TOO MANY OPTIONS
Nowadays, there are many online courses for those wanting to work with data. Since the data market is booming, schools are taking advantage of people willing to be part of this. Many people spend hundreds, or even thousands, of dollars with specializations, MBAs and so on. I understand them, there are too many options and most of them are just in the beginning of their career. So I want to help you to keep your savings for anything but studying Data Science/Analysis.
UDACITY PLATFORM
Udacity is my favorite online platform to learn about Data Science and Data Analysis. I believe a lot of people know about them, but most people don’t realize they offer amazing courses for free. So I filtered the best options for those willing to become a data analyst or a data scientist. If you do all these courses and apply them in some analysis, or write about some concepts on Medium, you’ll at least triple your chances of getting into the job market!
COURSES FOR DATA ANALYSTS
- Introduction to Python Programming
- Intro to Data Analysis
- Data Analysis and Visualization
- Data Visualization with Tableau
- Intro to Statistics
- Statistics
- SQL for Data Analysis
- Data Wrangling with Mongo DB
- A/B Testing
COURSES FOR DATA SCIENTISTS
- Introduction to Python Programming
- Intro to Data Analysis
- SQL for Data Analysis
- Data Wrangling with MongoDB
- Introduction to Machine Learning
- Intro to Statistics
- Intro to Inferential Statistics
- Time Series Forecasting
- Classification Models
- Machine Learning: Unsupervised Learning
- Model Building and Validation
- Machine Learning for Trading
- AWS Machine Learning
- Intro to Deep Learning with Pytorch
- Intro to Tensorflow for Deep Learning
As a mentor, what I usually recommend students is to do the following tasks: (1) complete a course; (2) apply the content learned in a data analysis or show some concept applied with Python — this is already part of your portfolio and you should upload it to Github — ; (3) write an article on Medium — this is also part of your portfolio. You can repeat steps (2) and (3) as many times as you want. As you can see, by doing steps 1, 2 and 3 you went from a regular resume to one with a certification and a reasonable portfolio. This will help you to impress recruiters.
Since there isn’t a specific list of subjects you must know as a data analyst / scientist, there are other courses that might also be important during your career. My list contemplates what I found most relevant in these 7 years working in this industry. I doubt you will have any trouble finding a job opportunity after studying all these courses I mentioned — you’ll probably get an opportunity way before finishing all of them. Other courses I find helpful:
- Database Systems Concepts and Design
- Spark
- Problem Solving with Advanced Analytics
- Introduction to Machine Learning with Microsoft Azure
- AI Fundamentals
- Segmentation and Clustering
- Reinforcement Learning
- Introduction to Computer Vision
- Version control with Git
CONCLUSION AND DISCUSSION
Bear in mind that you can get a job opportunity in data without spending a cent. People who work with data don’t really care if you have a paid certification. We just want to be sure you know how to deal with data. This is why we prefer to check your portfolio, ask the right questions during the interview and maybe apply some tests.
Besides, Udacity is a great platform. No one will ever reject your resume because you did some of its free courses. Additionally, these courses are great and better than those paid ones from other schools. My advice to you is to focus on studying the topics you need to learn to work in the position of a analyst or a scientist and build a great portfolio — which can be on Medium and/or Github. This is how you will get your chance.
