Master Data Analysis: Complete Python & SQL Course Outline

 

📊 Data Analysis Course Outline (Python & SQL, Beginner to Advanced, Latest Features)

📌 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, Seaborn) and SQL databases. By mastering these skills, learners can handle real-world datasets, build dashboards, and generate reports for business intelligence and decision-making.

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📘 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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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