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Fundamentals 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.

Prerequisities:

  • 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

Program:

  • 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

At the end of this course attendees will be able to:

  • create visualizations such as histograms and scatter plots to visually show data;
  • apply basic descriptive statistics to past data to gain greater insights;
  • combine descriptive and inferential statistics to analyze and forecast data;
  • utilize a regression analysis to spot trends in data and build a robust forecasting model.

Trainer:
Michael Burgess

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Fundamentals of Statistics For Data Analysis