In the dynamic landscape of data science, a robust and swiftly expanding field, untapped opportunities abound. According to LinkedIn's Emerging Jobs Report, the market is poised for significant growth, projecting an increase from $37.9 billion in 2019 to a staggering $230.80 billion by 2026. Consequently, individuals aspiring to establish enduring careers in the realm of IT would be wise to consider data science as their destination. Nevertheless, the process of acquiring proficiency in a new discipline poses its challenges. Mitigating these challenges involves crafting and executing a well-defined educational plan—a roadmap.

Need for Data Scientist

The urgency for data scientists has escalated in today's interconnected world, driven by the voluminous data generated by businesses, organizations, and individuals alike. Data science equips professionals with the tools and techniques essential for extracting meaningful insights from this data, fostering informed decision-making. This proficiency is pivotal for businesses seeking a competitive edge and improvement in operational efficiency. Furthermore, data science plays a pivotal role in addressing pressing global challenges such as healthcare, climate change, and social inequality. In essence, the demand for data science expertise is paramount in unlocking the full potential of data and making informed decisions in our data-driven era.

1. Learn Python:

Python serves as the backbone of data science. Mastering its basics is essential for seamless progression in the field. Start with the fundamentals:
1. Variables, Numbers, Strings
2. Lists, Dictionaries, Tuples
3. If condition, for loop
4. Functions, modules
5. Read, write files
6. Exception handling
7. Classes, Objects
7. Numpy

Resources to learn Python:
Programming with Mosh
Codebasics Python Playlist first 16 videos

2. Learn Tools for Handling Data Science:

Become proficient in essential tools like Pandas for data manipulation and Matplotlib for data visualization. These tools are the cornerstone of effective data handling and analysis in the realm of data science.
Resources to learn Pandas:Corey Schafer
Resources to learn Matplotlib:Codebasics

3. Mathematics for Data Science:

Understanding the mathematical foundations is crucial for unraveling the complexities of data science. Focus on key topics:
1. Variables, Numbers, Strings
2. Lists, Dictionaries, Tuples
3. If condition, for loop
4. Functions, modules
5. Read, write files

Resources:
Finish this excellent Khan academy course on statistics and probability Course link
Free Youtube playlist by Codebasics

4. Machine Learning:

Delve into the exciting realm of machine learning, covering fundamental topics:
1. Feature engineering
2. Regression
3. Classification
4. Clustering
5. Read, write files

Resources:
Free Youtube playlist by Codebasics (first 21 videos only)

5. Learn SQL:

Data Science is all about handling of data, and as these data is stored in databases like SQL it is important to master the language of databases with a focus on practical applications:
1. Basics of relational databases
2. Basic Queries: SELECT, WHERE LIKE, DISTINCT, BETWEEN, GROUP BY, ORDER BY
3. Advanced Queries: CTE, Subqueries, Window Functions
4. Joins: Left, Right, Inner, Full
5. Stored procedures and functions

Resources:
One shot video Chandoo
by w3schools

6. PowerBI Tool

Explore the significance of PowerBI in data science. Understand its role in data visualization and the importance of hands-on projects to solidify your skills.
OneShot video by Kevin Stratvert
Personal Finance Dashboard Codebasics
HR Data Analytics Dashboard Chandoo

This roadmap is your guide to navigating the complexities of the field. Stay curious, practice regularly, and immerse yourself in projects to apply your knowledge. As the data science landscape evolves, your solid foundation will empower you to unlock the vast potential of data and contribute meaningfully to the ever-growing field of data science. Happy learning!