Data Science Comprehensive Training

Your working lives are flooded by large amounts of information, but not all of them are useful data. Therefore, it is essential for us to learn how to apply data science into every aspect of our daily lives from personal finances, reading and lifestyle habits, to making informed business decisions. In this course you will learn how to leverage on data to ease life or unlock new economic value for a business. This course is a hands-on guided course for you to learn the concepts, tools, and techniques that you need to begin learning data science. We will cover the key topics from data science to Big Data, and the processes of gathering, cleaning and handling data. This course has a good balance between theory and practical applications, and key concepts are taught using case study references. Upon completion, participants will be able to perform basic data handling tasks, collect and analyse data, and present them using industry standard tools.

  • 5 Days Workshop
  • Completion Certificate awarded by GKK

  • BASED ON REQUEST
  • Please contact us directly for more details

DAY1


Introduction to Data Science

What is Data?
Types of Data
What is Data Science?
Knowledge Check
Lab Activity

Data Science Workflow

Data Gathering
Data Preparation & Cleansing
Data Analysis – Descriptive, Predictive, & Prescriptive

What are the course objectives?

Identify the appropriate model for different data types.
Create your own data process and analysis workflow.
Define and explain the key concepts and models relevant to data science.
Differentiate key data ETL process, from cleaning, processing to visualisation.
Implement algorithms to extract information from dataset.
Apply best practices in data science and become familiar with standard tools.
Data Visualisation & Model Deployment
Knowledge Check

Life of a Data Scientist

What is a Data Scientist?
Data Scientist Roles
What Does a Data Scientist Look Like?
T-Shaped Skillset
Data Scientist Roadmap
Data Scientist Education Framework
Thinking like a Data Scientist
Knowns & Unknowns
Demand & Opportunity
Labour Market
Applications of Data Science
Data Science Principles
Data-Driven Organisation
Developing Data Products
Knowledge Check

Data Gathering

Obtaining Data from Online Repositories
Importing Data from Local File Formats (json, xml)
Importing Data Using Web API
Scraping Website for Data
Knowledge Check

DAY2


Data Science Prerequisites

Probability and Statistics
Linear Algebra
Calculus
Combinatorics
Programming

Beginning Databases

Types of Databases
Relational Databases
NoSQL
Hybrid Databases
Lab Activity

Structured Query Language (SQL)

Performing CRUD (Create, Retrieve, Update, Delete)
Designing a Real World Database
Normalising a Table
Knowledge Check
Lab Activity

Introduction to Python

Basics of Python Language
Functions and Packages
Python Lists
Functional Programming in Python
Numpy & Scipy
iPython
Knowledge Check
Lab Activity: Exploring Data Using Python

DAY3


Data Preparation & Cleansing

Extract, Transform & Load (ETL) – Pentaho, Talend, etc.
Data Cleansing with OpenRefine
Aggregation, Filtering, Sorting, & Joining
Knowledge Check
Lab Activity

Data Quality

Raw vs Tidy Data
Key Features of Data Quality
Maintenance of Data Quality
Data Profiling
Data Completeness & Consistency

Exploratory Data Analysis (Descriptive)

What is EDA?
Goals of EDA
The Role of Graphics
Handling Outliers
Dimension Reduction

Introduction to R

Packages for Data Import, Wrangling, & Visualization
Conditionals & Control Flow
Loops & Functions
Knowledge Check
Lab Activity
Lab: Exploring Data Using R

DAY4


Machine Learning (Predictive)

Bayes’ Theorem
Information Theory
Natural Language Processing
Statistical Algorithms
Stochastic Algorithms

Introduction to Text Mining

What is Text Mining?
Natural Language Processing
Pre-Processing Text Data
Extracting Features from Documents
Using BeautifulSoup
Measuring Document Similarity
Knowledge Check
Lab Activity

Supervised, Unsupervised, & Semi-Supervised Learning

What is Prediction?
Sampling, Training Set, & Testing Set
Constructing a Decision Tree
Knowledge Check
Lab Activity

DAY5


Data Visualisation

Choosing the Right Visualisation
Plotting Data Using Python Libraries
Plotting Data Using R
Using Jupyter Notebook to Validate Scripts
Knowledge Check
Lab Activity

Data Analysis Presentation

Using Markdown Language
Converting Your Data Into Slides
Data Presentation Techniques
The Pitfall of Data Analysis
Knowledge Check
Lab Activity
Group Presentation Lab: Mini Project

Big Data Landscape

What is Small Data?
What is Big Data?
Big Data Analytics vs Data Science
Key Elements in Big Data
Extracting Values from Big Data
Challenges in Big Data

Big Data Tools & Applications

Introducing Hadoop Ecosystem
Cloudera vs Hortonworks
Real World Big Data Applications
Knowledge Check
Group Discussion

What are the course objectives?


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Who should take the course?


  • This workshop is intended for individuals who are interested in learning data science, or who want to begin their career as a data scientist.

    All participants should have a basic understanding of data, relations, and mathematics.

Who is your trainer for the program?


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We offer the following options:


  • Cash
  • HRDF Claimable
  • Maybank Ezpay (Up to 24 months @ 0% Interest)
  • CIMB Easy Pay (Up to 12 months @ 0% Interest)
  • Cash Installment (Case by case basis)

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