R for Data Science

This course includes:

Our R for Data Science course is accredited by NITA

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Course Information

Overview

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 statistical computing using R.

Prerequisites

Learn about Exploratory Data Analysis and why it is Important

  • Effective shortcuts that you wish you Ubiquity of Data, Explore Word Trends, Why Learn EDA
    (Exploratory Data Analysis), Goals of EDA.

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. i.e. Pseudo-User data and Histogram, Faceting,
    Outliers and Anomalies, Limiting the Axes e.t.c

Learn techniques of exploring relationship between two variables

  • Using scatter plots, line plots and correlations. i.e. Scatter plot, GGPlot Syntax, Overplotting
    e.t.c

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

Target Group

  • Data Analysts
  • Data Scientists
  • Risk Managers
  • Business Analysts
  • Geologists

Expected Outcome

  • Data Science
  • Python Programming
  • Numpy
  • Pandas

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