Module 1: Introduction to Data Analysis

• Understanding the Role of Data Analysts in Business
• Types of Data and Data Sources
• Overview of Data Analysis Process
• Introduction to Data Cleaning and Preprocessing
  
  

Module 2: Exploratory Data Analysis (EDA)

• Descriptive Statistics: Mean, Median, Mode, Variance, and Standard Deviation
• Data Visualization with Matplotlib and Seaborn
• Exploring Distributions and Histograms
• Correlation Analysis: Pearson and Spearman Correlation
  
  

Module 3: Statistical Methods for Data Analysis

• Probability Distributions: Normal, Binomial, Poisson
• Hypothesis Testing: T-tests, Chi-Square Test, ANOVA
• Confidence Intervals and Significance Levels
• Applying Statistical Tests in Real-World Scenarios
  
  

Module 4: Data Cleaning and Preparation

• Techniques for Handling Missing Data
• Outlier Detection and Treatment
• Feature Engineering: Creating New Variables
• Data Transformation: Scaling, Normalization, and Encoding
  
  

Module 5: Regression Analysis

• Introduction to Linear Regression
• Multiple Linear Regression and Model Evaluation
• Polynomial Regression and Overfitting
• Interpretation of Regression Coefficients
  
  

Module 6: Time Series Analysis

• Time Series Data Exploration and Visualization
• Trend Analysis and Seasonal Decomposition
• Forecasting Methods: Moving Average, Exponential Smoothing
• ARIMA Modeling for Time Series Forecasting
  
  

Module 7: Machine Learning Basics

• Overview of Machine Learning Algorithms
• Supervised vs. Unsupervised Learning
• Decision Trees and Random Forests
• Model Evaluation Metrics: Accuracy, Precision, Recall
  
  

Module 8: Clustering and Dimensionality Reduction

• K-Means Clustering and Hierarchical Clustering
• Principal Component Analysis (PCA) for Dimensionality Reduction
• Evaluating Clustering Performance
• Applications of Clustering in Marketing and Customer Segmentation
  
  

Module 9: Text Mining and Sentiment Analysis

• Introduction to Natural Language Processing (NLP)
• Text Preprocessing Techniques: Tokenization, Lemmatization
• Sentiment Analysis with NLTK and TextBlob
• Analyzing Customer Reviews and Social Media Texts
  
  

Module 10: Advanced Topics in Data Analysis

• Association Rule Mining and Market Basket Analysis
• Deep Dive into Big Data Technologies: Hadoop, Spark
• Web Scraping for Data Collection
• Ethics and Privacy in Data Analysis
  
  

Module 11: Data Visualization and Dashboards

• Interactive Data Visualization with Plotly and Bokeh
• Creating Dashboards in Tableau and Power BI
• Design Principles for Effective Data Communication
• Storytelling with Data: Crafting Compelling Narratives
  
  

Module 12: Project and Portfolio Development

• Applying Data Analysis Techniques to a Real-World Dataset
• Project Planning, Execution, and Presentation
• Building a Professional Data Analyst Portfolio
• Showcasing Analytical Skills and Project Outcomes