5 Days Data Analytics with R Specialization

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

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.

HRDF Funding

Please refer to this video https://youtu.be/Kzpd-V1F9Xs

1-     HRD Corp Grant Helper

How to submit grant applications for HRD Corp Claimable Courses

2-     Employers are required to apply for the grant at least one week before training commences.

Employers must submit their applications with supporting documents, including invoices/quotations, trainer profiles, training schedule and course content.

3-     First, Login to Employer’s e-TRIS account -https://etris.hrdcorp.gov.my

Second, Click Application

4-     Click Grant on the left side under Applications

5-     Click Apply Grant on the left side under Applications

6-     Click Apply

7-     Choose a Scheme Code and select HRD Corp Claimable Courses: Skim Bantuan Latihan Khas. Then, click Apply

8-     Scheme Code represents all types of training that suit the requirements provided by HRD Corp. Below are the list of schemes offered by HRD Corp:

9-     Select your Immediate Officer and click Next

10-  Select a Training Provider, then click Next

11-  Please select a training programme from the list, then key in all the required details and click Next

Select your desired training programme.

Give an explanation on why the participant is required to attend the training. E.g., related to their tasks/ career development, etc.

Explain the background and objective of this training.

Select a relevant focus area. For Employer-Specific Courses, select ‘Not Applicable’.

12-  If the training programme is a micro-credential programme, you are required to complete these 3 fields. Save and click Next

Insert MiCAS Application number

13-  Based on the nine (9) pillars listed below, HRD Corp Focus Area Courses are closely tied to support government initiatives towards nation building. As such, courses offered through the HRD Corp Focus Areas are designed to provide the workforce with skills required for current and future demands. Details of the focus areas are as follows:

14-  Please select a Course Title and Type of Training

15-  Select the correct type of training according to the actual type of training, or as mentioned in the training brochure:

16-  Please key in the Training Location and click Next

17-  Please select the Level of Certification and click Next

18-  Please follow the instructions and key in trainee details

19-  Click Add Batch, then click Save

20-  Click Add Trainee Details

21-  Please key in all the required details, then click Add

22-  Click Add if there are more participants. Once done, click Save

23-  Click Next

24-  Please key in the course fees and allowance details, then click Save

25-  Estimated cost includes the course fees/external trainer fees, allowances, and consumable training materials. Please comply with the HRD Corp Allowable Cost Matrix.

26-  Select Upfront Payment to Training Provider and key in the percentage from 0% to 30%. Then, click Save and Next

27-  Complete the declaration form and select a desired officer

28-  Add all the required documents, then click Add Attachment. Then, click Save and Submit Application

29-  Once the New Grant Application is successfully submitted, the Grant Officer will evaluate the application accordingly. The application may be queried if additional information is required.

The application status will be updated via the employer’s dashboard, email, and the e-TRiS inbox.

• Data Analyst
• Programmers
• IT Engineers
• Data Scientist

Lee Cheong Loong: Lee Cheong Loong is a manager with an EMBA – 21 years working experience indifference role/ Dept. (Sales, Logistic, IT, Online Store, Internal Trainer and Customer services), involved in multiple IT project (Server P2V upgrade, Network infra upgrade, and ERP system Deployment). He has switched career to Project management role, Big - data / Data scientist related project, with Professional Certification Big Data & Analytics, Professional Certificate in Tableau, & Pass the HRDF Certified Trainer Programme. He also involves in AI chatbot and application development to help small-medium practitioner (accounting industry).

Dr. Ghazaleh Babanejad: Dr. Ghazaleh Babanejad has received her Ph.D from University Putra Malaysia in Faculty of Computer ‎Science and Information Technology. She is working on recommender systems in the ‎field of skyline queries over Dynamic and Incomplete databases for her PhD thesis. She is also working ‎on Data Science field as a trainer and Data Scientist. She worked on Machine Learning and Process ‎Mining projects. She also has several international certificates in Practical Machine Learning (John ‎Hopkins University) Mining Massive Datasets (Stanford University), Process Mining (Eindhoven ‎University), Hadoop (University of San Diego), MongoDB for DBAs (MongoDB Inc) and some other ‎certificates. She has more than 5 years of experience as a lecturer and database administrator.