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CS3361- Data Science Laboratory Syllabus Regulation 2021 Anna University

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Anna University, Subject code – CS3361, deals with the B.E Computer Science and Engineering  Semester -III Data Science Laboratory syllabus regulation 2021 relating to affiliated institutions. From here, Students can get assistance in preparing notes to excel in academic performance.

We include every topic of the Data Science Laboratory Syllabus, to understand the subject very well. It will help you to improve your idea of syllabus of CS3361-Data Science Laboratory Syllabus on your finger tips to go ahead in a clear path of preparation. In this following article Data Science Laboratory Syllabus, will help you, Hope you share with your friends.

If you want to know more about the syllabus of B.E Computer Science and Engineering connected to an affiliated institution’s under four-year undergraduate degree programme. We provide you with a detailed Year-wise, semester-wise, and Subject-wise syllabus in the following link B.E Computer Science and Engineering Syllabus Anna University, Regulation 2021.

Aim Of Concept:

  • To understand the python libraries for data science
  • To understand the basic Statistical and Probability measures for data science.
  • To learn descriptive analytics on the benchmark data sets.
  • To apply correlation and regression analytics on standard data sets.
  • To present and interpret data using visualization packages in Python.

CS3361- Data Science Laboratory Syllabus

List Of Experiments:

  1. Download, install and explore the features of NumPy, SciPy, Jupyter, Statsmodels and Pandas packages.
  2. Working with Numpy arrays
  3. Working with Pandas data frames
  4. Reading data from text files, Excel, and the web and exploring various commands for doing descriptive analytics on the Iris data set.
  5. Use the diabetes data set from UCI and Pima Indians Diabetes data set for performing the following:
    a. Univariate analysis: Frequency, Mean, Median, Mode, Variance, Standard Deviation, Skewness and Kurtosis.
    b. Bivariate analysis: Linear and logistic regression modeling
    c. Multiple Regression analysis
    d. Also, compare the results of the above analysis for the two data sets.
  6. Apply and explore various plotting functions on UCI data sets.
    a. Normal curves
    b. Density and contour plots
    c. Correlation and scatter plots
    d. Histograms
    e. Three dimensional plotting
  7. Visualizing Geographic Data with Basemap

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