{"id":28677,"date":"2025-04-14T11:20:24","date_gmt":"2025-04-14T05:50:24","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/313307-statistical-modelling-for-machine-learning-syllabus-for-data-science-3rd-sem-k-scheme-msbte-pdf\/"},"modified":"2025-04-14T11:20:24","modified_gmt":"2025-04-14T05:50:24","slug":"313307-statistical-modelling-for-machine-learning-syllabus-for-data-science-3rd-sem-k-scheme-msbte-pdf","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/313307-statistical-modelling-for-machine-learning-syllabus-for-data-science-3rd-sem-k-scheme-msbte-pdf\/","title":{"rendered":"313307: Statistical Modelling for Machine Learning Syllabus for Data Science 3rd Sem K Scheme MSBTE PDF"},"content":{"rendered":"

Statistical Modelling for Machine Learning detailed Syllabus for Data Science (DS), 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 Data Science 3rd Sem K Scheme Syllabus PDF, do visit MSBTE Data Science 3rd Sem K Scheme Syllabus PDF Subjects<\/a>. The detailed Syllabus for statistical modelling for machine learning 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. Solve the given problem based on Statistic Techniques using R-Programming.<\/li>\n
  2. Implement Statistic methods using R-Programming.<\/li>\n
  3. Use Principles of Probability to solve given Problem.<\/li>\n
  4. Implement appropriate method based on the Interpolation.<\/li>\n
  5. Apply Sampling Methods to solve given problem using R-Programming.<\/li>\n<\/ol>\n

    Unit I<\/h4>\n

    Statistical Techniques 1.1\tFrequency Distribution: Definition, Basic terms. 1.2\tClassification of Data: Raw, Ungroup and Group data. 1.3\tMeasures of Central Tendency: Mean, Median and Mode for all types of data. 1.4\tConcept of Quartiles, Deciles and Percentiles for all types of data. 1.5\tGeometric mean and Harmonic mean and Combined mean for given data. 1.6\tGraphical Representation to find Mode (Histogram) and Median (Ogive curve ). 1.7\tMeasures of Dispersion: Range, Mean Deviation, Standard Deviation, Variance. 1.8\tSkewness: Types of skewness, Test of skewness, Co-efficient of skewness-Karl Pearson’s and Bowley’s coefficient. 1.9\tTypes of skewness in terms of Mean and Mode. 1.10\tMeasures of Kurtosis using central moment. Classroom Lecture Flipped Classroom Demonstration\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

    Probability of Random Variable 3.1 Probability : Definition, Terminologies. 3.2\tTheorem of Probability: Addition, Multiplication. 3.3\tConditional probability. 3.4\tBayes’ theorem. Classroom Lecture Flipped Classroom Demonstration\n<\/p>\n

    Unit IV<\/h4>\n

    Interpolation 4.1\tIntroduction. 4.2\tLagrange’s Interpolation formula. 4.3\tFinite Differences: Forward difference, Backward difference, Shift operator, Inverse shift operator. 4.4\tRelation between forward, backward, shift and inverse shift operator. 4.5\tNewton’s Gregory forward and backward difference Interpolation Formula. 4.6\tConcept of Extrapolation. Classroom Lecture Flipped Classroom Presentations\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

    Suggested Micro Project \/ Assignment<\/h4>\n<\/p>\n

    Assignment<\/i><\/p>\n

      \n
    1. Collect data of\tat least 05\treal world examples\tand test the Hypothesis of sampling distribution.<\/li>\n
    2. Collect data of\tat least 05\treal world examples\tand calculate Measures of skewness and kurtosis and prepare the document.<\/li>\n
    3. Collect data of\tat least 05\treal world examples\tand draw\/fit straight line and second-degree polynomial.<\/li>\n
    4. Collect data of\tat least 05\treal world examples\tand calculate probability using Bayes’ theorem.<\/li>\n
    5. Collect data of at least 03 city like cost of living and temperature data etc. and interpolate the missing index number for it and prepare the document.<\/li>\n<\/ol>\n

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