Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines expertise from various domains such as statistics, mathematics, computer science, and domain-specific knowledge to analyze and interpret complex data sets.

30 DAYS MASTERCLASS ON DATA SCIENCE

Python for Data Science:

Day 1: Introduction of data science

Day 2: Basic python programming- Part I

Day 3: Python programming– Part II

Day 4: Python programming– Part III

Libraries for Data science

Day 5: Python for data analytics- Pandas

Day 6: Python for data analytics- NumPy

Day 7: Python for data analytics- Matplotlib

Day 8: Python for data analytics- Seaborn

Data science element

Day 9: Data Collection, Data Wrangling, and Data Cleaning Techniques

Day 10: Exploratory Data Analysis (EDA) and Data Visualization Techniques

Day 11: Probability and Statistics for Data Science

Machine learning , Data processing and feature engineering

Day 12: Introduction to Machine Learning and Types of Learning

Day 13: Preprocessing Data for Machine Learning

Day 14: Feature Selection and Feature Engineering Techniques

Day 15: Handling Missing Data and Outliers

Day 16: Encoding Categorical Variables and Scaling Numerical Variables

Day 17: supervised learning regression and classification -model

Day 18: unsupervised learning clustering- model

Tools for visualization

Day 19: Introduction Tableau – data visualization

Day 20: Tableau – Data Sources, Worksheet

Day 21: Introduction power BI

Day 22: Visualize Data in the Form of Various Charts, Plots, and Maps BI tools - Power BI

Data science Project

Day 23: Credit score classification

Day 24: Stress prediction

Day 25: Social Media Ads Classification

Day 26: electricity price prediction

Day 27: Ground water level prediction

Day 28: churn analysis prediction

Day 29: drowsiness detection by using CNN

Day 30: chat bot

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