Computer Science Diploma

Data Science and Machine Learning Computer Science 6th Sem BTEUP Syllabus 2019-2020

Data Science and Machine Learning detail syllabus for Computer Science & Engineering, effective from 2019-2020, is collected from BTEUP 2017 Syllabus official website and presented for diploma students. PDF download is possible from official site but you can download the istudy mobile app for syllabus on mobile. The course details such as exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.

For all other bteup syllabus 6th sem computer science 2019-2020 you can visit BTEUP Syllabus 6th Sem Computer Science 2019-2020 Subjects. For all other elective subjects do refer to Electives. The detail syllabus for data science and machine learning is as follows.

Rationale:

The diploma holders in Computer Science and Engineering needs to understand about Data Science and Machine Learning and how to implement Machine Learning Algorithms. They should be able to solve real time problems using data science and Machine learning techniques. Hence this subject is introduced in the curriculum.

Learning Outcomes:

After undergoing the subject, the students will be able to:

  • Understand the basics of Data Science
  • Understand and develop Machine Learning Algorithms.
  • Implement Dimensionality Reduction Techniques

Detailed Content:

For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.

1. Introductionof data Science and Machine Learning (12 Periods)

Fundamentalsof Artificial Intelligence, need and applications of Data Science, Data Mining, data preparation,Machine Learning , Types and Applications of Machine learning

2. Data Preprocessing, Analysis and Visualization (10 Periods)

Data Pre-processing: Pre-processing Techniques- Mean Remova l, Scaling, Normalization, Binarization, One Hot Encoding, Label encoding, Data Analyses: Loading and summarizing the dataset, Data Visualization:Univariate Plots, Multivariate Plots, Training Data, Test Data,Performance Measures

3. Statistical Inference (12 Periods)

For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.

4. Exploratory Data Analysis and the Data Science Process (10 Periods)

Basic tools (plots, graphs and summary statistics) of EDA, Philosophy of EDA, The Data Science Process

5. Machine Learning Algorithms (12 Periods)

Introduction to Supervised Learning Algorithms -Decision Tree,Linear Regression, k-Nearest Neighbours (k-NN), SVM and Introduction to Unsupervised Learning Algorithms – K-means Clustering,MeanShiftAlgorithm,Dimensionality Reduction Techniques, Introduction to Neural Networks,

6. Mining Social-Network Graphs (10 Periods)

For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.

7. Data Science and Ethical Issues (16 Periods)

Discussions on privacy, security, ethics,A look back at Data Science, Next-generation data scientists

List of Experiments:

  1. WAP to implement the Decision Tree Algorithm
  2. WAP to implement the Linear Regression
  3. WAP to implement the k-Nearest Neighbors (k-NN)
  4. WAP to implement the SVM Algorithm
  5. WAP to implement the K-means Clustering
  6. WAP to implement various Distance Metrics
  7. WAP to implement Dimensionality Reduction Techniques

Instructional Strategy:

The subject is conceptual and practical based. Students should be given clear idea about the basic concepts of Data Science and Machine Learning. In practical session student should be asked to explain the algorithm and then write program for algorithm and run on computer. It is required that students should maintain records (files with printouts).

Means of Assessment:

  1. Assignments and quiz/class tests, mid-term and end-term written tests
  2. Actual laboratory and practical work, exercises and viva-voce
  3. Software installation, operation, development and viva-voce

Text Books:

  1. e-books/e-tools/relevant software to be used as recommended by AICTE/UPBTE/NITTTR.

Reference Books:

  1. http://www.spoken-tutorial.org
  2. http://swayam.gov.in

For detail syllabus of all other subjects of BE Computer Science, 2019-2020 scheme do visit Computer Science 6 syllabus for 2019-2020 Scheme.

Dont forget to download iStudy for latest syllabus and results, class timetable and more.

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