Introduction To Big Data For Business Intelligence

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Published 12/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.30 GB | Duration: 6h 24m
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

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