Statistics & Probability for Data Science

Duration: 2 days

This comprehensive course teaches statistics and probability for data analysis. Students learn key concepts, including probability distributions, hypothesis testing, statistical inference, and regression analysis. They also develop the ability to extract meaningful insights, predict outcomes, and assess model performance using practical exercises and real-world examples.

By the end of this course, you will learn to:

Learn foundational concepts of statistics and probability.
Analyse data with statistical techniques.
Conduct hypothesis testing and draw conclusions.
Develop and assess predictive models for insights.
Solve real-world data problems using statistical methods.

Programme Outline

Day 1: Foundations of Statistics for Data Science

Module 1: Introduction to Statistics and Probability

Understand the role of statistics and probability in data science.
Differentiate between descriptive and inferential statistics. Example: Summarising a customer dataset using descriptive measures.
Recognise how probability powers AI-driven applications and influences search, AEO, and GEO strategies in real-world digital ecosystems.
Explore basic probability concepts, including events, outcomes, and probability rules.

Module 2: Descriptive Statistics

Learn measures of central tendency: mean, median, and mode.
Understand measures of variability: range, variance, and standard deviation.
Visualize data using histograms, box plots, and scatter plots.
Exercise: Create visualisations for a dataset using the provided tools.

Module 3: Introduction to Probability Distributions

Explore key probability distributions: normal, binomial, and Poisson.
Understand the concept of the bell curve and its applications. Example: Using the normal distribution to model customer spending behaviour.
Hands-on activity: Analyze and visualise a dataset’s distribution.

Module 4: SWOT Analysis on Statistical Competency

Evaluate organisational strengths, weaknesses, opportunities, and threats in leveraging statistical tools.
Discuss and analyse with peers.

Day 2: Advanced Applications in Statistics and Probability

Module 5: Inferential Statistics

Understand sampling techniques and the importance of sample size.
Learn hypothesis testing: null and alternative hypotheses, p-values, and confidence intervals.
Conduct a hands-on hypothesis testing exercise using a sample dataset.

Module 6: Correlation and Regression Analysis

Understand relationships between variables through correlation. Example: Analysing the relationship between marketing spend and sales.
Explore regression analysis for prediction and trend analysis.
Hands-on activity: Develop and interpret a regression model using a business dataset.

Module 7: Probability for Decision Making

Explore Bayesian probability and its applications in data science.
Learn conditional probability and its role in predictive analytics. Example: Fraud detection in financial transactions.
Interactive exercise: Solve real-world problems using probability principles.

Q&A and Wrap-Up

Training Methodology

This workshop uses a blend of expert-led lectures, case studies, and practical exercises to build proficiency in statistics and probability for data science. Each day concludes with interactive activities, such as SWOT analysis and brainstorming sessions, to apply the concepts learned.

Training Methodology

Professionals, analysts, and aspiring data scientists seeking a strong foundation in statistics and probability for data-driven decision-making.