Introduction To Big Data For Business Intelligence

Data Advantage

What you'll learn

Understand basic concepts of Big Data and Data Science Life Cycle

Relate Big data , Data science and Statistics

Get basic understanding of Big Data Architecture and Modeling

Understand how businesses apply Big Data capabilities for achieving goals.

Understand application of Data science in health management with particular reference to pandemic COVID 19

Assess impact of Big Data and Data Science on Big Businesses through case studies.

Requirements

No programming is required. This course will help students of Management programs and practicing Managers to get new insights.

Description

In recent years, analytics has become increasingly important in the world of business, particularly as organizations have access to more and more data. Managers today no longer make decisions based on pure judgment and experience; they rely on factual data and the ability to manipulate and analyze data to support their decisions. No matter what your academic business concentration is, you will most likely be a future user of analytics to some extent and work with analytics professionals. Business analytics, or simply analytics, is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight into their business operations and make better, fact-based decisions. Business analytics is “a process of transforming data into actions through analysis and insights in the context of organizational decision-making and problem-solving.” Business analytics is supported by various tools, such as Microsoft Excel, commercial statistical software packages such as SAS or Minitab, and more complex business intelligence suites that integrate data with analytical software. The purpose of this course is to provide you with a basic introduction to the concepts, methods, and models used in big data analytics for business intelligence so that you will develop not only an appreciation for its capabilities to support and enhance business decisions but also the ability to use business analytics at an elementary level in your work. The course is spread over eight modules, and each module carries a quiz to reinforce the learning experience.

Overview

Section 1: Opening Remarks

Lecture 1 Course Overview

Section 2: Week 1: Module 1: Introduction to Big Data for Business Intelligence

Lecture 2 1IB 2.1: Introducing the basic terms

Lecture 3 1BI 2: Introducing Data Science

Lecture 4 1BI 3: History of Data Science & Types of Data

Lecture 5 1BI 4: Data Science Processes

Lecture 6 1BI 5: The Characteristics: 7 Vs of Big Data

Lecture 7 1BI 6: Markets for Data Science

Lecture 8 1BI 7: Learning Outcome

Section 3: Week 2: Module 2: Data Science and Big Data

Lecture 9 2 BI 0: Learning Objectives of Module 2

Lecture 10 2 BI 01: Data Science Life Cycle

Lecture 11 2 BI 02: Data Science & Statistics

Lecture 12 2 BI 03: Skill Sets for Data Scientists

Lecture 13 2 BI 04: Roles of Data Scientists in Businesses.

Lecture 14 2 BI 04.1: Roles of Big data Professionals in businesses.

Lecture 15 2 BI 05: Symbiotic Relationship between Big Data and Data Science

Lecture 16 2BI 06: How do Big Data and Data Science add value to businesses?

Lecture 17 2 BI 07: Learning Outcomes

Section 4: Week 3: Module 3: Big Data Models

Lecture 18 3 BI 0: Learning Objectives of Module 3.

Lecture 19 3 BI 01: Big Data Models

Lecture 20 3 BI 02: Differentiate RDBMS & NoSQL

Lecture 21 3 BI 03: Distributed Computing & MapReduce.

Lecture 22 3 BI 04: Stream Processing, Apache Kafka and Apache Flink for BI.

Lecture 23 3 BI 05: Machine Learning & Predictive Models: Transforming Businesses.

Lecture 24 3 BI 06: Deep Learning Models: Unleashing the Power of Neural Networks

Lecture 25 3 BI 07: Graph Analytics: Unveiling Insights in Interconnected Data

Lecture 26 3 BI 08: Big Data Frameworks: Empowering Scalable and Efficient Data Processing

Lecture 27 3 BI 09: The 9S of Big Data Framework.

Lecture 28 3 BI 10: Techno - Cultural Roles of Managers in the Big Data Landscape.

Lecture 29 3 BI 11: Learning Outcome

Section 5: Week 4: Module 4: Big Data Architecture:

Lecture 30 4 BI 0: Learning Objectives of Module 4.

Lecture 31 4 BI L1: Components of Big Data Architecture.

Lecture 32 4 BI L1a: APIs and Web Services.

Lecture 33 4 BI L1b: File Transfer and Copying.

Lecture 34 4 BI L1c: Data Governance and Security.

Lecture 35 4 BI L1d: Analytics and Visualization Tools.

Lecture 36 4 BI L1e: IoT Device Data Ingestion

Lecture 37 4 BI L1f: Big Data Storage Systems:

Lecture 38 4 BI L1g: Processing Engines and Computing Infrastructure:

Lecture 39 4 BI L2: Features of Big Data Architecture:

Lecture 40 4 BI L3: Importance and Impact:

Lecture 41 4 BI L4: Future Directions and Advancements:

Lecture 42 4 BI L5: Learning Outcomes:

Section 6: Week 5: Big Data for Business Intelligence.

Lecture 43 5 BI Lo: Learning Objectives

Lecture 44 5 BI L1: New Data Sources:

Lecture 45 5 BI L2a: Big Data Business Model

Lecture 46 5 BI L2b: Business Insights & Optimisation

Lecture 47 5BI L2c: Business Monitisation & Metamorphosis

Lecture 48 5 BI L2d: The Transition

Lecture 49 5 BI L3: The Observations

Lecture 50 5 BI L4: Data Monetisation & Business Impact

Lecture 51 5 BI L5: Business Data Analytics Lifecycle

Lecture 52 5 BI L6: Learning Outcomes:

Section 7: Week 6: Decision Analysis

Lecture 53 6 BI L0: Learning Objectives

Lecture 54 6BI L1: Formulating Decision Problems

Lecture 55 6BI L2: Decision Strategies without Outcome Probabilities

Lecture 56 6BI L3 : Opportunity-Loss Strategy

Lecture 57 6 BI L4: Decision Strategies for a Maximize Objective

Lecture 58 6 BI L5: Decision Trees:

Lecture 59 6BI L6: Learning Outcome:

Section 8: Week 7: Big Data in Health Management.

Lecture 60 7 BI L0: Learning Objectives

Lecture 61 7 BI L1: Technology Driven Healthcare

Lecture 62 7 BI L1a: Hadoop's MapReduce for Healthcare.

Lecture 63 7 BI L1b: Apache Spark for Healthcare.

Lecture 64 7 BI L1c: Arogya Sethu: India's Vibrant Healthcare Application.

Lecture 65 7 BI L2: Learning Outcomes

Section 9: Week 8: Case Studies.

Lecture 66 8 BI L0: Learning Objectives.

Lecture 67 8 BI L1: Case 1: WALMART: The Retailer.

Lecture 68 8 BI L2: CERN: Research Organisation.

Lecture 69 8 BI L3: NETFLIX: A Visual Media.

Lecture 70 8 BI L4: ROLLS ROYCE: Automobile Manufacturers.

Lecture 71 8 BI L5: FACEBOOK: Social Media Network.

Lecture 72 8 BI L6: Learning Outcomes.

Section 10: Concluding Remarks

Lecture 73 Thank you.

Students of Management programs,Practicing Managers,Entrepreneurs