Introduction to Data Science

Track: Enterprise Data Practitioner (EDP)
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.

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

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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;
  1. Understand what Data Science is and the skill sets needed to be a data analytics professional
  2. Understand the Data Science process and how its components interact
  3. 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.

CADS Certification​

Earn certification upon completion.

Minimum Qualification:
Undergraduate Degree

Training Track

Enterprise Data Practitioner (EDP)

Introduction to Data Science is one of the modules under our Enterprise Data Practitioner (EDP). EDP is an 8-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

  1. Overview –
    • Overview of Data Science
    • The Needs for Data Science
    • Responsibilities of data scientists
    • Roles of Data Scientists in Academia andIndustry
    • Data science Process
  2. 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)
  3. Data Analysis and Getting Started with Programming –
    • Categories of Data Analysis
    • Data Analysis Steps
    • Basic Concepts to Programming (Data Types, Operations and Structures)
  4. Data Visualization –
    • The Need to Visualize Data
    • Design Considerations; Graphs and Charts
  5. 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 Practitioner

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

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Introduction to Data Science