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Python for Data Analysis

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Overview:

This comprehensive course offers an in-depth exploration of Python as a powerful tool for data analysis. Over several modules, students will learn from setting up their Python environment to conducting advanced statistical analysis and interpreting real-world data.

What You'll Learn

Basic and advanced Python programming concepts necessary for data analysis.
How to manipulate, clean, and summarize datasets with Pandas.
Fundamental and advanced statistical analysis techniques using Python.
Creating various types of data visualizations to communicate findings effectively.
Application of statistical modeling and machine learning for predictive analytics.

Course Content

Module 1: Getting Started with Python

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Introduction to Python and its popularity in data science.

Features and benefits of using Python for data analysis.

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Step-by-step guide to installing Python.

Overview of essential Python packages for data analysis.

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Getting started with Jupyter Notebooks for interactive coding sessions.

Tips and tricks for using Jupyter Notebooks efficiently.

Module 2: Python Programming Fundamentals

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Understanding Python syntax and basic constructs.

Introduction to Data Types & Variables

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Implementing loops and conditional statements to control the flow of programs.

Defining and using functions and modules to organize code.

Module 3: Data Manipulation with Pandas

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Introduction to the Pandas library and its capabilities.

Understanding Series and DataFrames—the core data structures in Pandas.

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Techniques for handling missing data, duplicate data, and data type conversions.

Data filtering, sorting, and grouping for advanced data manipulation.

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Calculating mean, median, mode, variance, and standard deviation with Pandas.

Using Pandas for more complex data summarization techniques.

Module 4: Probability and Inferential Statistics

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Basics of probability theory and different types of probability distributions.

Application of random variables and distribution functions using SciPy.

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Conducting hypothesis tests and building confidence intervals.

Introduction to statistical modeling with StatsModels.

Module 5: Data Visualization Techniques

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Using Matplotlib and Seaborn for creating various types of plots.

Best practices for crafting insightful data visualizations.

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Advanced plotting techniques like heatmaps, pairplots, and facet grids.

Visualization as a tool for data analysis and hypothesis testing.

Module 6: Advanced Statistical Analysis

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Analyzing time series data.

Implementing linear and logistic regression models for predictive analysis.

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Techniques for cluster analysis and exploring dimensionality reduction methods such as PCA.

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Training sessions have been delivered at Microsoft, Bosch, Deloitte, Michelin, IBM, Collins Aerospace, and Cognizant. We are in no way associated with or sponsored by these companies.

This educational material is independently created and offered by Bot Campus AI. It is not sponsored by,endorsed by, or affiliated with Microsoft Corporation, Amazon Technologies, Inc., Google LLC, the Python Software Foundation, or Tableau Software, LLC. The use of these terms in our course material is for descriptive purposes only and does not imply any affiliation with or endorsement by the trademark owners.