AD3411 Data Science and Analytics Laboratory Syllabus:

AD3411 Data Science and Analytics Laboratory Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

• To develop data analytic code in python
• To be able to use python libraries for handling data
• To develop analytical applications using python
• To perform data visualization using plots

LIST OF EXPERIMENTS

Tools: Python, Numpy, Scipy, Matplotlib, Pandas, statmodels, seaborn, plotly, bokeh Working with Numpy arrays
1. Working with Pandas data frames
2. Basic plots using Matplotlib
3. Frequency distributions, Averages, Variability
4. Normal curves, Correlation and scatter plots, Correlation coefficient
5. Regression
6. Z-test
7. T-test
8. ANOVA
9. Building and validating linear models
10. Building and validating logistic models
11. Time series analysis

TOTAL: 60 PERIODS

COURSE OUTCOMES:

Upon successful completion of this course, students will be able to:
CO1. Write python programs to handle data using Numpy and Pandas
CO2. Perform descriptive analytics
CO3. Perform data exploration using Matplotlib
CO4. Perform inferential data analytics
CO5. Build models of predictive analytics

REFERENCES

1. Jake VanderPlas, “Python Data Science Handbook”, O’Reilly, 2016.
2. Allen B. Downey, “Think Stats: Exploratory Data Analysis in Python”, Green Tea Press, 2014.
3. Data Analysis and Visualization Using Python, Analyze Data to Create Visualizations for BI Systems — Dr. Ossama Embarak