What is Big Data

Duration: 2 min

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Module outline

  1. Quantitative Aptitude: Number System, Divisibility Rules, HCF LCM, Percentage, Ratio and Proportion (Ratios), Average, Profit and Loss, Simple Interest and Compound Interest, Mixture and Alligation, Partnership, Age Problems, Time and Work, Pipe and Cistern, Speed Time and Distance, Algebra, Arithmetic, Data Interpretation, Sequence and Series (Series), Logarithm, Powers and Exponents (Surds and Indices), Permutation and Combination, Probability, Speed Mathematics and Basic Mathematics, Trigonometry
  2. Logical Reasoning: Coding Decoding, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Seating Arrangements, Puzzle Solving (Puzzles), Data Arrangement, Syllogisms, Inequality, Data Sufficiency, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Series (Number and Letter Series) (Numerical Relations and Reasoning), Venn Diagram, Logical Sequence Of Words, Calendar, Clock, Cubes & Dices, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning)
  3. Verbal Ability: Noun, Pronoun, Adjectives, Verb, Adverb, Tenses, Subject Verb Agreement (Verb Noun Agreement), Articles, Preposition, Conjunction, Modals, Determiner, Vocabulary, 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), Active Passive Voice (Voice), Direct and Indirect Speech, Sentence Construction
  4. Data Structures (Domain Test): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  5. Algorithms (Domain Test): Algorithm Analysis, Sorting Algorithms, Time Complexity Analysis, Greedy Algorithms
  6. OOPs (Domain Test): Object Oriented Programming (OOPS)
  7. DBMS (Domain Test): 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
  8. SQL (Domain Test): SQL
  9. Operating Systems (Domain Test): 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
  10. Computer Networks (Domain Test): 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
  11. Coding: Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Java, Python for Placements by Yash Sir
  12. Written English Test: English Writing Ability
  13. Psychometric Test: Behavioral Assessment (Behavioral Competency Profiling) (Psychometric Test)
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video presents a lecture on the definition of Big Data, displayed on a PDF slide titled 'What is Big Data?'. The instructor explains that Big Data is a collection of large and complex datasets that cannot be processed using traditional computing techniques. The definition is broken down into three key points: the nature of the data (large and complex), the limitation of traditional methods, and the continuous act of generating, capturing, and processing enormous amounts of data. The instructor writes 'Relational Databases' and 'Multi-media' on the slide, and later adds 'Stream' and 'Sensor' to illustrate data sources. The term 'Genetic' is also written, likely referring to genetic data as another source. The instructor uses a yellow highlighter to emphasize the phrase 'enormous amounts of data on a continuing basis'. The video concludes with the slide transitioning to a new section titled 'Big Data Driving Factors'.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video shows a lecture slide titled 'What is Big Data?'. The instructor defines Big Data as a collection of large and complex data sets that cannot be processed using traditional computing techniques. The definition is presented in three bullet points. The instructor writes 'Relational Databases' and 'Multi-media' on the slide, then adds 'Stream' and 'Sensor' to illustrate data sources. The term 'Genetic' is also written. The instructor uses a yellow highlighter to emphasize the phrase 'enormous amounts of data on a continuing basis'. The slide transitions to a new section titled 'Big Data Driving Factors' at the end of the clip.

The lecture provides a foundational definition of Big Data, emphasizing its scale, complexity, and the need for non-traditional processing methods. The instructor uses the slide to build the definition incrementally, adding examples of data sources like relational databases, multimedia, streams, sensors, and genetic data. The key takeaway is that Big Data is not just about volume but also about the continuous and complex nature of data generation and processing, which necessitates new technologies and approaches.

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