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Tel: +60389418646
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Data Sciences Bootcamp 2019

Features

  • 4 intakes per year
  • 12 courses per intake
  • 1 hour optional weekend consultation session per course
  • Work on real world datasets
  • Evening classes (6.30pm – 10.00pm) to suit working individuals

Who Should Attend ?

  • Students (diploma, undergraduate and postgraduate) and academics
  • Data analyst, data entry or database administrator
  • Statistician
  • IT professional
  • Marketing and sales
  • Industry partner
  • Policy maker

Each course spans over eight (8) weeks; includes informative and interactive lectures, discussion, practicals, and activities. Participants will be guided in the application of what they have learnt during the Bootcamp to the real world data and scenario(s). Below are the structures of courses offered in our Bootcamp:

Course name Applied Regression & Time Series
Description This course emphasizes the concepts and the analysis of data sets with the application of key concepts, which are simple linear regression, multiple regression and matrix operations. Besides familiarise these two approaches, all the participants will reinforce the concepts in the real data sets and explore as well as evaluate the suitability of the selected regression variables and geometric interpretations.
Topics
  • Simple linear regression model
  • Model diagnostics
  • Multiple linear regression model, logistic regression
  • Introduction to Time series data
  • ARIMA models
  • Forecasting
Course structure     
The skills/tools you will gain/learn R, Linear Regression, CART

 

 

Course name Big Data
Description With the voluminous amount of data, this course aims to prepare the participants to enable dealing with large-scale data analysis. The patterns, trends and associated technologies will be exposed to all participants with both theoretical and hand-on approaches. So that, at the end of the course, they able to understand the application of big data in emerging areas of technology.
Topics
  • Intro to Big Data
  • Big Data Storage
  • Handling Big Data using Apache tools
  • Analysis Techniques of Big Data in Python
  • Big Data Strategy
  • Big Data in Commerce and Internet of Things
  • Big Data Applications in Bioinformatics
Course structure   
The skills/tools you will gain/learn Apache Spark, Hadoop

 

 

Course name Business Intelligence
Description Business Intelligence plays a central role in an organisation to handle large amounts of data and information as well as assists for the decision-making. With a brief understanding of BI concepts and principles, this course provides a demonstration platform to enable all participants to appreciate the business development model through the lens of a data-driven organisation.
Topics
  • Overview of Business Intelligence
  • Business Intelligence Life Cycle
  • Foundation and Technologies for Decision Making
  • Business Analytics
  • Model Based Decision Making
  • Business Strategy and Road Map
  • Knowledge management in Big Data startups and emulation
Course structure   
The skills/tools you will gain/learn BI Analytics Software

 

 

Course name Data Analytics Essentials
Description It is a pretty great success in any field by turning data into clear insights that support effective action, problem-solving and decision making. This course is intended to provide a better platform to apply the data analytics and statistical computing skills in real   data besides preparing all participants with a brief idea of data visualisation and data preparation for analysis.
Topics
  • Workflow: Basics, scripts and projects
  • Data transformation
  • Exploratory Data Analysis & Data Visualisation
  • Tibbles, Data import and Tidy data
  • Relational data, Strings and Factors
  • Pipes, vectors and  Iteration
  • Model basics and Model building
Course structure   
The skills/tools you will gain/learn R, tibbles, Python, pandas

 

 

Course name Data Driven Organisation (DDO)
Description Robust data in an organization are critical for data analysis and decision making. This course will introduce students to the shift in organizations in being data driven, leveraging on technological convergence to propel their vision and mission by developing the necessary skills to incorporate the results of data analysis within the decision-making process.
Topics
  • Introduction to Data Driven Organizations (DDO)
  • DDO maturity model and levels
  • DDO Design principles and framework
  • Case study
Course structure   
The skills/tools you will gain/learn Conceptual framework for DDO

 

 

Course name Data Visualization
Description Data visualization is an important visual method for effective communication and analyzing large datasets. A well-designed visual encoding can supplant cognitive calculations with simpler perceptual inferences and improve comprehension, memory, and decision making. Through this course, all the participants will get the exposure of the visual techniques and visual aids application.
Topics
  • Introduction of data visualization
  • Python and Javascript for visualization
  • Value of Visualization
  • Principles of perception, colour, design, and evaluation
  • Statistical Graph, multivariate data visualization
  • Text data visualization
  • Interactivity and Animation
  • Temporal, Multi-dimensional, Hierarchical and Network Data Visualization
Course structure
The skills/tools you will gain/learn ggplot, matplotlib, d3.js

 

 

 

Course name Digital Marketing
Description Digital Marketing or data driven marketing is product and services marketing using digital technologies. Understanding of sales and marketing function, use of contemporary digital tools and data analytics is needed to develop and operate a digital marketing program. This course will provide all the participants with a brief exposure to development, use of digital technologies and data analytics in sales and marketing function.
Topics
  • Principles of Marketing
  • Marketing Management
  • Marketing Strategies
  • Digital Marketing
  • Digital Marketing Strategies
  • Digital Marketing Data Analytics
  • Digital Marketing Program Development and Execution
Course structure   
The skills/tools you will gain/learn Marketing principles, Strategies

 

 

Course name Dimensionality Reduction
Description Dimensionality reduction is often performed in machine learning and data mining on big data. This course aims to guide all participants the awareness of main approaches and the issues. A better understanding of its algorithms and techniques to real datasets will enable the participants to appreciate the importance of this course.
Topics
  • Introduction to high dimensional data
  • Feature selection
  • Feature extraction by linear methods
  • Principal Component Analysis (PCA) & Kernel PCA
  • Other linear transformation
  • Feature extraction by non-linear methods
  • ISOMAP, Locally Linear Embedding (LLE), Self Organizing Maps (SOM)
  • Visualization for data pre-processing
Course structure   
Applied algorithms for dimensionality reduction

 

 

Course name Econometrics
Description With the concept of applying the statistical and mathematical analysis of economic data, this course provides a platform for all the participants with the knowledge of empirical content to economic relations. In particular, this course will equip the participants with the fundamentals of economics and application of mathematical tools in the field.
Topics
  • Introduction to Economics
  • Microeconomics
  • Macroeconomics
  • Introduction to econometrics
  • Regression analysis on cross sectional data
  • Regression analysis on time series data
Course structure   
The skills/tools you will gain/learn Econometrics and analysis methods

 

 

Course name Health Analytics and Data Mining
Description Health analytics plays a vital role in the healthcare field to efficiently extract and capture as much as possible fruitful information due to the tremendous public health data. This course provides a foundational knowledge of analytics with covering health information systems, data mining methods and strategies in performing data analysis. The ultimate goal of this course equips all participants to effectively contribute their analytics knowledge towards healthcare project to improve the healthcare.
Topics
  • Health Informatics
  • Data Standards & Regulations in Health Information Systems
  • Mining Healthcare Data I
    • Exploring healthcare data
    • Classification of healthcare data
  • Mining Healthcare Data II
    • Association analysis
    • Cluster analysis
    • Anomaly detection
  • Health Analytics
Course structure   
The skills/tools you will gain/learn PCA, data mining techniques, and machine learning

 

 

Course name Introduction to Data Science and Toolkits
Description Data Science is the study of the generalized extraction of knowledge from data. This foundation course aims to provide a brief and clear understanding of data science’s ideas or concepts and the toolbox as well in both conceptual framework and hands-on practical. This course will equip the participants with effective problem solving skills to prepare them deal with various data science problems out there.
Topics
  • Introduction to Data Science
  • Network Analysis
  • Recommender Systems
  • Cross-industry Standard Process for Data Mining
  • Advanced Analytics
  • Predictive and Prescriptive Analytics
  • Toolboxes for Data Scientists
  • Model Building Tools & Implementing into Production
Course structure   
The skills/tools you will gain/learn Graphs, Matrix factorisation, Python and R for Data Science

 

 

Course name Machine Learning
Description Machine learning is an emerging field of artificial intelligence that interfaces statistics and computer science. This course will provide the participants a platform to understand the ideas, develop the machine learning skills and apply the knowledge in life science/healthcare especially those popular research field of pharma and medicine include precision medicine and epidemic outbreak prediction. This algorithm allows the computer systems to learn from data and make decisions with minimal human intervention.
Topics
  • Introduction to machine learning
  • Data transformations & Feature Selection
  • Algorithms: The Basic Method
  • Credibility: Evaluating What’s Been Learned
  • Supervised learning (Neural networks, Probabilistic methods)
  • Unsupervised learning
  • Ensemble Learning
  • Natural Language Processing (KIV)
  • Case Study
Course structure    
The skills/tools you will gain/learn Hierarchical Clustering, Linear regression, Neural networks

 

 

Course name Web Programming and Scraping
Description As web development has become an essential step, this course is designed to guide all the participants on a path towards the study of web development and design using HTML, CSS, API etc. With that knowledge, the participants able to execute data fetching and extraction from World Wide Web.
Topics
  • Introduction to Web Programming
  • Markup Language Elements
  • Cascading style sheets (CSS)
  • Javascript programming
  • Introduction to web scraping
  • Advanced web parsing
  • Application programming interface (API)
  • Data storage and cleaning (SQL)
Course structure    
The skills/tools you will gain/learn Scrapy

 

*Bootcamp courses/topics are subject to changes as Data Sciences is an emerging field.

*Participants are advised to bring your own device(s).

*There’s an additional one hour optional consultation session on the weekend to discuss the problems that the participants may have experienced when doing the practical.

INTAKES

Intake 1
(Jan – Feb)
Intake 2
(Apr – May)
Intake 3
(Jul – Aug)
Intake 4
(Oct – Nov)
Day
Introduction to Data Science and Toolkits

Monday

Dimensionality Reduction
Big Data
Data Analytics Essentials

Tuesday

Web Programming and Scraping
Applied Regression & Time Series
Machine Learning

Wednesday

Health Analytics and Data Mining
Econometrics
Data Visualization

Thursday

Data Driven Organisation (DDO)
Digital Marketing

Friday

Business Intelligence


FEE

Category Rate (MYR) per course
Student 1,000
Academic 1,500
Corporate 2,000

Venue

Block B, MAEPS Building, MARDI Complex
Jalan MAEPS Perdana, 43400 Serdang
Selangor, Malaysia.


Job Prospects

  • Data Analyst
  • Data Engineer
  • Data Scientist
  • Data Journalist
  • Business Analyst
  • SQL Developer
  • Systems Engineer
  • Database Administrator
  • Big Data Engineer
  • Business Intelligence Analyst
  • Research Analyst
  • Software Engineer

Frequently Asked Questions (FAQ)

The number of hours are as below and are the same for each course:

No. of face-to-face hours per course = 8 topics x 4 hours = 32 hours
No. of self-study hours per course = 8 topics x 6 hours = 48 hours

Total hours per course = 80 hours (over a period of two months)

Implementation:

For each topic of each course, we will conduct a two-hour lecture session (6.30-7.30pm, with dinner/prayer break from 7.30-8.00pm, and continuation of lecture from 8.00-9.00pm) on the designated course day (see Table in Query #1 above), followed by an hour of practical on the same day. The students are to use the remaining weekdays for completion of the practical and self-study. An additional one hour (optional) consultation session will be conducted every Saturday (a time convenient for the students to be finalised on the first day of class) to discuss any problems that the students may have encountered during the practical or other questions related to the practical/lecture.

Example: All the topics of course 1 will be taught on Mondays. For each topic, the class will start with a two-hour lecture, followed by one hour practical. Students then spend the remaining weekdays to complete the practical and do a self-study on the lecture material covered. The students will meet the instructor again on Saturdays for an hour of consultation on the practical/lecture or other related questions.

Yes, a certificate of participation will be given to each participant of the course.

Registrations are open till a day before the start of the intake month (e.g. last day of Feb for March intake). However, registrations are on a first-come, first-served basis and will be closed if the maximum number of participants is reached.

We can make arrangements for this if a request is made earlier. Also, it would be preferred if the students travelled together, so multiple trips can be avoided. Alternatively, public transports are convenient and the students can opt for KLIA Ekspres, bus or budget taxi to reach their accommodation place.

The bootcamp will take place at Perdana University, which is located in Serdang.
 
The participants can opt to stay at Perdana University-arranged accommodations. Our staff will be able to provide you with information on the availability of the hostel rooms and the cost involved. We cannot guarantee a placement for the students in the hostel, but we will try to help them find an affordable accommodation, with access to public transport. Budget hotels at a reasonable cost are plenty, and the cost can be reduced if the students are prepared to share the room. The transport between their accommodation and Perdana University has to be taken care by the students themselves.
 
If the students stay in the Perdana University-arranged accommodations, the cost will also include transport to Perdana University (only) early in the morning and back in the evening. Students planning to also do research attachment with us, besides the bootcamp, can take advantage of this to travel to Perdana University during the day for discussion with the supervisor.

The cost is estimated to be between RM375 and RM500 per month depending on the size of the bedroom. Please contact Student Services Department for more information.


Academic Team

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