How to Become a Data Scientist in 2022: Courses, Bootcamps, and Other Training Resources

Data Collection

Data collection is the process of acquiring data from various sources, such as news sites, surveys, social media, and even website cookies. Data scientists must collect data generated from millions of users all over the world. It’s a significant part of the process and requires diligent work.

Data Cleaning

Data cleaning is the next step after collection. A data scientist scrubs through raw data and categorizes it accordingly. They have to look for inconsistencies and missing data to make sure there are no errors with their analysis. This requires strong attention to detail and technical skills to ensure the data scientist has clean data sets to work from.

Data Analysis

After the data is cleaned, the data scientist begins inspecting the information through data analysis. Data analysis is the process of inspecting data closely to find key patterns and trends. To make this information presentable to others, there is a great deal of statistical analysis and visualizations done in this stage. Data analysis highlights the points in need of further analysis. Visualization tools also allow data scientists to note the outliers and explore these further.

Data Modeling

Data modeling is the process of analyzing and organizing how data should be updated, collected, and stored. It’s an important aspect of any organization that wants to make use of large amounts of data. A data scientist uses various powerful tools and techniques to understand the best ways to create a data model for a business.

There are various ways to build a model. It could either be through machine learning techniques or statistical modeling. Only after the modeling can a data scientist start deriving insights from it.

Data Interpretation

Data interpretation is the derivation of insights from a data model. Data scientists must present these insights in a way that stakeholders can understand.

This is when one has to go back to the drawing board. How do the insights answer the questions posed at the beginning of the project? What’s the best way of communicating these insights?

For example, a data scientist could use information about user preferences to recommend products to a user on an e-commerce site.

Data Scientist

Data scientists work in every part of the data process, from collecting to organizing, analyzing, and interpreting. Data scientists must provide insights to help organizations come up with strategies to meet their goals.

Data Analyst

A data analyst is an entry-level data science job. Data analysts spend most of their days analyzing data and writing recommendations. They work with existing data and provide a summary of sorts that details the company’s performance.

Data Engineer

Data engineers work on building data infrastructure and dataset processes that data scientists use for analysis. Think of them as software engineers and data scientists combined. To thrive in the role, you must be fluent in programming languages such as Python, R, Java, and Scala.

• Ability to prepare data.
  As a data scientist, you’ll encounter cluttered datasets. You’ll need to know how to prepare data for analysis to solve a specific problem. This will involve sourcing, arranging, processing, modeling, and amending data into a readable format.
• Basic statistical skills.
  You must have a good understanding of the fundamentals of statistics and statistical analysis. These include distributions, probabilities, A/B statistical testing.
• Data visualization.
  Whether you’re a junior data scientist or a senior data engineer, visualizing data is critical, especially for clients that prefer seeing a graph to a dataset. You’ll need to use visualization tools, such as d3.js and Tableau.
• Fluency in programming languages.
  Using code to build your algorithms is an important part of the job. Some companies have specific languages they expect you to know. The most common ones are Python, R, SQL, and Scala. For an in-depth discussion of each, check out our guide on the best programming languages for data science.
• Excellent communication skills.
  As a data scientist, you’ll need to communicate with other data scientists to share your findings. You’ll also work with other departments to help them solve their data problems. To do these things effectively, you must know how to communicate your insights depending on your audience.
• Strong business mindset.
  Data science is all about solving business problems through data. You’ll have to approach problems as if you were an executive. This means having a strong understanding of particular business operations and strategies to give actionable insights.
• Critical Thinking.
  Being able to think critically is important when evaluating data and finding insights in large datasets. This entails tackling problems from different angles and perspectives.

1. Choose a data science career path.

   The first step is to consider what kind of work you would like to do as a data scientist. There are many applications for data scientists as discussed above. You can be a data analyst or a data engineer. Other roles include machine learning engineers and enterprise architects.

2. Choose your data science learning path.

   Do you wish to attend a university? Are you more suited for the skill-intensive training provided by coding bootcamps? Or do you perhaps wish to try your luck at self-study?

   
   

   Articles upon articles have pitted college degrees against bootcamp certifications. In the past, pursuing a computer science degree was the main path toward becoming a data scientist. Over the last few years, another popular option has emerged: coding bootcamps.

   
   

   Coding bootcamps are intensive programs designed to equip you with the skills you’d need to break into the tech industry. The allure of the bootcamp model lies in its short, immersive, and more affordable structure. Premier bootcamps even offer career support post-program to help you secure a job immediately and thrive in it.

   
   

   Each path has its benefits and drawbacks. Many have opted to take the best of each method to further their education.

3. Build your data science portfolio.

   To succeed in a career in data science, you’ll need to put what you learn into practice. Few employers hire data scientists without real-world experience. So, finding ways to build some projects to add to your portfolio is important.

   
   

   Check out our guide on data science projects for beginners. There, you’ll encounter some basic data science projects, with links to relevant datasets. Once you’ve accomplished these, your next step is to launch your project to the world. Read how to launch your portfolio project online through Career Karma here.

4. Gain data science experience through internship programs.

   Getting a data science internship is another way to boost your profile. Scour popular job websites, such as Indeed and LinkedIn, for great internship opportunities. If you already have a company in mind, check its website for any internships. Even better, go in person and inquire.

   
   

   Remember that you’re not the only one on the lookout for some work experience so use all the resources at your disposal. Do you know someone in the company who can add in a referral? Does your school have some kind of a partnership with the company? Most coding bootcamps have employer partnerships that you can leverage for an internship opportunity.

• Prepare a solid technical resume.
  Hiring managers glean their first impression of you through your resume. A neat-looking CV that represents your skills well is thus essentia
• Clean up your portfolio.
  Your portfolio acts as proof of your data science skills. It displays the quality of your work as well as how you work. Only include the projects you are most proud of. Remember quality over quantity.
• Prepare for a technical interview.
  While your portfolio may look good, hiring managers will want to make sure that you’re well-rounded. You need to exhibit an understanding of data science beyond the projects you’ve completed. A technical interview gives them the chance to test out your knowledge.

The Best Data Science Bootcamps

Below are the bootcamps that offer the best immersive data science programs. Data science bootcamps are career-focused and students often receive hands-on experience and work on real-world projects in their courses. Many data science bootcamps allow people to become data scientists without a degree.

Flatiron School

• Duration:
  15 weeks
• Cost:
  $15,000 – $17,000 (differs by market)

A consistently top-ranking bootcamp in Career Karma, Flatiron School offers one of the best data science bootcamps out there. It advances a holistic learning approach that boasts three main features: a curated community, passionate instructors, and a money-back guarantee.

Flatiron School’s data science curriculum gravitates toward developing your fluency in Python and skills in machine learning. The program starts with some prework before moving to the primary modules, each of which lasts three weeks:

1. Introduction to Data with Python and SQL
2. Statistics, A/B Testing, and Linear Regression
3. Machine Learning
4. Big Data, Deep Learning, and Natural Language Processing
5. Data Science Advanced Project

NYC Data Science Academy

• Duration:
  12 weeks
• Cost:
  $17,600

This New York-based bootcamp was specifically created to provide data science training. It offers a standard bootcamp delivered through remote live instruction and pre-recorded videos. . What sets NYC Data Science Academy apart from other data science bootcamps is its coverage of two programming languages, Python and R. Students also build statistical models while learning about linear regression, data classification, and visualization.

Data science course bundles Data science course bundles are also available. Among these are Data Science with R, Data Science Mastery, and Data Science Launchpad with Python.

General Assembly

• Duration:
  12 weeks
• Cost:
  $15,950

General Assembly is another top professional training school for tech fields, namely software engineering, UX design, digital marketing, and UX design. General Assembly programs come in online or on-campus formats. Its Data Science Immersive is a 12-week bootcamp that focuses on data science essentials, including Python programming, exploratory data analysis, data modeling, and machine learning.

If you’re looking for a cheaper option, consider taking General Assembly’s Data Science Course. Lasting 10 weeks for $3,950, this course is ideal for data science professionals just looking to upskill and perform more complex analysis.

Byte Academy

• Duration:
  10 weeks/26 weeks
• Cost:
  $14,950

Byte Academy’s Data Science Bootcamp offers a comprehensive curriculum that hones your knowledge in data science fundamentals. These are divided into five modules, namely Python, SQL, mathematics, data visualization, and machine learning. What makes Byte Academy’s programs unique is their AI tutor, Aiza, created to guide students as they learn.

MIT’s MicroMasters Program in Statistics and Data Science

The Massachusetts Institute of Technology (MIT) doesn’t disappoint when it comes to tech programs. This course in MIT’s MicroMasters program was developed by the Institute for Data, Systems, and Society. Through it, students learn everything there is to know about data science.

The program consists of four parts, all crammed with interactive projects, hands-on activities, and more. There’s a proctored exam at the end and you will earn a certificate if you pass. You will leave the course equipped with excellent foundational knowledge in data science.

The course is open to anyone. Whether you’re a recent high school graduate or already an intermediate data scientist, MIT’s program is among the best.

Data Science Specialization from Johns Hopkins University (via Coursera)

Another celebrated name in education, Johns Hopkins University is one of the best higher education schools in America. This specialized course will equip you with the tools needed to launch your career in data science.

Taught by Dr. Jeff Leek, an expert in biostatistics, this data science program emphasizes familiarity with the entire data science pipeline. Students will learn critical aspects such as regression analysis, programming languages, data cleansing, and data manipulation.

The course culminates with hands-on projects; once you complete them, you’ll earn a certificate.

Metis: Introduction to Data Science Course

In its Introduction to Data Science Course, Metis takes aspiring scientists into the minutiae of the field. This data science course explores aspects of the field not usually included in their data science bootcamp.

Students will learn:

1. Different forms of data manipulation
2. Data analysis and visualization
3. Computer science
4. Basic statistics

Metis designed this particular course for those who are interested in data science and who are considering pursuing further education after completing the class.

By the end, students will be able to use programming languages like Python and Ruby. Additionally, graduates of the data science course will know all there is to know about the data science pipeline.

Other courses teach various topics to help prepare students for work in the data science field. They include machine learning courses, programming courses to learn languages such as R, data wrangling, and data visualization. These are multi-level courses ranging from beginner to expert.

Thinkstats

Allen B. Downey

We went back and forth on whether to include a text devoted exclusively to probability theory on this list. But there’s almost nothing more foundational to data science than the said subject. Downey’s book does a great job of covering just what an aspiring analyst needs to know.

Data Science from Scratch

Joel Grus

If you want to learn how to become a data scientist from scratch, then this is the book for you. It gives you a great foundation on data science and covers the core fundamentals. It’s a great resource to have, even if you’re starting out in an online course or data science bootcamp.

An Introduction to Statistical Learning

Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

This is one of the foundational texts for data science. You’ll be hard-pressed to find a more pithy and thorough overview of the major data science algorithms.

Python for Data Analysis

Wes McKinney

This book will teach you data science through Python, NumPy, and Pandas, three of the most important tools you can learn. If you get a job in data science, you will almost certainly be using them regularly.

Understanding Machine Learning

Shai Shalev-Shwartz and Shai Ben-David

Machine Learning is all the rage these days, but there’s a lot of nonsense mixed in with exciting advancements. Whether you do ML or not, you need to understand the math behind it.

Certified Analytics Professional (CAP)

This is a global professional certificate that shows your ability to turn data into valuable insights and actions. The base price for this certification is $695.

Cloudera Certified Associate: Data Analyst

This certification teaches how to perform the core competencies required in Cloudera’s CDH environment using Impala and HIVE. The base price for this certification is $295.

Cloudera Certified Professional: CCP Data Engineer

This certification improves your data engineering skills. It also teaches data transformation, storage, and analysis. The cost of this certificate is $400.

Data Science Council of America (DASCA) Senior Data Scientist (SDS)

This is for data science professionals who want to further their careers into more challenging roles such as data scientists or analysts. The cost of this certification is $650.

Data Science Council of America (DASCA) Principle Data Scientist (PDS)

This certification is for the most experienced data scientists. It is essentially a stamp of approval from the most elite of data scientists. The cost of this certification ranges from $300-$950.

If you are a recent college graduate, or just trying to demonstrate your data knowledge, then a certification might be right for you. Essentially, if you have the time and money to get a certification, then there is no harm in doing so. Occasionally and in some circumstances, a certification can lead to a salary increase depending on where you are in your career.

Data scientists have an average salary of $108,971. Entry-level roles usually pay $95,000 while senior roles rack up to $185,500. As with other jobs, data scientist salaries are not set in stone. Their earnings may vary by company, location, and experience.