What is data science?
A data scientist is a professional who specializes in analyzing and interpreting data. They use their data science skills to help organizations make better decisions and improve their operations. Data scientists typically have a strong background in mathematics, statistics, and computer science. They use this knowledge to analyze large data sets and find trends or patterns. Additionally, data scientists may develop new ways to collect and store data.
1. Learning a programming language
Python and R are the most used programming languages in data science
Learn Python's basic syntax, e.g., data structures like variables, integers, and strings.
Lists, Dictionaries, Sets, and Tuples.
2. Learn Pandas, numpy, matplotlib/seaborn.
Pandas library in Python is mainly used for data analysis.
Numpy provides several techniques for data visualization.
Matplotlib is a popular Python library for displaying data and creating static, animated, and interactive plots. One can also learn Seaborn but matplotlib is mostly used.
3. Learn statistics and math.
Linear algebra: Scalar, vector matrices, and their operations.
Probability: Rules, Dependent and independent events, Mutually exclusive events, etc.
Calculus: Differentiation and its rules, partial differentiation, and integration and its rules.
Statistics(stats): Sampling techniques, testing data, regression modeling, etc.
4. Learn SQL or any other database management system.
SQL stands for Structured Query Language and is used to communicate with a database. It is the standard language for relational database management systems(RDBMS).
the SQL basic commands include like AND, AS, COUNT(), DELETE, GROUP BY, INSERT, IS NULL/IS NOT NULL, LIKE, MIN(), MAX(), ORDER BY, SELECT etc.
5. Learn and interact with BI tools.
Business Intelligence (BI) tools e.g. PowerBI and Tableau are all about helping you understand trends and derive insights from your data so that you can make tactical and strategic business decisions which also help you identify patterns.
I wish you all the success. But again remember, it should always be PROGRESS OVER PERFECTION!!