Intro to Data Science
What does a data scientist do? In this course, we will survey the main topics in data science so you can understand the skills that are needed to become a data scientist.
Reddacity may receive an affiliate commission if you enroll in a paid course after using these buttons to visit Udacity. Thank you for using these buttons to support Reddacity.
Reddit Posts and Comments
0 posts • 34 mentions • top 9 shown below
2 points • a-gentility
That's great! Coursera has some phenomenal courses on Data Analysis depending on if you want to stay as a quant analyst or go more into the machine learning route.
For more intro courses, I love Udacity and can't recommend it enough. Here's an intro course: https://www.udacity.com/course/intro-to-data-science--ud359
22 points • my_password_is______
all these are free -- don't know how good they are
this is free -- you only pay if you want a certificate
18 points • TextOnScreen
Is Udacity a good place to start learning?
I'm new to this sub (I hope I'm in the right place!), so hi everyone!
I've decided to learn Python, mainly cause I'd like to use it to further my data analysis skills. I already have beginner's knowledge of Java because I took an intro CS course in college.
I've never used Udacity, but discovered it recently and it seemed like a good starting point. I'm mainly looking at these classes:
[Intro to CS] (https://www.udacity.com/course/intro-to-computer-science--cs101)
[Programming Foundations] (https://www.udacity.com/course/programming-foundations-with-python--ud036)
[Design of Computer Programs] (https://www.udacity.com/course/design-of-computer-programs--cs212)
After having a Python foundation I'd take these courses:
[Intro to Data Analysis] (https://www.udacity.com/course/intro-to-data-analysis--ud170)
[Intro to Data Science] (https://www.udacity.com/course/intro-to-data-science--ud359)
[Model Building and Validation] (https://www.udacity.com/course/model-building-and-validation--ud919)
Do you think this is a decent curriculum?
Or perhaps I should dive right in to the [Intro to Python for Data Science] (https://www.datacamp.com/courses/intro-to-python-for-data-science) course that's linked on the Wiki?? I'm scared all the Udacity courses might take forever to complete and I'll lose steam...
Thanks for any help and advice!
15 points • _SleepyOwl
Help critique my ML curriculum!
Hi everyone! I've been doing some research on how to get started on machine learning as a beginner and have come up with the curriculum below for myself to follow. It would be great if those proficient in ML, or even those currently learning, help to critique or provide suggestions. Additional courses, literature, programs, or advice would be greatly appreciated! It may be difficult to critique without having firsthand experience of each course or program, but I'm more concerned the with subject matter included than the courses themselves. As I go through each course, I may decide to change them if I feel that it is not pertinent to the goal or lacking in quality. There's no way I will become an expert after this, but I'm simply looking for a good starting point. I hope this will help others as well.
As a point of reference, I've graduated with a B.S. in Mechanical Engineering, have taken several math courses (calculus, linear algebra, differential equations), and have taken a few programming courses (CS50, Intro to Comp Sci on eDX, CS 101 at University). I'm currently working part-time as a mechanical designer / energy analyst and have 40-50 hours a week to spare.
For the curriculum, I've broken it up into 5 sections (programming, statistics, linear algebra, data science, and machine learning) which will be overlapping. I plan on taking 3 courses at a time starting with Udacity's Intro to programming, Stanford's probability and stats, and Udacity's linear algebra refresher while reading "An Introduction to Statistical Learning."
Here it is!
- Udacity Introduction to programming https://www.udacity.com/course/intro-to-programming-nanodegree--nd000
- Learn the basics of programming through HTML, CSS, and Python.
- Stanford Online: Probability and Statistics https://lagunita.stanford.edu/courses/course-v1:OLI+ProbStat+Open_Jan2017/about
Broken into four sections: exploratory data analysis, producing data, probability, and inference.
Stanford Online: Statistical Learning http://online.stanford.edu/course/statistical-learning-winter-2014
- Introductory-level course in supervised learning, with a focus on regression and classification methods.
- Lectures cover all the material in An Introduction to Statistical Learning, with Applications in R.
Udacity Linear Algebra Refresher Course with Python: https://www.udacity.com/course/linear-algebra-refresher-course--ud953
UAustinX Linear Algebra - Foundations to Frontiers edx.org/course/linear-algebra
- Connections between linear transformations, matrices, and systems of linear equations.
- Partitioned matrices and characteristics of special matrices.
- Algorithms for matrix computations and solving systems of equations. Vector spaces, subspaces, and characterizations of linear independence. *Orthogonality, linear least-squares, eigenvalues and eigenvectors
- Udacity Introduction to Data Science https://www.udacity.com/course/intro-to-data-science--ud359
Focuses on Data Manipulation, Data Analysis with Statistics and Machine Learning, Data Communication with Information Visualization, and Data at Scale -- Working with Big Data
Udacity Data Science Nano-degree https://www.udacity.com/course/data-analyst-nanodegree--nd002
Learn to organize data, uncover patterns and insights, make predictions using machine learning, and clearly communicate critical findings.
(optional) UCSD Data Science Micro-masters Program (4 course program) https://www.edx.org/micromasters/data-science
- Four courses: Python for Data Science, Statistics and Probability in Data Science using Python, Machine Learning for Data Science, and Big Data Analytics Using Spark.
- Udacity Intro to Machine Learning https://www.udacity.com/course/intro-to-machine-learning--ud120
How to extract and identify useful features that best represent data, learn a few of the most important machine learning algorithms, and how to evaluate the performance machine learning algorithms.
Coursera Machine Learning by Andrew Ng https://www.coursera.org/learn/machine-learning
This course provides a broad introduction to machine learning, data-mining, and statistical pattern recognition.
(with job guarantee?) Udacity Machine Learning Nano-degree https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009
- Apply predictive models to massive data sets in fields like finance, healthcare, education, and more.
An Introduction to Statistical Learning http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf
The Elements of Statistical Learning: Data Mining, Inference, and Prediction https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf
Other (after curriculum):
Let me know your thoughts! Have you taken one of these courses before? Think there's too little programming or know of a better course/program? Chime in! Would be really grateful for any feedback.
2 points • lynda_
The nanodegree is the heart of the DMDA program: you get access to the nanodegree and completing it is a graduation requirement. It is difficult with and without an IT background and the degrees are harder. I would not recommend either program if the level of difficulty is holding you back.
Start with these ... you can do them for free without signing up for the nanodegree and they will be time-well-spent whether or not you decide to sign up for the nanodegree afterward.
Then watch this - https://www.youtube.com/watch?v=bB-ipD5HX9Y&list=PLea0WJq13cnAdItX9lNV3p4urGbEaO7Yd
If you find yourself engaged and wanting more, then this is the right path for you.
My background is in Accounting which is a prerequisite for MSDA. I did not have IT experience beyond my own research. I did have a heavier math background than other students which I found helpful. We all come in with gaps that we need to fill using outside sources like Udacity. It's not for everyone.
3 points • martor01
Everyone and their mother wants to do Data Science , most roles require an MS or PHD because back then it was the stuff what PHD researchers done, also companies not even know what the hell they are doing with the title so some of the data science is just SQL reporting or some basic Data Analysis role vica versa with Data Analysis.
What was your motivation in that specialization ?
You definitely need SQL , Python.
If you are like me and everybody just bores you with "just apply it to a real world problem" I would first look at packages to use from.
These are the official packages would definitely read through and try to use them .
Anaconda is a data science environment for Python and R and countless other and it comes with the basic 250 python packages preinstalled.
It also has a better package management system than pip , because pip is fucking ridiculous IMO.
This is good website to start with python.
This is a free course.
2 points • create_a_new-account
just the free course, not the nanodegrees
2 points • ksushbush
First of all, I could recommend to study Python. Why? There are some reasons.
1.Python is the fastest growing programming language. You can use it for ML applications, data analysis, visualization, web apps, API integrations, etc.
2.It’s one of the easier languages to pick up and learn.
Below some sources that I think will be good for your self learning.
If you haven't learnt ML before, check also these free online-courses
Hope you'll find it useful!
1 points • Guzikk
And the tips:
You can learn data science on your own or sign up for one of the free data-science courses at, for example, Udacity or Udemy. The courses will explain in detail what data science is and what are its uses.
If you decide to learn data science on your own, start by reading as much as possible on the subject. Read over the content on KDnuggets, FlowingData, or Simply Statistics to get into the mood for some data crunching. It wouldn’t hurt to read a few books on data science, too: