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5 Days Data Analytics with R Specialization in Singapore

Course Information

  • Sessions 5 days
  • Duration 37.5 hrs
  • Level Beginner to Intermediate
  • Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

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Certification

  • Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.

5 Days Data Analytics with R Specialization

Course Code: C1196

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What's This Course About

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.

Funding Options

No funding is available for this course

Course Fee

$1,500.00 (GST-exclusive)
$1,635.00 (GST-inclusive)

Course Date

Course Time

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Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

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

Course Info

Prerequisite

The learner must meet the minimum requirement below :

  • Read, write, speak and understand English

Target Audience

  • NSF
  • Full Time Students
  • Data Analysts

Software Requirement

This course will use Google Colab for training. Please ensure you have a Google account.

Job Roles

Job Roles

  • Data Analyst
  • Programmers
  • IT Engineers
  • Data Scientist

Trainers

Trainers

Dwight Nuwan Fonseka: Dwight Nuwan Fonseka is a ACLP certified trainer. He have a degree in Biotechnology from NUS ,Advanced diploma in Pharmaceutical management from MDIS and Masters in Education from NTU. He have 8 years experience of teaching biology at O and A levels/ IB level in international schools in Singapore and overseas. Marcel Leng: Marcel Leng is a ACTA certified. Marcel graduated with majors in Applied Mathematics and Physics from the National University of Singapore. His core specialisation skills are R, Python, Machine Learning, Statistical Analysis, and Data Visualisation in Tableau. His current interests include Machine Learning, Deep Learning, Artificial Intelligence, Internet of Things, Robotics and Programming.

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