Applied Statistics for Data Science

Introduction

This 5-day intensive course on "Applied Statistics for Data Science" is designed for professionals who aim to enhance their data interpretation and decision-making skills using statistical methods. The course provides a practical understanding of statistical concepts and their applications in the field of data science, through interactive sessions and hands-on exercises. By the end of this course, participants will be equipped with the essential statistical tools needed to analyze data effectively and make data-driven decisions.

Objectives

• Understand key statistical concepts and how they apply to data science.
• Gain proficiency in statistical programming and data analysis.
• Learn to apply statistical methodologies to solve real-world problems.
• Develop skills in data visualization and interpretation of statistical results.
• Improve the ability to communicate findings and inform decision-making processes.

Course Outlines

Day 1: Foundations of Statistics in Data Science

• Introduction to Data Science and the Role of Statistics
• Descriptive Statistics: Measures of Central Tendency and Variability
• Probability Theory and Probability Distributions
• Exploratory Data Analysis (EDA) Techniques
• Data Collection Methods and Sampling Techniques

Day 2: Inferential Statistics and Hypothesis Testing

• Concepts of Inferential Statistics
• Point Estimation and Confidence Intervals
• Formulating and Testing Hypotheses
• t-Test, Chi-Square Test, and Analysis of Variance (ANOVA)
• Understanding p-values and Statistical Significance

Day 3: Regression Analysis and Predictive Modeling

• Introduction to Regression Analysis
• Simple Linear Regression and Multiple Regression
• Model Evaluation and Interpretation
• Handling Categorical Variables and Interaction Effects
• Introduction to Logistic Regression

Day 4: Advanced Statistical Modeling

• Time Series Analysis and Forecasting Methods
• Cluster Analysis and Principal Component Analysis (PCA)
• Support Vector Machines and Decision Trees
• Bayesian Statistics and Methods
• Model Selection and Validation Techniques

Day 5: Data Visualization and Statistical Communication

• Principles of Effective Data Visualization
• Tools for Data Visualization: Matplotlib, Seaborn, and Plotly
• Building Interactive Dashboards
• Communicating Statistical Findings to Stakeholders
• Case Studies and Applied Projects