Introduction to Data Science

Track: Enterprise Data Analyst (EDA)

Being a data scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases and other branches of computer science along with a good understanding of the craft of problem formulation to engineer effective solutions.

Duration: 1 day
Fee: Request
Start Date: Request

Data Science is the study of the generalizable extraction of knowledge from data

This course will introduce students to a rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset. Students will learn the concepts, techniques and tools needed to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation and effective communication.

Learning outcome:

Upon completion, participants should be able to demonstrate each of the following;

Understand what Data Science is and the skill sets needed to be a data analytics professional
Understand the Data Science process and how its components interact
Understand the importance of effective communication for a data scientist

Who should attend:

Professionals that work with data

1 day of in depth learning

Face to face with experienced Data Scientist.

Course Methodology

This course will utilize a combination of Presentations and Workshops.

Pre-requisite:
N/A

HRDF Claimable

HDRF claimable. EPP option available for selected credit card.

CADS Certification

Earn certification upon completion.

Minimum Qualification:
Undergraduate Degree

Training Track

Enterprise Data Analyst (EDA)

Introduction to Data Science is one of the modules under our Enterprise Data Analyst (EDA). EDA is a 28-day training program that provides analysts with the tools to be immediate contributors to a data science team. They will assist to frame business requirements as analytics models.

Details of Subject

Overview –
- Overview of Data Science
- The Needs for Data Science
- Responsibilities of data scientists
- Roles of Data Scientists in Academia andIndustry
- Data science Process
Dealing with Data –
- Overview of Data Model
- Data Characterization
- Data Cleaning (The Need for Data Cleaning,Data Quality)
- Data Integration (Problems and Solutions)
- Data Storage (Models, Scalability Basics andChallenges)
Data Analysis and Getting Started with Programming –
- Categories of Data Analysis
- Data Analysis Steps
- Basic Concepts to Programming (Data Types, Operations and Structures)
Data Visualization –
- The Need to Visualize Data
- Design Considerations; Graphs and Charts
Ethics, security and privacy issues related to data science –
- Definition of ethics, security and privacy
- Importance of ethics and privacy
- Big Data best practices, Code of Conduct for data scientists

Lead Instructor

Dr. Jeremy Ooi

Dr. Jeremy’s PhD thesis was on the development of state estimation schemes using a popular conceptual tool known as the Sliding Mode Observer. It has elements of predictive analytics in the area of condition monitoring on account of observers having a primary function of using measurable signals to estimate unknown states for a given model of a system. Through data analytics, organizations gain valuable actionable insights. Dr. Jeremy’s PhD study was in the field of intelligent systems, and he is currently passionate about using intelligent systems for predictive analytics in the mid-stream of data science routine that combines “artificial” prowess of a machine and biological astuteness of a human person- to-drive improvements.

CADS Certification

EDA CADS Certified Enterprise Data Analyst

Certification for this module & track will be made available soon.

Hear from Our Alumni

The training has benefited me a lot with the skills required to be a data analyst and has upskilled me in data analysis. What I like about the program is where real life problems in the office is used as the materials during the trainings which provides more understanding of the topics. Eventually, it has broaden up my market in the career development.

The program started by setting an expectation on Day 1, that it is not a Read-And-Pass syllabus. The beauty of this program is that it requires the trainees to get their hands dirty and do the actual work throughout the course. It is indeed very different from a lot of programs in the market which just produces a certificate in the end without the trainee actually able to do some real stuff. The course is definitely benefiting anyone who really want to do Machine Learning and Data Science, by creating talents who are able to connect human thought with the machine algorithm, being the bridge within the corporate, into the digital worlds.

The training was beneficial as it trains myself to think from another perspective - one that may not be new, but wasn't practiced as often as I'd like. It was fun and interactive thanks to the facilitator, Jay, too. He was very helpful to answer any questions the attendees have. Believe that this training gives the attendees an extra edge by giving them the knowledge and perspective on how to better utilize data to what they want/need to do - for the organization, the business, and even for personal use, of which I believe will bring myself further in my career.

The training has been an eye opener for me. As someone who is not of programming background, it introduces me to the possibilities of what data can do. It has also helped me bridge the difference in thinking between the Business Team and the Data Science Team. Had a great time learning!

This training enhance on my skill of analyzing of using another tools of programming instead of excel and how to model and visualized our data in a better way instead of our traditional way. Especially when involve with a large scale of data. If using excel, there will a lot of time consuming there instead of R. For me, in market nowadays of big data, this course will brings more value and create another point of view on how to visualize our data especially in making any decision. And of course will enhance our career development.

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Introduction to Data Science

Introduction to Data Science

Data Science is the study of the generalizable extraction of knowledge from data

Learning outcome: