Module 1: Introduction to Data Analytics and Python

1.1 Overview of Data Analytics: Understanding the role and importance of data analytics in today's data-driven world.

1.2 Introduction to Python: Exploring the history, features, and applications of the Python programming language.

1.3 Setting up the Python Development Environment: Installing Python, configuring an IDE, and familiarizing with basic tools.

Module 2: Data Collection and Cleaning

2.1 Data Sources: Identifying and accessing various types of data sources, such as databases, APIs, and CSV files.

2.2 Data Cleaning Techniques: Handling missing values, identifying and correcting errors, and ensuring data consistency.

2.3 Data Preprocessing: Normalizing data, transforming variables, and preparing data for analysis.

Module 3: Exploratory Data Analysis (EDA)

3.1 Data Summarization: Describing data using measures of central tendency, dispersion, and distribution.

3.2 Data Visualization: Creating informative charts and graphs to visualize data patterns, trends, and relationships.

3.3 Univariate and Multivariate Analysis: Examining individual variables and their relationships with other variables.

Module 4: Data Wrangling with Pandas

4.1 Introduction to Pandas: Understanding the importance of Pandas for data manipulation and analysis.

4.2 DataFrames: Creating, manipulating, and exploring DataFrames, the fundamental data structure in Pandas.

4.3 Data Transformation Techniques: Filtering, sorting, merging, and aggregating data using Pandas methods.

Module 5: Statistical Analysis with Python

5.1 Descriptive Statistics: Applying statistical measures to describe and summarize data characteristics.

5.2 Hypothesis Testing: Understanding and performing hypothesis tests to evaluate claims about data.

5.3 Probability Distributions: Exploring common probability distributions and their applications in data analysis.

Module 6: Machine Learning with Python

6.1 Introduction to Machine Learning: Understanding the concepts and principles of machine learning.

6.2 Supervised Learning: Building predictive models using regression, classification, and decision tree algorithms.

6.3 Unsupervised Learning: Applying clustering algorithms to identify patterns and group data points.

Module 7: Data Visualization with Matplotlib and Seaborn

7.1 Introduction to Matplotlib and Seaborn: Understanding the role of Matplotlib and Seaborn in data visualization.

7.2 Creating Basic Plots: Generating line plots, bar charts, histograms, and scatter plots using Matplotlib.

7.3 Advanced Visualization Techniques: Creating complex visualizations, customizing plots, and building interactive dashboards.

Module 8: Model Evaluation and Selection

8.1 Model Evaluation Metrics: Understanding and applying evaluation metrics to assess model performance.

8.2 Model Selection and Comparison: Selecting the best model based on evaluation metrics and considering trade-offs.

8.3 Model Overfitting and Underfitting: Identifying and addressing overfitting and underfitting issues in machine learning models.

Module 9: Data Analytics Projects

9.1 Project Planning and Design: Defining project objectives, selecting appropriate data sources, and designing data analysis plan.

9.2 Data Analysis and Modeling: Applying data wrangling, EDA, machine learning techniques to analyze and extract insights from data.

9.3 Communication and Presentation: Effectively communicating data analysis findings and insights to stakeholders.