CS3361: Data Science Laboratory syllabus for AI&ML 2021 regulation

Data Science Laboratory detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the AI&ML students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Artificial Intelligence & Machine Learning 3rd Sem scheme and its subjects, do visit AI&ML 3rd Sem 2021 regulation scheme. The detailed syllabus of data science laboratory is as follows.

Course Objectives:

• 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.

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:
   1. Univariate analysis: Frequency, Mean, Median, Mode, Variance, Standard Deviation, Skewness and Kurtosis.
   2. Bivariate analysis: Linear and logistic regression modeling
   3. Multiple Regression analysis
   4. Also compare the results of the above analysis for the two data sets.
6. Apply and explore various plotting functions on UCI data sets.
   1. Normal curves
   2. Density and contour plots
   3. Correlation and scatter plots
   4. Histograms
   5. Three dimensional plotting
7. Visualizing Geographic Data with Basemap

List of Equipments:(30 Students Per Batch)

Tools: Python, Numpy, Scipy, Matplotlib, Pandas, statmodels, seaborn, plotly, bokeh

Note:

Example data sets like: UCI, Iris, Pima Indians Diabetes etc.

Course Outcomes:

At the end of this course, the students will be able to:

1. Make use of the python libraries for data science
2. Make use of the basic Statistical and Probability measures for data science.
3. Perform descriptive analytics on the benchmark data sets.
4. Perform correlation and regression analytics on standard data sets
5. Present and interpret data using visualization packages in Python.

For detailed syllabus of all other subjects of Artificial Intelligence & Machine Learning, 2021 regulation curriculum do visit AI&ML 3rd Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.