Curriculum

  • 1

    Introduction to the Basics of Applied Statistical Modelling

  • 2

    The Essentials of the R Programming Language

    • Rationale for this section

    • Introduction to the R Statistical Software & R Studio

    • Different Data Structures in R

    • Reading in Data from Different Sources

    • Indexing and Subsetting of Data

    • Data Cleaning: Removing Missing Values

    • Exploratory Data Analysis in R

    • Conclusion to Section 2

    • Section 2 Quiz
  • 3

    Statistical Tools to Learn More About Your Data

    • Summarize Quantitative Data

    • Measures of Center

    • Measures of Variation

    • Charting & Graphing Continuous Data

    • Charting & Graphing Discrete Data

    • Deriving Insights from Qualitative/Nominal Data

    • Conclusions to Section 3

    • Section 3 Quiz
  • 4

    Probability Distributions

    • Background

    • Data Distribution: Normal Distribution

    • Checking For Normal Distribution

    • Standard Normal Distribution and Z-scores

    • Confidence Interval-Theory

    • Confidence Interval-Computation in R

    • Conclusions to Section 4

    • Section 4 Quiz
  • 5

    Statistical Inference

    • What is Hypothesis Testing?

    • T-tests: Application in R

    • Non-Parametric Alternatives to T-Tests

    • One-way ANOVA

    • Non-parametric version of One-way ANOVA

    • Two-way ANOVA

    • Power Test for Detecting Effect

    • Conclusions to Section 5
  • 6

    Relationship Between Two Different Quantitative Variables

    • Explore the Relationship Between Two Quantitative Variables?

    • Correlation

    • Linear Regression-Theory

    • Linear Regression-Implementation in R

    • The Conditions of Linear Regression

    • Dealing with Multi-collinearity

    • What More Does the Regression Model Tell Us?

    • Linear Regression and ANOVA

    • Linear Regression With Categorical Variables and Interaction Terms

    • Analysis of Covariance (ANCOVA)

    • Selecting the Most Suitable Regression Model

    • Conclusions to Section 6

    • Section 6 Quiz
  • 7

    Other Types of Regression

    • Violation of Linear Regression Conditions: Transform Variables

    • Other Regression Techniques When Conditions of OLS Are Not Met

    • Model 2 Regression: Standardized Major Axis (SMA) Regression

    • Polynomial and Non-linear regression

    • Linear Mixed Effect Models

    • Generalized Regression Model

    • Logistic Regression in R

    • Poisson Regression in R

    • Goodness of fit testing

    • Conclusions to Section 7

    • Section 7 Quiz
  • 8

    Multivariate Analysis

    • Why Do Multivariate Analysis?

    • Cluster Analysis/Unsupervised Learning

    • Principal Component Analysis (PCA)

    • Linear Discriminant Analysis (LDA)

    • Correspondence Analysis

    • Similarity & Dissimilarity Across Sites

    • Non-metric multi dimensional scaling (NMDS)

    • Multivariate Analysis of Variance (MANOVA)

    • Conclusions to Section 8

    • Section 8 Quiz

About Your Instructor

Bestselling Instructor & Data Scientist (Cambridge University)

Minerva Singh

Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler.

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