Description

Module 1: Introduction to Data Science

• What is Data Science and why it matters

• Data Science lifecycle

• Roles: Data Analyst vs Data Scientist vs Data Engineer

• Real-world applications of data science

Module 2: Python for Data Science

• Python fundamentals: variables, data types, loops, functions

• Working with data structures: lists, dictionaries, tuples

• Introduction to Jupyter Notebook

• Popular libraries: NumPy, Pandas, Matplotlib

Module 3: Data Analysis with Pandas

• Reading and writing data (CSV, Excel)

• Data selection, filtering, and sorting

• Grouping, merging, and aggregating data

• Handling missing values and duplicates

Module 4: Data Visualization

• Visualizing data distributions, trends, and correlations

• Using Matplotlib and Seaborn for charts and plots

• Creating dashboards with Plotly (optional)

Module 5: Statistics and Probability

• Descriptive statistics: mean, median, mode, variance, standard deviation

• Probability theory: basic concepts, conditional probability

• Hypothesis testing: p-values, confidence intervals

• Distributions: normal, binomial, Poisson

Module 6: Exploratory Data Analysis (EDA)

• Identifying patterns and outliers

• Feature correlation analysis

• Using visualization tools to uncover insights

• Preparing data for modeling

Module 7: Machine Learning for Data Science

• Introduction to supervised and unsupervised learning

• Linear and Logistic Regression

• Decision Trees, Random Forest, K-Nearest Neighbors

• Model evaluation: accuracy, confusion matrix, ROC curve

• Overfitting and underfitting, cross-validation

Module 8: Feature Engineering and Selection

• Creating new features from raw data

• Encoding categorical variables

• Normalization and scaling

• Selecting the best features for modeling

Module 9: Time Series and Forecasting (Optional)

• Understanding time series data

• Trend, seasonality, and noise

• Moving average and exponential smoothing

• ARIMA models for forecasting

Module 10: Introduction to SQL for Data Science

• Basics of databases and relational data

• Writing SQL queries: SELECT, JOIN, GROUP BY

• Filtering and sorting data

• Combining Python with SQL (using SQLite or MySQL)

Module 11: Real-World Projects

• Sales prediction for a retail company

• Customer churn analysis

• Market basket analysis (association rules)

• Movie recommendation system

• Employee attrition prediction

Tools and Technologies Used

• Python

• Jupyter Notebook / Google Colab

• NumPy, Pandas, Matplotlib, Seaborn

• Scikit-learn

• SQL (SQLite, MySQL, or PostgreSQL)

• Plotly, Streamlit (for dashboard and deployment)

Requirements