Apply Statistics for Data Science Course
Tools / Frameworks: Advanced Excel
Duration: 2 weeks
Cost: $100
MODULE 1
INTRODUCTION TO STATISTICS
- Descriptive and inferential
statistics - Definitions
- Terms
- Types of data
MODULE 2
HARNESSING DATA
- Simple random sampling
- Stratified sampling
MODULE 3
EXPLORATORY ANALYSIS
- Mean
- Median and mode
- Data variability
- Standard deviation
- Z-score
- Outliers
MODULE 4
DISTRIBUTIONS
- Normal distribution
- Central limit theorem
- Histogram
- Normalization
- Normality tests
- Skewness
- Kurtosis
MODULE 5
HYPOTHESIS TESTING
- Understanding hypothesis
testing - Null and alternate hypotheses
- Making a decision
HYPOTHESIS TESTING – CRITICAL VALUE
METHOD
- Critical value method
- Critical value method – examples
HYPOTHESIS TESTING – P-VALUE METHOD
- P-value method
- P-value method – examples
- Types of errors
HYPOTHESIS TESTING – T-TESTS
- T distribution
- One sample t-test
- Independent and relational twosample
test - T-test hypothesis testing in Python
ONE WAY ANOVA TEST / F-TEST
- Analysis of variance (ANOVA) theory
- Hypothesis testing with more than
two variables with ANOVA - Industry example
- F-test hypothesis testing in Python
NON-PARAMETRIC HYPOTHESIS TESTING
- Chi-square test theory
- Application of chi-square in Python
MODULE 6
CORRELATION & REGRESSION
- Direct and indirect correlation
- Correlation with strong and weak
- Calculating correlation with Python
- Regression theory
- Simple linear regression with Python