...
Full Bio
Use Machine Learning To Teach Robots to Navigate by CMU & Facebook Artificial Intelligence Research Team
219 days ago
Top 10 Artificial Intelligence & Data Science Master's Courses for 2020
220 days ago
Is Data Science Dead? Long Live Business Science
248 days ago
New Way to write code is about to Change: Join the Revolution
249 days ago
Google Go Language Future, Programming Language Programmer Will Get Best Paid Jobs
570 days ago
Top 10 Best Countries for Software Engineers to Work & High in-Demand Programming Languages
722472 views
Highest Paying Programming Language, Skills: Here Are The Top Earners
669018 views
Which Programming Languages in Demand & Earn The Highest Salaries?
474117 views
Top 5 Programming Languages Mostly Used By Facebook Programmers To Developed All Product
458994 views
World's Most Popular 5 Hardest Programming Language
389268 views
I Climbed Every Intro to Data Science Course On The Internet, Based On Thousands Of Data Science Points
- It must teach the data science process. More on that soon.
- It must be on-demand or offered every few months.
- It must be an interactive online course, so no books or read-only tutorials. Though these are viable ways to learn, this guide focuses on courses.
- Python for Data Science and Machine Learning Bootcamp (Jose Portilla/Udemy): Full process coverage with a tool-heavy focus (Python). Less process-driven and more of a very detailed intro to Python. Amazing course, though not ideal for the scope of this guide. It, like Jose's R course below, can double as both intros to Python/R and intros to data science. 21.5 hours of content. It has a 4.7-star weighted average rating over 1,644 reviews. Cost varies depending on Udemy discounts, which are frequent.
- Data Science and Machine Learning Bootcamp with R (Jose Portilla/Udemy): Full process coverage with a tool-heavy focus (R). Less process-driven and more of a very detailed intro to R. Amazing course, though not ideal for the scope of this guide. It, like Jose's Python course above, can double as both intros to Python/R and intros to data science. 18 hours of content. It has a 4.6-star weighted average rating over 847 reviews. Cost varies depending on Udemy discounts, which are frequent.
- Data Science and Machine Learning with Python - Hands On! (Frank Kane/Udemy): Partial process coverage. Focuses on statistics and machine learning. Decent length (nine hours of content). Uses Python. It has a 4.5-star weighted average rating over 3,104 reviews. Cost varies depending on Udemy discounts, which are frequent.
- Introduction to Data Science (Data Hawk Tech/Udemy): Full process coverage, though the limited depth of coverage. Quite short (three hours of content). Briefly covers both R and Python. It has a 4.4-star weighted average rating over 62 reviews. Cost varies depending on Udemy discounts, which are frequent.
- Applied Data Science: An Introduction (Syracuse University/Open Education by Blackboard): Full process coverage, though not evenly spread. Heavily focuses on basic statistics and R. Too applied and not enough process focus for the purpose of this guide. Online course experience feels disjointed. It has a 4.33-star weighted average rating over 6 reviews. Free.
- Introduction To Data Science (Nina Zumel & John Mount/Udemy): Partial process coverage only, though good depth in the data preparation and modeling aspects. Okay, length (six hours of content). Uses R. It has a 4.3-star weighted average rating over 101 reviews. Cost varies depending on Udemy discounts, which are frequent.
- Applied Data Science with Python (V2 Maestros/Udemy): Full process coverage with a good depth of coverage for each aspect of the process. Decent length (8.5 hours of content). Uses Python. It has a 4.3-star weighted average rating over 92 reviews. Cost varies depending on Udemy discounts, which are frequent.
- Want to be a Data Scientist? (V2 Maestros/Udemy): Full process coverage, though the limited depth of coverage. Quite short (3 hours of content). Limited tool coverage. It has a 4.3-star weighted average rating over 790 reviews. Cost varies depending on Udemy discounts, which are frequent.
- Data to Insight: an Introduction to Data Analysis (University of Auckland/FutureLearn): Breadth of coverage unclear. Claims to focus on data exploration, discovery, and visualization. Not offered on demand. 24 hours of content (three hours per week over eight weeks). It has a 4-star weighted average rating over 2 reviews. Free with a paid certificate available.
- Data Science Orientation (Microsoft/edX): Partial process coverage (lacks modeling aspect). Uses Excel, which makes sense given it is a Microsoft-branded course. 12â??24 hours of content (two-four hours per week over six weeks). It has a 3.95-star weighted average rating over 40 reviews. Free with Verified Certificate available for $25.
- Data Science Essentials (Microsoft/edX): Full process coverage with good depth of coverage for each aspect. Covers R, Python, and Azure ML (a Microsoft machine learning platform). Several 1-star reviews citing tool choice (Azure ML) and the instructor's poor delivery. 18â??24 hours of content (three-four hours per week over six weeks). It has a 3.81-star weighted average rating over 67 reviews. Free with Verified Certificate available for $49.
- Applied Data Science with R (V2 Maestros/Udemy): The R companion to V2 Maestros' Python course above. Full process coverage with good depth of coverage for each aspect of the process. Decent length (11 hours of content). Uses R. It has a 3.8-star weighted average rating over 212 reviews. Cost varies depending on Udemy discounts, which are frequent.
- Intro to Data Science (Udacity): Partial process coverage, though good depth for the topics covered. Lacks the exploration aspect, though Udacity has a great, full course on exploratory data analysis (EDA). Claims to be 48 hours in length (six hours per week over eight weeks), but is shorter in my experience. Some reviews think the set-up to the advanced content is lacking. Feels disorganized. Uses Python. It has a 3.61-star weighted average rating over 18 reviews. Free.
- Introduction to Data Science in Python (University of Michigan/Coursera): Partial process coverage. No modeling and visualization, though courses #2 and #3 in the Applied Data Science with Python Specialization cover these aspects. Taking all three courses would be too in depth for the purpose of this guides. Uses Python. Four weeks in length. It has a 3.6-star weighted average rating over 15 reviews. Free and paid options available.
- Data-driven Decision Making (PwC/Coursera): Partial coverage (lacks modeling) with a business focus. Introduces many tools, including R, Python, Excel, SAS, and Tableau. Four weeks in length. It has a 3.5-star weighted average rating over 2 reviews. Free and paid options available.
- A Crash Course in Data Science (Johns Hopkins University/Coursera): An extremely brief overview of the full process. Too brief for the purpose of this series. Two hours in length. It has a 3.4-star weighted average rating over 19 reviews. Free and paid options available.
- The Data Scientist's Toolbox (Johns Hopkins University/Coursera): An extremely brief overview of the full process. More of a set-up course for Johns Hopkins University's Data Science Specialization. Claims to have 4â??16 hours of content (one-four hours per week over four weeks), though one reviewer noted it could be completed in two hours. It has a 3.22-star weighted average rating over 182 reviews. Free and paid options available.
- Data Management and Visualization (Wesleyan University/Coursera): Partial process coverage (lacks modeling). Four weeks in length. Good production value. Uses Python and SAS. It has a 2.67-star weighted average rating over 6 reviews. Free and paid options available.
- CS109 Data Science (Harvard University): Full process coverage in great depth (probably too in depth for the purpose of this series). A full 12-week undergraduate course. Course navigation is difficult since the course is not designed for online consumption. Actual Harvard lectures are filmed. The above data science process infographic originates from this course. Uses Python. No review data. Free.
- Introduction to Data Analytics for Business (University of Colorado Boulder/Coursera): Partial process coverage (lacks modeling and visualization aspects) with a focus on business. The data science process is disguised as the "Information-Action Value chain" in their lectures. Four weeks in length. Describes several tools, though only covers SQL in any depth. No review data. Free and paid options available.
- Introduction to Data Science (Lynda): Full process coverage, though the limited depth of coverage. Quite short (three hours of content). Introduces both R and Python. No review data. Cost depends on Lynda subscription.