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Training

R for Data Science

Use R for effective data analysis.

R for Data Science

Duration: 3 days | 18 hours

Available delivery methods:

Classroom

Use R for effective data analysis.

Course Information

In this course you will learn how to program in R and how to use R for effective data analysis. You will also understand the process of installing and configuring software necessary for statistics programming. It covers practical issues in statistics computing using R.

Key areas covered on the course include:

 Learn about Exploratory Data Analysis and why it is Important

  • Ubiquity of Data
  • Explore Word Trends
  • Why Learn EDA (Exploratory Data Analysis)
  • Goals of EDA
  • Our Approach for the Training
  • Training Overview 

R Basics

  • The power of R. Why R?
  • RStudio Installation on Windows and Mac.
  • RStudio Layout.
  • Demystify R & Getting Help on RStudio.
  • Read and Subset Data.
  • Factor Variables.
  • Data Munging.
  • Chit chat on R.

 One Variable.

Learn how to quantify and visualize individual variables within a dataset using histograms, boxplots and transforms.

  • What to do first!
  • Pseudo-User data and Histogram.
  • Faceting.
  • Outliers and Anomalies.
  • Limiting the Axes.
  • Explore with Bin Width. Adjusting Bin Width.
  • NA observations.
  • Labelling plots
  • Transforming Data.
  • Add a Scaling Layer.
  • Frequency Polygons. Box Plots and Quartiles.
  • Getting Logical and a Problem Set.

Explore Two Variables

Learn techniques of exploring relationship between two variables using scatter plots, line plots and correlations.

  • Scatter plot.
  • GGPlot Syntax.
  • Overplotting.
  • Conditional Means.
  • Overlaying Summaries with Raw Data.
  • Correlation.
  • Scatter plot again!
  • Making Sense of Data.
  • Understanding Noise.
  • Smoothing Conditional Mean.
  • Plot Choice.
  • Problem Set.

 Explore Many Variables

Learn powerful methods for examining relationships among multiple variables and how to reshape data

  • Multivariate Data.
  • Plot Conditional Summary.
  • Thinking in Ratios.
  • Reshape Data: Wide and Long Format.
  • Plotting.
  • Bias Variance Trade Off.
  • New Dataset!
  • Sampling Observations.
  • Cross Sectional Data and Its Limits.
  • Many Variables and Scatterplot Matrices.
  • Heat maps.
  • Problem Set.

Prerequisites

 

What’s included in this course?

3 days’ worth of Instructor-led, practical and interactive training, a course pack including all presentation slides, and a certificate.

Learners taking this course are:

  • Biostatisticians
  • Data Scientists
  • Data Analysts
  • Econometricians
  • Researchers
  • Economists

What key skills will I gain in this course?

  • Collect detailed information using R profiler
  • Configure statistical programming software
  • Make use of R loop functions and debugging tools
  • Understand critical programming language concepts

Do you offer In-house corporate training?

We offer corporate trainings for a minimum number of 6 participant at your preferred venue.

Find out more


Or call us now
+254 20 222 95 66
+254 716 56 88 99
+254 732 56 88 99

 

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