OCS353: Data Science Fundamentals syllabus for Robotics 2021 regulation (Open Elective-II)

Data Science Fundamentals detailed syllabus for Robotics & Automation Engineering (Robotics) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the Robotics 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 Robotics & Automation Engineering 7th Sem scheme and its subjects, do visit Robotics 7th Sem 2021 regulation scheme. For Open Elective-II scheme and its subjects refer to Robotics Open Elective-II syllabus scheme. The detailed syllabus of data science fundamentals is as follows.

Course Objectives:

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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

DATA MANIPULATION
Python Shell – Jupyter Notebook – IPython Magic Commands – NumPy Arrays-Universal Functions – Aggregations – Computation on Arrays – Fancy Indexing – Sorting arrays – Structured data – Data manipulation with Pandas – Data Indexing and Selection – Handling missing data – Hierarchical indexing – Combining datasets – Aggregation and Grouping – String operations – Working with time series – High performance

Unit III

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Unit IV

DATA VISUALIZATION
Importing Matplotlib – Simple line plots – Simple 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

Unit V

HANDLING LARGE DATA
Problems – techniques for handling large volumes of data – programming tips for dealing with large data sets- Case studies: Predicting malicious URLs, Building a recommender system – Tools and techniques needed – Research question – Data preparation – Model building – Presentation and automation.

Practical Exercises

1. Download, install and explore the features of Python for data analytics.
2. Working with Numpy arrays
3. Working with Pandas data frames
4. Basic plots using Matplotlib
5. Statistical and Probability measures

a) Frequency distributions
b) Mean, Mode, Standard Deviation
c) Variability
d) Normal curves
e) Correlation and scatter plots
f) Correlation coefficient
g) Regression

6. Use the standard benchmark data set for performing the following:

a) Univariate Analysis: Frequency, Mean, Median, Mode, Variance, Standard Deviation, Skewness and Kurtosis.
b) Bivariate Analysis: Linear and logistic regression modelling.

7. Apply supervised learning algorithms and unsupervised learning algorithms on any data set.
8. Apply and explore various plotting functions on any data set.

Note
Example data sets like: UCI, Iris, Pima Indians Diabetes etc.

Course Outcomes:

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Text Books:

1. David Cielen, Arno D. B. Meysman, and Mohamed Ali, “Introducing Data Science”, Manning Publications, 2016.
2. Jake VanderPlas, “Python Data Science Handbook”, O’Reilly, 2016.

Reference Books:

1. Robert S. Witte and John S. Witte, “Statistics”, Eleventh Edition, Wiley Publications, 2017.
2. Allen B. Downey, “Think Stats: Exploratory Data Analysis in Python”, Green Tea Press,2014.

For detailed syllabus of all the other subjects of Robotics & Automation Engineering 7th Sem, visit Robotics 7th Sem subject syllabuses for 2021 regulation.

For all Robotics & Automation Engineering results, visit Anna University Robotics all semester results direct link.