An effort to prepare and equip the data science/bioinformatics community with competencies and skills in the field. This short course offers a plethora of 25 courses of choice, with a proposed pathway. This pathway is proposed for anyone who would like to embark in this journey, starting from essential, and then progress to beginner, intermediate and advanced level for each discipline. It is recommended to have completed each level before progressing to the next higher level.
The short course offers logistical flexibility with courses offered during evenings of the weekdays (6.30pm to 10pm, with half an hour dinner/prayer break), or weekends (either full/ half day) making it attractive even for the working adults as an avenue to upskill. The total duration of this programme is 24 hours (8 classes spanning over 2 months), providing the participants with enough time to digest the material covered, in particular familiarising themselves with the practical hands-on aspects.
This short course is offered four times in a year (four intakes: Jan-Feb; Apr-May; July-Aug; Oct-Nov), providing even greater flexibility so the participants can plan out a training path that suits their own schedule and learning pace.
If there are enough interests from a specific organisation/entity, we are open to the idea of conducting the bootcamp at their location and date/time of their convenience.
These DSxBootcamps are organised by Academic Medical Centre Sdn. Bhd. in-collaboration with Perdana University School of Data Sciences.

DSxBootcamp phases

Essential

Intermediate

Beginner

Advanced

DSxBootcamp (Bioinformatics)

Phase

Course

Introduction to R

 

  • Basic commands, reading and writing data, indexing
  • Data objects, summary statistics, visualisation
  • Useful statistical analyses: correlation analysis, linear regression
  • Useful statistical analyses: t-test, ANOVA
  • chi-squared test, Fisher’s test
  • Multivariate data analyses (PCA)
  • Capstone project (analysis of real data sets).

Who should attend: Anyone who is interested in developing basic R skills to analyse their own data.

Introduction to Python

  • The concept of programming language
  • Introduction, variable and expressions
  • Flow control
  • Repetition
  • Input/Output
  • Method, Biopython
  • Biopython I
  • Biopython II.

Who should attend: Anyone who is interested in developing basic computer programming skills for problem solving in Bioinformatics/Data Science.

Introduction to Linux and Shell Scripting

  • Introduction to Linux operating system
  • Basic command line interface
  • Linux utilities and text editor
  • Shell scripting (variable, array and expression)
  • Shell scripting (flow control and repetition)
  • Shell tricks for one-liner bioinformatics I
  • Shell tricks for one-liner bioinformatics II.

Who should attend: Anyone who is interested in developing Linux and shell scripting skills.

Introduction to Bioinformatics

  • Biological databases and tools
  • Sequence comparisons
  • Biological patterns and profiles
  • Molecular evolution
  • Structural biology
  • Genomic & next generation sequencing (NGS)
  • Proteomics
  • Network and pathway of bioinformatics.

Who should attend: Anyone who wants an introduction to the world of bioinformatics.

Structural Bioinformatics and Molecular Modelling

  • Fundamentals of biological structures I
  • Fundamentals of biological structures II
  • Fundamentals of three dimensional structure determination
  • Three dimensional data representation and visualization
  • Computational structure prediction I
  • Computational structure prediction II
  • Computational structure prediction III
  • Application.

Who should attend: Anyone who is interested in structural bioinformatics and learn to predict three dimensional (3D) protein structure using computational methods.

Molecular Mechanics and Docking

  • Fundamentals of biological structures
  • Empirical force field models: molecular mechanics I
  • Empirical force field models: molecular mechanics II
  • Macromolecular interactions
  • Fundamentals of molecular docking I
  • Fundamentals of molecular docking II
  • Virtual screening and drug designing
  • Application.

Who should attend: Anyone who wants to learn about various topics in molecular modelling and perform molecular docking studies.

Genome Informatics (NGS Analysis)

  • Introduction to high throughput sequencing
  • Inspection of sequence quality
  • Sequence alignment and assembly genomics
  • Comparative genomics & evolutionary analysis I
  • Comparative genomics & evolutionary analysis II
  • Metagenomics
  • Genome annotation
  • Computing resources for sequencing informatics.

Who should attend: Anyone who wants an introduction to the world of genomics analysis.

High-throughput and Ensemble-based Screening for Drug Discovery

  • Introduction on computer-aided drug discovery
  • Development of compound library and compound optimization
  • High-throughput virtual screening I
  • High-throughput virtual screening II
  • Ranking, interaction analysis and selection of potential hits
  • Connecting protein dynamics to ensemble conformations I
  • Connecting protein dynamics to ensemble conformations II
  • Clustering analysis and trajectory extraction.

Who should attend: Advanced course for those who already understands the fundamentals in docking and molecular dynamics simulation.

Applied Bioinformatics

  • Transcriptomics
  • Epigenomics & non-coding genome
  • Cancer genomics
  • Personal & medical genomics
  • Vaccine informatics
  • Immuno-informatics
  • Viral informatics
  • Data curation & visualization.

Who should attend: Anyone would like to embark on advanced level bioinformatics research and studies.

Membrane Protein Simulation

  • Biochemistry of lipid and phospholipid
  • About membrane protein
  • About molecular dynamics simulation I
  • About molecular dynamics simulation II
  • Modelling of the membrane protein using homology modelling I
  • Modelling of the membrane protein using homology modelling II
  • Embedding and optimization of membrane protein in the membrane bilayer
  • Embedding and optimization of membrane protein in the membrane bilayer II

Who should attend: Anyone who is interested in membrane protein modelling and dynamics simulation.

Modelling and Simulation of Gene Expression and Metabolism

  • Concepts of gene expression and its impact on metabolic pathways I
  • Concepts of gene expression and its impact on metabolic pathways II
  • Ordinary differential equation modelling and Petri net modelling
  • Mathematical modelling of gene expression
  • Mathematical modelling of gene regulation
  • Mathematical modelling of metabolic pathways
  • Principles in simulation
  • Sensitivity analysis of models.

Who should attend: Anyone interested in in silico analysis of gene expression and metabolism.

Molecular Dynamics and Simulation

  • Fundamentals of biological structures
  • Energy Minimization
  • Fundamentals of Molecular Simulations I
  • Fundamentals of Molecular Simulations II
  • Fundamentals of Molecular Dynamics Simulation I
  • Fundamentals of Molecular Dynamics Simulation II
  • Fundamentals of Molecular Dynamics Simulation III
  • Application.

Who should attend: Anyone who wants to know about molecular simulations and perform molecular dynamics simulation.

DSxBootcamp (Data Sciences)

Phase

Course

Introduction to R

  • Basic commands, reading and writing data, indexing
  • Data objects, summary statistics, visualisation
  • Useful statistical analyses: correlation analysis, linear regression
  • Useful statistical analyses: t-test, ANOVA
  • chi-squared test, Fisher’s test
  • Multivariate data analyses (PCA)
  • Capstone project (analysis of real data sets).

Who should attend: Anyone who is interested in developing basic R skills to analyse their own data.

Introduction to Python

  • The concept of programming language
  • Introduction, variable and expressions
  • Flow control
  • Repetition
  • Input/Output
  • Method, Biopython
  • Biopython I
  • Biopython II.

Who should attend: Anyone who is interested in developing basic computer programming skills for problem solving in Bioinformatics/Data Science.

Introduction to Linux and Shell Scripting

  • Introduction to Linux operating system
  • Basic command line interface
  • Linux utilities and text editor
  • Shell scripting (variable, array and expression)
  • Shell scripting (flow control and repetition)
  • Shell tricks for one-liner command I
  • Shell tricks for one-liner command II.

Who should attend: Anyone who is interested in developing Linux and shell scripting skills.

Introduction to Data Science

  • Introduction to data science toolkits
  • Applications of big data
  • Data analytics essential
  • Data visualisation
  • Data mining
  • Machine learning
  • Web programming and scraping
  • Application.

Big Data

  • Introduction 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.

Data Analytics Essentials

  • Workflow: Basics, scripts and projects
  • Data transformation
  • Exploratory data analysis & data visualization
  • Tibbles, data import and tidy data
  • Relational data, strings and factors
  • Pipes, vectors and iteration
  • Model basics and model building.

Data Driven Organizations

  • Introduction to data driven organizations (DDO) I
  • Introduction to data driven organizations (DDO) II
  • DDO maturity model and levels I
  • DDO maturity model and levels II
  • DDO Design principles and framework I
  • DDO Design principles and framework II
  • Case study I
  • Case study II.

Web Programming and Scraping

  • 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).

Data Visualization

  • Introduction of data visualization
  • Python and Javascript for visualization
  • Value of visualization
  • Principles of perception, color, design, and evaluation
  • Statistical Graph, multivariate data visualization
  • Text data visualization
  • Interactivity and animation
  • Temporal, multi-dimensional, hierarchical and network data visualization.

Machine Learning

 

  • 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.

 

Digital Marketing

  • Principles of marketing
  • Marketing management
  • Marketing strategies
  • Digital marketing
  • Digital marketing strategies
  • Digital marketing data analytics
  • Digital marketing program development and execution I
  • Digital marketing program development and execution II.

Business Intelligence

  • 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.

Health Analytics and Data Mining

  • Health informatics
  • Data standards & regulations in health information systems
  • Mining healthcare data
  • Exploring healthcare data
  • Classification of healthcare data
  • Association and cluster analysis
  • Anomaly detection
  • Health Analytics.

Dimensionality Reduction

  • 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.

Econometrics

  • Introduction to Economics
  • Microeconomics
  • Macroeconomics
  • Introduction to econometrics
  • Regression analysis on cross sectional data I
  • Regression analysis on cross sectional data II
  • Regression analysis on time series data I
  • Regression analysis on time series data II.

Applied Regression & Time Series

  • Simple linear regression model
  • Model diagnostics
  • Multiple linear regression model
  • Logistic regression
  • Introduction to Time series data
  • ARIMA models
  • Forecasting.

Features

4 intakes per year

12 courses per intake

Consultation session

1 hour optional per course

hands-on training

Evening/weekend classes

Evening/weekend class to suit working individuals

*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.

Fee & payment information

SAVE UP TO 50% REGISTRATION WAIVER!

Contact us to learn more

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Student

MYR 1,500 Per course

Academic

MYR 2,000 Per course

Corporate

MYR 2,500 Per course

PAYMENT MODE

Payment mode 1: Telegraphic Transfer / Bank Draft

Please make your payment to:

CIMB Bank Berhad, Account Name : PERDANA UNIVERSITY
Account No : 8001539175
Bank Address : CIMB Bank Berhad, Plaza Damansara Branch, Ground Floor, Menara SBB 83,Medan Setia 1, Bukit Damansara, 50490 Kuala Lumpur, MALAYSIA.
Swift Code: CIBBMYKL

Please add special annotation in your payment details:  “DSxBootcamp 2019”, “Participant’s name”.

Payment mode 2: Cheque / Online Banking

An invoice will be issued once the registration is completed.

Please send the cheque and bank draft to the CIMB Bank Berhad

Account Name : PERDANA UNIVERSITY
Account No : 8001539175

Please add special annotation in your cheque or when online banking: “DSxBootcamp 2019”, “Participant’s name”.

PAYMENT CONFIRMATION

Upon completion, please email us the proof of payment to [email protected]

CANCELLATION POLICY

  1. Any cancellation of registration must be submitted in writing to the Organizing Committee.
  2. There will be a partial refund (50%) of registration fee for cancellation made ONE MONTH before the bootcamp date.
  3. There will be a partial refund (30%) of registration fee for cancellation made TWO WEEKS before the bootcamp date.
  4. There will be no refund of registration fee for cancellations made ONE WEEK before the bootcamp date, however a substitute participant will be welcomed.
  5. A full refund will be made in the event that the registered course(s) is(are) cancelled by the Organizer.

Should you have any questions about the bootcamp registration, kindly drop us an email.

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Frequently Asked Questions (FAQs)

How many hours are allocated for each course?

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.

Is there any certificate to be given to the participants after the course?

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

What is the maximum number of participants in each class?

Maximum of 20 participants

When is the closing date for application?

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.

I am an international applicant. Upon arriving in Kuala Lumpur, is there any person in charge from the University that will welcome the participants at the airport?

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.

Can I apply University accommodation?

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.

How much does the accommodation cost (estimation)?

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.