What you will learn in Introduction to Statistics Course
Learn core concepts in descriptive and inferential statistics
Visualize and summarize data using graphs and numerical techniques
Understand probability, distributions, and sampling methods
Conduct hypothesis testing and interpret results
- Apply basic regression techniques and analyze relationships
- Learn resampling methods including bootstrapping
- Gain experience in real-world statistical problem solving
Program Overview
Descriptive Statistics & Data Visualization
⏱️ 1 week
- Explore data types and graphical representations
- Calculate central tendency (mean, median) and spread (variance, SD)
- Learn to summarize large datasets meaningfully
Producing and Sampling Data
⏱️ 1 week
- Learn how to design surveys and experiments
- Understand sampling methods and potential biases
- Distinguish observational studies from experimental design
Probability Concepts
⏱️1 week
- Learn probability rules, conditional probability, and independence
- Work with discrete and continuous distributions
- Understand how probability supports inference
Sampling Distributions & Central Limit Theorem
⏱️ 1 week
- Learn how sample statistics vary
- Apply the Central Limit Theorem
- Understand the basis of inferential statistics
Regression Analysis
⏱️ 1 week
- Introduce simple linear regression
- Interpret regression output
- Use correlation to measure variable relationships
Significance Tests
⏱️ 1 week
- Perform t-tests and chi-square tests
- Understand p-values and confidence intervals
- Learn about errors in hypothesis testing
Resampling Techniques
⏱️ 1 week
- Explore bootstrapping and permutation testing
- Use simulations to draw conclusions
Multiple Comparisons
⏱️ 1 week
- Learn methods for dealing with multiple hypotheses
- Control false discovery rates
Get certificate
Job Outlook
- Strong demand across industries for statistical knowledge
- Relevant for roles in data analysis, business intelligence, and research
- Serves as a stepping stone to more advanced fields like data science
- Employers value statistical reasoning for decision-making
- Complements tools like Python, R, and Excel in analytics jobs
- Builds foundational knowledge required for AI and ML pathways
- Applicable in fields like economics, medicine, marketing, and policy
Specification: Introduction to Statistics
|
