AI News, For learning data science, is Data Camp better than Coursera's data science specialization?

For learning data science, is Data Camp better than Coursera's data science specialization?

The testing within the python datascience course is trivially easy - basically ‘fill in the blanks for this very simple bit of code’ - and there is minimal repetition to embed any learning.

I also found that questions posted on the forums were not answered, which in the case of a platform based around a subscription fee model is pretty disappointing.

If you’re a complete novice, they do make you feel like you are getting somewhere, which can be good for your confidence, but the material and testing really is so basic that, in my opinion, they’re of relatively limited use.

Although it is desperately in need of some real cleaning up, and removal of many errors, the Andrew Ng Coursera offering (focusing specifically on ML) is still a very good option, coming from a theoretical position (NB It’s MATLAB/Octave based - free MATLAB license whilst you’re enrolled!).

He does not focus on theory - you are expected to already have a grasp of that or do plenty of reading yourself (The recommended text is “Introduction to Statistical Learning” - which is one of the best introductory/intermediate texts out there).

The course is very up to date, and very hands-on… you’ll be downloading a lot of libraries, you’ll have the odd installation issue to deal with, you’ll be setting up accounts with Kaggle, with Amazon Web Services, etc.

If you want to learn Data Science, start with one of these programming classes

started creating my own data science master’s degree using online courses shortly afterwards, after realizing it was a better fit for me than computer science.

For this guide, I spent 20+ hours trying to find every single online introduction to programming course offered as of August 2016, extracting key bits of information from their syllabi and reviews, and compiling their ratings.

Borrowing this answer from Programmers Stack Exchange: The course we are looking for introduces programming and optionally touches on relevant aspects of computer science that would benefit a new programmer in terms of awareness.

The professors kindly and promptly sent me detailed course syllabi upon request, which were difficult to find online prior to the course’s official restart in September 2016.

Learn to Program: The Fundamentals (LTP1) Timeline: 7 weeks Estimated time commitment: 6–8 hours per week This course provides an introduction to computer programming intended for people with no programming experience.

It covers the basics of programming in Python including elementary data types (numeric types, strings, lists, dictionaries, and files), control flow, functions, objects, methods, fields, and mutability.

Modules Learn to Program: Crafting Quality Code (LTP2) Timeline: 5 weeks Estimated time commitment: 6–8 hours per week You know the basics of programming in Python: elementary data types (numeric types, strings, lists, dictionaries, and files), control flow, functions, objects, methods, fields, and mutability.

There are two programming assignments in LTP2 of similar size.” He emphasized that the estimate of 6–8 hours per week is a rough guess: “Estimating time spent is incredibly student-dependent, so please take my estimates in that context.

Sometimes someone will get stuck on a concept for a couple of hours, while they might breeze through on other concepts … That’s one of the reasons the self-paced format is so appealing to us.” In total, the University of Toronto’s Learn to Program series runs an estimated 12 weeks at 6–8 hours per week, which is about standard for most online courses created by universities.

With 6,000+ reviews and the highest weighted average rating of 4.93/5 stars, this popular course is noted for its engaging videos, challenging quizzes, and enjoyable mini projects.

The condensed course description and full syllabus are as follows: “This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications … To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple.

For students interested in some light preparation prior to the start of class, we recommend a self-paced Python learning site such as codecademy.com.” Timeline: 5 weeks Estimated time commitment: 7–10 hours per week Week 0 — statements, expressions, variables  Understand the structure of this class, and explore Python as a calculator.

Week 2 — event-driven programming, local/global variables Learn the basics of event-driven programming, understand the difference between local and global variables, and create an interactive program that plays a simple guessing game.

Week 4 — lists, keyboard input, the basics of modeling motion Learn the basics of lists in Python, model moving objects in Python, and recreate the classic arcade game “Pong.” Week 5 — mouse input, list methods, dictionaries Read mouse input, learn about list methods and dictionaries, and draw images. Week 6 — classes and object-oriented programming Learn the basics of object-oriented programming in Python using classes, and work with tiled images.

Though the latter three come at a price point of $25/month, DataCamp is best in category for covering the programming fundamentals and R-specific topics, which is reflected in its average rating of 4.29/5 stars.

The series breakdown is as follows: Estimated time commitment: 4 hours Chapters: Estimated time commitment: 6 hours Chapters: Estimated time commitment: 4 hours This follow-up course on intermediate R does not cover new programming concepts.

Aspiring Data Scientist! Here are 8 free online courses to start…

If you are a Data Scientist, you need to know 3 major areas: Fortunately there are free courses for all the 3 around the internet.

If you want to go further, this is your book (but it’s not free): http://datascienceatthecommandline.com Python is very popular in Machine Learning, predictive analytics and text-mining.

Free course: https://www.codecademy.com/learn/python Free book: https://learnpythonthehardway.org/book/ Not free, but really great data+python book: Python for Data Analysis Worst name for anything, it’s not even googleable, right? :-) But, it’s a very useful language designed by mathematicians for mathematicians.

nice GitHub depo: https://github.com/zoltanctoth/smalldata-training And if you want to practice (maybe because you are trying to prepare yourself to a job interview), this a good place to do that: https://www.hackerrank.com/ The business part is tricky, because mostly you need to learn it on the job — as different companies have very different businesses.

If you want to try out, what it is like being a junior data scientist at a true-to-life startup, check out my new 6-week online data science course: The Junior Data Scientist’s First Month!

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