Data Management and Statistical Data Analysis using R Course

COURSE OVERVIEW

Introduction

R Statistical software for data visualization and data analysis, used by researchers, statisticians, & data miners for quantitative data analysis. Statistical Data Management and Analysis using R course provides an insight into quantitative data management and analysis (exploring, summarizing, statistical analyzing, visualizing). R is an open source software with many features for quantitative data management and analysis. Making Sense of Data is an important skill. Our modern world is a complicated place, and we are bombarded by data at every turn. The first step to understanding and making sense of data is being able to summarise data.

R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical data analysis. You will start with the most basic importing techniques and advanced ways to handle even the most difficult datasets to import. R has “become the de-facto standard for writing statistical software among statisticians. This course will give you a solid foundation in creating statistical analysis solutions using the R language, and how to carry out a range of commonly used analytical processes. Finally, you will learn to implementation and data analysis.

Duration

5 days

Who Should Attend?

Statistician, analyst, or a budding data scientist and beginners who want to learn how to analyze data with R,

Course Objective:

o   Introduce participants to R as a quantitative data analysis tool

o   Enable learner master R software and R-studio as a user interface

o   Enable learner import data from various sources

o   Introduce basic statistics for exploratory data analysis including methods for describing and summarizing variable distributions

o   Provide essential skills for data manipulation including selecting subsets and recoding

o   Introduce the visual representation of variables in scatter graphs, bar charts, and histograms

o    Import and export data in various formats in R

o    Perform advanced statistical data analysis

o   Enhance your data analysis skills and learn to handle even the most complex datasets

o   Learn how to handle vector and raster data in R

COURSE OUTLINE

Introduction to Statistical Analysis

• Explain the basic steps of the research process 
• Explain differences between populations and samples 
• Explain differences between experimental and non-experimental research designs 
• Explain differences between independent and dependent variables
  

Introduction to R software for statistical computing

• Overview of the R Studio IDE 
• Installing, loading and updating R packages 
• Creating objects in R 
• Data types 
• Data structures 
• Sorting vectors and data frames 
• Directory management commands 
• Direct data entry in R (for small data sets) 
• Importing data from other software 
• Decision structures (if, if-else, if-else if-else) 
• Repetitive structures (for and while loops) 
• Other important programming functions (break, next, warn, stop)
  

Data Wrangling and Cleaning in R

• Working with variables 
• Transform continuous variables to categorical variables 
• Add new variables to data frames
• Handling missing values 
• Sub-setting data frames 
• Appending and merging data frames 
• Spit data frames 
• Stack and unstack data frames
  

Explanatory Data Analysis (EDA) in R

• Creating tables of frequencies and proportions  
• Cross tabulations of categorical variables 
• Descriptive statistics for continuous variables
  

Data Visualization using R base package

• Introduction to graphs and charts in R 
• Customizing graph attributes (titles, axes, text, legends) 
• Graphs for categorical variables 
• Graphs for continuous variables 
• Graphs to investigate relationship between variables
  

Mean Comparison Tests in R

• One Sample T Test 
• Independent Samples T Test  
• Paired Samples T Test 
• One-way analysis of variance (ANOVA)
  

Tests of Associations in R

• Chi-Square test of independence 
• Pearson's Correlation 
• Spearman's Rank-Order Correlation
  

Predictive Regression Models using R

• Linear Regression 
• Multiple Linear Regression 
• Binary Logistic Regression 
• Ordinal Logistic Regression

Methodology

The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web-based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.

All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.

Certification

Upon successful completion of this training, participants will be issued with Steady Development Centre certificate.

Training Venue

The training will be held at Steady Development Centre. The course fee covers the course tuition, training materials, two break refreshments and lunch.

All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.

Accommodation and Airport Pickup

Accommodation and airport pickup are arranged upon request. For reservations contact the training officer.

• Email: training@steadydevelopmentcentre.com
• Mobile: +254 718108851

 

Tailor- Made

This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our Steady Development Centre or at a convenient location.

Payment

The course fee payment should be transferred to Steady Development Centre account through bank, 3 working days before commencement of the training so as to enable us to prepare better.

For further inquiries, please contact us on:

• Tel: +254 718108851
• Email: training@steadydevelopmentcentre.com

Our Upcoming Training Schedule