Best Data Science Certification Courses to Boost Your Career in 2021

Best Data Science Certification Courses to Boost Your Career in 2021

Data science has been rapidly growing in popularity, with impressive salaries and roles in the company. There’s a lot more in the field than you might expect, whether it’s designing the right graphs or analyzing that hundredth correlation matrix. You’ll find yourself with many more job prospects once you put a data science certification on your resume.

After all, sources say that we currently have a shortage of data scientists. Job postings looking for data scientists increased by 31% in 2018, while job searches for data scientists only increased by 14%. More people are hiring than looking, and a certificate can be your first step in entering the data science market.

Learning data science can be challenging at first with all the technical jargon. However, picking out a data science course can make the process much easier, whether you want to study the statistics behind analysis or look at machine learning models. Your resume and job prospects will definitely thank you.

In this article, we’ll cover the details of what you can expect in some of the best data science courses. After charting the best online options, we’ll analyze any extra information you’ll need to get started on your data science journey.

Orientation

Data science has evolved over the past decade as datasets grow larger and more complex. Today, these scientists must be skilled in data mining, programming, analysis, and more. They have to be confident with a wide range of skills and get comfortable with manipulating data to get accurate results.

However, data science is also one of the harder fields to teach yourself. A data science class can be a solid opportunity for you to learn the skills needed, and course projects can be a good addition to your resume. Below, we’ll cover some of the most important things to look for in your ideal data science certification course.

Scope

Data science is a complex field, and it can cover many topics. When you first start, you’ll want to have an idea of what you want to master. Is it looking into visualization and graphs so you can better communicate your results with the audience? Or is it more along the lines of machine learning, so you can create models that forecast future quarters?

Some data science courses are focused on the fundamentals, setting up the stage for you to explore on your own. Others take you through a comprehensive view of the course, studying more advanced topics and techniques for data analysis. The right fit for you will depend on your goals.

Certificates Offered

Data science certificates can be an easy way to stand out from the competition to the recruiter. They’re evidence that you have solid data analysis skills, and they show you know how to manipulate and collect data as well. While there isn’t one officially accepted data science certificate, you do have many choices.

One of the most common certifications is the Certified Analytics Professional, or CAP. However, you’ll have to create an account before taking this exam and pay the fee, which can cost close to $700. There are many other certifications as well, and many of them fall within similar price ranges.

If you don’t feel like paying for another certification exam, you can also get a certification of completion for various edX courses in our course list. Examples of classes that come with a certificate include Computational Thinking and Data Science and Introduction to Data Science.

Prerequisite Knowledge

Courses can cover different amounts of information, and they appeal to different types of students. While we have a wide variety of courses that we’ve selected, only one or two of them might fit your needs. Below, we’ll cover the main categories of students that we’ll be referring to throughout the article.

  • Beginner students have very little background in data analysis and coding. If you’re in this category, you’ve likely not had prior training with Python, R, or other statistical packages, and you’ll want to focus on the fundamentals.
  • Intermediate students have tried statistics or coding in the past, and they may know some of the basics already. They’re often looking to expand their skills and start exploring more data science techniques with regression and forecasting.
  • Advanced students already have extensive experience with programming and analysis, and they’re often students who want to top off their skills with a more in-depth understanding of the theoretical side. They’re looking to learn more advanced techniques as well, such as incorporating Excel or looking at simulation techniques.

Time and Cost

A data science course can take a long time to finish, and you’ll need to set aside at least a month to fully learn the basics. Some courses may take multiple months, such as Computational Thinking and Data Science, which is a particularly time-intensive class.

These courses typically run around $100, so most of your budgeting will go towards a certification exam (if you choose to take one with an outside vendor). Many courses also have payment plans if you’re worried about cost. As long as you watch all the modules and complete the assignments, you’ll be getting the most value for your money.

Cheat Sheet

blue infographic showing cloud computing and data analysis

Below, you’ll find a summarized list of our favorite data scientist certification courses. We’ve tried to cover courses that can help any data science topic you might have in mind, so we hope that you’ll fit into one or two of the categories below.

  1. If you’re looking for a course that gives a solid overview of everything you can do with data science, our regression line matches the Complete Data Science Bootcamp with your needs.
  2. If you’re just starting out and want a high-level overview of how data scientists work, the Introduction to Data Science course shows up on our graphs.
  3. If you want to learn how to use machine learning for data science, the Machine Learning A-Z class charts out the right path for you.
  4. If you want a course that has lots of hands-on practice and examples, the Data Science A-Z course has that base covered.
  5. If you have little experience but want to learn more about data science, Data Science: Visualization serves as a solid median.
  6. If you already know how to use Python and R for data science but you want to deepen your understanding, Computational Thinking and Data Science can take your statistical ability to the next level.
  7. If you want to learn how to use data science when it comes to spreadsheets, we’d recommend you test out the Python for Excel course.

Top 7 Best Data Science Certification Courses 2021


1. Most Comprehensive Data Science Course: Complete Data Science Bootcamp

  • 29 hours $99.99 Our rating  
  • Course Highlights
  • Instructor: 365 Careers
  • Programs Used: Python (NumPy, statsmodels, scikit-learn)
  • Skill Level: All Levels

Why we like it

This course gives an in-depth look into everything you’ll need to start your data science career. Covering topics from statistics to machine learning, you’ll be prepared for any big data problem you encounter in the field.

Our Review

The Complete Data Science Bootcamp is taught by 365 Careers, which is the #1 best-selling provider of finance courses in the world. Having taught over one million students, they’ve fine-tuned their lectures and exercises to make learning much easier for you. The course itself goes over dozens of modules and packages so that you’ll have options when it comes to analyzing data.

The course is mainly taught in Python, and it has 63 different modules so that you can easily distinguish between topics. It covers everything from the statistics behind the packages to an introduction to deep learning. It even covers TensorFlow and can help with software integration towards the end of the course.

Best Suited For

This class is great for students who want to have lessons on everything that data science can cover. Because of the wide range of topics and in-depth discussion, it’s also a longer course, so it’s best for students who have time to sit down and spend time learning.

However, it’s not a great course for students who might want to only learn the basics, or students who only want to look at more advanced topics. While you can choose to study only certain modules, you won’t be getting as much out of the course.

What Sets This Course Apart?

The depth and breadth that this course covers are unmatched by any others we’ve seen. It builds up the fundamentals by discussing the general field of data science, covering possible careers and high-level overviews of topics. Then it digs deeper into mathematics, even covering matrices and how to use them for data science. Topped off with deep learning modules, you’ll be well versed in any data science topic.

Pros

  • Gives a comprehensive overview of what data science can offer
  • Instructors have extensive teaching experience
  • Covers a wide range of python modules
  • Gives an in-depth look into mathematics and statistics
  • Discusses software integration and more advanced topics

Cons

  • Not suited for students who only want to learn a few topics


2. Best Data Science Course for Beginners: Introduction to Data Science

  • 6 weeks, 6 hours per week Free ($39 Certificate) Our rating  
  • Course Highlights
  • Instructor: IBM
  • Programs Used: Python, High-Level Statistics
  • Skill Level: Beginner

Why we like it

This course serves as an introduction to data science by teaching you the vocabulary, roles, opportunities, and expectations you’ll face. With a focus on high-level content covering how data science works, it sets you up for harder data science courses in the future.

Our Review

Data science is very STEM-focused, and it can be hard to learn all the jargon in the field. In this course, you’ll be able to spend time learning the ideas behind data science from a higher-level perspective. You’ll be able to see the ideas behind machine learning without digging into the Python code yourself and find out how models function to analyze the data.

This course is taught by IBM and you can access it for free, though you can also receive a certificate of completion for a fee. It doesn’t offer as many practices throughout the course, but each module ends with some readings and review questions. Modules also end with a summary so that you can review the lessons quickly.

Best Suited For

This course is suited for beginners, since it doesn’t dig too deeply into Python code or R code. It teaches the higher-level concepts instead, so you know why you’re coding what you’re coding as you get into more advanced courses. However, since this course is more basic, you may want to take other courses as well before taking a data science certification exam.

For students who want to get deeper into data science and already have a background in coding, we would recommend you try the Computational Thinking and Data Science course instead.

If you’re not as advanced but you want to get a more comprehensive overview of data science, we’d recommend the Complete Data Science Bootcamp course.

What Sets This Course Apart?

This course doesn’t get caught up in coding and mathematical calculations. Instead, it focuses on developing the fundamental knowledge needed for the field by covering the jargon (such as machine learning and deep learning) or discussing careers in data science. It takes a look at the role of data science in business as well.

Pros

  • Discussing opportunities as a data scientist
  • Covers the fundamentals of what data scientists do
  • Teaches jargon used in the data science field
  • Offers summaries and quizzes after each module

Cons

  • Does not cover coding as a data scientist in-depth


3. Best Machine Learning Course: Machine Learning A-Z

  • 45 hours $94.99 Our rating  
  • Course Highlights
  • Instructor: Kirill Eremenko
  • Programs Used: Python, R
  • Skill Level: All Levels

Why we like it

This machine learning course gives you enough techniques to conquer any big data problem you’ll run into on the job. Delving deep into various models, approaches, and solutions, you’ll be well-versed in everything from logistic regression to decision trees.

Our Review

This machine learning course focuses on teaching how to create various modules using Python and R. Both software packages are often used in the data analyst field, and you’ll be able to add your experience with them on your resume once you finish the course. You’ll learn advanced techniques like dimensionality reduction while gaining new intuition about these models.

With exercises inserted throughout the course, you can try out your new skills as you learn. You’ll also gain access to Python and R code templates for you to modify, add on to, and run on your own device. By the end of the course, you’ll find yourself practicing on data sets for preprocessing and running your own analysis through the machine learning models.

Best Suited For

This course is best suited for data scientists who are interested in exploring machine learning and forecasting techniques. It starts the course from fundamentals, so beginners won’t find themselves being left behind. However, it quickly delves into advanced topics as well, such as principal component analysis and artificial neural networks.

However, the course itself is on the longer side, so we recommend it for those who want a deeper look into machine learning. If you find yourself more interested in other aspects of data science as well, we would recommend you try the Data Science A-Z course instead.

What Sets This Course Apart?

This course takes a deeper dive into machine learning and how you can use it to expand your data science skills. With this focus, you’ll learn about any R packages you’ll need for regression and how to use Python modules for classification. In addition, it also covers the differences between various machine learning models so that you’ll know how to attack any data problem you see.

Pros

  • Helps you develop intuition with machine learning models
  • Gain access to code templates
  • Covers advanced machine learning concepts
  • Packed with exercises for practice
  • Covers the data preprocessing stage

Cons

  • Focuses solely on machine learning


4. Most Practical Data Science Course: Data Science A-Z

  • 21 hours $94.99 Our rating  
  • Course Highlights
  • Instructor: Kirill Eremenko
  • Programs Used: SSIS, SQL, Tableau, Gretl
  • Skill Level: All

Why we like it

Learning a new skill is difficult, and you’ll need to spend hours practicing before it becomes second nature. This course is packed with real-world examples and exercises to help you on your data science journey and prepare you to work in the industry.

Our Review

This course is filled with examples and exercises to help you hone your data science skills. As it teaches you about multicollinearity, correlation matrices, and CAP curves, you’ll be manipulating your own set of data, so you’ll be prepared for serious work once you finish. However, it focuses more on the analysis and doesn’t go as in-depth when looking at the mathematical and statistical side.

The course is shorter than other courses but still covers its own unique topics. It even covers how you can present your findings as a data scientist. The instructor also teaches a machine learning course if you’re more interested in deep learning techniques.

Best Suited For

This course is suited for all learners. As it introduces any new software, it starts with the fundamental steps so that beginners won’t be left behind. In addition to learning how to install and navigate through the software, they’ll quickly cover practical applications for intermediate and advanced learners. With more than enough practice, it’s best for students who enjoy a hands-on learning process.

However, since this course is a little shorter than others, you won’t be getting as much material from the lessons. You’ll also be working with software such as Tableau instead of Python, so you may have to learn how to use Python for data science from another higher-level course. It all depends on which types of software you may prefer to use.

What Sets This Course Apart?

The first thing the sets this course apart is the numerous exercises included as you learn. The course itself is full of real-life exercises that train you in data mining, modeling, visualization, and more. In addition, the programs used varies as well. Instead of using Python, they’ll teach SQL, SSIS, Tableau, and Gretl, which are other examples of software that data scientists find useful.

Pros

  • Contains practice exercises and problems for all modules
  • Teaches how to navigate new software
  • Tailored for students of all levels
  • Covers programs such as SQL and Tableau
  • Offers advice on presenting as a data scientist

Cons

  • Doesn’t use Python for data analysis
  • Doesn’t cover in-depth statistics


5. Best Intermediate Data Science Course: Data Science: Visualization

  • 8 weeks, 2 hours per week $149 Our rating  
  • Course Highlights
  • Instructor: Rafael Irizarry
  • Programs Used: R (ggplot2)
  • Skill Level: Beginner, Intermediate

Why we like it

This visualization course for data science takes a deep dive into ggplot2 as it teaches the strengths, weaknesses, and capabilities of dozens of graphs. Its focused approach ensures you know how to use data visualization for your future job.

Our Review

Taught by a Harvard professor of biostatistics, this course helps solidify your data visualization skills. As a data scientist, creating graphs and charts is essential for analyzing the data you have, and they’re one of the best ways to communicate results with others in your department. The course also shows how to quickly identify mistakes when it comes to larger data analysis projects.

The course itself is free for you to audit, but you’ll have to pay for the full course experience (with graded assignments, projects, and more). You’ll also receive a verified certificate of completion with the full class experience to add to your resume. However, this course does focus mostly on data visualization, so if you want a course that covers more data science roles, we’d recommend the Data Science A-Z course.

Best Suited For

This course is best suited for beginner or intermediate students who want to expand their skills with data visualization. The class does use R when it generates graphs, but it teaches how to use the software step-by-step. This way, no one gets left behind. If you’re looking for a more challenging course, however, we’d recommend the class Computational Thinking and Data Science.

This course is taught by a Harvard professor with years of experience, and lessons are streamlined and concise. The course itself also takes less time per week when compared to other classes, so it’s suitable for someone who might have a busier schedule. However, the lower time required also means not as much material gets covered.

What Sets This Course Apart?

This course’s intense focus on data visualization is unmatched by other classes. You’ll be learning about how to use ggplot2 while working with real data sets on world health, economics, and infectious disease trends. In addition, the course looks at the weaknesses behind each plot, so you’ll know the pros and cons of various techniques before applying them in the field.

Pros

  • Doesn’t take as much time per week
  • Taught by a Harvard professor
  • Teaches the strengths and weaknesses of various graphs
  • Covers all the functionalities of ggplot2 in R

Cons

  • Solely focuses on data visualization
  • Doesn’t cover as much material


6. Best Advanced Data Science Course: Computational Thinking and Data Science

  • 9 weeks, 16 hours per week $75 Our rating  
  • Course Highlights
  • Instructor: John Guttag
  • Programs Used: Python (pylab)
  • Skill Level: Advanced

Why we like it

If you already know how to program, this course can take your data science skills to the next level. Taught by professors from MIT, you’ll get an in-depth look at advanced theories that can set you apart from the competition.

Our Review

Coming from MIT’s online series of courses, this class covers how to use computation when it comes to data analysis. You’ll be studying computational problem solving when it comes to dynamic programming, graph optimization, pylab plotting, and more. You’ll be studying several advanced concepts when it comes to programming and mathematics to apply them to your field.

It’s focused more on the theoretical than the practical, and you’ll have to connect the concepts taught to data science on your own. For a class that emphasizes practical applications more, we’d recommend the Data Science A-Z class. It also includes a verified certificate of completion once you finish and pass the class.

Best Suited For

This course is best suited for advanced students who are serious about improving their data science skills. It’s one of the longest courses on the list, and since it’s tailored for advanced students, it’s also very time intensive. It’ll be difficult to balance this course with a full-time job, so you might need solid time management skills to succeed.

This course assumes you start out with past Python programming experience and an understanding of computational complexity. As a result, if you’re just a beginner, we recommend you take courses that do a better job of covering the fundamentals, such as Introduction to Data Science.

What Sets This Course Apart?

This course does a great job at covering some of the most difficult concepts in data science from its theoretical roots. From Monte Carlo simulations to advanced Python programming, it ensures you’ll have a strong background in data science. It’s also taught by a team of three MIT professors and lecturers!

Pros

  • Covers higher-level theoretical statistics
  • Teaches Monte Carlo simulations
  • Taught by MIT professors
  • Gives an in-depth look at computational programming

Cons

  • Very time-intensive
  • Not suitable for beginners


7. Best Data Science Excel Course: Python for Excel

  • 16 hours $109.99 Our rating  
  • Course Highlights
  • Instructor: Alexander Hagmann
  • Programs Used: Python (Pandas, Seaborn), Excel
  • Skill Level: All Levels

Why we like it

You’ll learn how to effectively use spreadsheets as part of the data analysis projects, learning how to combine Python with Excel as you generate plots and create various Dashboard Apps.

Our Review

This course is best for anyone who encounters data that always comes in spreadsheets and wants to analyze it more effectively. You’ll be able to automate Excel and learn how to work with Excel Dashboard Apps to complete your tasks more efficiently. Unfortunately, only 85% of the course works for Apple users, though many of the techniques are cross-platform.

Alexander Hagmann is a Bestselling Udemy Instructor for multiple courses with more than 10 years of experience in finance and investment. As a result, his lessons are very practical and can be easily applied to the field. You’ll get the chance to work on three specially created projects just for the course and deal with datasets.

Best Suited For

This course is best suited for anyone looking to improve their data science skills alongside their Excel skills. Excel comes with many powerful functions and macros, and you’ll learn how to run Python scripts within Excel. It’s suitable for all levels of learners who aren’t already familiar with xlwings since that’s one of the main focuses.

In addition, this course also gives a crash course on Python for those who have no experience, so you won’t have to worry about being left behind. It also shows how to use Python data visualization packages within Excel (such as Seaborn). If you truly have no experience in Python, the code is already provided for you to run with the click of a button.

However, if you’re looking for a course that gives a better overview of all of data science rather than focusing on Excel, we recommend you check out the Complete Data Science Bootcamp.

What Sets This Course Apart?

This course takes a look at how to incorporate Excel into your data analysis skills. Many datasets can be manipulated through a spreadsheet service and being able to directly work with Excel when analyzing can make your job much smoother. The course studies xlwings in Excel which is a useful package that’s not talked about elsewhere.

Pros

  • Teaches how to integrate Excel and Python
  • Provides all Python scripts used
  • Contains three projects designed to deepen your understanding

Cons

  • Only 85% of the course works on Apple computers
  • Focuses on Excel and not on all of data science


Data Science Course FAQ

Online data science courses make the learning process much easier, and they’re worth it if you want to save time and energy through the learning process. While most of the information is available online freely, having a teacher, projects, and assignments can make the learning process significantly easier, quicker, and more enjoyable.

Data science basics can take one to six months to learn, depending on the scope and structure of the course. You’ll want to set aside several hours a week to have a proper learning experience and make sure you give yourself time to go through all the practice questions in class. As a new skill, you don’t want to rush through the class too quickly!

Data science certification exams can be difficult to pass, so you’ll want to spend a month familiarizing yourself with the basics in a course. However, for more advanced certifications, you may need several months covering all the details before you’re adequately prepared. The more projects you can do during this time frame, the better your skills will be.

While you don’t need to get a data science certification, having one can help your resume stand out among others. A certification isn’t necessary but will only help your job prospects, and they’ll let the recruiter know that you have a solid data science background.

Data science is used in many fields, from the traditional data scientist role to being a business intelligence analyst. You might even want to be a data mining engineer, which has an average salary of over $100,000. You’ll have a chance at any role that requires data analysis.



Extra Credit

We’ve covered the best data science certification courses above, so now it’s time to start looking into the details of how you can succeed. We’ll be taking a dive into the best strategies you can use to get the most out of these data science courses – and the most value for your money. We’ll cover common software used before looking at the most frequently asked questions when it comes to data science.

What You Need to Succeed

With these online courses, all you’ll need is a computer that can run Python, R, and other software packages required by the class. With the exception of the Python for Excel class, which is primarily tailored for Windows, all of the other courses on our list are compatible with all operating systems.

Commonly Used Software

Often, you’ll want to either pre-install or make sure your computer can run the necessary software packages for data science. We’ve listed the software used in each course in our list, so it’s a good idea to make sure they’re all compatible with your device. Below is a list of the common software and a quick summary of what they entail.

Python

Python is the most used programming language for data science. Packages you’ll likely use include ones for computation (such as NumPy or Pandas) and ones for data visualization (such as matplotlib). Many courses, such as the Machine Learning A-Z course, will involve using Python to some extent.

R

R is a statistical software to help analyze large amounts of data. They have built-in statistical functions, ranging from forecasting to t-tests. Just like Python, they also have many packages you can import. Some classes, such as Data Science: Visualization will focus on R’s data visualization packages. In addition, R is commonly used for statistical analysis after you use Python for preprocessing data.

Tableau

Tableau is a newer data visualization and analytics tool, growing more popular by the year. As a more recent software, there won’t be as many courses which cover it. However, Data Science A-Z discusses the uses for Tableau and how to make the most of it.

Overcoming Obstacles in Online Learning

Online learning can be a challenge when it comes to data science. It’s different than traditional courses, both in structure and in teaching methods. Below, we’ll quickly highlight two of the biggest road bumps you might encounter and our suggestions for how to overcome them.

Asking Questions

As a STEM-focused course, not being able to ask questions in live lectures is the hardest part. Many of the instructors we’ve listed will either have online communities where you can get help or list an email for you to reach out to. We recommend that you keep a notepad close by so that you can write down questions you have as soon as they arise so that you can reach out as needed.

Because STEM courses tend to build on prior information, misunderstanding a lesson from one of the first modules can make later modules more difficult to learn. While it can be intimidating to reach out to the teacher, you’ll want to clear up confusion when it occurs to make things easier for yourself in later lessons.

Practicing Your Skills

If you don’t use it, you lose it. This is a common saying for many skills, and it’s especially true with data science. Whether you’re learning the theory behind a statistic or looking at how to chart out certain data points, we recommend finding all the practice you can to be prepared. You might even want to look for courses that come with practice included.

For example, the Data Science A-Z course comes with exercises in every module for you to practice your new skills. And if you want to get experience with projects while you learn, you may enjoy the Python for Excel course.

Analyzing Your Future

When it comes to getting that data scientist certification, one of the hardest parts is getting started. Once you’ve picked out the best data science class, you’ll learn all the skills needed to draw your own conclusions from large datasets. Whether it’s creating a graphical representation of your conclusions, designing that first logistic regression run, or choosing the right statistic, any one of these courses will help you on your way!