{"id":29006,"date":"2025-04-14T14:37:45","date_gmt":"2025-04-14T09:07:45","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/314316-probability-and-statistics-syllabus-for-cloud-computing-big-data-4th-sem-k-scheme-msbte-pdf\/"},"modified":"2025-04-14T14:37:45","modified_gmt":"2025-04-14T09:07:45","slug":"314316-probability-and-statistics-syllabus-for-cloud-computing-big-data-4th-sem-k-scheme-msbte-pdf","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/314316-probability-and-statistics-syllabus-for-cloud-computing-big-data-4th-sem-k-scheme-msbte-pdf\/","title":{"rendered":"314316: Probability and Statistics Syllabus for Cloud Computing & Big Data 4th Sem K Scheme MSBTE PDF"},"content":{"rendered":"

Probability and Statistics detailed Syllabus for Cloud Computing & Big Data (BD), K scheme PDF has been taken from the MSBTE<\/a> official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical\/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below. <\/p>\n

For all other MSBTE Cloud Computing & Big Data 4th Sem K Scheme Syllabus PDF, do visit MSBTE Cloud Computing & Big Data 4th Sem K Scheme Syllabus PDF Subjects<\/a>. The detailed Syllabus for probability and statistics is as follows.<\/p>\n

Rationale<\/h4>\n

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
\"Get<\/a>.<\/p>\n

Course Outcomes:<\/h4>\n

Students will be able to achieve & demonstrate the following COs on completion of course based learning<\/p>\n

    \n
  1. Apply the basics of probability to solve the engineering problems.<\/li>\n
  2. Use the concepts of random variable and types of probability distribution for getting the spread of d<\/li>\n
  3. Apply network analysis to estimate the complex pattern of relationship and network structure.<\/li>\n
  4. Apply the Bivariate frequency distribution and correlation for data analysis.<\/li>\n
  5. Apply Regression analysis to determine the correlation of data.<\/li>\n<\/ol>\n

    Unit I<\/h4>\n

    Probability 1.1\tDefinition and types of events :Simple event, Occurrence & non-occurrence of an event, Sure event, Impossible event, Complementary event 1.2\tAlgebra of events (Union, Intersection and Mutually exclusive) , Elementary Properties of Probability 1.3\tAddition theorem of two event using axiomatic definition of property and using Venn diagram 1.4\tConditional probability, Multiplication theorem, Independent events 1.5\tProblems on Bayes’ Theorem (without proof)\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nImprove lecture Chalk Bo Tutorial Flipped classroo\n<\/p>\n

    Unit II<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    Unit III<\/h4>\n

    Network Analysis (PERT and CPM) 3.1\tConstruction of Network Diagram A-O-A Network (Activity on Arrow Network), Guidelines, Fulkerson’s rule for numbering events, Interdependency, Node Relationship, Dummy activity, Common errors in Networking 3.2\tDetermination of Critical path: Network diagram, Paths in network 3.3\tPERT Algorithm: Estimate Three kinds of time estimates in PERT, Networking diagram, Probability calculation, Expected project completion date\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nImprove lecture Chalk Bo Tutorial Flipped Classroo\n<\/p>\n

    Unit IV<\/h4>\n

    Bivariate frequency distribution and correlation 4.1\tCorrelation: Definition and Types of correlation(Positive,Negative,Linear and Non linear) 4.2\tScatter diagram, Karl Pearson’s co-efficient of correlation and Spearman’s Rank co-efficient of correlation 4.3\tMarginal frequency distribution, Conditional frequency distribution\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nImprove lecture Chalk Bo Tutorial Flipped Classroo\n<\/p>\n

    Unit V<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    List of Experiments:<\/h4>\n
      \n
    1. Find the union, intersection and complement of events based on Venn diagram for the given problem. 1\tC<\/li>\n
    2. * Calculate the probability using concepts of conditional probability and Bayes theorem for the given problem. 1\tC<\/li>\n
    3. Find the Expected value, Variance and Standard Deviation for the given problem. 1\tC<\/li>\n
    4. * Calculate the probability using Bernoulli distribution for the given problem. 1\tC<\/li>\n
    5. *Calculate the probability using Uniform distribution for the given problem. 1\tC<\/li>\n
    6. Calculate the probability using Exponential distribution for the given problem. 1\tC<\/li>\n
    7. Construct the Network diagram (A-O-A ) Activity on Arrow network. 1\tC<\/li>\n
    8. Find the critical path using Fulkerson’s rule for numbering events for the given problem. 1\tC<\/li>\n
    9. *Calculate the Expected time using PERT algorithm for the given problem. 1\tC<\/li>\n
    10. * Calculate the Karl Pearson’s coefficient of correlation to the given data. 1\tC<\/li>\n
    11. * Calculate the Spearman’s Rank co-efficient of correlation to the given data. 1\tC<\/li>\n
    12. Find the Marginal frequency distribution and Conditional frequency distribution for the given problem. 1\tC<\/li>\n
    13. * Find the Standard deviation of data,covariance,coefficient of correlation and coefficient of regression for the given problem. 1\tC<\/li>\n
    14. Determine the best way of fitting the curve by least square method. 1\tC<\/li>\n
    15. * Find the equation to line of regression (X on Y and Y on X) for the given problem. 1\tC<\/li>\n<\/ol>\n

      Self Learning<\/h4>\n<\/p>\n

      Micro Project<\/i><\/p>\n