Ratings and Reviews for Introduction to Data Science in Python

Very little explanation on how the libs actually work. Assignments cover stuff that sometimes is not even lectured.

Overall good. Assignment wording could have been more precise.

Can't learn anything. Nothing is explained properly, everything is just dumped at once and you are expected to know all

While the information provided is not bad and I liked the style of teaching, with the Jupyter Notebook, the assignments are unnecessarily ambiguous, to the point that you have to go through the forums simply to understand the meaning of the question and what to return. And the autograder mechanism was also quite misleading.

Lectures are descriptive but do not explain. Very few practice exercises. Assignments do not reinforce lectures but require you to learn and implement new things on your own. You will finish each week having practiced a line of code once and not be any better prepared than if you watched YouTube and practiced your own project. This course prepares you only to look everything up on Stackoverflow. If you want a course that drills into you the language so you have a base level of proficiency, skip this.

Issues: 1. The lectures are a chore to sit through. Dry, slow, and unorganized. The lecture on pivot tables was not used in any assignment. So why have it? 2. Assignments. Ooof. Lets break this down. a. Autograder is poor. Aside from the oddities that just break it sometimes, the hidden test feedback is lacking. If my answer is off at the 15th decimal point because my dataframe is 226 rows and not 227. I need another assert and feedback telling me that. Instead of a lesson on logic a lot of these problems became frustrating cases of github searching for other peoples passing code and then working backwards b. Assignments felt rushed. Each assignment had poorly written questions that frequently popped up in the forums asking for clarification. The assignments themselves had odd jumps in difficulty and assumptions. Some would build upon the lectures but other times they would jump and assume that we would figure out the middle. Oftentimes we did, but imperfectly, and the assignments penalized us for that imperfection. For example, if question says clean the data and we do using one of a dozen different ways why are we penalize if we have 224 clean rows, and the answer requires 227. If that level of end accuracy is required, then we need more guidance to achieve exactly that. 3. Forums. Useless. Filled with garbage, and the useful ones are unstructured mess. For one, the autograders output is small grey typwriter text which is undecipherable and the TAs always wanted it posted. This lead to long chains of code blocks and one line responses. I also think the TAs emphasis on posting zero code is wrong. The entire web is built on Stack Overflow, so why not allow code snippets in the forum?

Its assignments take too long to be accomplished. Somewhere videos were too fast to catch.

Well organized course with lots of materials available for self study, specially the discussion forum was extraordinary helpful to complete this course. Ans this is bit tough one to handle to new comer in programming. But with a little extra effort can be completed.

Its an exciting and ground-breaking experience into a career of data analytics

Absolutely awesome one that everyone aspiring to be a data scientist must start with...