Exploratory and Predictive Analytics Using Python

Introduction to Python: Overview of Python, its applications in business analytics, and setting up Python environment (Anaconda, Jupyter Notebook). Basic Python Syntax: Variables, data types, operators, and expressions. Control Structures: Conditional statements (if, else, elif), loops (for, while), and iteration techniques. Functions and Modules: Defining functions, using built-in functions, importing and using Python libraries. Basic Input/Output Operations: Reading from and writing to files, handling user input.

Introduction to Pandas: Understanding data structures in Pandas (Series, Data Frames). Data Importing and Exporting: Reading data from CSV, Excel, and other file formats, writing data to files. Data Cleaning: Handling missing data, removing duplicates, and data filtering. Data Manipulation: Sorting, indexing, and grouping data, merging and concatenating Data Frames. Working with Dates and Times: Handling date and time data, extracting and modifying date and time components.

Introduction to EDA: Importance of EDA in business analytics, steps in performing EDA. Descriptive Statistics: Calculating and interpreting mean, median, mode, variance, standard deviation, and quartiles. Data Visualization with Matplotlib and Seaborn: Creating basic plots (line, bar, histogram, boxplot), customizing plots (titles, labels, colors). Advanced Visualization Techniques: Pair plots, heatmaps, and correlation matrices. Identifying Patterns and Trends: Analyzing relationships between variables, detecting outliers, and understanding distributions.

Introduction to Predictive Analytics: Understanding predictive analytics, use cases in business. Introduction to Machine Learning: Overview of supervised vs. unsupervised learning, key concepts (features, labels, training, testing). Linear Regression: Understanding and implementing simple linear regression, evaluating model performance (R-squared, MAE, RMSE). Introduction to Classification: Basics of classification, implementing logistic regression, confusion matrix, accuracy, precision, recall. Introduction to Scikit-Learn: Using Scikit-Learn for building and evaluating machine learning models.

Case Study 1: Sales Data Analysis: Performing EDA and visualization on sales data, identifying key trends and patterns. Case Study 2: Customer Segmentation: Using clustering techniques (e.g., K-Means) for customer segmentation and analysis. Case Study 3: Predictive Modeling: Building a predictive model for customer churn, interpreting results, and making business recommendations.