Data Science Roadmap

Data science is one of the highest-paying career options in this data-driven world. Every company now makes decisions based on the data generated by its business. A company, therefore, needs skilled data science professionals to turn raw data into actionable insights. If you’re looking for a step-by-step roadmap for learning data science, this article is for you. In this article, I will introduce you to a data science roadmap that you can follow to learn data science.

Data Science Roadmap

Step 1: Learn Statistics

Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. Statistics is a fundamental part of data science, and learning the basics of statistical concepts such as probability, hypothesis testing, regression analysis, and data visualization is essential to understanding how to analyze data effectively.

Below are some of the best resources on the internet to learn statistics for Data Science:

1. Think Stats
2. Statistics 101
3. Statistics for Data Science by Great Learning

Step 2: Learn Python

Python is a popular programming language widely used in data science. Learning Python is important because it lets you write code to manipulate and analyze data, and it provides a wide range of libraries and tools that make working with data more efficient.

Below are some of the best resources on the internet to learn Python:

1. Python Full Course by Tech with Tim
2. Introduction to Python programming by Udacity
3. Think Python

Step 3: Learn Python Libraries for Data Science

Python libraries such as Pandas, NumPy, Matplotlib and Scikit-learn are essential tools for data scientists. These libraries provide a range of functions for data manipulation, visualization, and machine learning. Learning to use these libraries will allow you to work more effectively with data.

So below are all the Python libraries that you need to learn with their learning resources:

1. NumPy
2. Pandas
3. Matplotlib
4. Seaborn
5. Plotly
6. Scikit-learn
7. Keras
8. Tensorflow

Step 4: Learn Machine Learning

Machine learning is a subfield of artificial intelligence that focuses on building systems that can learn from data. It involves using data and algorithms to build intelligent systems. Machine learning is essential for data scientists who want to create predictive models and develop intelligent systems.

Below are some of the best resources on the internet to learn Machine Learning:

1. Hands-on Machine Learning with Scikit-learn, Keras, & Tensorflow
2. Machine Learning Crash Course by Google Developers
3. Mastering Machine Learning Algorithms

Step 5: Learn Essential Data Science Tools

Besides statistics, Python, Python libraries, and machine learning, data scientists also need to know how to work with some essential tools that helps in working with data and solving business problems with data.

Below are some of the essential tools that you need to learn:

1. Microsoft Excel
2. SQL
3. Tableau or Power BI
4. Google Colab
5. Jupyter Notebook
6. Cloud Computing Platforms

Final Step: Work on Projects

The last step in the data science roadmap is often overlooked by many beginners. The ultimate goal is to learn data science and get a job as a data science professional. To get your first data science job, you need to show how you can solve problems with all the skills you have.

So work on projects! It will help you show your approaches to solving different problems with your data science skills. You can find some amazing data science projects solved and explained from here. You can practice your skills by going through these projects.

Summary

So this is the complete data science roadmap that I will recommend you to follow to learn data science:

1. Learn Statistics
2. Learn Python
3. Learn Python Libraries for Data Science
4. Learn Machine Learning
5. Learn Essential Data Science Tools
6. Work on Projects

At each step of the roadmap above, I’ve mentioned some of the best resources you can follow to learn data science. I hope you liked this article on a comprehensive roadmap to learning data science. Feel free to ask valuable questions in the comments section below.