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Overview
A broad coverage of statistics for Data Analysis.
This three day course is designed for anyone who's going to make a career working in data. It is practical in nature and will take you through the statistical fundamentals that you're going to need to thrive as a data analyst or scientist.
Whether you work with Excel, R, Python or any other data solution, you will need to understand statistics to get your data analysis off the ground.
The course is taught using R for programming illustration with a focus on statistic that applies across domains.
Target Audience
Aimed at fledging data practitioners who wish to have a practical understanding of statistical methods.
Prerequisites
- GCSE mathematics or above
- An interest in mathematical and logical thinking
- No prior experience of R is assumed, although prior experience will be an advantage
Course Outline
Introduction to R
- RStudio
- Data Structures
- Flow and Functional Programming
Introduction to Data
- Exploring Data
- Summarizing Data
Probability
- Bayes Rule and Conditional Probability
- Random Variables
Statistical Distributions
- Bernoulli
- Normal
- Binomial
- Poisson
Inferential Statistics
- Point Estimates
- Hypothesis Testing
- Confidence Levels
Inference for Numerical Data
- T-tests
- ANOVA
Inference for Categorical Data
- Proportions
- Chi-Square
Machine Learning as Statistical Inference
- Regression
- Classification
AWS learning paths
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GCP learning paths
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Azure learning paths
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Data, Analytics & AI learning paths
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