EtusivuHae koulutuksiaFundamentals of Statistics for Data Analysis

# Fundamentals of Statistics for Data Analysis

An introduction to python, and a broad coverage of mathematics for Data Analysis, Data Science and Machine Learning: algebra, linear algebra, calculus, probability and statistics.
This 3-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 mathematical fundamentals that you're going to need to thrive as a data scientist or data analyst.
Whether you work with Excel, SQL, Hadoop, or any other data solution, you're going to be able to understand a model more effectively with mathematics. You'll learn to improve existing statistical models and start developing the right skills to ask more advanced questions.

Koulutusmuoto
Remote

Kesto
3 päivää

Hinta
3534 €

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 Content

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 