Anna University, Subject code – CS3352, deals with the B.E Computer Science and Engineering Semester -III Foundations Of Data Science syllabus regulation 2021 relating to affiliated institutions. From here, Students can get assistance in preparing notes to excel in academic performance.

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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 data science fundamentals and process.
- To learn to describe the data for the data science process.
- To learn to describe the relationship between data.
- To utilize the Python libraries for Data Wrangling.
- To present and interpret data using visualization libraries in Python

**CS3352 -Foundations Of Data Science Syllabus**

**Unit I:** Introduction

Data Science: Benefits and uses – facets of data – Data Science Process: Overview – Defining research goals – Retrieving data – Data preparation – Exploratory Data analysis – build the model– presenting findings and building applications – Data Mining – Data Warehousing – Basic Statistical descriptions of Data

**Unit II:** Describing Data

Types of Data – Types of Variables -Describing Data with Tables and Graphs –Describing Data with Averages – Describing Variability – Normal Distributions and Standard (z) Scores

**Unit III:** Describing Relationships

Correlation –Scatter plots –correlation coefficient for quantitative data –computational formula for correlation coefficient – Regression –regression line –least squares regression line – Standard error of estimate – interpretation of r2 –multiple regression equations –regression towards the mean

**Unit IV:** Python Libraries For Data Wrangling

Basics of Numpy arrays –aggregations –computations on arrays –comparisons, masks, boolean logic – fancy indexing – structured arrays – Data manipulation with Pandas – data indexing and selection – operating on data – missing data – Hierarchical indexing – combining datasets – aggregation and grouping – pivot tables

**Unit V:** Data Visualization

Importing Matplotlib – Line plots – Scatter plots – visualizing errors – density and contour plots – Histograms – legends – colors – subplots – text and annotation – customization – three dimensional plotting – Geographic Data with Basemap – Visualization with Seaborn.

**Text Books:**

- David Cielen, Arno D. B. Meysman, and Mohamed Ali, “Introducing Data Science”, Manning Publications, 2016. (Unit I)
- Robert S. Witte and John S. Witte, “Statistics”, Eleventh Edition, Wiley Publications, 2017. (Units II and III)
- Jake VanderPlas, “Python Data Science Handbook”, O’Reilly, 2016. (Units IV and V)

**References:**

- Allen B. Downey, “Think Stats: Exploratory Data Analysis in Python”, Green Tea Press,2014.

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