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.

Unlock the wealth of fortune with Data Science

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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 importance of effective communication for a data practitioner
  3. Understand the fundamentals and capabilities of Big Data
  4. Learn the data science process: acquire, wrangle, analyze, model, visualize, share

Who should attend:

Professionals that work with Data & Excel

1 day of in depth learning

Face to face with experienced Data Scientist.

Course Methodology

This course will utilize a combination of Lectures and Workshops.

CADS Certification​

Earn certification upon completion.

Pre-requisite:
N/A
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) programme. EDP is a nine-day training program that super-charges Business Intelligence analysts with new skills to analyse and communicate insights effectively.

Details of Subject

Overview – Explore the basics of Data Science
  • Overview of Data Science
  • The Needs for Data Science
  • Responsibilities of data scientists
  • Data Science Process
Dealing with Data – Explore the data with data mining techniques
  • Acquiring data
  • Wrangling 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 and Challenges)
Data Analysis – Explore analyze data method
  • Categories of Data Analysis
  • Data Analysis Process
  • Learn the methods of data analysis such as Statistics and Machine Learning
Data Visualization and Storytelling – Learn to make the data understandable to all
  • Visualizing data into knowledge
  • Adding wisdom with storytelling
  • The Need to Visualize Data
  • Design Considerations;
  • Graphs and Charts
The Data Science Industry Learn to utilize the skills, tools, and talent to build a Data Science team
Applications of Big Data in Business Learn the capabilities of Big Data and managing expectations on what Big Data can deliver to help your bottom line.

Lead Instructor

Dr. Vinod A. R. Ramachandran
Dr. Vinod Ramachandran is a certified Data Science specialist with 10 years of Data Modelling experience. His expertise also covers Statistics and Computational Intelligence where he researched the implementation of computational algorithms and techniques in the medical field (A full list of his publications is available at www.goo.gl/S2CC2i). On top of that, Dr. Vinod has also been granted a patent for his work on applying Genetic Algorithms in modelling Epileptic Seizures. Currently, his interests revolve around not only Data Science but also Geometric Modelling and Topological Data analysis.

CADS Certification

EDP CADS Certified Intro to Data Science

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.

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Intro to Data Science