Data Analytics With R Training in Preston

Data Analytics with R Course Outline

Module 1: Introduction to Data Analytics and R

• Understanding Data Analytics and Its Importance
• Data Analytics Lifecycle and Phase
• Types of Data Analysis
• Applications of Data Analytics in Different Industries
• Overview of R Programming for Data Analytics
• Setting Up R and RStudio

Module 2: Data Handling and Preprocessing in R

• Introduction to Data Handling and Preprocessing in R
• Understanding Data Structures in R
• Working with Vectors in R
• Lists in R
• Matrices and Arrays in R
• Data Frames in R
• Importing and Exporting Data in R
• Handling Missing Values in R
• Removing Duplicates in R
• Identifying and Handling Outliers
• Data Transformation Using dplyr
• Data Tidying Using tidyr
• Introduction to Exploratory Data Analysis (EDA)
• Visualising Data in R for EDA
• Summary and Best Practices in Data Preprocessing

Module 3: Business Intelligence and Analytics

• Introduction to Business Intelligence and Data Visualisation in R
• Fundamentals of Business Intelligence (BI) and Analytics
• Data Visualisation Principles
• Creating Static Visualisations with ggplot2
• Interactive Dashboards with Shiny
• Enhancing Visualisations with Plotly
• Data Storytelling and Reporting
• Best Practices in BI and Data Visualisation

Module 4: Statistical Analysis and Hypothesis Testing

• Introduction to Statistical Analysis in R
• Descriptive Statistics –Overview
• Understanding Measures of Central Tendency
• Understanding Measures of Dispersion
• Visualising Data with Histograms and Boxplots
• Inferential Statistics – Confidence Intervals
• Introduction to Hypothesis Testing
• Correlation and Covariance Analysis
• Understanding Probability Distributions
• Normality Testing in R
• t-Tests in R
• ANOVA (Analysis of Variance) in R
• Chi-Square Test for Categorical Data
• Visualising Statistical Results

Module 5: Machine Learning

• Introduction to Machine Learning in R
• Applications of Machine Learning
• Supervised vs. Unsupervised Learning
• Understanding Supervised Learning
• Understanding Unsupervised Learning
• Importance of Feature Engineering
• Handling Missing Values in ML
• Splitting Data for Training and Testing
• Model Evaluation Metrics for Regression
• Model Evaluation Metrics for Classification
• Hyperparameter Tuning in Machine Learning
• Cross-Validation for Reliable Models
• Overfitting vs. Underfitting in ML Models
• Comparing Different ML Models in R
• Best Practices in ML with R

Module 6: Regression and Classification Models in R

• Introduction to Regression and Classification Models in R
• Linear Regression
• Evaluating Linear Regression Models
• Logistic Regression
• Evaluating Logistic Regression Models
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• k-Nearest Neighbours(kNN)
• Handling Imbalanced Data in Classification Models

Module 7: Unsupervised Learning and Advanced Analytics

• Introduction to Unsupervised Learning and Advanced Analytics
• Clustering Techniques
• k-Means Clustering in R
• Hierarchical Clustering in R
• Principal Component Analysis (PCA) for Dimensionality Reduction
• Association Rule Mining – Apriori Algorithm
• Time Series Analysis
• Time Series Forecasting with ARIMA in R
• Text Mining and Sentiment Analysis
• Sentiment Analysis in R
• Summary and Best Practices in Unsupervised Learning & Advanced Analytics

Who should attend this Data Analytics with R Course?  

The Data Analytics with R Course is designed for individuals seeking to harness the power of data to make informed decisions. This course offers insights and tools to propel your analytical capabilities. This course can be beneficial for a wide range of professionals, including:

• Data Analysts
• Statisticians
• Business Analysts
• Financial Analysts
• Marketing Analysts
• Researchers
• Healthcare Data Professionals
• IT and Software Engineers

Prerequisites of the Data Analytics with R Course 

There are no formal prerequisites for this Data Analytics with R Course.

Data Analytics with R Course Overview

Data Analytics with R Course is a crucial component of Big Data and Analytics Training. In today's data-driven world, the power of R for data analysis is paramount. R, a programming language and environment widely used for statistical analysis and data visualisation, holds the key to uncovering actionable insights from vast datasets. Its relevance is evident in its ability to aid professionals in making informed decisions based on data-driven findings.

Proficiency in R is the ability to harness R's capabilities, enabling them to extract meaningful patterns from complex datasets, aiding in informed decision-making. For instance, data scientists proficient in R can explore trends and uncover hidden insights in various industries, making it a critical skill for career advancement.

The 1-day course offered by the Knowledge Academy is designed to empower delegates with the knowledge and practical skills they need for data analytics with R. Through hands-on experience in data manipulation, statistical analysis, and data visualisation using R, participants will gain a deep understanding of the subject. The course emphasizes real-world applications, ensuring that delegates are well-equipped to tackle data-related challenges effectively.

Data Analytics with R Course Objectives

• To gain expertise in exploratory data analysis and visualisation techniques
• To acquire knowledge of advanced statistical methods for data interpretation
• To understand machine learning algorithms and their application in data analytics
• To learn to create interactive dashboards for data presentation
• To implement real-time analytics solutions using R and Big Data technologies
• To apply ethical considerations and best practices in data analytics projects

After completing this Big Data and Analytics Course, you will receive a certification, validating your proficiency in R for data analytics. This certification serves as a tangible proof of your skills and enhances your career prospects.

Benefits of Data Analytics with R Training 

Data Analytics with R Training in in Preston by The Knowledge Academy, a global training provider, enables learners to apply analytical techniques using the R programming language. It helps professionals interpret and manage datasets effectively.

Professionals can obtain the following benefits from this course:

• Enhanced Data Analytics with R Programming: Strengthen the ability to work confidently with R and RStudio for data analysis and reporting tasks.
• Effective Data Handling: Clean, transform, and structure datasets using R functions, packages and R data structures such as vectors, lists, and data frames to ensure accurate analysis.
• Improved Statistical Analysis: Apply statistical techniques in R to interpret data patterns and support analytical conclusions.
• Advanced Data Visualisation: Create clear and meaningful visualisations from the datasets using tools such as ggplot2, Plotly, and Shiny.
• Applied Machine Learning: Implement Machine Learning techniques in R to build predictive models and improve data-driven forecasting.
• Strengthened Regression and Classification: Develop and evaluate regression and classification models in R to predict outcomes and enrich analytical decision-making.
• Improved Data Interpretation: Convert analytical results into clear, actionable insights that can be easily understood by stakeholders.

What’s included in this Data Analytics with R Course?

• World-Class Training Sessions from Experienced Instructors
• Data Analytics with R Certificate
• Digital Delegate Pack