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10 Modules / ~100 pages
Wizard Mode
~25 Modules / ~400 pages
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Probability and Statistics
( 25 Modules )

Module #1
Introduction to Probability and Statistics
Overview of the importance and applications of probability and statistics in real-world scenarios
Module #2
Basic Concepts of Probability
Understanding probability, sample spaces, events, and the concept of randomness
Module #3
Types of Probability
Exploring theoretical, experimental, and subjective probability
Module #4
Rules of Probability
Learning the addition, multiplication, and complement rules of probability
Module #5
Conditional Probability
Understanding conditional probability, independence, and Bayes theorem
Module #6
Random Variables
Introduction to discrete and continuous random variables
Module #7
Probability Distributions
Exploring Bernoulli, Binomial, Poisson, and Uniform distributions
Module #8
Normal Distribution
Understanding the normal distribution, its properties, and applications
Module #9
Statistical Measures
Calculating mean, median, mode, variance, and standard deviation
Module #10
Data Visualization
Understanding the importance of data visualization and different types of plots
Module #11
Descriptive Statistics
Summarizing and describing data using statistics and data visualization
Module #12
Inferential Statistics
Introduction to making inferences about populations based on sample data
Module #13
Sampling Distributions
Understanding sampling distributions and the central limit theorem
Module #14
Hypothesis Testing
Formulating and testing hypotheses using statistical methods
Module #15
Confidence Intervals
Constructing and interpreting confidence intervals for population parameters
Module #16
Regression Analysis
Introduction to simple and multiple linear regression
Module #17
Correlation Analysis
Understanding correlation coefficients and their interpretation
Module #18
Chi-Square Tests
Using chi-square tests for goodness of fit and independence
Module #19
Non-Parametric Tests
Introduction to non-parametric tests for nominal and ordinal data
Module #20
ANOVA and F-Tests
Using ANOVA and F-tests for comparing means and variances
Module #21
Experimental Design
Designing experiments and understanding blocking, randomization, and replication
Module #22
Survey Research Methods
Understanding survey research methods, including questionnaire design and sampling
Module #23
Big Data and Data Mining
Introduction to big data and data mining techniques
Module #24
R and Python for Probability and Statistics
Using R and Python for probability and statistical analysis
Module #25
Course Wrap-Up & Conclusion
Planning next steps in Probability and Statistics career


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