Master Data Analysis: Complete Python & SQL Course Outline

📌 Introduction

Data Analysis is essential for transforming raw data into actionable insights. This course roadmap teaches you how to collect, clean, analyze, and visualize data using Python libraries (Pandas, NumPy, Matplotlib, and Seaborn) and SQL databases. By mastering these skills, learners can effectively handle real-world datasets, build informative dashboards, and generate actionable reports for business intelligence and informed decision-making.

📘 Detailed Course Outline

Module 1. Introduction to Data Analysis

• What is Data Analysis, and its importance

• Types of data: structured, semi-structured, unstructured

• Data Analysis workflow: Collection → Cleaning → Analysis → Visualization → Reporting

• Tools overview: Python, SQL, Jupyter Notebook, VS Code, Excel

Module 2. Python Basics for Data Analysis

• Python installation and environment setup

• Variables, data types, operators, conditionals, loops

• Functions and modules

• Reading and writing data using Python

• Latest Python 3.12+ features relevant to data analysis

Module 3. Working with SQL for Data Analysis

• Introduction to databases and SQL

• Basic SQL queries: SELECT, WHERE, ORDER BY, GROUP BY

• Joins: INNER, LEFT, RIGHT, FULL

• Aggregations and subqueries

• Window functions and advanced SQL features (SQL 2022/2025)

• Connecting Python with SQL using sqlite3, SQLAlchemy, or PyODBC

Module 4. Python Libraries for Data Analysis

• NumPy: arrays, indexing, slicing, operations

• Pandas: Series, DataFrame, indexing, selection, filtering

• Data cleaning: handling missing data, duplicates, outliers

• Data transformation: merge, concat, pivot, melt

• Latest Pandas 2.x features

Module 5. Data Cleaning & Preprocessing

• Handling missing and null values

• Data type conversion and normalization

• Handling categorical data (one-hot encoding, label encoding)

• Feature scaling and standardization

• Working with dates and timestamps

• Data validation and integrity checks

Module 6. Exploratory Data Analysis (EDA)

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

• Correlation analysis and covariance

• Grouping, aggregation, and pivot tables

• Identifying trends, patterns, and anomalies

• Using Python libraries for EDA: Pandas, NumPy

Module 7. Data Visualization

• Matplotlib: line, bar, scatter, histogram, pie charts

• Seaborn: heatmaps, pairplots, regression plots, categorical plots

• Advanced visualization: time series plots, dual-axis charts

• Interactive visualization: Plotly, Bokeh

• Best practices for effective data visualization

Module 8: Advanced SQL for Data Analysis

• Common table expressions (CTE)

• Window and analytic functions

• Ranking, lag, lead, and cumulative functions

• Advanced aggregations

• Indexing, optimization, and query performance tuning

Module 9. Combining Python & SQL

• Extracting data from SQL to Python DataFrames

• Performing transformations using Pandas

• Joining SQL data with external datasets

• Automating SQL queries using Python

• Building end-to-end analysis workflows

Module 10. Advanced Data Analysis & Reporting

• Time series analysis and forecasting basics

• Grouping and multi-level aggregation

• Pivot tables and cross-tabulations

• Generating reports using Python (PDF, Excel, HTML)

• Dashboards using Plotly Dash or Streamlit

• Latest 2025 tools & techniques for automated analysis

Module 11. Advanced Data Analysis & Reporting

Real Life Project

📘 Master Data Analysis: Complete Python & SQL Course Outline

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