Embark on a rewarding journey into the world of data analytics with our 5-day Data Analytics with R Specialization course. This comprehensive training is designed to equip you with in-depth knowledge and practical skills in using R, a powerful programming language for statistical analysis and data visualization. The course begins with the basics of R programming, ensuring a solid foundation even for beginners. As you progress, you will explore advanced data manipulation techniques and delve into the intricacies of data analysis. Our expert instructors will guide you through real-world scenarios, helping you understand how to apply these skills in a practical setting.
The second half of the course focuses on more complex aspects of data analytics, including predictive modeling and machine learning using R. You will learn to create compelling data visualizations, a crucial skill in interpreting and presenting data effectively. This course not only enhances your analytical capabilities but also prepares you to tackle real-world data challenges in various industries. Whether you're a professional looking to upskill, a student interested in data science, or an enthusiast eager to dive into data analytics, this course will set you on the path to mastering data analytics with R in just five days, opening doors to numerous career opportunities in the ever-growing field of data science.
Certificate
All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.
Funding and Grant
HRDF SBL Claimable for Employers Registered with HRDF
Day 1
Topic 1: R Fundamental
Topic 1.1 Getting Started in R
- What is R
- Install R and RStudio IDE
- Explore RStudio Interface
Topic 1.2. Data Types
- Numbers
- String
- Vector
- Matrix
- Array
- Data Frame
- List
- Factor
Topic 1.3. R Packages & Data I/O
- Import R Packages
- Import R Data Sets
- Import External Data
- Export Data
Topic 1.4. Data Visualization
- Scatter Plot
- Boxplot
- Bar chart
- Pie chart
- Histogram
Topic 1.5. R Programming
- Conditional
- Loop
- Break & Next
- Function Syntax
- Default Arguments
Topic 1.6. Statistics Analysis with R
- Descriptive Statistics
- Correlation
- Linear and Multiple Regression
- Hypothesis Testing
- Analysis of Variance (ANOVA)
Day 2
Topic 2: Data Analytics and Visualization with R
Topic 2.1 Data Preparation and Transformation
- Overview of Data Analysis of Research Data
- Install R Data Analysis Packages - Tidyverse and ggplot2
- Import and Export Dataset
- Filter and Slice Data
- Clean Data
- Join Data
- Transform Data
- Aggregate Data
- Pipe Data
Topic 2.2 Data Summary
- Categorical vs Continuous Data
- Quantitative vs Qualitative Data
- Descriptive Statistics of Data
- Summarize Data
- Basic Plots and Tables
Topic 2.3 Quantitative Data Analysis
- Quantitative Data Analysis Overview
- Correlation Analysis
- Regression Analysis
- Hypothesis Testing
- Analysis of Variances (ANOVA)
Topic 2.4 Qualitative Data Analysis
- Qualitative Data Analysis Overview
- Install R Packages for Qualitative Data Analysis
- Word Cloud Analysis
- Text Analysis
Topic 2.5 Data Visualization
- Grammar of Graphics
- Plots for Quantitative Data
- Plots for Qualitative Data
- Customize Visualizations
- Interpret Findings
Day 3
Topic 3: Basic Machine Learning with R
Topic 3.1 Overview of Machine Learning
- Introduction to Machine Learning
- Pattern Recognition Problems Suitable for Machine Learning
- Supervised vs Unsupervised Learnings
- Types of Machine Learning
- Machine Learning Techniques
- R Packages for Machine Learning
Topic 3.2 Regression
- What is Regression
- Applications of Regression
- Least Square Error Minimization
- Data Pre-processing
- Bias vs Variance Trade-off
- Regression Methods with Regularization
- Logistic Regression
Topic 3.3 Classification
- What is Classification
- Applications of Classification
- Classification Algorithms
- Confusion Matrix
- Classification Performance Evaluation
Day 4
Topic 4: Pattern Recognition with R
Topic 4.1 Clustering
- What is Clustering
- Applications of Clustering
- Distance Measure
- Clustering Algorithms
- Clustering Performance Evaluation
- Anomaly Detection Problem
Topic 4.2 Principal Component Analysis
- Principal Component Analysis (PCA) and Dimension Reduction
- Applications of PCA
- PCA Workflow
Topic 4.3 Deep Learning
- What is Neural Network
- Activation Functions
- Loss Function Minimization
- Gradient Descent Algorithms and Learning Rate
- Deep Neural Network for Visual Recognition
- Improve Visual Recognition with Convolutional Neural Network
- The Future of AI
- AI Ethics
Day 5
Topic 5: Text Mining with R
Topic 5.1: Introduction to Text Mining
- What is text mining
- Applications of text mining
Topic 5.2: Basic Text Functions
- Text manipulation functions
- Working with strings
- Working with gsub
- Advanced methods
- Convert to corpus
Topic 5.3: Importing Data
- Converting docx into corpus
- Converting pdf into corpus
- Converting html to corpus
- Web scraping
Topic 5.4: Tidytext Package
- Tidying text objects
- Tidying document term matrix objects
- Tidying document frequency matrix objects
- Tidying corpus objects
- Mining literacy works
Topic 5.5: Word Frequencies & Relationships
- Pre-processing text
- Wordcloud
- Frequency analysis
- nGrams & bigrams
- Bigrams for sentiment analysis
- Visualizing bigrams network
Topic 5.6: Sentiment Analysis
- Sentiment libraries
- Analyzing positive & negative words
- Comparing 3 sentiment libraries
- Common positive & negative words
Topic 5.7: Topic Modelling
- Latent Semantic Indexing (LSI)
- Latent Dirichlet Allocation (LDA)
- Word topic probabilities
- Document - topic probabilities
- Chapters probabilities
- Per document classification
Topic 5.8: Document Similarity & Classifier
- Text alignment & pairwise comparison
- Minihashing and locality sensitive hashing
- Extract key words
- Classify by location, language, topic
Wed, Dec 18, 2024
9:30 - 17:30 Malaysia Time
Business & Professional
Other
Class, Course, Training or Workshop