DATA SCIENCE

Course Overview

Prerequisite(s)

Probability and Statistics, Algebra, Matrices, Calculus, Algorithms, Python Programming.

Course Objectives

1. To identify the types of data, understand about how to collect the data, manage the data.
2. Familiarize the student about the concepts of data visualization and formal inference procedures.
3. Demonstrate the applications of Data Science in solving real-world problems

Course Outcomes

After completion of this course, the students will be able to:

• CO1: Understand the fundamental concepts, life cycle, and process of data science for solving real-world problems.
• CO2: Apply statistical techniques such as correlation, regression, ANOVA, and exploratory data analysis for data interpretation and prediction.
• CO3: Develop machine learning models including logistic regression, recommendation systems, and financial modeling techniques for practical applications.
• CO4: Analyze social networks, data visualization, fraud detection, and ethical issues in data science for informed decision-making.

Syllabus Units

UNIT-I: Introduction (9 Lectures)

Topics: What is Data Science? Data science life cycle: Problem definition, data collection, data cleaning, data exploration, model building, model evaluation, and deployment. Statistical Inference ANOVA, Exploratory Data Analysis, and Data Science Process: Statistical thinking in the Age of Big Data.

Learning Outcomes: At the end of this unit, the students will be able to understand the basic concepts of data science and understand the data science process.

UNIT-II: Describing Relationships (9 Lectures)

Topics: 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.

Learning Outcomes: At the end of this unit, the students will be able to use correlation and regression to analyze data and make predictions.

UNIT-III: Financial Modelling (9 Lectures)

Topics: Logistic Regression: Classifiers, Time stamps and Financial Modelling: Timestamps, Financial Modelling.

Learning Outcomes: At the end of this unit, the students will be able to apply Logistic regression classifier for binary classification problem and understand Financial Modeling.

UNIT-IV: Visualization (9 Lectures)

Topics: Recommendation Engines: Building a User-Facing Data Product at Scale: A Real World Recommendation Engine. Data Visualization and Fraud Detection: Visualization tools, Marks Data Visualization Projects, Data Science and Risk.

Learning Outcomes: At the end of this unit, the students will be able to implement Recommendation Engine and understand data visualization projects.

UNIT-V: Social Networks (9 Lectures)

Topics: Social Networks and Data Journalism: Social Network Analysis at Morning Analytics, social network analysis, terminology from social networks, morning side analytics, Data Journalism. Ethical Issues in Data Science.

Learning Outcomes: At the end of this unit, the students will be able to understand the various concepts of social network analysis and understand data journalism.

Learning Resources

Text Books

1. Cathy O'Neil, Rachel Schutt, Doing Data Science, Straight Talk from the Frontline, O'Reilly, 2013.

References

1. Python Data Science Handbook: Essential Tools for Working with Data, from Shroff/O'/Reilly; First edition (1 January 2016)
2. Jure Leskovek, AnandRajaraman, Jeffrey Ullman, Mining of Massive Datasets. v2.1, Cambridge University Press, 2014.
3. David Cielen, Arno D. B, Meysman and Mohamed Ali, "Introducing Data Science", Manning Publications, 2016