Courses
Statistics for Data Science Course
This course provides a solid foundation in statistical methods tailored for data science, covering probability, inference, hypothesis testing, regression, and advanced modeling techniques. Through theoretical concepts and practical applications using programming tools, students will learn to analyze datasets, draw insights, and apply statistics to machine learning and data-driven decision-making.
About Course
Career Prospects
- Data Scientist
- Statistician
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- Financial Analyst
- Biostatistician
- Quantitative Analyst
- Research Analyst
- Analytics Manager
Course Curriculum
Module 1: Introduction to Statistics and Data Science
- Overview of Statistics
- Role in Data Science
- Types of Data
- Data Collection Methods
- Ethical Considerations
Module 2: Descriptive Statistics
- Measures of Central Tendency
- Measures of Dispersion
- Data Summarization
- Graphical Representations
- Exploratory Data Analysis
Module 3: Probability Fundamentals
- Basic Probability Concepts
- Conditional Probability
- Bayes’ Theorem
- Independence and Dependence
- Random Variables
Module 4: Probability Distributions
- Discrete Distributions
- Continuous Distributions
- Normal Distribution
- Binomial and Poisson
- Sampling Distributions
Module 5: Inferential Statistics Basics
- Point Estimation
- Confidence Intervals
- Margin of Error
- Central Limit Theorem
- Parameter Estimation
Module 6: Hypothesis Testing
- Null and Alternative Hypotheses
- Type I and Type II Errors
- P-Values and Significance
- One-Sample Tests
- Two-Sample Tests
Module 7: Regression Analysis
- Simple Linear Regression
- Multiple Linear Regression
- Model Assumptions
- Coefficient Interpretation
- Residual Analysis
Module 8: Advanced Statistical Methods
- ANOVA and MANOVA
- Chi-Square Tests
- Non-Parametric Tests
- Correlation Analysis
- Time Series Basics
Module 9: Statistics in Machine Learning
- Feature Selection
- Model Evaluation Metrics
- Cross-Validation
- Bias-Variance Tradeoff
- Bayesian Statistics
Module 10: Projects and Applications
- Real-World Case Studies
- Capstone Project
- Data Interpretation
- Reporting Insights
- Career Preparation
Skills and Tools Covered
- Python
- R
- Pandas
- NumPy
- SciPy
- Statsmodels
- Matplotlib
- Seaborn
- Jupyter Notebook
- Excel
- SAS
- Tableau
- Power BI
Duration
1 Month
Cost
KES. 15,000