This module introduces students to Python, one of the key programming languages in data science. The journey begins with foundational Python knowledge, installation and setup of the programming environment, and a look into Python's simple syntax. Students learn about Python's variables, data types, and operators, essential for manipulating and storing data. The second part of this module explores control flow tools, and functions in python. Students get hands-on experience with lists, tuples, dictionaries, conditional statements, loops, and functions. Complementing the Python-specific curriculum, the module also intertwines mathematical concepts like number theory, vectors, and matrices, enabling students to use advanced Python libraries like NumPy, effectively for complex calculations and data analytics.

LEARNING OUTCOMES

• Students will be able to recall and explain the basic syntax, variables, data types, and operators in Python.
• Demonstrate comprehension of Python's primary data structures (lists, tuples, dictionaries) and control flow tools (conditional statements, loops) by explaining their usage and significance in data manipulation and storage.
• Apply the knowledge of Python in creating and modifying different programs.
• Utilize Python for problem-solving and data analytics, integrating the mathematical concepts of number theory, vectors, and matrices to perform complex computations and analyses.

This module delves into the mathematical and statistical methodologies necessary for data treatment, underscored with the application of Python libraries - NumPy, Pandas, and Matplotlib. NumPy is introduced for numerical computations, followed by Pandas for data manipulation. Later, students get hands-on experience with Matplotlib, a potent tool for data visualization in Python. In sync with the Python libraries, this module unpacks foundational statistics, dividing them into descriptive and inferential branches. Hypothesis formulation and testing are introduced, empowering students to make data-based decisions. The module wraps up with a comprehensive look into probability theory and Bayes' theorem, Correlation and regression analysis providing students with a holistic understanding of mathematical and statistical data treatments.

LEARNING OUTCOMES

• Students will comprehend the functionality and usage of Python libraries such as NumPy, pandas, and matplotlib for data treatment.
• Apply descriptive and inferential statistical methodologies to analyze and interpret data.
• Formulate and test hypotheses, using these as a foundation for making data-driven decisions.
• Implement probability theory and Bayes' theorem to evaluate the uncertainty and update probabilities based on evidence in data science scenarios.

This module delves into the pivotal skill of data visualization, which transforms complex data into insightful visuals that drive informed decision-making. The course highlights popular data visualization tools such as PowerBI and Tableau, equipping students with practical skills to navigate these platforms effectively. Throughout the module, students will understand the significance of effective data visualization, learn to choose the right visual representation for their data, and get hands-on experience in using PowerBI/Tableau. They will learn to manipulate data, create interactive dashboards, and customize visual reports that can communicate complex datasets in a clear, concise, and visually appealing manner.

LEARNING OUTCOMES

• Students will comprehend the importance of data visualization and the role of tools like PowerBI and Advanced Excel in this process.
• Apply the knowledge to select the appropriate visual representation for various datasets.
• Create interactive dashboards and visual reports using PowerBI/ Advanced Excel.
• Analyze and interpret data visualizations to derive meaningful insights and aid decision-making.

This module highlights the importance of database management in today's data-driven world and learn to design, create, and manage databases to store and retrieve data efficiently and securely. This module takes a deep dive into Data mining to discover patterns in large data sets. The course provides an opportunity to learn concepts, principles, and skills to practice and engage in scalable pattern discovery methods on massive data; discuss pattern evaluation measures; study methods for mining diverse kinds of frequent patterns, sequential patterns, and sub-graph patterns; and study constraint-based pattern mining, pattern-based classification, and explore their applications. Following the preprocessing, the course advances into Data cleaning and preprocessing are essential steps in the data mining process. Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in the data to improve its quality. It includes tasks such as removing duplicates, filling in missing values, and correcting invalid data. Preprocessing, on the other hand, involves transforming raw data into a format that is suitable for analysis.

LEARNING OUTCOMES

• Students will be able to create and manage databases, tables, and other database objects.
• Apply various data preprocessing techniques and manage data quality issues effectively.
• Develop Competency in Various Types of Data Cleaning and Preprocessing
• Acquire Practical Experience in Managing Numerical and Text Data

This module provides an in-depth understanding of established methods of artificial intelligence and machine learning techniques that enable computers to learn without being explicitly programmed. The module discusses various parts of artificial intelligence, which include ML (Machine Learning) and aims to explain real-world application. The module provides foundational understanding of AI concepts and terms, be able to describe several issues and ethical concerns surrounding AI, and articulate advice from experts about learning and starting a career in AI. Following the preprocessing, the course advances into algorithms such as linear regression, k-NN, decision trees, random forest, etc. for machine learning by supervised and unsupervised learning.

LEARNING OUTCOMES

• Understand Artificial Intelligence and Machine Learning fundamentals.
• Understand the nature of the dataset and the methods for pre-processing it for machine learning.
• Introduction to Supervised and Unsupervised Machine Learning Algorithms
• Demonstrate a deep critical understanding of algorithms and mathematics behind established ML approaches

In this capstone project, students will be required to demonstrate the application of data science and analytics skills learned throughout the course. The project involves the end-to-end process of addressing a real-world problem using data analytics from various professional backgrounds. This includes data gathering, cleaning, exploratory data analysis, visualization, and applying machine learning algorithms for predictive analysis.

Objectives: Formulate a real-world problem that can be addressed using data analytics. Gather and clean data relevant to the problem. Conduct exploratory data analysis to understand trends, patterns, and anomalies in the data. Visualize data in a way that is meaningful and helps in decision-making. Apply appropriate machine learning algorithms to make predictions or draw conclusions. Evaluate the effectiveness of your solution. Communicate your results effectively to both technical and non-technical audiences.