• Certified Digital Transformation Professional (CDTP)
  • Python Programming Essentials (PPE)
  • Data Analytics Essentials (DAE) with Python
  • Advanced Big Data Professional (ABDP)
  • Advanced Data Science Professional with Python (ADSP)
  • Certified Machine Learning Expert (CMLE)
  • Cyber Security Attack / Defend Strategist (CSAD)
  • Advanced Deep Learning Professional (ADLP)
  • Business Competitive Advantage Analytics (BCAA)
  • Leading Digital Transformation (LDT)
  • Digital Transformation Framework (DTF)
  • Advanced Python Programming Professional (APPP)

Course Information

  • Course Date:
    • 29-31 March 2021, 9.30am – 5.30pm
  • Duration: 3-Days / 24 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Business Leaders, CEO, Directors, CTO, CSO, CISO, CIO, System Analyst, Technologist, System Engineer, IT Professionals and Anyone seeking to acquire advanced knowledge on Digital Transformation Analytics
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Digital Transformation based on the syllabus covered

Pre-Requisite

  • N/A

Course Objective

  • Acquire advanced knowledge and strategies required to lead and manage digital transformation in an organization
  • Learn how to design a Digital Transformation Framework based on your organizations specific requirements
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Programme Details

Module 1 Introduction to Digital Transformation

  • What is Digital Transformation?
  • Need for Digital Transformation
  • Benefits of Digital Transformation for Business
  • Key Elements to a Successful Digital Transformation
  • Common Mistakes in Digital Transformation
  • Designing a Mobile-First Strategy

Module 2 Industry 4.0 and Its Impact on Businesses

  • What is Industry 4.0?
  • Impact of Industry 4.0 on Businesses
  • Deep Dive into Artificial Intelligence
  • Understanding Data Analytics / Big Data
  • Cyber Security Overview
  • Cloud Computing Essentials
  • Social Media for Businesses
  • Opportunities / Challenges for Businesses

Module 3 Digital Transformation Framework

  • What is a Digital Transformation Framework
  • Key Components of a Digital Transformation Framework
  • Identify Your Customers and Their Pain Points
  • Design Your Digital Transformation Journey
  • Choosing the Right Framework for Your Organization

Module 4 Developing a Digital Transformation Framework

  • Redefine the Purpose of the Organization
  • Understand the Objective of Digital Transformation
  • Building a Digital Transformation Strategy
  • Data Governance and Cyber Security Policies
  • Disrupt Your Business Model
  • Identify and Digitally Transform Your Business Core
  • Choosing the Right Framework for Your Organization

Module 5 Data Strategies for Digital Transformation

  • Data Analytics Overview
  • Leverage on Data Analytics and Big Data
  • Establishing Target Metrics
  • Designing of a Data Analytics Strategy
  • Time-to-Market Analytics
  • Competitive Advantage Analytics
  • Human Capacity Analytics
  • Business Growth Analytics

Module 6 Developing an Eco-System Strategy

  • What is an Eco-System
  • Key Components of an Eco-System
  • Managing an Eco-System
  • Eco-system Beyond Borders
  • Achieving Successes Being Part of an Eco-System

Module 7 Putting Everything Together

  • Becoming an Inclusive Leader
  • Organization Cultural Change
  • Organization Management Methodologies
  • Putting Everything Together
  • Continuous Refinement of Framework
  • Creating a Business Model
  • What is Digital Transformation?
  • Need for Digital Transformation
  • Benefits of Digital Transformation for Business
  • Key Elements to a Successful Digital Transformation
  • Common Mistakes in Digital Transformation
  • Designing a Mobile-First Strategy
  • What is Industry 4.0?
  • Impact of Industry 4.0 on Businesses
  • Deep Dive into Artificial Intelligence
  • Understanding Data Analytics / Big Data
  • Cyber Security Overview
  • Cloud Computing Essentials
  • Social Media for Businesses
  • Opportunities / Challenges for Businesses
  • What is a Digital Transformation Framework
  • Key Components of a Digital Transformation Framework
  • Identify Your Customers and Their Pain Points
  • Design Your Digital Transformation Journey
  • Choosing the Right Framework for Your Organization
  • Redfine the Purpose of the Organization
  • Understand the Objective of Digital Transformation
  • Building a Digital Transformation Strategy
  • Data Governance and Cyber Security Policies
  • Disrupt Your Business Model
  • Identify and Digitally Transform Your Business Core
  • Choosing the Right Framework for Your Organization
  • Data Analytics Overview
  • Leverage on Data Analytics and Big Data
  • Establishing Target Metrics
  • Designing of a Data Analytics Strategy
  • Time-to-Market Analytics
  • Competitive Advantage Analytics
  • Human Capacity Analytics
  • Business Growth Analytics
  • What is an Eco-System
  • Key Components of an Eco-System
  • Managing an Eco-System
  • Eco-system Beyond Borders
  • Achieving Successes Being Part of an Eco-System
  • Becoming an Inclusive Leader
  • Organization Cultural Change
  • Organization Management Methodologies
  • Putting Everything Together
  • Continuous Refinement of Framework
  • Creating a Business Model

Certified Digital Transformation Professional (CDTP) involves rigorous usage of real-time case studies, role playing and group discussion

Course Information

  • Course Date:
    • 22 to 24 Mar, 2021, 9.30am – 5.30pm (3 Lessons)
    • 23, 25, 30 Mar, 1, 6, 8 Apr 2021, 7.00pm – 10.00pm (6 Lessons)
  • Duration: 3-days / 24 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Anyone interested in acquiring knowledge and skills on Python Programming
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Python Programming based on the syllabus covered

Pre-Requisite

  • N/A

Course Objective

  • Acquiring the essential knowledge and skills required to begin Python Programming.
  • Learn how to start developing applications in Python Programming through extensive practical / hands-on sessions.
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Programme Details

Module 1 Introduction to Python Programming

  • What is Python Programming?
  • Setup & Installation
  • Understanding the Structure of Python Script with First Python Program
  • Basic Variables: Numbers , Strings
  • Lists & Dictionaries
  • If Else Statements
  • Control Statements
  • Loops
  • Functions

Module 2 File-Handling in Python

  • Introduction to File-Handling in Python
  • Creating a Text & Excel File
  • Writing Content to File
  • Appending Content to File
  • Renaming Files
  • Open & Save File

Module 3 Handling Errors and Exceptions in Python

  • Types of Errors
  • Error Handling
  • Try and Except

Module 4 Class and Objects in Python

  • Defining classes
  • Creating objects
  • O-O Programming
  • Constructors
  • Inheritance
  • Creating instances

Module 5 Working with Modules and Request with Python

  • Packages
  • Modules
  • Time & date module
  • Math Module, Numpy Module, Pandas Module
  • Sending mail
  • Sending SMS
  • What is Python Programming?
  • Setup & Installation
  • Understanding the Structure of Python Script with First Python Program
  • Basic Variables: Numbers , Strings
  • Lists & Dictionaries
  • If Else Statements
  • Control Statements
  • Loops
  • Functions
  • Introduction to File-Handling in Python
  • Creating a Text & Excel File
  • Writing Content to File
  • Appending Content to File
  • Renaming Files
  • Open & Save File
  • Types of Errors
  • Error Handling
  • Try and Except
  • Defining classes
  • Creating objects
  • O-O Programming
  • Constructors
  • Inheritance
  • Creating instances
  • Packages
  • Modules
  • Time & date module
  • Math Module, Numpy Module, Pandas Module
  • Sending mail
  • Sending SMS
  •  

Python Programming Essentials (PPE) involves rigorous usage of real-time case studies, hands-on exercises and group discussion

Course Information

  • Course Date: 
    • 22, 24, 29, 31 Mar 5, 7, 12, 14, 19, 21 Apr 2021, 7:00pm – 10:00pm (10 Lessons)
  • Duration:  4 days / 32 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Aspiring Data Scientist, Data Analyst, HR Analyst, and Anyone interested in pursuing a career in the areas of Business Analytics / Data Analytics. 
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Data Analytics and Python Programming based on the syllabus covered

Pre-Requisite

  • It is preferred that participants successfully completed and pass Python Programming Essentials (PPE)

Course Objective

  • Acquire the essential knowledge on how to use data analytics to make better business or organisational decisions.
  • Learn the different components of Data Analytics, Data Mining, Data Warehousing and Visualization using Python
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Programme Details

Module 1 Introduction to Data Analytics

  • Data Analytics Overview
  • Concepts of Data Analytics
  • Importance and Advantages of Data Analytics
  • Developing / Application of Data Analytics Strategies

Module 2 Different Types of Analytics and Application

  • Data Analytics Maturity Model
  • Understanding Descriptive, Predictive and Prescriptive Analytics
  • Different Application of analytics method
  • Concepts of Text Analytics and Web Analytics
  • Different Application of Analytics Methods

Module 3 Different Types of Analytics and Application

  • Data / information architecture
  • ETL Architecture
  • What is Data Warehouse
  • Business intelligence vs Data Analytics
  • Application of Analytics in an Organisation
  • Case Studies

Module 4 Deep Dive into Python Programming for Data Analytics

  • Introduction to Python Programming
  • Fundamentals of Python Programming for Data Analytics
  • Understanding Python Modules e.g. NumPy, Pandas, Matplotlib

Module 5 Data Mining and Processes for Data Analytics

  • Fundamentals of Data Mining
  • Objectives of Data Mining
  • Key aspects of Data Mining
  • Concepts of Knowledge Discovery in Databases (KDD)
  • Models in Data Mining
  • Data Mining Model vs Statistical Model
  • Data Mining Processes

Module 6 Data Mining Techniques

  • Descriptive Analytics: Clustering Models
  • Descriptive Analytics: Association Models
  • Descriptive Analytics: Visualisation
  • Predictive Analytics: Classification Models
  • Predictive Analytics: Regression Models

Module 7 Introduction to Machine Learning

  • Supervised Learning vs Unsupervised Learning
  • Linear Regression Analysis
  • Logistic Regression Analysis
  • Random Forest Analysis
  • Data Analytics Overview
  • Concepts of Data Analytics
  • Importance and Advantages of Data Analytics
  • Developing / Application of Data Analytics Strategies
  • Data Analytics Maturity Model
  • Understanding Descriptive, Predictive and Prescriptive Analytics
  • Different Application of analytics method
  • Concepts of Text Analytics and Web Analytics
  • Different Application of Analytics Methods
  • Data / information architecture
  • ETL Architecture
  • What is Data Warehouse
  • Business intelligence vs Data Analytics
  • Application of Analytics in an Organisation
  • Case Studies
  • Introduction to Python Programming
  • Fundamentals of Python Programming for Data Analytics
  • Understanding Python Modules e.g. NumPy, Pandas, Matplotlib
  •  
  • Fundamentals of Data Mining
  • Objectives of Data Mining
  • Key aspects of Data Mining
  • Concepts of Knowledge Discovery in Databases (KDD)
  • Models in Data Mining
  • Data Mining Model vs Statistical Model
  • Data Mining Processes
  • Descriptive Analytics: Clustering Models
  • Descriptive Analytics: Association Models
  • Descriptive Analytics: Visualisation
  • Predictive Analytics: Classification Models
  • Predictive Analytics: Regression Models
  • Supervised Learning vs Unsupervised Learning
  • Linear Regression Analysis
  • Logistic Regression Analysis
  • Random Forest Analysis

Data Analytics Essentials (DAE) involves rigorous usage of real-time case studies, hands-on exercises and group discussion

Course Information

  • Course Date: 
    • 22 to 25 Mar 2021, 9:30am to 5:30pm (4 Lessons)
  • Duration: 4-Days / 32 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Data Analyst, Finance Analyst, HR Analyst, Software Engineers, Database Administrator, CIO or Anyone interested in pursuing a career in Big Data, Data Analytics, and Data Engineering
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Big Data and Hadoop based on the syllabus covered

Pre-Requisite

  • It is preferred that participants successfully completed and pass Data Analytics Essentials (DAE)

Course Objective

  • Acquiring advanced knowledge and skills on how to use Hadoop in Big Data to identify correlation and causation statistically valid models as part of their organisation strategic decision making and planning.
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Programme Details

Module 1 Data Types and Relational Database Management System (RDBMS)

  • Data Types & RDBMS Concepts
  • Structured Query Language – SQL Basics
  • Data Warehousing Concepts
  • Data Modeling Concepts

Module 2 Introduction to Big Data and Hadoop

  • Introduction to Big Data and Hadoop
  • Hadoop Architecture
  • Hadoop Deployment
  • Hadoop Troubleshooting
  • Introduction to Hadoop Distributed File System (HDFS)
  • Introduction to MapReduce

Module 3 Processing Data in Hadoop

  • Overview on MapReduce
  • When to Use MapReduce
  • Introduction to Spark
  • Components and Concept of Spark
  • Understanding Abstraction
  • Working with Pig
  • Overview on Hive
  • Apache Hive – Hive Query Language

Module 4 Fundamentals to NoSQL Data Management for Big Data

  • Introduction to NoSQL
  • Concepts of NoSQL
  • Key-Value Stores
  • Document Stores
  • Object Data Stores
  • Graph Databases

Module 5 Real-Time Data Processing with Hadoop

  • Understand Stream Processing
  • Introduction to Apache Storm
  • Understanding Storm Architecture and Topologies
  • Integration Storm with HDFS
  • Integration Storm with HBase

Module 6 Spark Streaming

  • Overview on Spark Streaming
  • Spark Streaming with Simple Count
  • Spark Streaming with Multiple Inputs
  • Maintaining State
  • Windowing in Spark Streaming
  • Streaming vs. ETL Code
  • Batch Analytics with Spark
  • SparkSQL and DataFrame
  • DataFrame APIs and SQL API

Module 7 Visualizing in Big Data

  • Big Data Visualization Tools
  • Quick Overview on the Different Chart Types
  • Using Python to Visualize Data
  • Data Types & RDBMS Concepts
  • Structured Query Language – SQL Basics
  • Data Warehousing Concepts
  • Data Modeling Concepts
  • Introduction to Big Data and Hadoop
  • Hadoop Architecture
  • Hadoop Deployment
  • Hadoop Troubleshooting
  • Introduction to Hadoop Distributed File System (HDFS)
  • Introduction to MapReduce
  • Understanding Abstraction
  • Working with Pig
  • Overview on Hive
  • Apache Hive – Hive Query Language
  • Design Your Digital Transformation Journey
  • Choosing the Right Framework for Your Organization
  • Introduction to NoSQL
  • Concepts of NoSQL
  • Key-Value Stores
  • Document Stores
  • Object Data Stores
  • Graph Databases
  • Understand Stream Processing
  • Introduction to Apache Storm
  • Understanding Storm Architecture and Topologies
  • Integration Storm with HDFS
  • Integration Storm with HBase
  • Overview on Spark Streaming
  • Spark Streaming with Simple Count
  • Spark Streaming with Multiple Inputs
  • Maintaining State
  • Windowing in Spark Streaming
  • Streaming vs. ETL Code
  • Batch Analytics with Spark
  • SparkSQL and DataFrame
  • DataFrame APIs and SQL API
  •  
  • Big Data Visualization Tools
  • Quick Overview on the Different Chart Types
  • Using Python to Visualize Data

Advanced Big Data Professional (ABDP) involves rigorous usage of real-time case studies, hands-on exercises and group discussions

Course Information

  • Course Date:
    • 8 to 12 Mar 2021, 9:30am – 5:30pm (5 Lessons)
  • Duration: 5 Days / 40 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Professionals or Anyone interested in pursuing a career as a data scientist and use data to understand the world, uncover insights, and make better decisions
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Data Science and Python Programming based on the syllabus covered

Pre-Requisite

  • It is preferred that participants successfully completed and pass Data Analytics Essentials (DAE) or Python Programming Essentials (PPE)

Course Objective

  • Acquire advanced knowledge on how to use Data Science with Python Programming to uncover business insights and trend.
  • Learn how to use algorithms and basic Artificial Intelligence / Machine Learning techniques to make predictions.
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Programme Details

Module 1 Introduction to Data Science

  • What is Data Science
  • Data Science Vs. Analytics
  • What is Data warehouse
  • Online Analytical Processing (OLAP)
  • MIS Reporting
  • Data Science and its Industry Relevance
  • Problems and Objectives in Different Industries
  • How to Harness the power of Data Science?
  • ELT vs ETL

Module 2 Deep Dive into Python Programming

  • Python Editors & IDE
  • Custom Environment Settings
  • Basic Rules in Python
  • Most Common Packages / Libraries in Python (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
  • Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels –  Date & Time Values
  • Basic Operations – Mathematical – string – date
  • Reading and writing data
  • Simple plotting/Control flow/Debugging/Code profiling

Module 3 Importing / Exporting Data with Python

  • Importing Data into from Various sources
  • Database Input (Connecting to database)
  • Viewing Data objects – sub setting, methods
  • Exporting Data to various formats

Module 4 Data Cleansing with Python

  • Cleaning of Data with Python
  • Steps to Data Manipulation
  • Python Tools for Data manipulation
  • User Defined Functions in Python
  • Stripping out extraneous information
  • Normalization of Data and Data Formatting
  • Important Python Packages e.g.Pandas, Numpy etc)

Module 5 Data Visualization with Python

  • Exploratory Data Analysis
  • Descriptive Statistics, Frequency Tables and Summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs
  • Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)

Module 6 Statistics Fundamentals

  • Basic Statistics – Measures of Central Tendencies and Variance
  • Building blocks (Probability Distributions, Normal distribution, Central Limit Theorem)
  • Inferential Statistics (Sampling, Concept of Hypothesis Testing)
  • Statistical Methods: Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi-square
  • Statistical Methods: ANOVA
  • Statistical Methods: Correlation and Chi-square

Module 7 Introduction to Machine Learning

  • Statistical Learning vs Machine Learning
  • Iteration and Evaluation
  • Supervised Learning vs Unsupervised Learning
  • Predictive Modelling – Data Pre-processing, Sampling, Model Building, Validation
  • Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Cross Validation Train & Test, Bootstrapping, K-Fold validation etc

Module 8 Understanding Predictive Analytics

  • Introduction to Predictive Modelling
  • Types of Business Problems
  • Mapping of Techniques
  • Linear Regression
  • Logistic Regression
  • Segmentation – Cluster Analysis (K-Means / DBSCAN)
  • Decision Trees (CHAID/CART/CD 5.0)
  • Time Series Forecasting

Module 9 Understanding A/B Testing Concepts

  • Introduction to A/B Testing
  • Measuring Conversion for A/B Testing/li>
  • T-Test and P-Value
  • Measuring T-Statistics and P-Values using Python
  • A/B Test Gotchas
  • Novelty Effects, Seasonal Effects, and Selection of Bias
  • Data Pollution
  • What is Data Science
  • Data Science Vs. Analytics
  • What is Data warehouse
  • Online Analytical Processing (OLAP)
  • MIS Reporting
  • Data Science and its Industry Relevance
  • Problems and Objectives in Different Industries
  • How to Harness the power of Data Science?
  • ELT vs ETL
  • Python Editors & IDE
  • Custom Environment Settings
  • Basic Rules in Python
  • Most Common Packages / Libraries in Python (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
  • Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels –  Date & Time Values
  • Basic Operations – Mathematical – string – date
  • Reading and writing data
  • Simple plotting/Control flow/Debugging/Code profiling
  • Importing Data into from Various sources
  • Database Input (Connecting to database)
  • Viewing Data objects – sub setting, methods
  • Exporting Data to various formats
  • Cleaning of Data with Python
  • Steps to Data Manipulation
  • Python Tools for Data manipulation
  • User Defined Functions in Python
  • Stripping out extraneous information
  • Normalization of Data and Data Formatting
  • Important Python Packages e.g.Pandas, Numpy etc)
  • Exploratory Data Analysis
  • Descriptive Statistics, Frequency Tables and Summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs
  • Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)
  • Basic Statistics – Measures of Central Tendencies and Variance
  • Building blocks (Probability Distributions, Normal distribution, Central Limit Theorem)
  • Inferential Statistics (Sampling, Concept of Hypothesis Testing)
  • Statistical Methods: Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi-square
  • Statistical Methods: ANOVA
  • Statistical Methods: Correlation and Chi-square
  • Statistical Learning vs Machine Learning
  • Iteration and Evaluation
  • Supervised Learning vs Unsupervised Learning
  • Predictive Modelling – Data Pre-processing, Sampling, Model Building, Validation
  • Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Cross ValidationTrain & Test, Bootstrapping, K-Fold validation etc
  • Introduction to Predictive Modelling
  • Types of Business Problems
  • Mapping of Techniques
  • Linear Regression
  • Logistic Regression
  • Segmentation – Cluster Analysis (K-Means / DBSCAN)
  • Decision Trees (CHAID/CART/CD 5.0)
  • Time Series Forecasting
  • Introduction to A/B Testing
  • Measuring Conversion for A/B Testing/li>
  • T-Test and P-Value
  • Measuring T-Statistics and P-Values using Python
  • A/B Test Gotchas
  • Novelty Effects, Seasonal Effects, and Selection of Bias
  • Data Pollution

Advanced Data Science Professional (ADSP) involves rigorous usage of real-time case studies, hands-on exercises and group discussions

Course Information

  • Course Date:
    • 22 to 25, 27 Feb Feb 2021, 9.30am – 5.30pm (5 Lessons)
  • Duration: 5 Days / 40 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Data Analyst, Finance Analyst, HR Analyst, System Analyst, CIO, or Anyone who are interested in pursuing a career in the areas of Artificial Intelligence / Machine Learning
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Machine Learning and Python Programming based on the syllabus covered

Pre-Requisite

  • It is preferred that participants successfully completed and pass Data Analytics Essentials (DAE) or Python Programming Essentials (PPE)

Course Objective

  • Acquire advanced knowledge on how to use Python to apply powerful Artificial Intelligence / Machine Learning techniques to gain real-world insight.
  • Learn how to use Python for Data Mining, Data Engineering, and apply Machine Learning algorithms to Analyze and Predict with Structured and Unstructured Data
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Programme Details

Module 1 Introduction to AI and Machine Learning

  • What is Artificial Intelligence (AI)
  • Concepts of machine learning
  • Data and machine learning
  • Real world applications of machine learning
  • How machine learning works

Module 2 Data Structures & Managing Data Using Python

  • Data and Data Types
  • Deep Dive into Python
  • Data Types
  • Variable Operators in Python
  • Data Vectors and Data Frames
  • Reading and Writing Data Files to Python
  • Communicating with Database via Python
  • Executing SQL Using Python
  • Joining Structured & Semi Structured Data with Python
  • Big Data Concepts & Application of Python

Module 3 Exploring Data Using Python

  • Bar Chart
  • Pie Chart
  • Trend Chart
  • Histogram
  • Box Plot
  • Scattered Plot & Correlation
  • Other Chart

Module 4 Basic Classification Models & Techniques

  • Concept of Classification
  • Supervised and Unsupervised Classification
  • Decision Tree Classification
  • Random Forest Classification
  • Naive Bayes Classification
  • Support Vector Machine

Module 5 Regression Methods and Forecasting

  • Concept of Regression Modelling
  • Modelling Stages
  • Simple linear Regression
  • Multiple Linear Regression
  • Refining the Model
  • Model Validation and Prediction
  • Logistic Regression

Module 6 Finding Data Patterns Using Association Rules

  • Concepts of Association Rules
  • Market Basket Analysis (MBA)
  • Support, Confidence & Lift
  • Other Techniques of Association
  • Application of Association

Module 7 K-Means Clustering

  • Cluster Analysis
  • Hierarchical Clustering
  • K-Means Clustering

Module 8 Evaluating and Improving Model Performance

  • Model Evaluation and Comparison
  • Parameters to Evaluate the Model Accuracy
  • Selection of Right Parameters for a Model

Module 9 Deep Dive Into Deep Learning

  • Understanding Learning Representation of Data
  • Fundamentals of Deep Learning
  • How Deep Learning Works
  • Deep Learning and Its Application
  • Future of Deep Learning
  • What is Digital Transformation?
  • Need for Digital Transformation
  • Benefits of Digital Transformation for Business
  • Key Elements to a Successful Digital Transformation
  • Common Mistakes in Digital Transformation
  • Designing a Mobile-First Strategy
  • What is Industry 4.0?
  • Impact of Industry 4.0 on Businesses
  • Deep Dive into Artificial Intelligence
  • Understanding Data Analytics / Big Data
  • Cyber Security Overview
  • Cloud Computing Essentials
  • Social Media for Businesses
  • Opportunities / Challenges for Businesses
  • What is a Digital Transformation Framework
  • Key Components of a Digital Transformation Framework
  • Identify Your Customers and Their Pain Points
  • Design Your Digital Transformation Journey
  • Choosing the Right Framework for Your Organization
  • Redfine the Purpose of the Organization
  • Understand the Objective of Digital Transformation
  • Building a Digital Transformation Strategy
  • Data Governance and Cyber Security Policies
  • Disrupt Your Business Model
  • Identify and Digitally Transform Your Business Core
  • Choosing the Right Framework for Your Organization
  • Data Analytics Overview
  • Leverage on Data Analytics and Big Data
  • Establishing Target Metrics
  • Designing of a Data Analytics Strategy
  • Time-to-Market Analytics
  • Competitive Advantage Analytics
  • Human Capacity Analytics
  • Business Growth Analytics
  • What is an Eco-System
  • Key Components of an Eco-System
  • Managing an Eco-System
  • Eco-system Beyond Borders
  • Achieving Successes Being Part of an Eco-System
  • Becoming an Inclusive Leader
  • Organization Cultural Change
  • Organization Management Methodologies
  • Putting Everything Together
  • Continuous Refinement of Framework
  • Creating a Business Model
  • Model Evaluation and Comparison
  • Parameters to Evaluate the Model Accuracy
  • Selection of Right Parameters for a Model
  • Understanding Learning Representation of Data
  • Fundamentals of Deep Learning
  • How Deep Learning Works
  • Deep Learning and Its Application
  • Future of Deep Learning

Certified Machine Learning Expert (CMLE) involves rigorous usage of real-time case studies, hands-on exercises and group discussion

Course Information

  • Course Date:
    • 8 to 10 Feb 2021, 9.30am – 5.30pm 
    • 8 to 10 Mar 2021, 9.30am – 5.30pm 
    • 7 to 9 Apr 2021, 9.30am – 5.30pm 
  • Duration: 3-Days / 24 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend:  Anyone interested in acquiring knowledge and skills on Cyber Security Attack and Defense strategies. 
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Cyber Security based on the syllabus covered

Pre-Requisite

  • It is preferred that participants successfully completed and pass Cyber Security Essentials (CSE)

Course Objective

  • Acquiring the advanced knowledge and technical skills in Cyber Security attacks and defending strategies
  • Learn how identify potential threats and take preemptive measures to secure cyber assets
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Programme Details

Module 1 Building a Hacking Lab

Participants will be introduced the latest advancement of Cyber Security Concepts. Participants will also acquire knowledge on how to install / setup Kali Linux (Attacker), Vulnerable Virtual Machines (Target), Cloud Computing and Virtualization concepts.

  • Introduction to Real-World Hacking
  • Kali Linux VM Configuration
  • Configure Vulnerable VMs
  • Cloud VM vs Container

Module 2 Hiding Identity

In the process of hacking, it is an important to hide your identity or achieving anonymity. Anonymity will allow you to do things on the internet invisibly.

  • ProxyChains
  • VPN Services
  • TOR Network

Module 3 Open-Source Intelligence

Acquire essential knowledge on how to perform opensource intelligence on enumerate sensitive information of Target to make the process of hacking look seamless.

  • OSINT
  • Internet Archives / WHOIS / Netcraft
  • Google Hacking Database & Shodan
  • Network Scanning Methodology
  • Identify Live Hosts & Port Scanning
  • Enumerating Sensitive Information

Module 4 Password Cracking

In this module, participants will learn how to perform different types of password attacks, including cracking hashes and brut-force.

  • Attack Vector
  • Password Cracking Tools
  • Cracking the Hashes
  • Sniffing Password

Module 5 Malware Threats

Participants will understand the different types of malware and how attackers are distributing malware using undetectable to security devices.

  • Type of Malware
  • Creating & Distributing Malware
  • Art to Avoiding Detection

Module 6 Social Engineering Attack

Discover popularly techniques used to perform attacks like sniffing, MITM, spoofing and phishing using social engineering. This increases the ease of conducting a mass attack surface area.

  • MITM Description
  • Sniffing & Spoofing Attack
  • Phishing & Spear Phishing Attack
  • Social Media Phishing
  • Web Based Delivery

Module 7 Hacking Web Server & Database

Web Servers and Database technologies are widely used everywhere and most of them are vulnerable. In this module, participants will learn how to identify vulnerabilities and mitigation.

  • Basic WEB Technologies
  • OWASP Top 10
  • Hacking WEB Server
  • SQL Injection Methodlogy
  • SQL Injection Tools

Module 8 Wireless Hacking

Open and Free WIFI Access Points / Network are often used by hackers to spy on sensitive data of users. In this module, participants will gain an in-depth understanding on how to to securely use and configure wireless networks.

  • Wireless Basics
  • WPA / WPA2 Attack
  • Rogue Access Points

Module 9 Evading IDs, Firewalls and Honeypots

100% security is only marketing strategies. Learn how hackers bypass security domains of targets. Participants will also learn how to harden / defend IT infrastructure through extensive practical / hands-on exercises.

  • IDS / IPS Configuration
  • Honeypots
  • Firewalls (Vendor-Centric vs Open Source)
  • ACL – Access Control List
  • Introduction to Real-World Hacking
  • Kali Linux VM Configuration
  • Configure Vulnerable VMs
  • Cloud VM vs Container
  • ProxyChains
  • VPN Services
  • TOR Network
  • OSINT
  • Internet Archives / WHOIS / Netcraft
  • Google Hacking Database & Shodan
  • Network Scanning Methodology
  • Identify Live Hosts & Port Scanning
  • Enumerating Sensitive Information
  • Attack Vector
  • Password Cracking Tools
  • Cracking the Hashes
  • Sniffing Password
  • Type of Malware
  • Creating & Distributing Malware
  • Art to Avoiding Detection
  • MITM Description
  • Sniffing & Spoofing Attack
  • Phishing & Spear Phishing Attack
  • Social Media Phishing
  • Web Based Delivery
  • Basic WEB Technologies
  • OWASP Top 10
  • Hacking WEB Server
  • SQL Injection Methodlogy
  • SQL Injection Tools
  • Wireless Basics
  • WPA / WPA2 Attack
  • Rogue Access Points
  • IDS / IPS Configuration
  • Honeypots
  • Firewalls (Vendor-Centric vs Open Source)
  • ACL – Access Control List

Course Information

  • Course Date: 8 to 12 March 2021, 9.30am – 5.30pm (5 Lessons)
  • Duration: 5-Days / 40 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend:  Data Scientist, Data Analyst, Analyst, System Analyst, Technologist, System Engineer, IT Professionals and Anyone seeking to acquire advanced knowledge on Deep Learning.
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Deep Learning based on the syllabus covered

Pre-Requisite

  • It is preferred that participants have some prior experience in Python Programming, Data Analytics or successfully completed and received a Certificate of Competency in Python Programming Essentials (PPE).

Course Objective

  • Advanced Deep Learning Professional (ADLP) is designed for anyone interested in acquiring the advanced knowledge and skills required to implement / manage Deep Learning an organization
APPLY NOW

Programme Details

Module 1 Introduction to Deep Learning

  • What is Artificial Intelligence and Machine Learning?
  • Understanding Learning Representation of Data
  • Fundamentals of Deep Learning
  • How Deep Learning Works
  • Deep Learning and Its Application
  • Future of Deep Learning

Module 2 Understanding Machine Learning Essentials

  • Probabilistic Modeling
  • Kernel Methods
  • Decision trees, Random Forests, and Gradient Boosting Machines
  • Understanding Neural Networks
  • What Makes Deep Learning Different
  • Hardware, Data, and Algorithms of Deep Learning

Module 3 Data Representation in Neural Networks

  • Scalars, Vectors, Matrices
  • 3D Tensors and higher-dimensional tensors
  • Key Attributes
  • Manipulating Tensors in Numpy
  • The Notion of Data Batch
  • Examples of Data Tensors
  • Vector Data
  • Timeseries Data or Sequence Data
  • Image and Video Data

Module 4 Neural Networks: Tensor Operations

  • Element-wise Operations
  • Broadcasting
  • Tensor Dot
  • Tensor Reshaping
  • Geometric Interpretation of Tensor Operations
  • Geometric Interpretation of Deep Learning

Module 5 Neural Networks: Gradient-Based Operations

  • Introduction to Derivative
  • Derivative of a Tensor Operation: Gradient
  • Stochastic Gradient Descent
  • The Backpropagation Algorithm

Module 6 Structure of a Neural Network

  • What are the Layers for Deep Learning?
  • Neural Network Models
  • Core Elements to Configuring a Learning Process
  • Introduction to Keras, TensorFlow, Theano, and CNTK
  • Brief Overview of Keras

Module 7 Getting Ready for Deep Learning

  • Key Considerations
  • Setting up Jupyter Notebooks
  • Setting up of Keras
  • Deep Learning in a Cloud
  • Identifying the Best GPU for Deep Learning

Module 8 Binary Classification

  • Preparing the Data
  • Building Network
  • Validating Approach
  • Using a Trained Network to Generate Predictions on New Data

Module 9 Multi-Class Classification

  • Preparing the Data
  • Building Network
  • Validating Approach
  • Generate Predictions on New Data
  • Alternative Ways to Handle Labels and Loss
  • Importance of Having Sufficiently Large Intermediate Layers

Module 10 Regression Model

  • Preparing the Data
  • Building Network
  • Validating Approach using K-Fold Validation

Module 11 Deep Learning for Computer Vision

  • Introduction to Convnets
  • Understanding Convolution Operation and Max Pooling Operation
  • Training a Convnet on a Small Dataset
  • Understanding the Relevance of Deep Learning for Small-Data Problems
  • Downloading the Data and Building the Network
  • Data Pre-processing and Data Augmentation
  • Visualizing Intermediate Activations
  • Visualizing Convnet Filters
  • Visualizing Heatmaps of Class Activation

Module 12 Deep Learning for Text and Sequences

  • Encoding of Words or Characters
  • How is Word Embedding Being Used?
  • From Raw Text to Word Embeddings
  • Understanding Recurrent Neural Networks
  • What is LSTM and GRU Layers?
  • What is a First Recurrent Baseline
  • Using Recurrent Dropout to Fight Overfitting
  • Stacking Recurrent Layers
  • Using Bidirectional RNNs
  • Understanding 1D Convolution for Sequence Data
  • 1D Pooling for Sequence Data
  • Implementing a 1D Convnet
  • Combing CNNs and RNNs to Process Long Sequences

Module 13 Advanced Deep Learning Techniques

  • Introduction to Functional APIs
  • Multi-Input and Multi-Output Modes
  • Directed Acyclic Graphs of Layers
  • Layer Weight Sharing
  • Using Keras Callbacks and TensorBoard
  • Understanding TensorFlow Visualization Framework
  • Advanced Architecture Patterns
  • Hyperparameter Optimization
  • Model Ensembling

Module 14 Generative Deep Learning

  • Text Generation with LSTM
  • Implementing Deep Dream in Keras
  • Neural Style Transfer in Keras
  • Generating Images with Variational Autoencoders
  • What is a Generative Adversarial Networks
  • Training DCGAN
  • What is Artificial Intelligence and Machine Learning?
  • Understanding Learning Representation of Data
  • Fundamentals of Deep Learning
  • How Deep Learning Works
  • Deep Learning and Its Application
  • Future of Deep Learning
  • Probabilistic Modeling
  • Kernel Methods
  • Decision trees, Random Forests, and Gradient Boosting Machines
  • Understanding Neural Networks
  • What Makes Deep Learning Different
  • Hardware, Data, and Algorithms of Deep Learning
  • Scalars, Vectors, Matrices
  • 3D Tensors and higher-dimensional tensors
  • Key Attributes
  • Manipulating Tensors in Numpy
  • The Notion of Data Batch
  • Examples of Data Tensors
  • Vector Data
  • Timeseries Data or Sequence Data
  • Image and Video Data
  • Element-wise Operations
  • Broadcasting
  • Tensor Dot
  • Tensor Reshaping
  • Geometric Interpretation of Tensor Operations
  • Geometric Interpretation of Deep Learning
  • Introduction to Derivative
  • Derivative of a Tensor Operation: Gradient
  • Examples of Data Tensors
  • Vector Data
  • Timeseries Data or Sequence Data
  • Image and Video Data
  • What are the Layers for Deep Learning?
  • Neural Network Models
  • Core Elements to Configuring a Learning Process
  • Introduction to Keras, TensorFlow, Theano, and CNTK
  • Brief Overview of Keras
  • Key Considerations
  • Setting up Jupyter Notebooks
  • Setting up of Keras
  • Deep Learning in a Cloud
  • Identifying the Best GPU for Deep Learning
  • Preparing the Data
  • Building Network
  • Validating Approach
  • Using a Trained Network to Generate Predictions on New Data
  • Preparing the Data
  • Building Network
  • Validating Approach
  • Generate Predictions on New Data
  • Alternative Ways to Handle Labels and Loss
  • Importance of Having Sufficiently Large Intermediate Layers
  • Preparing the Data
  • Building Network
  • Validating Approach using K-Fold Validation
  • Introduction to Convnets
  • Understanding Convolution Operation and Max Pooling Operation
  • Training a Convnet on a Small Dataset
  • Understanding the Relevance of Deep Learning for Small-Data Problems
  • Downloading the Data and Building the Network
  • Data Pre-processing and Data Augmentation
  • Visualizing Intermediate Activations
  • Visualizing Convnet Filters
  • Visualizing Heatmaps of Class Activation
  • Encoding of Words or Characters
  • How is Word Embedding Being Used?
  • From Raw Text to Word Embeddings
  • Understanding Recurrent Neural Networks
  • What is LSTM and GRU Layers?
  • What is a First Recurrent Baseline
  • Using Recurrent Dropout to Fight
  • Stacking Recurrent Layers
  • Using Bidirectional RNNs
  • Understanding 1D Convolution for Sequence Data
  • 1D Pooling for Sequence Data
  • Implementing a 1D Convnet
  • Combing CNNs and RNNs to Process Long Sequences
  • Overfitting
  • Introduction to Functional APIs
  • Multi-Input and Multi-Output Modes
  • Directed Acyclic Graphs of Layers
  • Layer Weight Sharing
  • Using Keras Callbacks and TensorBoard
  • Understanding TensorFlow Visualization Framework
  • Advanced Architecture Patterns
  • Hyperparameter Optimization
  • Model Ensembling
  • Text Generation with LSTM
  • Implementing Deep Dream in Keras
  • Neural Style Transfer in Keras
  • Generating Images with Variational Autoencoders
  • What is a Generative Adversarial Networks
  • Training DCGAN

Advanced Deep Learning Professional (ADLP) involves rigorous usage of real-time case studies, hands-on exercises and group discussion

Course Information

  • Course Date:  Coming Soon 
  • Duration: 2 hours
  • Certification: Participants will receive a Certificate of Completion upon successfully completing and fulfilling all course requirements course
  • Who Should Attend: HR Managers, HR Directors, CHRO, HR Business Partners, Analyst, Operation Directors, CDTO, CEOs, IT Professionals, IT Infrastructure, Business Leaders, or Anyone interested in acquiring essential knowledge on how to analyse Business Competitive Advantage

Course Objective

  • Business Competitive Advantage Analytics (BCAA) is a 2-Hour Executive Workshop aimed to provide participants with the essential knowledge and technical know-how on Business Competitive Advantage Analytics
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Programme Details

Overview on Business Competitive Advantage Analytics

  • Identifying the Sources of Competitive Advantage
  • Individual Performance vs Group Performance
  • Operational Continuity vs Strategic Improvements
  • Organization Strength and Weaknesses
  • Where to Focus the Analytics
  • Restructuring the Organization based on Competitive Advantage

Course Information

  • Course Date:  Coming Soon 
  • Duration: 4 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Business Leaders, Aspiring Entrepreneurs, Managers, or Anyone interested in acquiring knowledge and skills required to lead in digital transformation

Course Objective

  • Leading Digital Transformation (LDT) is a 4-Hour Executive Workshop designed for leaders, managers and anyone seeking to enhance their leadership competency in digital economy and acquire knowledge on how to strategically lead an organization through digital changes
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Overview

It is never easy leading any form of corporate transformation, let alone starting and sustaining a digital transformation effort. Becoming digital is no longer an option for companies today. The new wave of technology accessible to the corporate world – Data Analytics, AI, Internet of Things, Big Data – are redefining the nature of work and productivity. However, there is no one-size fits all formula or best practices that is applicable for all companies.

Programme Details

Module 1 Understanding the Digital Opportunities and Threat

  • Impact of Industry 4.0
  • Redefining Customer Experience
  • Overview on Data Analytics / Artificial Intelligence / Machine Learning
  • Beefing up your Cyber Security
  • Digital Transformation Platform

Module 2 Incumbency, Talent, and Culture

  • Disrupting the Incumbent Organization
  • Managing Talent with People Analytics
  • Fostering an Innovative Culture
  • Disrupt Without Being Disrupted

Module 3 Overcoming the Key Reasons Why Companies Are Not Advancing in Transformation

  • Outsourcing / Partnership in the Creation of Digital Capabilities
  • Moving Beyond In-house IT or Chief Information Officer (CIO)
  • Development of Digital Capabilities to Cater for all Business Units
  • Going All-in Instead of Being Selective

Module 4 Strategies in Executing Digital Transformation

  • Objective of Digital Transformation
  • Building a Digital Transformation Strategy
  • Becoming an Inclusive Leader
  • From Blueprint to Action Items
  • What’s Next?
  • Impact of Industry 4.0
  • Redefining Customer Experience
  • Overview on Data Analytics / Artificial Intelligence / Machine Learning
  • Beefing up your Cyber Security
  • Digital Transformation Platform
  • Disrupting the Incumbent Organization
  • Managing Talent with People Analytics
  • Fostering an Innovative Culture
  • Disrupt Without Being Disrupted
  • Outsourcing / Partnership in the Creation of Digital Capabilities
  • Moving Beyond In-house IT or Chief Information Officer (CIO)

  • Development of Digital Capabilities to Cater for all Business Units

  • Going All-in Instead of Being Selective

  • Objective of Digital Transformation
  • Building a Digital Transformation Strategy
  • Becoming an Inclusive Leader
  • From Blueprint to Action Items
  • What’s Next?
  • From Blueprint to Action Items
  • What’s Next?

Course Information

  • Course Date:  Coming Soon 
  • Duration:8 hours / One-Day
  • Certification: Participants will be awarded a Certificate of Completion upon completing the course and fulfilling all course requirements
  • Who Should Attend: Digital Transformation Framework (DTF) is designed for Professionals and Anyone interested in acquiring essential knowledge required to design a Digital Transformation Framework (DTF) for their organization. Participants will have the opportunity to learn in a supportive and encouraging environment. Class is limited to 20 participants as hands-on sessions and real-time demonstration is expected.
  • Examination: N/A

Pre-Requisite

  • N/A

Course Objective

  • Digital Transformation Framework (DTF) is a 8-Hour Executive Workshop designed for Professionals and Anyone interested in acquiring essential knowledge required to design a Digital Transformation Framework (DTF) for their organization.
APPLY NOW

Programme Details

Module 1 Introduction to Digital Transformation  

  • What is Digital Transformation?
  • Need for Digital Transformation
  • Benefits of Digital Transformation for Businesses
  • Key Elements to a Successful Digital Transformation
  • Common Mistakes in Digital Transformation
  • Case Studies, Exercises and Group Discussion

Module 2 Digital Transformation Framework 

  • What is a Digital Transformation Framework?
  • Benefits of a Digital Transformation Framework
  • Choosing the Right Framework for Your Organization
  • Case Studies, Exercises and Group Discussion

Module 3 Developing a Digital Transformation Framework  

  • Objective of Digital Transformation
  • Building a Digital Transformation Strategy
  • Case Studies, Exercises and Group Discussion
  • What is Digital Transformation?
  • Need for Digital Transformation
  • Benefits of Digital Transformation for Businesses
  • Key Elements to a Successful Digital Transformation
  • Common Mistakes in Digital Transformation
  • Case Studies, Exercises and Group Discussion
  • What is a Digital Transformation Framework?
  • Benefits of a Digital Transformation Framework
  • Choosing the Right Framework for Your Organization
  • Case Studies, Exercises and Group Discussion
  • Objective of Digital Transformation
  • Building a Digital Transformation Strategy<
  • Case Studies, Exercises and Group Discussion
  •  

Course Information

  • Course Date: 1 to 4 Mar 2021, 9:30am – 5:30pm (4 Lessons)
  • Duration: 4 Days / 32 Hours
  • Certification: Participants will be awarded a Certificate of Competency in Advanced Python Programming Professional upon meeting the requirements and passing the examination.
  • Who Should Attend: Anyone interested in acquiring the advanced knowledge and skills required for dashboard designing and API Integration using Python Programming
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Python Programming based on the syllabus covered

Pre-Requisite

  • It is preferred that participants have some knowledge in software development or successfully completed Python Programming Essentials (PPE).

Course Objective

  • Advanced Python Programming Professional (APPP) is designed for anyone interested in acquiring the advanced knowledge and skills required for dashboard designing and API Integration using Python Programming.
APPLY NOW

Programme Details

Module 1 Introduction to Python Programming

  • Introduction
  • Set up & Installation
  • Structure of Python Script with first Python Program
  • Basic Variables : Numbers, Strings
  • Lists & Dictionaries
  • If else Statements
  • Control Statements
  • Loops
  • Functions

Module 2 File-Handling

  • Introduction to File-Handling in Python
  • Creating a Text & Excel File
  • Writing Content to File
  • Appending Content to File
  • Renaming Files
  • Open & Save File

Module 3 Handling Errors and Exceptions

  • Types of Errors
  • Error Handling
  • Try and except
  • Hands-On and Practical Exercises

Module 4 Classes and Objects

  • Defining classes
  • Creating objects
  • Object Oriented Programming
  • Constructors
  • Inheritance
  • Creating instances

Module 5 Working with Modules

  • Packages
  • Modules

Module 6 Introduction to Dashboard Development

  • What is the objective of a Dashboard?
  • Creating a New Environment using Dash
  • Introduction to Dash
  • Developing a Multi-page Application

Module 7 Beginning Dashboard Development

  • Create a Data Selector Element
  • Develop your First Data Table
  • Changing of Dates Presented in Data Table
  • Calculating Changes in Metrics

Module 8 Advanced Dashboard Development

  • Development of a Download Data Link
  • Create a Second Data Table
  • Updating Graphs by Selecting Rows in a Dash Data table
  • Updating Graphs and Calculating Metrics Real-time

Module 9 Dashboard Deployment

  • Introduction to Plotly Graph Tools
  • Deployment of Dashboard
  • Introduction
  • Set up & Installation
  • Structure of Python Script with first Python Program
  • Basic Variables : Numbers, Strings
  • Lists & Dictionaries
  • If else Statements
  • Control Statements
  • Loops
  • Functions
  • Introduction to File-Handling in Python
  • Creating a Text & Excel File
  • Writing Content to File
  • Appending Content to File
  • Renaming Files
  • Open & Save File
  •  
  • Types of Errors
  • Error Handling
  • Try and except
  • Hands-On and Practical Exercises
  • Defining classes
  • Creating objects
  • Object Oriented Programming
  • Constructors
  • Inheritance
  • Creating instances
  • Packages
  • Modules
  • What is the objective of a Dashboard?
  • Creating a New Environment using Dash
  • Introduction to Dash
  • Developing a Multi-page Application
  • Create a Data Selector Element
  • Develop your First Data Table
  • Changing of Dates Presented in Data Table
  • Calculating Changes in Metrics
  • Development of a Download Data Link
  • Create a Second Data Table
  • Updating Graphs by Selecting Rows in a Dash Data table
  • Updating Graphs and Calculating Metrics Real-time
  • Introduction to Plotly Graph Tools
  • Deployment of Dashboard

Advanced Python Programming Professional (APPP) involves rigorous usage of real-time case studies, hands-on exercises and group discussion

Course Information

  • Course Date:
    • 29-31 March 2021, 9.30am – 5.30pm
  • Duration: 3-Days / 24 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Business Leaders, CEO, Directors, CTO, CSO, CISO, CIO, System Analyst, Technologist, System Engineer, IT Professionals and Anyone seeking to acquire advanced knowledge on Digital Transformation Analytics
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Digital Transformation based on the syllabus covered

Pre-Requisite

  • N/A

Course Objective

  • Acquire advanced knowledge and strategies required to lead and manage digital transformation in an organization
  • Learn how to design a Digital Transformation Framework based on your organizations specific requirements
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APPLY NOW

Programme Details

Module 1 Introduction to Digital Transformation

  • What is Digital Transformation?
  • Need for Digital Transformation
  • Benefits of Digital Transformation for Business
  • Key Elements to a Successful Digital Transformation
  • Common Mistakes in Digital Transformation
  • Designing a Mobile-First Strategy

Module 2 Industry 4.0 and Its Impact on Businesses

  • What is Industry 4.0?
  • Impact of Industry 4.0 on Businesses
  • Deep Dive into Artificial Intelligence
  • Understanding Data Analytics / Big Data
  • Cyber Security Overview
  • Cloud Computing Essentials
  • Social Media for Businesses
  • Opportunities / Challenges for Businesses

Module 3 Digital Transformation Framework

  • What is a Digital Transformation Framework
  • Key Components of a Digital Transformation Framework
  • Identify Your Customers and Their Pain Points
  • Design Your Digital Transformation Journey
  • Choosing the Right Framework for Your Organization

Module 4 Developing a Digital Transformation Framework

  • Redefine the Purpose of the Organization
  • Understand the Objective of Digital Transformation
  • Building a Digital Transformation Strategy
  • Data Governance and Cyber Security Policies
  • Disrupt Your Business Model
  • Identify and Digitally Transform Your Business Core
  • Choosing the Right Framework for Your Organization

Module 5 Data Strategies for Digital Transformation

  • Data Analytics Overview
  • Leverage on Data Analytics and Big Data
  • Establishing Target Metrics
  • Designing of a Data Analytics Strategy
  • Time-to-Market Analytics
  • Competitive Advantage Analytics
  • Human Capacity Analytics
  • Business Growth Analytics

Module 6 Developing an Eco-System Strategy

  • What is an Eco-System
  • Key Components of an Eco-System
  • Managing an Eco-System
  • Eco-system Beyond Borders
  • Achieving Successes Being Part of an Eco-System

Module 7 Putting Everything Together

  • Becoming an Inclusive Leader
  • Organization Cultural Change
  • Organization Management Methodologies
  • Putting Everything Together
  • Continuous Refinement of Framework
  • Creating a Business Model
  • What is Digital Transformation?
  • Need for Digital Transformation
  • Benefits of Digital Transformation for Business
  • Key Elements to a Successful Digital Transformation
  • Common Mistakes in Digital Transformation
  • Designing a Mobile-First Strategy
  • What is Industry 4.0?
  • Impact of Industry 4.0 on Businesses
  • Deep Dive into Artificial Intelligence
  • Understanding Data Analytics / Big Data
  • Cyber Security Overview
  • Cloud Computing Essentials
  • Social Media for Businesses
  • Opportunities / Challenges for Businesses
  • What is a Digital Transformation Framework
  • Key Components of a Digital Transformation Framework
  • Identify Your Customers and Their Pain Points
  • Design Your Digital Transformation Journey
  • Choosing the Right Framework for Your Organization
  • Redfine the Purpose of the Organization
  • Understand the Objective of Digital Transformation
  • Building a Digital Transformation Strategy
  • Data Governance and Cyber Security Policies
  • Disrupt Your Business Model
  • Identify and Digitally Transform Your Business Core
  • Choosing the Right Framework for Your Organization
  • Data Analytics Overview
  • Leverage on Data Analytics and Big Data
  • Establishing Target Metrics
  • Designing of a Data Analytics Strategy
  • Time-to-Market Analytics
  • Competitive Advantage Analytics
  • Human Capacity Analytics
  • Business Growth Analytics
  • What is an Eco-System
  • Key Components of an Eco-System
  • Managing an Eco-System
  • Eco-system Beyond Borders
  • Achieving Successes Being Part of an Eco-System
  • Becoming an Inclusive Leader
  • Organization Cultural Change
  • Organization Management Methodologies
  • Putting Everything Together
  • Continuous Refinement of Framework
  • Creating a Business Model

Certified Digital Transformation Professional (CDTP) involves rigorous usage of real-time case studies, role playing and group discussion

Course Information

  • Course Date:
    • 22 to 24 Mar, 2021, 9.30am – 5.30pm (3 Lessons)
    • 23, 25, 30 Mar, 1, 6, 8 Apr 2021, 7.00pm – 10.00pm (6 Lessons)
  • Duration: 3-days / 24 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Anyone interested in acquiring knowledge and skills on Python Programming
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Python Programming based on the syllabus covered

Pre-Requisite

  • N/A

Course Objective

  • Acquiring the essential knowledge and skills required to begin Python Programming.
  • Learn how to start developing applications in Python Programming through extensive practical / hands-on sessions.
APPLY NOW

Programme Details

Module 1 Introduction to Python Programming

  • What is Python Programming?
  • Setup & Installation
  • Understanding the Structure of Python Script with First Python Program
  • Basic Variables: Numbers , Strings
  • Lists & Dictionaries
  • If Else Statements
  • Control Statements
  • Loops
  • Functions

Module 2 File-Handling in Python

  • Introduction to File-Handling in Python
  • Creating a Text & Excel File
  • Writing Content to File
  • Appending Content to File
  • Renaming Files
  • Open & Save File

Module 3 Handling Errors and Exceptions in Python

  • Types of Errors
  • Error Handling
  • Try and Except

Module 4 Class and Objects in Python

  • Defining classes
  • Creating objects
  • O-O Programming
  • Constructors
  • Inheritance
  • Creating instances

Module 5 Working with Modules and Request with Python

  • Packages
  • Modules
  • Time & date module
  • Math Module, Numpy Module, Pandas Module
  • Sending mail
  • Sending SMS
  • What is Python Programming?
  • Setup & Installation
  • Understanding the Structure of Python Script with First Python Program
  • Basic Variables: Numbers , Strings
  • Lists & Dictionaries
  • If Else Statements
  • Control Statements
  • Loops
  • Functions
  • Introduction to File-Handling in Python
  • Creating a Text & Excel File
  • Writing Content to File
  • Appending Content to File
  • Renaming Files
  • Open & Save File
  • Types of Errors
  • Error Handling
  • Try and Except
  • Defining classes
  • Creating objects
  • O-O Programming
  • Constructors
  • Inheritance
  • Creating instances
  • Packages
  • Modules
  • Time & date module
  • Math Module, Numpy Module, Pandas Module
  • Sending mail
  • Sending SMS
  •  

Python Programming Essentials (PPE) involves rigorous usage of real-time case studies, hands-on exercises and group discussion

Course Information

  • Course Date: 
    • 22, 24, 29, 31 Mar 5, 7, 12, 14, 19, 21 Apr 2021, 7:00pm – 10:00pm (10 Lessons)
  • Duration:  4 days / 32 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Aspiring Data Scientist, Data Analyst, HR Analyst, and Anyone interested in pursuing a career in the areas of Business Analytics / Data Analytics. 
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Data Analytics and Python Programming based on the syllabus covered

Pre-Requisite

  • It is preferred that participants successfully completed and pass Python Programming Essentials (PPE)

Course Objective

  • Acquire the essential knowledge on how to use data analytics to make better business or organisational decisions.
  • Learn the different components of Data Analytics, Data Mining, Data Warehousing and Visualization using Python
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Programme Details

Module 1 Introduction to Data Analytics

  • Data Analytics Overview
  • Concepts of Data Analytics
  • Importance and Advantages of Data Analytics
  • Developing / Application of Data Analytics Strategies

Module 2 Different Types of Analytics and Application

  • Data Analytics Maturity Model
  • Understanding Descriptive, Predictive and Prescriptive Analytics
  • Different Application of analytics method
  • Concepts of Text Analytics and Web Analytics
  • Different Application of Analytics Methods

Module 3 Different Types of Analytics and Application

  • Data / information architecture
  • ETL Architecture
  • What is Data Warehouse
  • Business intelligence vs Data Analytics
  • Application of Analytics in an Organisation
  • Case Studies

Module 4 Deep Dive into Python Programming for Data Analytics

  • Introduction to Python Programming
  • Fundamentals of Python Programming for Data Analytics
  • Understanding Python Modules e.g. NumPy, Pandas, Matplotlib

Module 5 Data Mining and Processes for Data Analytics

  • Fundamentals of Data Mining
  • Objectives of Data Mining
  • Key aspects of Data Mining
  • Concepts of Knowledge Discovery in Databases (KDD)
  • Models in Data Mining
  • Data Mining Model vs Statistical Model
  • Data Mining Processes

Module 6 Data Mining Techniques

  • Descriptive Analytics: Clustering Models
  • Descriptive Analytics: Association Models
  • Descriptive Analytics: Visualisation
  • Predictive Analytics: Classification Models
  • Predictive Analytics: Regression Models

Module 7 Introduction to Machine Learning

  • Supervised Learning vs Unsupervised Learning
  • Linear Regression Analysis
  • Logistic Regression Analysis
  • Random Forest Analysis
  • Data Analytics Overview
  • Concepts of Data Analytics
  • Importance and Advantages of Data Analytics
  • Developing / Application of Data Analytics Strategies
  • Data Analytics Maturity Model
  • Understanding Descriptive, Predictive and Prescriptive Analytics
  • Different Application of analytics method
  • Concepts of Text Analytics and Web Analytics
  • Different Application of Analytics Methods
  • Data / information architecture
  • ETL Architecture
  • What is Data Warehouse
  • Business intelligence vs Data Analytics
  • Application of Analytics in an Organisation
  • Case Studies
  • Introduction to Python Programming
  • Fundamentals of Python Programming for Data Analytics
  • Understanding Python Modules e.g. NumPy, Pandas, Matplotlib
  •  
  • Fundamentals of Data Mining
  • Objectives of Data Mining
  • Key aspects of Data Mining
  • Concepts of Knowledge Discovery in Databases (KDD)
  • Models in Data Mining
  • Data Mining Model vs Statistical Model
  • Data Mining Processes
  • Descriptive Analytics: Clustering Models
  • Descriptive Analytics: Association Models
  • Descriptive Analytics: Visualisation
  • Predictive Analytics: Classification Models
  • Predictive Analytics: Regression Models
  • Supervised Learning vs Unsupervised Learning
  • Linear Regression Analysis
  • Logistic Regression Analysis
  • Random Forest Analysis

Data Analytics Essentials (DAE) involves rigorous usage of real-time case studies, hands-on exercises and group discussion

Course Information

  • Course Date: 
    • 22 to 25 Mar 2021, 9:30am to 5:30pm (4 Lessons)
  • Duration: 4-Days / 32 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Data Analyst, Finance Analyst, HR Analyst, Software Engineers, Database Administrator, CIO or Anyone interested in pursuing a career in Big Data, Data Analytics, and Data Engineering
  • Examination: Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Big Data and Hadoop based on the syllabus covered

Pre-Requisite

  • It is preferred that participants successfully completed and pass Data Analytics Essentials (DAE)

Course Objective

  • Acquiring advanced knowledge and skills on how to use Hadoop in Big Data to identify correlation and causation statistically valid models as part of their organisation strategic decision making and planning.
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Programme Details

Module 1 Data Types and Relational Database Management System (RDBMS)

  • Data Types & RDBMS Concepts
  • Structured Query Language – SQL Basics
  • Data Warehousing Concepts
  • Data Modeling Concepts

Module 2 Introduction to Big Data and Hadoop

  • Introduction to Big Data and Hadoop
  • Hadoop Architecture
  • Hadoop Deployment
  • Hadoop Troubleshooting
  • Introduction to Hadoop Distributed File System (HDFS)
  • Introduction to MapReduce

Module 3 Processing Data in Hadoop

  • Overview on MapReduce
  • When to Use MapReduce
  • Introduction to Spark
  • Components and Concept of Spark
  • Understanding Abstraction
  • Working with Pig
  • Overview on Hive
  • Apache Hive – Hive Query Language

Module 4 Fundamentals to NoSQL Data Management for Big Data

  • Introduction to NoSQL
  • Concepts of NoSQL
  • Key-Value Stores
  • Document Stores
  • Object Data Stores
  • Graph Databases

Module 5 Real-Time Data Processing with Hadoop

  • Understand Stream Processing
  • Introduction to Apache Storm
  • Understanding Storm Architecture and Topologies
  • Integration Storm with HDFS
  • Integration Storm with HBase

Module 6 Spark Streaming

  • Overview on Spark Streaming
  • Spark Streaming with Simple Count
  • Spark Streaming with Multiple Inputs
  • Maintaining State
  • Windowing in Spark Streaming
  • Streaming vs. ETL Code
  • Batch Analytics with Spark
  • SparkSQL and DataFrame
  • DataFrame APIs and SQL API

Module 7 Visualizing in Big Data

  • Big Data Visualization Tools
  • Quick Overview on the Different Chart Types
  • Using Python to Visualize Data
  • Data Types & RDBMS Concepts
  • Structured Query Language – SQL Basics
  • Data Warehousing Concepts
  • Data Modeling Concepts
  • Introduction to Big Data and Hadoop
  • Hadoop Architecture
  • Hadoop Deployment
  • Hadoop Troubleshooting
  • Introduction to Hadoop Distributed File System (HDFS)
  • Introduction to MapReduce
  • Understanding Abstraction
  • Working with Pig
  • Overview on Hive
  • Apache Hive – Hive Query Language
  • Design Your Digital Transformation Journey
  • Choosing the Right Framework for Your Organization
  • Introduction to NoSQL
  • Concepts of NoSQL
  • Key-Value Stores
  • Document Stores
  • Object Data Stores
  • Graph Databases
  • Understand Stream Processing
  • Introduction to Apache Storm
  • Understanding Storm Architecture and Topologies
  • Integration Storm with HDFS
  • Integration Storm with HBase
  • Overview on Spark Streaming
  • Spark Streaming with Simple Count
  • Spark Streaming with Multiple Inputs
  • Maintaining State
  • Windowing in Spark Streaming
  • Streaming vs. ETL Code
  • Batch Analytics with Spark
  • SparkSQL and DataFrame
  • DataFrame APIs and SQL API
  •  
  • Big Data Visualization Tools
  • Quick Overview on the Different Chart Types
  • Using Python to Visualize Data