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DATA SCIENCE FOR EXECUTIVES

HRDF claimable courses at i 4 academy

FAST FACTS

Level: C Level & Managers

Duration: 2 Days

Price:RM 3000  (Early Bird Offer RM 2500)

DATE: September 30-October 1 2017  & October 27-October 28

Programme:
Certificate Of Attendance

Overview

The new business environment is played on a cloud based infrastructure where both internal and external data is readily available and companies must utilise this data for growth and resilience. This course will provide an understanding on the importance of Big Data Analytics and why it is needed for your business to improve revenue, boost productivity, understand and predict customer needs and create new business opportunities. This course is geared for executives whose requirements are to understand the fundamental models of data science without programming skills.

Data Science has the second highest impact on modern businesses; the first impact being the introduction of computers. Having said that, one of the very many hurdles faced by businesses are the entry barriers due to a lack of know-how, organisational inertia, difficulties in hiring the right manpower, and the apparent need for upfront commitment, and more.

Data Science functions within your organisation, taking the anxiety-factor out of the Big Data revolution and demonstrating how data-driven decision-making can be integrated into ones’ organisation to harness existing advantages and to create new opportunities.

Assuming minimal prior knowledge, this course provides complete coverage of the key aspects, including data wrangling, modelling and analysis, predictive-, descriptive- and prescriptive-analytics, data management and curation, standards for data storage and analysis, the use of structured, semi-structured and unstructured data of open public data, and the data-analytic value chain, all covered at a functional level.

The past several years have been marked by a paradigm shift in the role of data in organisations. Whereas, historically, data was retained for compliance reasons or where needed for day-to-day business operations, the advent of cheap, readily-available storage options has made organisations more inclined not to erase stored data, and the boom of equally-cheap, equally-available processing power has opened the door to advanced analytics on this stored data, unlocking the business value hidden in the bits.

Today, this trend has been taken to its extremes: data is collected by any available means, far beyond what is necessary for standard business operations or, indeed, beyond information that has clearly-defined future uses; deep analysis is done both retrospectively and on-the-fly, often driving split-second business decisions; insights are and gained by combining otherwise unrelated – and historically soiled – data sets, including ones that are publicly available or that are purchasable, such as social media archives or demographic data; and long-held rules-of-thumb are being systematically replaced by quantitatively-superior data-driven decision-making. Use of data analysis has become ubiquitous, from traditional uses such as risk analysis by banks and insurance companies to new domains such as consumer-behavior analysis, churn prediction and efficacy measurement and optimisation for all types of customer incentives. Also emerging are intra-organisational applications and uses, such as in The Internet of Things: Big Data analytics, over telemetry data from industrial appliances and networked devices (e.g., smart meters) are now used from manufacturing to mining, from transportation to health, from energy to cyber-security. Wherever a digital footprint can be created, data is gathered and analysed in order to model behaviors, understand causes and effects, predict the future and allow decision optimisation for profit maximisation and cost minimisation, which is why even small and medium businesses today are accumulating Big Data and experimenting with cloud-based data analytics, and why this data is proving vital for creating and maintaining their competitive edge.

Speaker
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Tarun Sukhani

IT Entrepreneur, Educator, Author, Speaker, Data Scientist, Machine Learning/AI Developer, Security Expert & Agile Coach.

He is an IT executive, educator, author, speaker, data scientist, security expert, agile coach, polyglot coder, and entrepreneur with over 20 years of combined professional experience both in the U.S. and internationally. As a seasoned veteran, his expertise lies in leading teams in the design and delivery of highly scalable, concurrent, and performant enterprise and web-scale software solutions with budgets of up to $100 million. He is particularly adept at building productive, self-managing agile teams with predictable velocities and delivery timeframes.

Skilled in all phases of the SDLC/ALM, with a solid foundation in Agile (XP, SAFe, Lean, Scrum, Kanban, and Scrumban) and traditional (PMI and PRINCE2) project management frameworks and methodologies.

Learning Outcomes
  • Why you need to make data science the core of your business.
  • Understand the importance of data and analytics for your business to improve revenue, boost productivity, understand & predict customer needs and to create new business opportunities.
  • Gain confidence in the management of data-analytic projects.
  • Learn the skills necessary to allow your organisation a pain-free migration into the “data-driven enterprise” world and to increase your organisations’ foothold in data analysis.
  • Acquire an understanding of the key trends in Data Science and how these are influencing the future of businesses.
Who Should Attend?
  • Decision Makers in Industry who want to understand the “WHAT” and learn “HOW” Data Science will make them more efficient, increase revenues and provide insights into their customers.
  • Builders and SME who want a general overview of IOT
  • Heads of strategy and innovation
  • Project leads
Course Outline

Day 1

  • Introduction to Data Science (60 mins)
  • Data as an Asset (30 mins)
  • Data Lifecycle (60 mins)
  • Data Wrangling and EDA (60 mins)
  • Fundamentals of Statistics (90 mins)
  • Model Creation and Validation (90 mins)

Day 2

  • Data Visualization (30 mins)
  • Data Engineering for Analysis (60 mins)
  • Operationalization and Data Model Lifecycle (90 mins)
  • Deep Learning (60 mins)
  • Hands-on Exercise – Panel Discussion on Building a Data-Driven Enterprise (60 mins)
  • Case Studies (60 mins)
  • Q&A Session(30 mins)
Benefits
  • Develop a high-level understanding of Data Science.
  • Understanding the difference between structured and unstructured data.
  • Understanding what types of data is useful for your organisation.
  • Gain insights into the fundamental modeling techniques.
  • How to use data science in your organisation to improve revenues, understand and predict customers and create new business opportunities.
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