Big Data Driving Factors

Duration: 1 min

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Module outline

  1. Communication Round: Vocabulary, Subject Verb Agreement (Verb Noun Agreement), Tenses, Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions), Cloze Test (Cloze Passage), Sentence Correction (Error Correction), Sentence Completion (Fill in the blanks), Sentence Re-arrangements (Para Jumbles) (Narrative Sequencing), Direct and Indirect Speech, Active Passive Voice (Voice), Verbal Analogy (Word Based Analogy), Articles, Preposition, Conjunction, Sentence Construction, English Writing Ability, Communication Assessment
  2. Quantitative Aptitude: Ratio and Proportion (Ratios), Divisibility Rules, Data Interpretation, Mixture and Alligation, Permutation and Combination, Logarithm, Number System, HCF LCM, Speed Time and Distance, Percentage, Average, Powers and Exponents (Surds and Indices), Time and Work, Mensuration and Geometry, Age Problems, Pipe and Cistern, Probability, Profit and Loss, Simple Interest and Compound Interest, Algebra
  3. Analytical and Logical Reasoning: Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Data Sufficiency, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Directions (Direction Test), Ranking, Order and Sequence, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Selection Decision Table (Decision Making), Logical Sequence Of Words, Puzzle Solving (Puzzles), Syllogisms, Seating Arrangements, Calendar, Clock, Blood Relations, Cubes & Dices, Venn Diagram, Analytical Reasoning (Counting Figures Reasoning)
  4. Cognizant Gaming Aptitude: Gamified / Game Based Aptitude
  5. Java (Cluster 1): Java
  6. ANSI SQL MySQL (Cluster 1,2,3): SQL
  7. HTML, CSS, JavaScript (Cluster 1,3): HTML, CSS, JavaScript
  8. Python (Cluster 2): Python for Placements by Yash Sir
  9. Cloud Fundamentals (Cluster 2): Emerging Tech Trends
  10. C# (Cluster 3): C#
  11. OOPs: Object Oriented Programming (OOPS)
  12. Technical Assessment (Coding): Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Pseudo Codes
  13. Data Structures: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  14. DBMS: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, ORDBMS, Database Security & Authorization, Query Processing & Optimization, Enhanced Data Models, Data Warehousing & Mining, Big Data Systems, NoSQL
  15. Operating Systems: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling, File Management, Windows OS, Linux OS, Security, Distributed Systems, Virtual Machines
  16. Computer Networks: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security, Mobile Technology, Cloud Computing and IoT, Cyber and Info Security
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video presents a lecture on the driving factors behind the exponential growth of Big Data. The instructor explains that the quantity of data in the world is increasing rapidly due to the widespread use of the internet and web, which results in nearly all human activity being digitized. The lecture focuses on the major sources of Big Data, which are listed on a slide. These sources include social media sites, sensor networks, digital images/videos, cell phones, transaction records, web logs, digital records, documents, surveillance records, e-commerce data, and complex scientific research data. A diagram on the slide visually represents 'Big Data' as a central concept connected to these various sources. The instructor uses a digital pen to highlight and write on the slide, emphasizing key points such as 'Data mining' and 'Data Science' as related fields.

Chapters

  1. 0:00 – 1:18 00:00-01:18

    The video displays a presentation slide titled 'Big Data Driving Factors'. The slide's text explains that the world's data quantity is growing exponentially due to the increasing use of the internet and web, which digitizes everything from logs and messages to images and videos. The slide then lists the major sources of Big Data, including social media sites, sensor networks, digital images/videos, cell phones, transaction records, web logs, digital records, documents, surveillance records, e-commerce data, and complex scientific research data. A diagram on the right shows 'Big Data' at the center, connected to these sources. The instructor, visible in a small window, uses a digital pen to write 'Data mining' and 'Data Science' on the slide, highlighting key concepts related to the topic.

The lecture establishes a clear cause-and-effect relationship: the proliferation of internet and web technologies is the primary driver for the exponential growth of digital data. This data, in turn, is generated from a wide array of sources, from personal devices like cell phones to large-scale systems like sensor networks and e-commerce platforms. The core message is that this massive, diverse, and rapidly expanding data is the foundation of the 'Big Data' field, which is further supported by the related disciplines of data mining and data science.

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