In our era of “information overload,” is it any wonder there’s such a high demand for Data Science professionals? From machine learning to statistical analysis, data scientists help “code the flows” of our hi-tech society.
Not only is Data Science an intellectually stimulating career, it’s also incredibly lucrative. Indeed, data may be more valuable than oil in the 21st century—especially if Tesla has its way! Recent stats from the University of Wisconsin suggest Data Science grads make an average of $100,000 per year.
Since there’s such a high demand for Data Science skills, there has been a significant uptick in online courses available in this field. While this is excellent news for at-home learners, it could make finding the perfect Data Science course extra challenging. With so many high-quality lectures to choose from, it’s nearly impossible for newcomers to pick the “ideal” course.
The Best Online Data Science Courses
Thankfully, there are a few Data Science courses that have risen above the pack in recent years. Whether you’re starting your journey into machine learning or you want to perfect your Python skills, you should find one Data Science course below that meets your skill level, expectations, and budget. If you’re looking to become a Data Scientist, check our extensive write up here.
Statistics with R Specialization — Coursera
A large part of Data Science involves rigorous statistical computation. Hence, you should feel confident in the basics of Statistics before stepping into a more specialized Data Science course. One great way to prepare for more advanced data mining topics is to enroll in a lecture series like Coursera’s Statistics with R Specialization.
Taught by professors at Duke University, this course will give you a thorough understanding in using R to analyze statistics. As you progress in this course, you should feel more comfortable with topics like linear regression and Bayesian statistical inference. Most importantly, instructors in this highly-rated course claim you’ll be able to visualize and communicate statistical probability without resorting to hi-tech “mumbo-jumbo.”
While this course isn’t in the “Data Science world” per se, it’s a great way to enter the field. Anyone who’s just dipping their toes into Data Science will gain a great deal from this course. You should also investigate this Coursera series if you need a refresher in statistical analysis or R.
To learn more about this platform, read our Coursera review here.
Python Data Science by IBM — edX
In addition to R, Python is one of the fundamental coding languages used in Data Science. If you’ve never coded with Python before, it’s a good idea to check out edX’s highly ranked Python Data Science for a fantastic introduction.
While there are plenty of Intro to Python courses online, edX’s offering has one significant advantage: IBM specialists created it. Not only will students get to learn from professionals at IBM, they can interact with the IBM Cloud. Instructors will also share real-world data to help students understand how data analytics works in big business.
Taking IBM Python courses on your own typically takes seven months. As a perk, you will receive a certificate after finishing this course (which is mighty impressive considering it has the IBM label!). Not only is this course a great way to gain insights into coding with Python, it’s a fantastic way for professionals to see how Data Science works at one of America’s leading companies.
Introduction to Data Science — Metis
The Intro to Data Science course on Metis earns high marks for its live teaching format. Anyone who enrolls in this course will get the opportunity to listen to lectures in real-time. Not only that, you could easily interact with your professor in multiple Q&A sessions throughout the series.
All of these features make Metis’s course one of the most interactive series you’ll find online. Anyone who’s looking for a college lecture format will feel right at home purchasing this Intro to Data Science curriculum.
This six-week course starts by reviewing Data Science’s foundations (e.g., CS and Python) before moving on to data visualization and modeling. After each lecture, you will be assigned problem sets to give you real-world practice of the theories presented in each lesson.
Please keep in mind, students should have a basic understanding of algebra and statistics before entering this course. While there’s no requirement to know Python before enrolling, it’s helpful to learn the basics beforehand.
Data Science Ready — Harvard University
Another popular introductory course in Data Science is Harvard University’s “Data Science Ready” protocol. Developed by leading minds from Harvard’s prestigious Business School, this four-week course introduces the core concepts of data visualization and organization without complex coding jargon.
Data scientists aren’t the target audience for this primer. Instead, professors developed this course to help business professionals in fields like advertising or healthcare. The skills taught in the Data Scient Ready modules should help people outside of Data Science better understand how to use these concepts to enhance their business operations.
So, if you’re someone who wants to gain a non-technical, working knowledge of Data Science, then the Harvard “Data Science Ready” program may be right for you.
By the way, Harvard offers a plethora of specialized Data Science programs for people who want more advanced training. For instance, you could apply for a Business Data Science course to discover how data flows could enhance your company’s bottom line. There’s also an advanced Healthcare Data Science course that helps hospital workers use machine learning to provide optimal patient care.
After completing Harvard’s initial Data Science Ready course, it’s a good idea to look through the wide range of other online courses offered on this website.
Data Science Specialization — Coursera
With almost 83,000 positive ratings, it’s impossible to ignore Coursera’s Data Science Specialization program. Created by Johns Hopkins University, this 11-month course is one of the most comprehensive introductory courses to Data Science.
Students will begin by learning the essential questions to ask when presented with large pools of data. Professors will then introduce tools like GitHub and RStudio to plot and analyze information. These preliminary considerations lead to a comprehensive guide to R programming before moving on to complex topics like machine learning and regression models.
After submitting your capstone project, you should feel more confident in all aspects of Data Science.
Intro To Data Science — Udacity
Are you looking for a free Data Science course on an accredited MOOC? If so, you’ve got to research Udacity’s Intro To Data Science catalog.
This two-month course is organized into five lessons that teach you core concepts like data wrangling, MapReduce, and Big Data. Udacity’s course also features special lecturers from Twitter and Google to help you understand how Data Science is used in Silicon Valley. Along the way, you’ll take interactive quizzes to gauge your comprehension.
Best of all, this self-paced class is 100 percent free to download. Plus, the skills you learn in this course should help you successfully pass one of Udacity’s many Nanodegree programs.
Although Udacity’s Intro To Data Science is free, it is an intermediate-level course. Udacity strongly advises students to have a basic understanding of Python before applying for these modules. Luckily, Udacity also offers a free beginner-friendly Python training course.
MicroMasters Program in Data Science — edX
Students who are interested in graduate-level work should take advantage of edX’s MicroMasters programs. These rigorous classes give young professionals a chance to sharpen their Data Science skills while earning potential credits in Master’s Degree programs. Indeed, those who complete the MicroMasters Program in Data Science could apply for partial credit at RIT and Curtin University.
As for the course itself, this UC San Diego program is broken down into four sections: Python, probability, machine learning, and Big Data analytics. Each of these sections takes roughly ten weeks with an average course time of 8 – 10 hours per week. Course designers are keen to teach students how to apply their mathematical skills to create clear data points for business clients.
This edX course is ideal for intermediate Data Science students looking to improve their business prospects.
Data Analyst Nanodegree Program — Udacity
Udacity offers a unique “Nanodegree” program for various tech-related fields like Data Science. What sets Nanodegrees apart from other programs is their emphasis on project-based learning. Rather than the standard lecture-based series, students who enroll in the Data Analyst Nanodegree Program will learn by perfecting a program with professors.
Specific areas of interest in the Data Analyst Nanodegree course include SQL and Python coding. You’ll have the opportunity to use both languages to wrangle real-world data sets and present clear findings to your class. Along the way, you will have plenty of access to instructors who can ensure you’re on the right path. When you complete Udacity’s program, you will get to place the impressive Udacity Nanodegree on your resume.
Since this Nanodegree program isn’t an intro course, you should have some experience with Data Science before applying. Indeed, the Nanodegree program was designed to help those interested in “upskilling” their computer science skills. This makes Udacity’s offering well-suited for those already in Data Science who want to enhance their professional career.
Machine Learning — Coursera
If you’re most interested in becoming a master machine programmer, you should research Coursera’s top-rated Machine Learning modules. Taught by AI expert Andrew Ng, this Stanford-backed course has a sterling rating from over 155,000 global students.
A few of the top topics of discussion in this course include clustering, logistic regression, and algorithmic programming. Students will also gain insights into Deep Learning, database mining, and programming AI robots. As a bonus, Coursera’s Machine Learning programs will share an insightful look into Silicon Valley’s latest innovations and practices.
Introduction to Data Analysis using Excel — edX
First developed in the 1980s, Microsoft’s Excel remains a critical tool for Data Scientists. Even if you think you know how to use an Excel spreadsheet, you may want to consider taking edX’s Intro to Data Analysis using Excel. This free course will teach you all the tricks you need to get the most out of your spreadsheet data.
A few crucial topics this class will go over include pivots and slicers, both of which should help you manage and share data. Instructors will also place a great emphasis on visualizing data using Excel’s tools.
This self-paced intro course should take one month with about 2 – 4 hours of coursework per week. Although you could use any Microsoft Excel program, instructors will demonstrate on the 2016 version. Also, while this is an intro course, you should have a basic familiarity with Excel beforehand.
Please note that Microsoft offers many other free courses on edX’s platform. For those who enjoyed this Excel course, you could move on to the more rigorous class entitled, “Analyzing and Visualizing Data with Excel.” Many data scientists also enjoy taking edX’s Querying Data with Transact-SQL.
FYI: You could earn a certificate from each of edX’s Microsoft courses for a small fee. While the info in these courses is free, you can’t advertise a certification on your resume unless you pay.
Data Scientist Career Path — Codecademy
Founded in 2018, Codecademy is one of the largest online platforms dedicated to, well, coding! As one of the top-tier names in coding education, Codecademy has a lot of clout with businesses in the Data Science field. You could use Codecademy’s status to your advantage by applying for its Data Scientist Career Path class.
This 21-lesson course is designed to get you ready to successfully launch a Data Science dream career. You’ll start by learning the fundamentals of coding languages like Python and SQS before using these systems in real-world applications. As the course progresses, you’ll also learn how to use machine learning and AI to effectively analyze data, test hypotheses, and present clear reports to clients. Codecademy also promises to help you advance your career path throughout this robust training program.
If you’re not sure about applying for Codecademy’s Data Scientist Career Path, you should first try this website’s free lessons. After you get a feel for how Codecademy’s system works, you should know whether this learning model is right for you.
Machine Learning Crash Course — Google
When it comes to AI, few companies can compete with the innovation going on at Google. Therefore, it makes sense to go straight to the source if you’re interested in Machine Learning. Luckily for us, Google doesn’t mind sharing its deep experience with Deep Learning. In fact, anyone could take Google’s Machine Learning Crash Course online.
Students begin their journey into AI by looking at “machine learning framing” before moving on to issues like validation sets, logistic regression, and neural networks. Most of these lessons begin with a video tutorial that’s followed by exercises, case studies, and visualizations. In total, this Crash Course has 15 hours of content you could explore at your own pace.
While this course will teach you invaluable info on Machine Learning, you can’t use it to apply for college credit. Also, you should feel comfortable with algebra, statistics, calculus, and Python before taking this Crash Course.
MicroMasters MIT Statistics and Data Science — edX
Another popular course in edX’s MicroMasters program is MIT’s Statistics and Data Science protocol. This five-lesson series is designed for intermediate students who want a graduate-level experience with real-world applications. This course is well-suited for those interested in working in fields like statistics and probability.
Professors emphasize issues like probabilistic models and hypothesis testing, especially in the first two seminars. Later on, you will practice your Python skills as you investigate the latest advances in machine learning.
One exciting feature about this course is you can choose your final lesson. Currently, edX offers modules on statistical applications in the social sciences or computation.
As with other MicroMasters degrees, you could use the certificate from this program to gain credit at certain universities. Be sure to investigate which colleges accept edX degrees before applying for this program.
Intro to Self-Driving Cars — Udacity
Along with electric vehicles, self-driving cars are becoming more commonplace in contemporary life. In fact, recent reports suggest the autonomous driving industry could reach over $1.3 billion before 2030. If you’d like to put your Data Science skills to work in this exciting new field, you may want to consider applying for Udacity’s Nanodegree course entitled, “Intro to Self-Driving Cars.”
As mentioned above, these Nanodegrees are designed to give students practical experience in tech-related fields. In the Intro to Self-Driving Cars course, students will see how C++, Python, and calculus could be used to power autonomous technology.
After completing this first course, you may want to consider applying for Udacity’s “Become A Self-Driving Car Engineer” Nanodegree. This more advanced course will teach students how Deep Learning and computer vision affect self-driving vehicles. You’ll even get to code a successful program for one of Udacity’s autonomous cars.
If all this self-driving info goes over your head, no worries! Udacity also offers a Nanodegree for intermediate coders. This Intro to Programming Nanodegree is designed to equip you with the skills necessary to understand the nuances in both self-driving modules.
Blockchain Specialization — Coursera
There’s no question that blockchain technology is becoming more mainstream. While this decentralized ledger is often associated with cryptocurrencies, it has proven to be a beneficial technology in other fields—including Data Science. Indeed, many people involved in Data Science are excited about the possibilities of using high-trust blockchains to share sensitive info.
While blockchain science isn’t necessary for those in the Data Science field, it’s certainly worthy of exploration. Since blockchain is relatively new, data scientists who understand this skill could have a substantial competitive advantage.
Anyone who’s interested in adding these skills to their resume might want to check out Coursera’s Blockchain Specialization course. Created by the University of Buffalo, this course will teach students the foundations of blockchain technology with an emphasis on the Ethereum network. The info in this course should help you code your own decentralized apps (aka “Dapps”) using Ethereum’s Solidity language.
If you’re most interested in refining your coding expertise with an intensive protocol, then you might like what DataCamp has to offer. This “boot camp style” platform is designed to supercharge your coding skills in a short span of time.
Unlike other coding platforms, DataCamp only focuses on languages you need to succeed in Data Science. Those who enroll in DataCamp’s course will refine their Python, R, and SQL skills, which could dramatically improve your professional prospects.
While this isn’t a college-style lecture course, it’s an invaluable training tool for those who need extra practice coding for Data Science. As a bonus, you could try DataCamp’s desktop and mobile platforms for free before signing up for the premium program. You can learn more in our extensive DataCamp review here.
Pro Tip: Don’t Neglect Data Science Apps!
Learning code shouldn’t stop when you leave your desktop. While it’s challenging to get a comprehensive education on your smartphone, there are plenty of fantastic smartphone apps students could use to practice Python on-the-go.
As mentioned above, both Codecademy and Datacamp now offer mobile services to supplement your core curriculum. However, there are loads of other apps like Enki, ProgrammingHub, and SoloLearn that could help you practice core concepts on the fly. Best of all, you don’t have to pay a penny to use many of these coding apps. Be sure to look through the many Data Science apps now available to help perfect your coding.