Teachmind

Introduction to Pandas Library
- Installing and Importing Pandas
- DataFrames and Series
Data Manipulation Techniques
- Loading Data from CSV, Excel, JSON
- Data Inspection: Head, Tail, Describe, Info
- Indexing and Slicing DataFrames
- Filtering and Sorting Data
- Handling Missing Data (Imputation Techniques)
- Grouping and Aggregation
- Merging, Joining, and Concatenating DataFrames
Case Study: Real-world Data Cleaning and Manipulation

Introduction to Data Visualization
- Importance of Visualization in Analytics
Matplotlib Library
- Basic Plots: Line, Bar, Histogram, Scatter, Pie
- Customizing Plots (Labels, Titles, Legends)
- Subplots and Multi-figure Plots
- Saving and Exporting Plots
Seaborn Library
- Advanced Visualizations: Pairplot, Heatmap, Violin Plot, Box Plot
- Styling and Aesthetic Customization
Case Study: Visualizing Trends and Patterns in a Dataset

Introduction to NumPy Library
- Array Creation and Operations
- Mathematical and Statistical Functions
- Random Number Generation
Introduction to SciPy Library
- Statistical Distributions and Tests
- Hypothesis Testing (t-test, chi-square test)
- Correlation and Regression Analysis
- Optimization Techniques
Case Study: Statistical Inference from Data

Importance of EDA in Data Analytics
EDA Techniques
- Univariate, Bivariate, and Multivariate Analysis
- Detecting Outliers and Anomalies
- Feature Engineering and Transformation
Visualization Techniques for EDA
- Distribution Plots, Correlation Heatmaps
- Pair Plots, Box Plots, Violin Plots
Case Study: Performing EDA on a Business Dataset

Data Preprocessing Techniques
- Data Normalization and Standardization
- Handling Missing Values and Outliers
- Encoding Categorical Data (One-Hot Encoding, Label Encoding)
- Feature Scaling (Min-Max, Standard Scaler)
Feature Selection Techniques
- Correlation-based Selection
- Chi-square, ANOVA, Mutual Information
Dimensionality Reduction Techniques
- Principal Component Analysis (PCA)
- Feature Extraction
Case Study: Preparing Data for Machine Learning

Introduction to Machine Learning
- Supervised vs. Unsupervised Learning
Scikit-learn Library Overview
- Model Selection and Training
- Train-Test Split, Cross-Validation
Supervised Learning Algorithms
- Regression Models (Linear, Ridge, Lasso)
- Classification Models (Logistic Regression, KNN, SVM, Decision Trees)
Unsupervised Learning Algorithms
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)
Model Evaluation Metrics
- Accuracy, Precision, Recall, F1-Score, AUC-ROC
Case Study: Building and Evaluating Predictive Models

Introduction to Time Series Data
- Characteristics of Time Series
Time Series Analysis with Pandas
- Resampling, Rolling, and Shifting
- Decomposition of Time Series (Trend, Seasonality, Residuals)
Time Series Forecasting Techniques
- ARIMA, SARIMA Models
- Moving Average and Exponential Smoothing
Case Study: Forecasting Business Metrics

Introduction to SQL for Data Analytics
- Basic SQL Queries (Select, Join, Aggregate)
- Integrating SQL with Python (SQLAlchemy, Pandas)
Data Wrangling with SQL
- Filtering, Grouping, and Sorting Data
- Advanced SQL (Subqueries, Window Functions, CTEs)
Python-SQL Integration for Data Analytics
Case Study: Data Wrangling for a Business Analytics Project

Project Overview
- Combining All Learnings into a Complete Data Analysis Project
- Dataset Selection and Problem Definition
Building a Data Analysis Pipeline
- Data Collection, Cleaning, Analysis, and Visualization
Presentation of Findings and Insights
Project Review and Evaluation

Data Analyst