New Data Science Certificate Program – Cal State Fullerton

If you are interested in earning a data science certificate, California State University Fullerton has just announced a new data science certificate program.

The program is entirely online and runs 1 course at a time starting in late February 2015. The 5th and final course should complete sometime in late 2015. Thus, the entire program lasts less than a year. Below are some more details on the program.

What do you need to get into the program?

  • Bachelor’s Degree in math, CS, stats, business, science, or other related field
  • A couple year’s work experience

What type of people should attend this program?

  • Someone intested in transitioning to data science career
  • Managers whom wish to better understand data-driven decisions
  • Programmers and Statisticians looking to become data scientists
  • Analysts looking to move beyond Excel


Full Disclosure: I am a member of the Advisory Board for this program.

About these ads

Considering Julia? Here Are Some Resources

Julia is a new programming language that is quickly gaining traction in the statistics and data science world. It is a high-level language, yet the speed is comparable with lower-level languages like C and fortran. Below are some resources for various types of people.

Do you love the academic paper?

Julia: A Fresh Approach to Numerical Computing – An academic paper that includes a brief overview of the language as well as a description of the Julia architecture, benchmarks and much more.

You want to start with the Official Language Docs

The Julia Language is well-documented. There is even a style guide in the docs.

Forget the paper and docs, You want to try it Now!

JuliaBox – Web-hosted implementation of IJulia. IJulia is a collaboration with IPython to provide an interface for writing Julia code in a web browser. Use this option if you want to start right now and figure the details out later.

Slow down, Try some tutorials first

Forio Julia Tutorials – The tutorials expect you to install Julia Studio IDE, but JuliaBox above should be sufficient. The tutorials contain enough code to get you started. Then the tutorials advance to larger and more complex problems.

You prefer to see someone use Julia first

3 Questions When Choosing a Data Science Program

I was honored to write a guest blog post for Master’s in Data Science. The site contains a very detailed list of graduate programs in data science. The post I authored is title:

3 Questions to Ask Before Choosing a Data Science Program

Not to ruin the post, but the 3 questions are:

  1. What is my Background?
  2. What are my goals?
  3. Does location matter?

Head on over to Master’s in Data Science to see all the details about why those are 3 important questions.

3 Great Data Science Books You Can Read Now…for free

Just this week, I have become aware of 3 free online books for data science.

Data Visualization with Javascript

If you are looking for a tutorial to teach you how to make wonderful visualizations on the web, look no further. Data Visualization with JavaScript is a free online book for learning data visualization with Javascript. It provides tons of examples and step by step instructions for how to create the graphs, charts, and other visualizations. Here is a quick list of the topics:

  • Graphs
  • D3.js
  • Interactive Charts
  • Geographic Plots
  • Timelines

Frontiers in Massive Datasets

Frontiers in Massive Datasets is a report all about how science, business, communications, national security and others need to learn to handle massive amounts of data. Whether the data has been sitting in a database for years or it is now just screaming into the systems, massive data is now a problem for almost every industry. This report covers many of the topics that need to be addressed when dealing with big data. Here is a very brief overview of the topics:

  • Limitations
  • Sampling
  • Building Models from Massive Data
  • Real-time Algorithms
  • 7 Computational Giants of Massive Data Analysis

Foundations of Data Science

Foundations of Data Science is a draft of textbook written by John Hopcroft and Ravindran Kannan. It is intended to be a text for computer science with an emphasis more on probability and statistics rather than discrete mathematics. The authors argue that knowledge of working with data is a necessary skill for computer scientists of the future. This is clearly the most technical and academic of the 3 books, but if that is your thing, your should really enjoy browsing through this book. Here are some of the topics.

  • High-Dimensional Space
  • Clustering
  • Algorithms for Massive Data Problems
  • Singular Value Decomposition
  • Graphical Models
Follow

Get every new post delivered to your Inbox.

Join 4,341 other followers

%d bloggers like this: