Foundation of Data Science

Course Title: Foundation of Data Science

Course No: ENCT202

Nature of the Course: Theory + Lab

Semester: 3

Full Marks: 40 + 60 + 50

Pass Marks: 16 + 24 + 20

Credit Hours: 3

Course Description

This course introduces the core concepts, tools, and methodologies of data science, covering the entire data science process from data acquisition, data manipulation, visualization, probability, statistics, and machine learning, with applications in business and engineering.

Course Objectives

The objective of this course is to introduce the core concepts, tools, and methodologies of data science, focusing on the tools and techniques needed to analyze and interpret data effectively. Using data science tools, students will cover the entire data science process, from data acquisition, data manipulation, visualization, probability, statistics, and machine learning, with applications in business and engineering.

Course Contents

1. Introduction to Data Science

3 hrs6 marks

1.1. Overview of data science

1.2. Jargons of data science

1.3. Modern data ecosystem

1.4. Data science lifecycle

1.5. Trends, markets and applications of data science

1.6. Tools and technologies in data science

1.7. Data scientist and their roles

2. Mathematics for Data Science

10 hrs12 marks

2.1. Introduction to linear algebra for data science

2.2. Vectors, matrices and matrix factorization

2.3. Gradient descent for optimization

2.4. Introduction to probability and random variable

2.5. Probability distributions: Normal, Bernoulli, Binomial, Poisson

2.6. Descriptive and inferential statistics

2.7. Central limit theorem and sample distribution concepts

2.8. Normal approximation; hypothesis testing procedures: Tests about the mean of a normal population

2.9. The t-test, Z-tests for differences between two populations means, the two-sample t-test, confidence interval for mean of normal population

2.10. ANOVA

3. Data Understanding and Preprocessing

10 hrs12 marks

3.1. Types of data: Structured, unstructured, semi-structured

3.2. Data preprocessing requirements

3.3. Data sources and collection methods

3.4. Data cleaning and preparation

3.5. Data wrangling and associated tools

3.6. Data enrichment, validation and publishing

3.7. Data transformation and normalization

3.8. Dimensionality reduction linear factor model, principal component analysis (PCA)

4. Data Analysis

8 hrs9 marks

4.1. Data analytics: Descriptive, diagnostic, predictive and prescriptive analytics

4.2. Exploratory data analysis using descriptive statistics

4.3. Data visualization

4.4. Data visualization techniques

4.5. Principles of effective data visualization

4.6. Feature engineering and other aspects of data manipulation

5. Regression and Predictive Modeling

5 hrs6 marks

5.1. Empirical models, simple linear regression, MLE and least square estimator

5.2. Multiple linear regression, matrix approach to multiple linear regression, polynomial regression models, categorical regressors, indicator variables, selection of variables and model building

5.3. Logistic regression

6. Modeling and Validation Processes

6 hrs9 marks

6.1. Introduction to machine learning

6.2. Introduction to supervised, unsupervised and reinforcement learning

6.3. Modeling process, training/validating model, cross validation methods, predicting new observations interpretation

6.4. Measures for model performance and evaluation: Classification accuracy, confusion matrix, sensitivity, specificity, precision, recall, F-score, ROC curve, clustering performance measures, other measures

7. Ethics and Recent Trends

3 hrs6 marks

7.1. Ethical considerations in data science

7.2. Data privacy regulations

7.3. Responsible data usage

7.4. The five Cs

7.5. Future trends

Laboratory Works

Practical sessions covering data science tools, statistical analysis, hypothesis testing, central limit theorem, data wrangling and ETL, data visualization, feature engineering, linear and logistic regression, and K-means clustering. Students are required to submit a project developing a prototype to solve a real-world problem. (45 hours)

1.Get acquainted with data science tools and perform statistical analysis
2.Hypothesis tests on sample datasets to compare population means
3.Simulate and apply the central limit theorem (CLT)
4.Perform data wrangling and ETL processes on a dataset, followed by exploratory data analysis (EDA)
5.Utilize tools to create effective data visualizations to derive key insights from the dataset
6.Implement feature extraction and selection techniques
7.Develop a simple linear regression model and extend it to multiple linear regression
8.Apply logistic regression and evaluate the model
9.Apply K-means clustering and assess cluster quality

Reference Books

1.Ozdemir, S. (2016). Principles of Data Science. Germany: Packt Publishing.
2.Maheshwari A. (2018). Data Science for Dummies, Wiley.
3.Grus, J. (2019). Data Science from Scratch: First Principles with Python. United States: O'Reilly Media.
4.Bruce, P., Bruce, A. (2017). Practical Statistics for Data Scientists: 50 Essential Concepts. United States: O'Reilly Media.
5.VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. United States: O'Reilly Media.
6.Provost, F., Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. United States: O'Reilly Media.

Notes:

This syllabus follows the official BCT curriculum of Tribhuwan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative. https://ioe.tu.edu.np/pages/computer-engineering-curriculum-structure-2635

Data Science