Are you excited to dive into the dynamic realm of Data Science? Whether you are a novice or a seasoned coder, this Best Courses Guide (BCG) is your gateway to discovering the top online courses to kickstart your journey.
Data Science is an evolving domain that blends statistics, programming, and domain expertise to uncover insights from data. With the right skills and knowledge, you can harness the potential of big data and make a significant impact in your career. So, grab a cup of coffee and let’s venture into the realm of Data Science together!
One important note: While data science often includes data analytics, the latter has gained significant traction on its own. Hence, this BCG includes courses specifically focusing on data analytics. Additionally, we have included Python and R courses, as they are the predominant programming languages in data science.
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What is Data Science?
Data science leverages scientific reasoning to derive general principles from specific observations. In today’s digital age, with the massive volume of data generated daily, the task of uncovering trends would be insurmountable for humans. Data scientists step in to offload the computational heavy lifting to machines through machine learning or deep learning.
Data scientists play a crucial role in ensuring that the data fed to machines is clean, selecting the appropriate algorithms, and conveying the findings to non-technical stakeholders. This dynamic and gratifying field has garnered immense popularity, being termed as the “sexiest job of the 21st century” by HBR and offering a median salary of $159K per year in the United States.
Data analytics, a subset of data science, has also been on the rise, with Python and R emerging as the preferred programming languages. With data abundance and constant technological advancements, data science is poised to become a pivotal and stimulating field for years to come.
My Experience with Data Science
I (Elham) collaborated with my colleague @manoel to curate this guide. Both of us hail from computer science backgrounds and are avid online learners, having completed around 45 MOOCs collectively. While Manoel holds an online bachelor’s in computer science, I am currently pursuing my foundation in computer science. Data science has been the prime motivator for my interest in CS!
Why You Should Trust Us
Class Central, akin to a Tripadvisor for online education, has assisted 60 million learners in discovering their next course. With over a decade of experience in scouring online education, we have compiled a repository of 200,000 online courses and 200,000 reviews penned by our users. Furthermore, we are avid online learners ourselves; as a team, we have collectively completed over 400 online courses, including online degrees.
Courses Overview
- All courses collectively boast 9M enrollments and YouTube views, with the most popular course attracting 3.8M views
- Seven of the courses are free or offer free audits, while the remaining three are paid
- Seven courses cater to beginners, with the rest tailored for intermediate learners
- This guide encompasses a diverse compilation of courses from 6 providers, with Coursera being the most represented
- Three courses employ Python, two utilize R, with the remainder not involving coding
- Approximately 356K individuals are pursuing Data Science Courses on Class Central
Introduction to Computational Thinking and Data Science by the Massachusetts Institute of Technology on edX. This free-to-audit course is designed to equip you with a diverse range of concepts and methods to excel in computational thinking and data science, presented with the rigor you’d expect from an MIT course.
Building on the foundations laid by Introduction to Computer Science and Programming Using Python, this course is ideal for those with prior Python programming experience and a basic understanding of algorithms and complexity.
Be prepared for challenges as this course reflects the coursework and assignments of MIT students, ensuring a robust learning experience.
You’ll delve into:
- Efficient algorithms like greedy algorithms, breadth-first search, and depth-first search to tackle optimization problems
- Stochastic thinking focusing on probabilities for simulating problem solutions
- Statistical techniques covering plotting probability density functions, confidence intervals, sampling, and standard error
- Machine learning encompassing supervised and unsupervised learning, linear regression, and clustering
- Pitfalls and constraints of statistics to avoid common statistical inaccuracies that mislead individuals
The course material is based on the book Introduction to Computation and Programming Using Python, Second Edition.
| Institution | Massachusetts Institute of Technology |
| Provider | edX |
| Part of | Computational Thinking using Python |
| Instructors | Eric Grimson, John Guttag, Ana Bell |
| Level | Intermediate |
| Workload | 100–140 hours |
| Enrollments | 249K |
| Exercises | Free assessments, problem sets and exams |
| Certificate | Paid |
