This is a beginner-friendly guide for career shifters who are interested into Data Science. This will cater to learners who are just starting their self-study journey.
- Learning the basics of data science
- Programming
- Applied Data Science Track
- Hands-on Experience
Learning data science fundamentals is the most important part of your journey because it will prepare you for the learning challenges.
Begin by watching beginner-friendly video tutorials to learn the fundamentals of statistics and data science. These non-technical videos will help you understand various aspects of data science, including data ingestion, data cleaning, data analysis, modeling, and applications. If you don't like watching videos, you can read blogs and books instead. Learning the fundamentals of data science will help you understand the career path and introduce you to the most commonly used tools for data analytics and machine learning.
After learning the fundamentals, it's time to master Python, as you'll be coding for the majority of your time at work. Learning Python will help you with research, data visualization, creating cutting-edge applications, and passing technical interviews. Then, as the most important tool for data ingestion and analysis, learn SQL. Almost all data science interviews will include SQL questions, so learning it early on will improve your chances of getting hired.
Data management, exploratory analysis, statistical experimentation, model development, programming, and reporting are all covered in the data science track. The career path also includes a structure and interactive coding exercises. These courses will teach you the fundamentals of statistics, machine learning, and natural language processing.
- Numpy
- Data Cleaning
- Exploratory Data Analysis
- Matplotlib
- Seaborn
- Data Visualization
- Tableau
- Statistics for Data Science
- Google Machine Learning
- Introduction to Machine Learning
- Intermediate Machine Learning
It's time to put our skills to use in the real world, but you won't be able to find work right away unless you have some prior experience. What is the best way to gain experience? Participate in data science competitions, volunteer in non-profit projects, or apply for internships.
Look into Omdena's projects. Look for beginner-friendly projects to add to your data science portfolio. You don't have to limit yourself to jobs to gain experience; you can also gain experience by contributing to open-source projects.