August 02, 2019

My Online Data Science Curriculum

At the start of 2019, I decided that I want to switch from web development to data science. I am already studying BBA, Business Information Technology at the Turku University of Applied Sciences. I am doing an exchange year at the University of Alicante in Spain. There I was able to add a lot of math to my curriculum, but not any machine learning or data science courses to it.

After some research I decided to create my own online curriculum to fill the gaps in my university studies. Due to my web development background I have some existing skills that helped in the transition.

Existing skills

Python
Git / Github
(HTML, CSS ,Javascript)
(Web development)
Data structures & algorithms
Databases (SQL, MongoDB)
Data Visualization basics
Jupyter & Anaconda

During this curriculum I will study calculus, linear algebra, statistics & discrete mathematics at the University of Alicante. So many of the math courses will be reinforced with corresponding university courses.

Important skills I need to learn or get better with

Statistics
Calculus & linear algebra
Communication skills (storytelling with the data)
Critical thinking
Data preprocessing
Machine learning
SQL (I have basic knowledge, but that is not enough)
Domain/business knowledge

Phase 1 - DS, ML & math fundamentals

Most important thing during this phase is to learn the basics and get a broader understanding about data science. I also want to learn the basics of machine learning during this time.

It is also important to learn what to do with the data before you can use it. Data scientists spend most of their time preparing the data for use.

Phase 1 is composed mostly about online courses that teach the fundamentals.

Here are some optional resources to help with the process.

Phase 2 - DL, advanced ML & Kaggle

The goal of phase 2 is to deepen the understanding of the concepts used in data science and introduce myself to deep learning.

Even more important in this phase is to start applying the skills learned during phase 1 by building simple projects and participating in Kaggle beginner competitions.

Deep Learning by Andrew Ng | Coursera
Advanced Machine Learning | Coursera
Kaggle beginner competitions
Projects (simple projects that apply what you have learned)

Phase 3 - Start building & complement with courses

The meaning of phase 3 is to apply the learned knowledge in competitions and projects. I want to start building my portfolio during this time while simultaneously deepening my understanding of data science principles.

Kaggle competitions
Projects (Portfolio-ready projects that showcase your skills)
Deeper courses/books??
Keep the daily habit of learning and building

Phase 4 - Apply for a job or internship

Finally, time to get a job in data science. During this time I am going to practice interviewing and simultaneously build my portfolio.

Practice interviewing
Research the companies that you would want to work with
Polish your resume and portfolio
Apply for jobs
Most importantly, keep building projects and keep learning!

Phases 1-3 should take 2-4 months each, but I’m prepared for phase 4 to last much longer. I don’t have a Ph.D. or a masters in data science. Neither do I have work experience. That is why I’m prepared for job hunting to last a long time.

Back to blog index