New Online Data Science Tracks for 2017
In 2017 there are many new and revamped data science tracks that are much more comprehensive for beginners than ever before. The tracks are designed to give you the skills you need to grab a job in data science, and some even have a job guarantee.
By Brendan Martin, LearnDataSci.
Back in my original data science courses post, which was aggregated in 2015, there were already a ton of data science courses and bootcamps available.
Recently, there have been many new and revamped data science tracks that are much more comprehensive for beginners than ever before. The tracks below are designed to give you the skills you need to grab a job in data science, and some even have a job guarantee.
Even though a lot of the platforms below aren’t new, I’ve collected all of the updates and changes make that make these the best options for learning data science online.
Springboard (re-branded from SlideRule) is one of the most impressively built data science online bootcamps. They have two options, Career Tracks and Workshops.
The career track is the brand new addition, so I’ll detail that below. The main differences between the Data Science Career Track and the Foundations of Data Science Workshop that they’ve offered for a while are:
- The Career Track has twice the content compared to the Workshop
- The Career Track costs twice as much compared to the Workshop
- The Career Track has a job guarantee, the Workshop does not
- The Career Track is Python-focused and the Workshop is R-focused
- There’s more career resources in the Career Track
- The Career Track has prerequisites
- The Career Track has higher acceptance standards than the Workshop
The Python-focused career track comes with a job guarantee, so if you don’t get a qualifying job offer within 6 months of graduating, you’ll get your tuition refunded. There aren’t many platforms (or even universities for that matter) that are willing to back up their curriculum to that extent, but with that comes a heftier price tag to join.
Highlights of the Career Track
- Price: $1000/month or $4800 one-time
- Language: Python
- Personalized career coaching
- Interview prep
- Twice the content when compare to their workshops
- Job guarantee
- Employer partnerships
- Prerequisites: some programming, college-level statistics,
Career Track Curriculum
- Programming in Python
- Data Wrangling
- Inferential Statistics
- Machine Learning
- Working with Big Data
- Advanced Data Visualization
- Capstone Project
- Career Resources
The more beginner friendly Foundations of Data Science Workshop
I’m sure a lot of readers have heard of DataCamp, but just recently they’ve reorganized and added additional content to their platform to create Career Tracks.
DataCamp used to be stronger with R for data science, but now they’ve boosted their Python courses up to an equivalent level, and are still expanding. At $29/month for all of their courses and tracks, DataCamp is the best bang-for-the-buck for beginners that just want to get started fast.
If you can’t decide if you want to start the R or Python data science tracks, see this R vs Python post. There’s no stopping you from taking both!
You can be an absolute beginner with Python and start taking this track right away. DataCamp begins by teaching you the Python skills needed for data science, then you’ll start working with machine learning, visualization, and statistics and data storage with the language. You’ll end this series with a very strong foundation of doing data science with Python.
- 19 Courses
- 67 Hours
- Intro to Python for Data Science
- Intermediate Python for Data Science
- Python Data Science Toolbox (Part 1)
- Python Data Science Toolbox (Part 2)
- Importing Data in Python (Part 1)
- Importing Data in Python (Part 2)
- Cleaning Data in Python
- pandas Foundations
- Manipulating DataFrames with pandas
- Merging DataFrames with pandas
- Introduction to Databases in Python
- Introduction to Data Visualization with Python
- Interactive Data Visualization with Bokeh
- Statistical Thinking in Python (Part 1)
- Statistical Thinking in Python (Part 2)
- Supervised Learning with scikit-learn
- Unsupervised Learning in Python
- Network Analysis in Python (Part 1)
- Machine Learning with the Experts: School Budgets
Similar to the previous track with Python, but now you’re working with R and you have about 4 more hours of content. Here, you’re also getting the full intro to the language so you don’t need to worry about knowing R beforehand. After getting comfortable with R, you’ll be digging deep into data analysis, machine learning tools, and visualization tools. Everything you need to start doing data science with R.
- 23 Courses
- 95 Hours
- Introduction to R
- Intermediate R
- Intermediate R – Practice
- Importing Data in R (Part 1)
- Importing Data in R (Part 2)
- Cleaning Data in R
- Importing & Cleaning Data in R: Case Studies
- Writing Functions in R
- Data Manipulation in R with dplyr
- Joining Data in R with dplyr
- Data Visualization in R
- Data Visualization with ggplot2 (Part 1)
- Data Visualization with ggplot2 (Part 2)
- Data Visualization with ggplot2 (Part 3)
- Introduction to Data
- Exploratory Data Analysis
- Exploratory Data Analysis in R: Case Study
- Correlation and Regression
- Foundations of Inference
- Machine Learning Toolbox
- Machine Learning Toolbox
- Text Mining: Bag of Words
- Reporting with R Markdown
This series is a neat variation of the Data Science with R Career Track, where you’re still learning the basics of R, but the curriculum has a financial focus. You won’t be getting into machine learning as much, but it’s definitely a great way to expand your practical R knowledge and have fun doing it.
- 12 Courses
- 51 Hours
- Introduction to R for Finance
- Intermediate R for Finance (Coming soon)
- Manipulating Time Series Data in R with xts & zoo
- Importing and Managing Financial Data in R
- Introduction to Time Series Analysis
- ARIMA Modeling with R
- Manipulating Time Series Data in R: Case Studies
- Introduction to Portfolio Analysis in R
- Intermediate Portfolio Analysis in R
- Bond Valuation and Analysis in R
- Credit Risk Modeling in R
- Financial Trading in R
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