

Push forward to grow your skills with intermediate and advanced courses. As you gain more experience, you will identify your strengths and weaknesses. Whether you have already landed a job or are in the process of applying, focus on what you can control. That includes actively working on becoming a stronger candidate for a present or future position.

Continually building your foundation and learning new skills can help you become more attractive to leading employers. Filling gaps in your knowledge and reinforcing what you know will give you greater confidence and experience. Consider online programs and courses that will bolster your qualifications and help you stand out from other candidates.

Interested in Becoming a Data Scientist from Scratch?

NYC Data Science Academy has been voted the “Best Data Science Bootcamp” in the United States five years running. Our application process is simple: apply, talk to an admissions officer, complete a technical assessment, and begin the program. We offer various options to cater to different experience levels, time commitments, budgets, and preferred levels of support.

Apply to NYC Data Science Bootcamp


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Vivian Zhang

Vivian is the CTO and School Director of NYC Data Science Academy and CTO of SupStat. With her extensive experience working in the data science field, she developed expertise in multiple programming languages, including R, Python, Hadoop, and Spark. In August 2016, Forbes ranked her amongst one of the nine women leading the pack in data analytics. In 2013, she created the NYC Open Data Meetup group, which stands as one of the largest data science communities offering meetups, conferences, and a weekly newsletter. In her spare time, Vivian enjoys meeting people and sharing her motivational stories with our students and other professionals

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