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Data Science – Python with SQL Integration
Module 1: Connecting Python with Databases
Introduction to Python database libraries (SQLite3, MySQL Connector, psycopg2).
Connecting Python to a database.
Executing SQL queries from Python.
Fetching results into Pandas DataFrames.
Hands-on: Fetch employee records into Pandas.
Module 2: Data Cleaning & Preprocessing
Using Pandas to clean SQL-fetched data.
Handling null/missing values.
Removing duplicates & formatting.
Data type conversions.
Hands-on: Clean and prepare student results dataset from SQL.
Module 3: Exploratory Data Analysis (EDA)
Descriptive statistics (mean, median, std dev).
Correlation & covariance.
Grouping and aggregating SQL data in Pandas.
Hands-on: Analyze sales trends by product category.
Module 4: Data Visualization
Matplotlib + Seaborn for SQL dataset visualization.
Bar charts, line graphs, scatter plots, histograms.
Heatmaps for correlation analysis.
Hands-on: Visualize monthly sales from SQL database.
Module 5: Applied Data Science
Case study: Customer Segmentation (SQL + Pandas).
Case study: Inventory Management (SQL + Python dashboard).
Case study: Employee Performance Analysis.
Hands-on: Build insights using real-world datasets.
Module 6: Mini Project
Exciting and impactful projects that drive innovation, growth, and real-world results.
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