My learning path in data science

For more than two years now, in parallel to my daily data-related work, I’ve enrolled in several interesting courses. In this post I’d like to describe these as part of my learning path, even when some of them are no longer active you can still grab the resources.

• Coursera | Johns Hopkins University | Executive Data Science Specialization:

  • A Crash Course in Data Science (link)
  • Building a Data Science Team (link)
  • Managing Data Analysis (link)
  • Data Science in Real Life (link)

• Coursera | Johns Hopkins University | Data Science Specialization:

  • The Data Scientist’s Toolbox (link)
  • R Programming (link)
  • Getting and Cleaning Data (link)
  • Exploratory Data Analysis (link)
  • Reproducible Research (link)
  • Statistical Inference (link)
  • Regression Models (link)

• Coursera | University of Washington | Machine Learning Foundations: A Case Study Approach (link)

• Stanford University | Statistical Learning (link)

• Stanford University | Writing in the Sciences (link)

• Udacity | Data Visualization and D3.js. Communicating with Data (link)

• edX | MIT + Kaggle | 15.071x The Analytics Edge (link)

• Coursera | Eindhoven University of Technology | Process Mining: Data science in Action (link)

• edX | University of California, Berkeley | XSeries - Data Science and Engineering with Apache Spark:

  • CS105x Introduction to Apache Spark (link)
  • CS120x Distributed Machine Learning with Apache Spark (link)
  • CS110x Big Data Analysis with Apache Spark (link)

• edX | University of Michigan | DS101x Data Science Ethics (link)

• LinkedIn Learning | Building Deep Learning Applications with Keras 2.0 and Tensorflow/Theano (link)

If you are into data science and in need of any help with your career just drop me a line or two.