Introduction

Statistical analysis forms the backbone of data science, enabling data professionals to draw meaningful insights from complex datasets. This course focuses on the essential statistical techniques needed for data analysis, from foundational concepts to more advanced methods that are directly applicable to data science. Participants will learn how to apply statistical techniques to real-world data, interpret results, and make data-driven decisions. Emphasizing both theory and practical application, the course equips participants with the statistical tools necessary to handle data science tasks, such as hypothesis testing, regression analysis, and predictive modeling.

Objectives

By the end of this course, participants will:

• Understand core statistical concepts and their application in data science.
• Learn how to collect, prepare, and analyze data for statistical testing.
• Gain hands-on experience with essential statistical tests, including t-tests, chi-square tests, and ANOVA.
• Learn how to build regression models for prediction and assess their performance.
• Understand and apply statistical methods for model validation and diagnostics.
• Explore advanced statistical techniques, such as time-series analysis and multivariate statistics.
• Understand how to communicate statistical findings clearly and effectively.

Who Should Attend?

This course is ideal for:

• Data scientists, analysts, and researchers who want to deepen their understanding of statistical methods.
• Professionals who need to apply statistical analysis in their work but lack formal statistical training.
• Individuals who wish to pursue a career in data science and need a strong statistical foundation.
• Anyone looking to gain skills to analyze and interpret data more effectively and make data-driven decisions.

Day 1: Introduction to Statistics and Data Preparation

Morning Session: Statistical Fundamentals for Data Science

• What is statistics and its role in data science?
• Descriptive statistics: Measures of central tendency (mean, median, mode) and variability (standard deviation, variance)
• Data distributions: Normal distribution, skewness, kurtosis
• Understanding populations and samples: Types of data (nominal, ordinal, interval, ratio)
• Sampling methods: Random sampling, stratified sampling, and sample size determination

Afternoon Session: Data Cleaning and Exploration

• Data preprocessing: Handling missing values, outliers, and duplicates
• Exploratory data analysis (EDA): Visualizing distributions using histograms, box plots, and density plots
• Summary statistics: Interpreting mean, standard deviation, skewness, and kurtosis
• Hands-on: Performing EDA and cleaning a real-world dataset using Python and libraries like Pandas and Seaborn
• Data visualization: Correlation matrix, pair plots, and visualizing categorical data

Day 2: Probability and Hypothesis Testing

Morning Session: Introduction to Probability and Distributions

• Probability basics: Events, probability laws, and conditional probability
• Discrete and continuous distributions: Binomial, Poisson, normal, and exponential distributions
• The concept of probability density functions (PDF) and cumulative distribution functions (CDF)
• Sampling distributions and the Central Limit Theorem (CLT)

Afternoon Session: Hypothesis Testing

• What is hypothesis testing? Null hypothesis, alternative hypothesis, and significance levels (p-values)
• Types of tests: One-tailed vs. two-tailed tests
• Common hypothesis tests: t-tests, z-tests, and chi-square tests
• Hands-on: Performing t-tests and chi-square tests to analyze datasets and interpret the results
• Type I and Type II errors, power analysis, and sample size considerations

Day 3: Analysis of Variance (ANOVA) and Correlation

Morning Session: ANOVA (Analysis of Variance)

• When to use ANOVA: Comparing means across multiple groups
• One-way and two-way ANOVA: Concepts, assumptions, and interpretation
• Post-hoc tests: Tukey’s HSD, Bonferroni correction
• Assumptions of ANOVA: Homogeneity of variances and normality
• Hands-on: Performing one-way and two-way ANOVA using Python and interpreting the results

Afternoon Session: Correlation and Regression Analysis

• Correlation: Pearson, Spearman, and Kendall correlation coefficients
• Understanding correlation vs. causation
• Introduction to linear regression: Simple vs. multiple linear regression
• Regression diagnostics: Residual analysis, R-squared, adjusted R-squared, and p-values
• Hands-on: Calculating correlations and fitting a simple linear regression model

Day 4: Advanced Regression Techniques

Morning Session: Multiple Linear Regression

• Assumptions in multiple linear regression: Linearity, independence, homoscedasticity, and normality of errors
• Multicollinearity: Identifying and addressing multicollinearity in regression models
• Stepwise regression: Forward, backward, and bidirectional selection
• Model validation: Cross-validation and model evaluation metrics (e.g., RMSE, MAE)

Afternoon Session: Logistic Regression and Classification

• Introduction to logistic regression: Binary outcomes and the logistic function
• Evaluating logistic regression models: Confusion matrix, accuracy, precision, recall, F1-score, ROC curve, and AUC
• Understanding classification metrics and trade-offs
• Hands-on: Building and evaluating a logistic regression model using real-world datasets

Day 5: Time-Series Analysis and Multivariate Statistics

Morning Session: Time-Series Analysis

• What is time-series data? Components of time series: Trend, seasonality, and noise
• Time-series decomposition: Additive and multiplicative models
• Moving averages and smoothing techniques
• Autoregressive models (AR), moving average models (MA), and ARIMA models
• Hands-on: Analyzing and forecasting time-series data using Python

Afternoon Session: Multivariate Analysis and Course Wrap-Up

• Introduction to multivariate analysis: Principal Component Analysis (PCA), factor analysis
• Cluster analysis: K-means clustering and hierarchical clustering
• Multivariate regression analysis: Multiple predictors and multicollinearity
• Hands-on: Applying PCA and clustering techniques to real-world datasets
• Final project: Participants will work through a case study that involves applying statistical analysis to a dataset and communicating the results
• Course wrap-up: Review key concepts, provide additional resources, and discuss next steps in learning

Materials and Tools:

• Required tools: Python (for hands-on activities with libraries like NumPy, Pandas, Matplotlib, and Statsmodels)
• Jupyter Notebooks for running and documenting analysis
• Access to real-world datasets for practice (e.g., Kaggle, UCI Machine Learning Repository)
• Templates and guides for hypothesis testing, regression modeling, and statistical analysis

Conclusion and Final Assessment

• Recap of key concepts: Descriptive statistics, hypothesis testing, regression analysis, ANOVA, and time-series analysis
• Final project presentations and peer feedback
• Certification of completion for those who successfully complete the course and demonstrate practical application of statistical techniques