EIE

# CS3352: Foundations of Data Science syllabus for EIE 2021 regulation (Professional Elective-VII)

Foundations of Data Science detailed syllabus for Electronics & Instrumentation Engineering (EIE) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the EIE 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 Electronics & Instrumentation Engineering 6th Sem scheme and its subjects, do visit EIE 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to EIE Professional Elective-VII syllabus scheme. The detailed syllabus of foundations of data science is as follows.

#### 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 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.

#### Course Outcomes:

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

1. Define the data science process
2. Understand different types of data description for data science process
3. Gain knowledge on relationships between data
4. Use the Python Libraries for Data Wrangling
5. Apply visualization Libraries in Python to interpret and explore data