Data Analytics with Python for Beginners
What You’ll Learn
Python is the most powerful tool for data analytics today. This course teaches you to work with real datasets using NumPy, Pandas, Matplotlib, Seaborn, and Plotly. Every step is covered practically — from data cleaning and EDA to visualization and business insights.
Benefits
- NumPy — Array Computing Mastery
- Pandas — Advanced Data Manipulation
- Matplotlib & Seaborn — Static Visualizations
- Plotly — Interactive Charts & Dashboards
- Data Cleaning & Preprocessing
- Exploratory Data Analysis (EDA)
- Statistical Analysis with Python
- Business Insight Reporting
Curriculum
🐍 Module 1 — Python for Analytics (Quick Revision)
- Python revision: Data Types, Lists, Dicts, Functions, OOPs basics
- List Comprehensions, Lambda, Map, Filter
- Working with CSV, JSON, Excel files natively
🔢 Module 2 — NumPy Mastery
- Array Creation: arange, linspace, zeros, ones, random
- Indexing, Slicing, Boolean Masking, Fancy Indexing
- Broadcasting: Shape rules, Operations on different-shaped arrays
- Mathematical Operations: dot product, matrix operations, linear algebra
- Aggregations: sum, mean, std, percentile along axes
🐼 Module 3 — Pandas Deep Dive
- Series & DataFrame creation, attributes, methods
- Data Access: loc, iloc, at, iat, Boolean filtering
- Data Cleaning: isnull, fillna, dropna, duplicated, replace
- Type Conversion, String Operations (str accessor), Regex in Pandas
- GroupBy: Split-Apply-Combine, agg, transform, apply
- Merge, Join, Concat, Append operations
- Pivot Table, Crosstab, Melt, Stack, Unstack
- Time Series: datetime, resample, rolling, shift
- Performance: Vectorization vs loops, Categorical dtype, chunking large files
📊 Module 4 — Data Visualization
- Matplotlib: Figure, Axes, Subplots, Line, Bar, Scatter, Pie, Histogram
- Matplotlib customization: Colors, Markers, Annotations, Legends
- Seaborn: Distributions, Categorical Plots, Regression, Heatmap, Pairplot
- Plotly Express & Graph Objects: Interactive Bar, Scatter, Funnel, Sunburst
- Plotly Dash: Basic interactive dashboard building
- Visualization Best Practices: Chart selection, Color theory, Storytelling
🔍 Module 5 — EDA & Business Insights
- EDA Framework: Understand → Clean → Explore → Conclude
- Univariate, Bivariate, Multivariate Analysis
- Outlier Detection: IQR Method, Z-score, Visual Methods
- Correlation Analysis: Pearson, Spearman, Cramer’s V
- Feature Distribution: Skewness, Kurtosis, Normality Tests
- Business Insight Report: Executive Summary, Key Findings, Recommendations
- Projects: IPL Analysis, Zomato Dataset, Global COVID-19 Data, Netflix EDA
