August 2, 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.

- Intro to Data Science | Udacity
- Intro to Descriptive Statistics | Udacity
- Intro to Inferential Statistics | Udacity
- Machine Learning by Stanford (Andrew Ng) | Coursera
- Applied Data Science with Python | Coursera
- Algebra I | Math |Khan Academy
- Algebra II | Math |Khan Academy
- Statistics and Probability |Khan Academy
- Calculus 1 | Math |Khan Academy
- Calculus 2 | Math |Khan Academy
- Multivariable Calculus |Khan Academy
- Linear Algebra |Khan Academy

Here are some optional resources to help with the process.

- Automate the Boring Stuff with Python Online Book
- Learning How to Learn | Coursera
- YouTube | Statistics 110: Probability

## 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.