Data Science

Course Overview

Data Science called as “the sexiest job of the 21st century” by Harvard Business Review., and in this period “the world’s most valuable resource is no longer oil, but data”. This course helps students to understand the basic concepts of Data Science like information extraction from the vast amounts of data using different scientific methods. It explains how the data is manipulated and processed to unravel useful underlying information from the raw and unstructured data. The course follows an example-based approach in terms of providing a better understanding on how data science techniques are applied in the real-world problems. Furthermore, the course provides insights to the ethical use of data, which has become very crucial with the rampant and abundant generation of data.

Programming Language used: Python, Julia, R

Course Objective

After completiton of the course, students should be able to:

explain the basic concepts and techniques of Data Science.
Ask appropriate questions, create appropriate data visualization and provide insight based on it.
Apply Data Science techniques for descriptive, diagnositive, predictive & prescriptive analysis in real-world problems.

Prerequisites

It is expected that students have prior knowledge of mathematical preliminaries such as Probabilities and Statistics, Calculus and Linear Algebra. Besides, students should have the knowledge of high-level programming languages like C and C++ or Python to understand the concepts of Data Science and implement projects.

Chapter Breakdown

Chapter 1: Introduction to DataScience [6 Hrs.]

Evolution of Science: Empirical, Theoretical, Computer Science and Data Science
Current Trends in Data Science
DIKW Pyramid
Asking Good Questions
Data Types and Properties
Data Analytics Value Escalator
Data Science Hierarchy
Data Science Lifecycle

Chapter 2: Data Wrangling [9 Hrs.]

Languages for Data Science
Data Discovery
Data Structuring
Data Cleaning
Data Transformation
Data Sampling Techniques
Data Enriching
Feature Selection
Data Validating
Data Publishing

Chapter 3: Data to Models: Score, Rank, Class, Clusters [6 Hrs.]

Why Model ?
- Philosophies of Modeling
Data to Score:
- The Body Mass Index (BMI)
- Developing Scoring Systems
- Linear Regression
- Metrics
Data to Rank
- Advanced Ranking Techniques: PageRank, Elo Ranking, Machine-learned ranking (MLR)
- Arrow’s Impossibility Theorem
- Metrics
Data to Class
- Types of Classification
- Metrics
Data to Clusters
- Types of Clustering
- Cluster Validation

Chapter 4: Visualizing Data [6 Hrs.]

Exploratory (EDA) and Explantory Data Analysis
Grammar of Graphics
Developing a Visualization Aesthetic
- Principles of Visual Design
Chart Types
Interactive Visualization
Common tools for Data Visualization
Storytelling with data
Misinformation and Disinformation

Chapter 5: Data Analysis [12 Hrs.]

Descriptive Analysis
Diagnostic Analysis
Predictive Analysis
Prescriptive Analysis
Case Studies
Optional
- Process Analysis
- Text Analysis
- Qualitative Research & Analysis

Chapter 6: Big Data [6 Hrs.]

Introduction to Big Data
Big Data Ecosystem
Parallelism
Hadoop and MapReduce
Apache Spark

Chapter 7: Future Trends in Data Science [3 Hrs.]

Current Operational trends
Next-Generation Data Scientists
Cultivating Soft Skills
Being an Ethical Data Scientist

Reference

Steven S. Skiena. The Data Science Design Manual. Springer Cham, 2017
Cathy O’Neil, Rachel Schutt. Doing Data Science. O’REILLY Media, Inc., 2013
Joel Grus. Data Science from Scratch 2e: First Principles with Python. O’REILLY, 2019
Ivo D. Dinov. Data Science and Predictive Analytics. Springer, 2018