Learn practical skills, build real-world projects, and advance your career
Updated 4 years ago
There's no shortage of data science courses, tutorials and learning materials available online. However, it can often feel intimidating as a beginner to figure out where to start. This short & concise guide is meant to help you build your data science career the right way and answer the most common doubts you may have: which topics you should learn, in what order, which projects you should/shouldn't do, what you should put in your resume etc. It would help to bookmark this page and refer to it from time to time, whenever you need some guidance or feel lost.
Ingredients
- Skills and Domains
- Courses, Certifications & Books
- Projects
- Job roles
- Resume & Professional profile
- Job Hunting & Interviews
All the above ingredients are important for getting started with a career in data science, so don't ignore any of them. Depending on where you currently stand, it might take anywhere from 3-4 months to a year to build a strong profile. But don't worry, you'll have a lot of fun along the way.
Skills & Domains
- Domains: Wrangling, EDA, Visualization, Machine Learning, Deep Learning
- Coding: Python, R, Frameworks (SKLearn, TensorFlow, PyTorch etc.)
- Math: Linear Algebra, Statistics, Basic Calculus
- Misc.: Experimentation, Documentation, Presentation, Storytelling, Excel
- Deployment (bonus): Backend, UI, Cloud (AWS/Heroku), DevOps