As an engineer I like to put some structure to the chaos. Inspired by Roadmap: How to Learn Machine Learning in 6 Months and Tetiana Ivanova – How to become a Data Scientist in 6 months a hacker’s approach to career planning I build my own learning road map for this year:
So 2018 will be all about Data Science. Hearing about the Personal Knowledge Mastery concept at SWEC17 I am going to tackle the learning process on different levels.

Watch the Pros

Thanks to open course ware there are a ton of awesome university courses online e.g.:

MIT 6.0002 Introduction to Computational Thinking and Data Science

Learn the tools

There is already a whole bunch of tools we can consider belonging to a standard data science stack. Because my main language is Python the focus is of course on mostly python modules.

• JuPyter Notebook
• numpy
• pandas
• seaborn
• bokeh
• holoviews
• scikit-learn
• keras / TensorFlow
• Tableau

Finishing Udacity / Udemy courses

To brush up my python skills and my knowledge of basic computer science I will finish some already started online courses:

• • [  ] Introduction to Machine Learning
  • [  ] Python Bootcamp
  • [  ] Algorithms and Data Structures
  • [  ] Introduction to Artificial Intelligence
  • [  ] Introduction to computer vision
  • [  ] Artificial Intelligence for Robotics

Reading data science books

To get a broad overview I bought two books on DS / ML

• [  ] Data Science from Scratch
• [  ] Hands on Machine Learning

Do Exercises on Kaggle

• [x] Create Account at Kaggle
• [  ] Do first exercise
• [  ] Participate in a contest

Visit Meetups about Data Science

[  ] Visit Big Data Meetup Events

Add some Peer Pressure

My brother in law and I teemed up and build a Whatsapp learn & exchange group. We are currently four members.

Write Blog Articles

I will try to incorporate some of the stuff I’ve learned into blog articles.

I already did

• Bayes’ Theorem Part 1
• Data Science Overview
• Classification: Precision and Recall
• Confusion Matrix
• UD120 Intro to Machine Learning
• Lesson 2: Naive Bayes
• Lesson 3: Support Vector Machines

So stay tuned!

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