Big Data Driving Factors

Duration: 1 min

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Module outline

  1. Quantitative Ability: Percentage, Profit and Loss, Ratio and Proportion (Ratios), Average, Time and Work, Speed Time and Distance, Simple Interest and Compound Interest, Age Problems, Number System, Divisibility Rules, HCF LCM, Algebra, Permutation and Combination, Probability, Data Interpretation, Logarithm, Sequence and Series (Series), Powers and Exponents (Surds and Indices), Speed Mathematics and Basic Mathematics
  2. Logical Reasoning: Seating Arrangements, Puzzle Solving (Puzzles), Data Arrangement, Coding Decoding, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Data Sufficiency, Venn Diagram, Inequality, Series (Number and Letter Series) (Numerical Relations and Reasoning), Logical Sequence Of Words, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Selection Decision Table (Decision Making), Analogy, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning)
  3. Verbal Ability: Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions), Subject Verb Agreement (Verb Noun Agreement), Tenses, Articles, Preposition, Conjunction, Modals, Active Passive Voice (Voice), Direct and Indirect Speech, Vocabulary, Sentence Re-arrangements (Para Jumbles) (Narrative Sequencing), Sentence Completion (Fill in the blanks), Sentence Correction (Error Correction), Cloze Test (Cloze Passage), Facts, Inference and Judgements
  4. Programming Fundamentals (Technical): C Fundamentals
  5. Data Structures (Technical): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  6. Algorithms (Technical): Algorithm Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Time Complexity Analysis, Advanced Algorithms
  7. OOPs (Technical): Object Oriented Programming (OOPS)
  8. DBMS (Technical): 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
  9. SQL (Technical): SQL
  10. Operating Systems (Technical): 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
  11. Computer Networks (Technical): 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, Cloud Computing and IoT, Cyber and Info Security
  12. Software Engineering (Technical): Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  13. Coding/Automata Round: Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Java, Python for Placements by Yash Sir, Pseudo Codes
  14. Personality / Behavioral Assessment: Behavioral Assessment (Behavioral Competency Profiling) (Psychometric Test)
  15. Writing Assessment: English Writing Ability
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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