CS3353 FOUNDATIONS OF DATA SCIENCE L T P C 3 0 0 3
COURSE OBJECTIVES:
· 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
UNIT I INTRODUCTION 9
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 9
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 9
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 9
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
9
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:
CO1: Define the data
science process
CO2: Understand different
types of data description for data science process
CO3: Gain knowledge on
relationships between data
CO4: Use the Python
Libraries for Data Wrangling
CO5: Apply visualization Libraries in Python
to interpret and explore data
TOTAL:45 PERIODS
TEXTBOOKS:
1. David Cielen, Arno D.
B. Meysman, and Mohamed Ali, “Introducing Data Science”, Manning Publications,
2016. (Unit I)
2. Robert S. Witte and
John S. Witte, “Statistics”, Eleventh Edition, Wiley Publications, 2017. (Units
II and III)
3. Jake VanderPlas,
“Python Data Science Handbook”, O’Reilly, 2016. (Units IV and V)
REFERENCE:
1. Allen B. Downey,
“Think Stats: Exploratory Data Analysis in Python”, Green Tea Press,2014
