Here’s How to Learn Data Science: A Complete Guide

Data science broadly encompasses data analysis, machine learning, data engineering, and statistical modelling. The path you take can vary based on the role you’re aiming for, such as a Data Analyst, Data Scientist, Machine Learning Engineer, or Data Engineer. So, if you are looking for a complete step-by-step guide to learn Data Science, this article is for you. This article will take you through a comprehensive guide to learn Data Science for all data science roles.

Here’s How to Learn Data Science: A Complete Step-by-Step Guide

Step 1: Foundational Knowledge

Foundational knowledge to learn Data Science includes basic programming (Choose Python or R), basic statistics and mathematics, and Data Visualization.

Below are all the learning resources to get all the foundational knowledge for Data Science:

• Python: Python As Fast as Possible by Tech with Tim
• R: R Programming Course
• Statistics: Basic Statistics by Khan Academy
• Data Visualization: Fundamentals of Data Visualization by Claus O. Wilke

This step is essential for all roles: Data Scientists, Data Analysts, Data Engineers, and Machine Learning Engineers.

Step 2: Necessary Python or R Libraries

The next step is to learn all the necessary Python or R libraries for Data Science.

Below are all the libraries with their learning resources you should learn:

• NumPy and Pandas (Python)
• dplyr and tidyr (R)
• SciPy (Python)
• R for Statistical Analysis
• Matplotlib (Python)
• ggplot2 (R)
• Plotly for Python and Plotly for R
• Scikit-learn and StatsModels (Python)
• caret (R)
• BeautifulSoup (Python)
• rvest (R)
• TensorFlow and PyTorch (Python)
• TensorFlow for R

This step is essential for all roles: Data Scientists, Data Analysts, Data Engineers, and Machine Learning Engineers.

Step 3: Intermediate Data Analysis

Data Analysis includes data manipulation, exploratory data analysis, working with databases (SQL) and data visualization tools.

Below are all the resources you can follow to learn Data Analysis:

• Data Manipulation: Data Manipulation using Python
• EDA: EDA using R | EDA using Python
• SQL: SQL for Data Science
• Data Visualization Tools: Tableau Specialization | Power BI Certification

This step is also essential for all roles: Data Scientists, Data Analysts, Data Engineers, and Machine Learning Engineers.

Step 4: Getting Started with Machine Learning Fundamentals

Getting started with Machine Learning fundamentals involves learning fundamental Machine Learning Algorithms, Techniques, and Concepts.

Below are the best resources you can follow to learn the fundamentals of Machine Learning:

• Machine Learning Algorithms Handbook
• IBM Machine Learning Professional Certification

This step is essential for Data Analysts, Data Scientists and Machine Learning Engineers.

Step 5: Advanced Statistics and Machine Learning Concepts

The next step is to learn advanced statistics and Machine Learning concepts. It involves advanced statistical methods and advanced Machine Learning concepts like deep learning and NLP.

Below are the best resources you can follow to learn advanced statistics and Machine Learning concepts:

• Advanced Statistics: Advanced Statistics for Data Science Specialization
• Deep Learning: Neural Networks and Deep Learning
• NLP: NLP Specialization

This step is essential for Data Scientists and Machine Learning Engineers.

Step 6: Data Engineering Fundamentals

Data Engineering Fundamentals includes learning about data warehousing, ETL processes, Cloud Services and Big Data Technologies.

Below are the best resources you can follow to learn the fundamentals of Data Engineering:

• Data Warehousing: Data Warehouse Guide
• Data ETL Pipelines: ETL and Data Pipelines Course
• Cloud Services: Cloud Data Engineering Certification

This step is essential for Data Engineers.

Step 7: Work on Projects

The last step is to work on projects based on real-time business problems based on your desired Data Science roles. Below are some resources from where you can find solved and explained projects:

1. Data Science Projects
2. Data Analysis Projects
3. Machine Learning Projects
4. Data Engineering Project Ideas

Working on projects is essential for all Data Science roles.

Summary

So, Data science broadly encompasses data analysis, machine learning, data engineering, and statistical modelling. The path you take can vary based on the role you’re aiming for, such as a Data Analyst, Data Scientist, Machine Learning Engineer, or Data Engineer. I hope you liked this article on a complete guide to learn Data Science step by step.