A Plus Topper

Improve your Grades

AD3411 - Data Science And Analytics Laboratory Syllabus Regulation 2021 Anna University

by

Subject code AD3411 deals with semester IV of B.Tech Artificial Intelligence and Data Science regarding affiliated institutions of Anna University Regulation 2021 Syllabus. In this article, you can gather certain information relevant to the Data Science And Analytics Laboratory. We added the information by expertise.

We included the proper textbooks and references to assist in some way in your preparation. It will enhance your preparation and strategies to compete with the appropriate spirit with others in the examination. If you see, you can find the detailed syllabus of this subject unit-wise without leaving any topics from the unit. In this article AD3411 – Data Science And Analytics Laboratory Syllabus, You can simply read the following syllabus. Hope you prepare well for the examinations. I hope this information is useful. Don’t forget to share with your friends.

If you want to know more about the syllabus of B.Tech Artificial Intelligence And Data Science connected to an affiliated institution’s four-year undergraduate degree program. We provide you with a detailed Year-wise, semester-wise, and Subject-wise syllabus in the following link B.Tech. Artificial Intelligence And Data Science Syllabus Anna University, Regulation 2021.

Aim of 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, stat models, 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.

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.

Related Posts On Semester – IV:

You May Also Visit: