Module 1: Introduction to R and RStudio

• Installing R and RStudio.
• Navigating the RStudio interface: panes, console, and script editor.
• Basic R syntax, data types (vectors, lists, data frames), and operators.
• Importing and exporting data from various file formats (CSV, Excel, etc.).
• Practical session: Loading a dataset into RStudio, performing basic operations, and saving the results.

Module 2: Data Wrangling with dplyr and tidyr

• Introduction to the Tidyverse principles of data manipulation.
• Using dplyr functions for data filtering, selecting, arranging, and summarizing.
• Pivoting data with tidyr to go from wide to long format and vice versa.
• Handling missing values and outliers in R.
• Practical session: Cleaning and transforming a messy dataset using a combination of dplyr and tidyr functions.

Module 3: Exploratory Data Analysis and Visualization

• The importance of EDA in understanding data and informing analysis.
• Calculating descriptive statistics to summarize data.
• Introduction to ggplot2 for creating powerful visualizations.
• Creating scatter plots, bar charts, histograms, and box plots.
• Practical session: Generating a series of visualizations to explore and understand a new dataset.

Module 4: Descriptive Statistics and Data Summarization

• Measures of central tendency: mean, median, and mode.
• Measures of dispersion: standard deviation, variance, and interquartile range.
• Understanding data distributions: normal, skewed, and uniform.
• Calculating and interpreting summary statistics for a dataset.
• Practical session: Calculating and visualizing descriptive statistics for a business dataset to identify key trends and summaries.

Module 5: Hypothesis Testing and t-Tests

• Fundamental concepts of hypothesis testing: null vs. alternative hypothesis, p-value, and significance level.
• Performing a one-sample t-test to compare a sample mean to a known value.
• Conducting a two-sample t-test to compare the means of two groups.
• Paired t-test for dependent samples.
• Practical session: Using R to perform one-sample and two-sample t-tests to answer a research question about group differences.

Module 6: ANOVA and Chi-Squared Tests

• One-way ANOVA for comparing the means of three or more groups.
• Two-way ANOVA for examining the effect of two independent variables.
• The Chi-squared test for independence between two categorical variables.
• Interpreting the results of ANOVA and Chi-squared tests.
• Practical session: Analyzing a dataset with multiple groups using ANOVA and performing a Chi-squared test on a contingency table.

Module 7: Linear Regression Analysis

• Introduction to linear regression: understanding the relationship between variables.
• Fitting a simple linear regression model in R.
• Interpreting the regression coefficients, R-squared, and p-values.
• Assumptions of linear regression and how to check them.
• Practical session: Building a simple linear regression model to predict a continuous variable from another.

Module 8: Multiple Regression and Model Diagnostics

• Extending simple linear regression to multiple regression with several predictors.
• Understanding multicollinearity, interaction terms, and variable selection.
• Advanced model diagnostics: residual plots and influence plots.
• Creating and interpreting confidence and prediction intervals.
• Practical session: Constructing a multiple regression model to predict housing prices and diagnosing its performance.

Module 9: Introduction to Time Series Analysis

• What is time series data and its unique characteristics?
• Components of a time series: trend, seasonality, and randomness.
• Visualizing time series data in R.
• Basic forecasting techniques: naive, mean, and seasonal naive methods.
• Practical session: Loading a time series dataset, visualizing its components, and creating a simple forecast.

Module 10: Building an End-to-End Statistical Project

• A comprehensive capstone project that ties together all course modules.
• Participants will define a research question, collect or load data, clean and prepare the data, perform appropriate statistical tests, and visualize the results.
• Writing a project report to communicate the findings clearly and concisely.
• Peer review and presentation of projects.
• Practical session: Participants will work on a final project that involves a full statistical analysis pipeline from start to finish.

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588