- Types of data
- Levels of measurement
Categorical variables: Visualization
Continous Variables: Visualization
Histogram charts
Descriptive Statistics
Measure of Central Tendencies
Mean Median Mode
Measure of Dispersion
Range, Variance, Standard deviation
Standard deviation and co-efficient of variation
Measures of asymmetry
Skewness and Kurtosis
Determining relationship between two variables
Cross Tabulation
Scatter plot
Correlation
Distributions
The normal distribution
Central Limit thoerem
Standard error
Hypothesis testing and linear regression
Alternate and Null Hypothesis
Rejection of Null Hypothesis and significance level
Type 1 and Type 2 Error
Lessons in reading order
Read these lessons in the order given below. Each lesson is a blog post on this site with examples and R codes.
16 lessons. Read them in this order.
Module 1 Describe your data
- Basics of statistics for data science: Type of data measurement scale
- Basics of statistics for Data science: Descriptive statistics with R codes
- Calculate Skewness and Kurtosis
- Basics of Statistics for Data Science: How to calculate Z score
- Data Visualization with R codes: Barplots and Scatter plots
- Data visualization with R: Histogram, Boxplot, Piechart, Mosiacplot, Correlation
Module 2 Test your ideas
- 10 important statistics concept with R codes
- What is p-value?
- When to use different statistical test for your business problems?
- How to perform one-way anova in SPSS and interpret the results
Module 3 Build models
- Linear regression in R with codes: Analysis and interpretation
- What are R, R^2 and Adjusted R in a regression output?
- Everything you would like to know about logistic regression
- Logistic regression with R codes
- Metrics to evaluate classification models with R codes: Confusion Matrix, Sensitivity, Specificity, Cohen’s Kappa Value, Mcnemar’s Test
- Unsupervised machine learning Creating Segments using RFM data Kmeans clustering in R