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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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