Python for Data Analysis

Module 1: Introduction to Python and Data Analysis

• Python basics and environment setup
• Python libraries for data analysis (Pandas, NumPy)

Module 2: Importing and Cleaning Data

• Loading data from CSV, Excel, databases
• Handling missing values, data formatting, and normalization

Module 3: Data Manipulation with Pandas

• DataFrames, Series, indexing
• Sorting, filtering, grouping, and aggregation

Module 4: Data Visualization

• Plotting with Matplotlib and Seaborn
• Creating charts, histograms, and scatter plots

Module 5: Exploratory Data Analysis (EDA)

• Statistical summaries
• Correlations and distributions

Module 6: Advanced Data Techniques

• Merging and joining datasets
• Working with time series and categorical data

Module 7: Introduction to Machine Learning Concepts

• Basic ML algorithms overview
• Using scikit-learn for predictive modeling

Module 8: Building Data Pipelines

• Automating data workflows and transformations

Module 9: Project Work and Case Studies

• Real-world data analysis projects
• End-to-end data analysis examples

Module 10: Certification Preparation and Practice

• Review, practice exams, and tips

Experience in the Industry Gain knowledge from experts with practical Python for Data Analysis project experience in a variety of sectors.

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Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

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