Training on Statistics for Data Science

Module 1: Introduction to Statistics and Data Science

• Overview of statistics and its role in data science
• Types of data: qualitative and quantitative
• Data collection methods and sources
• Introduction to data science workflows and applications
• Practical Exercise: Identifying and categorizing different types of datasets

Module 2: Descriptive Statistics and Data Summarization

• Measures of central tendency: mean, median, and mode
• Measures of variability: range, variance, and standard deviation
• Data summarization and interpretation techniques
• Introduction to data visualization concepts
• Practical Exercise: Calculating and interpreting descriptive statistics using Excel or Python

Module 3: Data Visualization Techniques

• Creating charts, graphs, histograms, and box plots
• Understanding distributions: normal distribution, skewness, and kurtosis
• Best practices for presenting statistical data
• Storytelling with data visualization
• Case Study: Visualizing sales and customer trends for business insights
• Practical Exercise: Building visual dashboards and charts from sample datasets

Module 4: Fundamentals of Probability

• Introduction to probability concepts and rules
• Probability distributions: binomial, Poisson, and normal distribution
• Sampling methods and sampling distributions
• Law of Large Numbers and Central Limit Theorem
• Practical Exercise: Solving probability problems using real-world scenarios

Module 5: Hypothesis Testing and Statistical Inference

• Understanding null and alternative hypotheses
• Confidence intervals and significance levels
• T-tests and chi-square tests
• Understanding p-values and decision-making in statistics
• Case Study: Using hypothesis testing to evaluate customer satisfaction data
• Practical Exercise: Conducting hypothesis tests using Excel, SPSS, or Python

Module 6: Correlation and Simple Linear Regression

• Correlation and covariance analysis
• Understanding relationships between variables
• Introduction to simple linear regression
• Interpreting regression outputs and coefficients
• Practical Exercise: Building a simple regression model for predictive analysis

Module 7: Multiple Regression and Model Evaluation

• Multiple linear regression concepts
• Model evaluation metrics: R-squared, RMSE, MAE
• Detecting overfitting and underfitting
• Introduction to model validation techniques
• Case Study: Predicting business performance using regression models
• Practical Exercise: Developing and evaluating multiple regression models

Module 8: Classification and Logistic Regression

• Introduction to classification problems in data science
• Logistic regression for binary outcomes
• Evaluating classification models
• Applications of classification in business and research
• Practical Exercise: Creating a logistic regression model for customer behavior prediction

Module 9: Advanced Statistical Methods and Time Series Analysis

• Introduction to ANOVA (Analysis of Variance)
• Time series analysis and forecasting concepts
• Trend and seasonality analysis
• Applications of advanced statistical techniques in data science
• Case Study: Forecasting sales and operational trends using time series data
• Practical Exercise: Performing ANOVA and basic forecasting analysis

Module 10: Capstone Project and Real-World Applications

• End-to-end statistical analysis project
• Data cleaning, analysis, visualization, and interpretation
• Presenting insights and recommendations from data
• Case Study: Solving a real-world business or research problem using statistics and data science techniques
• Practical Exercise: Group project presentation and interpretation of analytical findings