Data Science Course Syllabus

Great Learning offers a broad range of world-class courses in data science to suit various needs and skill levels. The data science syllabus serves as a roadmap for students and instructors, enabling them to navigate through the different aspects of data science, which typically include programming, data manipulation, analysis, visualization, business analytics, machine learning, and artificial intelligence.
 

Data Science Course Syllabus

Here is in-depth information on the syllabus for each course, designed to support budding data scientists in advancing their careers:
 

• Post Graduate Program in Data Science and Business Analytics - The University of Texas at Austin (UT Austin)
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• Post Graduate Program in Data Science and Business Analytics (Classroom) - Great Lakes Executive Learning
   

• Module-1: Data Science Foundations: This foundational module introduces students to the core concepts and techniques that form the basis of data science, providing a solid groundwork for further exploration. By delving into various disciplines, learners will gain a well-rounded understanding of the field's applications and acquire the essential skills to excel in data-driven decision-making.
   

• Statistical Methods for Data Science - Grasp fundamental statistical techniques, hypothesis testing, and probability distributions to analyze and interpret data.
   

• Business Finance - Understand critical financial concepts, financial statement analysis, and the impact of financial decisions on business performance.
   

• Marketing and CRM - Explore marketing strategies, customer segmentation, and Customer Relationship Management (CRM) to enhance customer satisfaction and drive business growth.
   

• SQL Programming - Learn Structured Query Language (SQL) to manage and manipulate databases, extract insights, and perform complex data operations.
   

• Python for Data Science - Master Python programming, data structures, and libraries to clean, analyze, and visualize data, as well as implement machine learning models.
   

• Module-2: Data Science Techniques: This module delves deeper into advanced data science methods and techniques, equipping students with the knowledge and tools to tackle complex data-driven challenges. Learners will master a range of powerful approaches to extract valuable insights, make predictions, and optimize decision-making processes.
   

• Advanced Statistics - Expand your statistical knowledge by exploring multivariate analysis, regression, and other advanced techniques for sophisticated data interpretation.
   

• Data Mining - Uncover hidden patterns, associations, and trends in large datasets using data mining algorithms and techniques for effective decision-making.
   

• Predictive Modeling - Build and deploy predictive models to forecast future outcomes, identify trends, and support strategic planning across various industries.
   

• Time Series Forecasting - Analyze time-dependent data to predict future values and trends, helping businesses adapt and optimize their strategies accordingly.
   

• Machine Learning - Learn the fundamentals of machine learning, including supervised and unsupervised learning, to create intelligent models that improve over time.
   

• Optimization Techniques - Apply linear, integer, and nonlinear optimization methods to solve complex problems, enhancing efficiency and resource allocation in diverse applications.
   

• Module-3: Domain Exposure - Business Analytics: This module focuses on the application of data science and business analytics across various industry domains. Students will gain practical experience leveraging data-driven techniques to solve real-world challenges, enhancing their understanding of the field's impact and relevance.
   

• Demystifying ChatGPT and Applications - Explore the inner workings of ChatGPT, its architecture, and diverse applications to comprehend the potential of AI-driven language models.
   

• Marketing & Retail Analytics - Uncover the power of data in marketing and retail, using analytics to optimize pricing, promotions, customer segmentation, and inventory management.
   

• Web & Social Media Analytics - Delve into the world of web and social media analytics, discovering how to extract valuable insights from online user behaviour, social media interactions, and content engagement, enabling you to optimize digital marketing campaigns, improve user experience, and make informed decisions for your online presence.
   

• Finance & Risk Analytics - Learn to apply data science techniques in finance, risk management, and investment, improving prediction accuracy and financial decision-making.
   

• Supply Chain & Logistics Analytics - Discover how data analytics can optimize supply chain operations, reduce costs, and improve overall efficiency in logistics management.
   

• Module-4: Data Visualization and Insights: The final module emphasizes the importance of effectively communicating data-driven insights through visualization. Students will learn to harness the power of data visualization tools to present complex information in a visually engaging and easily digestible format, allowing for more informed decision-making.
   

• Data Visualization using Tableau - Master Tableau, a leading data visualization tool that creates interactive and shareable dashboards that effectively convey insights and support data-driven decisions.
   

• Capstone Project - It serves as the culmination of the data science course, allowing students to apply the knowledge and skills acquired throughout the program to real-world problems. By working on a comprehensive, industry-relevant project, learners will showcase their proficiency in data analysis, visualization, and modelling techniques, demonstrating their readiness to excel in a data-driven career. This hands-on experience not only solidifies the understanding of core concepts but also enhances the student's ability to tackle complex challenges in their future professional endeavours.
   

• Career Assistance: Resume building and Mock interviews- The program offers dedicated career assistance to support students in their transition to a successful data-driven career. It includes guidance on crafting a compelling resume highlighting their skills, accomplishments, and experiences in data science. Additionally, students will participate in mock interviews, simulating real-world scenarios and helping them build the confidence needed to excel in actual job interviews. This comprehensive career support aims to prepare learners to effectively navigate the job market and secure rewarding positions in the field of data science.
   

• Data Science and Machine Learning Online Course - MIT Institute for Data, Systems, and Society
   
• Weeks 1-2: Foundations of Data Science: In the initial two weeks of the program, students are introduced to the foundational concepts and techniques required for a successful career in Data Science. This phase focuses on building a solid base in programming and statistical methods, setting the stage for more advanced topics later in the program.
   
• Python for Data Science - Learn the essentials of Python programming, libraries, and data visualization, equipping you with the tools to manipulate, analyze, and visualize data.
   
• Statistics for Data Science - Gain a solid understanding of statistical concepts, probability distributions, descriptive statistics, and inferential statistics, which are critical for effective data analysis.
   

• Project 1 - Apply the knowledge gained in Python and statistics to a real-world problem, demonstrating your ability to transform raw data into meaningful insights.
   

• Week 3: Learning Break
   

• Week 4: Making Sense of Unstructured Data

In the fourth week of the program, students explore the challenges and techniques associated with analyzing unstructured data. This module delves into methods that help reveal hidden patterns, relationships, and information from complex, unstructured datasets, which are prevalent in today's data-rich world.
 

• Introduction - Gain an overview of unsupervised learning, its significance and examples, and the unique challenges they present in the realm of data science and machine learning.
   

• Clustering - Learn about clustering algorithms, such as K-means and hierarchical clustering, which group similar data points together to uncover underlying patterns and structures.
   

• Spectral Clustering, Components, and Embeddings - Discover advanced techniques like spectral clustering, principal component analysis (PCA), and embeddings, which help identify latent relationships and simplify high-dimensional data for further research.
   

• Week 5: Learning Break with Hands-on Masterclass 1
   

• Week 6: Regression and Prediction

In the sixth week of the program, students dive into the world of regression and prediction, exploring various techniques to model relationships between variables and forecast future outcomes. This module covers both classical and modern regression approaches, addressing the challenges posed by high-dimensional data and emphasizing the importance of causal inference.
 

• Classical Linear and Nonlinear Regression and Extensions - Understand the fundamentals of linear and nonlinear regression to model relationships between variables.
   

• Modern Regression with High-Dimensional Data - Learn advanced techniques for handling high-dimensional data, including regularization methods, dimensionality reduction, ridge and LASSO regression, and random forests, to improve predictive accuracy.
   

• The Use of Modern Regression for Causal Inference - Delve into the role of modern regression methods in establishing causal relationships, allowing for more reliable and actionable insights in real-world scenarios.
   

• Week 7: Learning Break with Hands-on Masterclass 2
   

• Week 8: Classification and Hypothesis Testing

In the eighth week of the program, students explore the essential concepts of classification and hypothesis testing. This module focuses on methods for distinguishing between different groups or classes in a dataset and techniques to validate statistical claims, which are crucial components of data-driven decision-making.
 

• Hypothesis Testing and Classification - Learn the principles of hypothesis testing, statistical significance, and classification algorithms like logistic regression, decision trees, and support vector machines to make informed decisions based on data.
   

• Project 2 - Apply the concepts of hypothesis testing and classification to a real-world problem, demonstrating your ability to build accurate predictive models and validate statistical claims.
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• Week 9: Learning Break with Hands-on Masterclass 2
   

• Week 10: Deep Learning

In the tenth week of the program, students delve into the fascinating domain of Deep Learning, a subset of Machine Learning that utilizes artificial neural networks to solve complex problems. This module introduces the fundamentals of deep learning architectures and techniques, empowering learners to create intelligent systems capable of processing vast amounts of data and adapting over time.
 

Deep Learning - Learn the basics of deep learning, including artificial neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), to build robust models for image recognition, image classification, speech recognition, natural language processing, and more.
 

• Week 11: Recommendation Systems

In the eleventh week of the program, students explore the realm of recommendation systems, which are widely used in e-commerce, entertainment, and other industries to provide personalized suggestions to users. This module covers the essential techniques and algorithms behind these systems, enabling learners to create tailored recommendations that enhance user experiences and drive engagement.
 

• Recommendations and Ranking - Understand the importance of recommendations, the challenges of ranking items, and the metrics used to evaluate the effectiveness of recommendation systems.
   

• Collaborative Filtering - Learn about collaborative filtering techniques, including user-based and item-based approaches, which leverage the collective preferences of users to generate personalized recommendations.
   

• Personalized Recommendations - Dive into advanced methods like content-based filtering and matrix factorization, which further improve the personalization and accuracy of recommendations.
   

• Project 3 - Apply the concepts of recommendation systems to a real-world problem, showcasing your ability to design and implement personalized, data-driven solutions that cater to user preferences.
   

• Week 12: Networking and Graphical Models (Non-Graded)

In the twelfth week of the program, students are introduced to the concepts of networking and graphical models, which offer powerful ways to represent complex relationships and dependencies among variables. Although this module is non-graded, it provides valuable insights into how these models can be used to analyze and extract information from intricate systems.
 

• Introduction - Gain an overview of the significance of networking and graphical models in capturing the structure and relationships within complex systems.
   

• Networks - Learn about different types of networks, their properties, and the algorithms used to analyze and optimize network-based systems.
   

• Graphical Models - Explore graphical models, including Ising and Gaussian models and Markov networks, which offer a flexible framework for representing and reasoning complex, probabilistic relationships among variables.
   

• Week 13: Self-Paced: Predictive Analytics

In the thirteenth week of the program, students engage in a self-paced exploration of predictive analytics, focusing on techniques and best practices for dealing with temporal data and feature engineering. This module empowers learners to create more accurate and effective predictive models by understanding how to handle time-dependent data and extract meaningful features.
 

• Predictive Modeling for Temporal Data - Learn specialized techniques and approaches for handling time-series data in predictive modelling, including autoregressive models, moving averages, and exponential smoothing.
   

• Feature Engineering - Discover the art and science of feature engineering, including feature selection, extraction, and transformation, to improve the performance of predictive models and uncover hidden insights in your data.
   

• Self-Paced Modules

The program also offers self-paced learning modules focused on ChatGPT, a powerful AI-driven language model. These modules provide insights into the inner workings, development, and applications of ChatGPT, equipping learners with the knowledge to harness its potential for various use cases and industries.
 

• Module 1 - Demystifying ChatGPT and Applications - Gain an in-depth understanding of ChatGPT, its architecture, and diverse applications, exploring the possibilities and implications of AI-driven language models in real-world scenarios.
   

• Module 2 - ChatGPT: The Development Stack - Learn about the development stack behind ChatGPT, including the tools, frameworks, and libraries required to create, fine-tune, and deploy powerful AI language models for a wide range of applications.
   

• Data Science Online Certificate Program - Great Lakes Executive Learning

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• Data Science PG Course (Bootcamp) - Great Lakes Executive Learning
   

• Module-1: Preparatory Module

The introductory module of the PG program in data science serves as a stepping stone for learners, ensuring they have a solid foundation in programming before diving into more advanced topics. This initial phase focuses on building a solid base in Python, the most popular programming language for data science applications.
 

Basics of Python - Learn Python programming essentials, including syntax, data structures, control structures, and libraries, equipping you with the tools to effectively manipulate, analyze, and visualize data throughout the program.
 

• Module-2: Foundations of Data Science

In the second module, students are introduced to the core concepts and techniques required for a successful career in the field. This phase reinforces programming skills while also delving into data analysis, statistics, and database management, providing learners with a comprehensive understanding of the critical aspects of data science.
 

• Introduction to programming using Python - Expand your Python knowledge, learn more about its applications in data manipulation, analysis, and visualization, and explore essential libraries like NumPy and Pandas.
   

• Exploratory Data Analysis - Discover the techniques and tools to summarize, visualize, and interpret data, identifying patterns, trends, and relationships that inform decision-making processes.
   

• Statistical Methods for Decision Making - Gain a solid understanding of statistical concepts, probability distributions, hypothesis testing, and regression analysis, among many others, which are crucial for effective data-driven decision-making.
   

• SQL Programming - Learn SQL, a powerful language for querying and managing databases, enabling you to efficiently retrieve, manipulate, and analyze data stored in relational database management systems.
   

• Module-3: Machine Learning Techniques

In the third module, students delve into the world of machine learning, exploring various techniques to create intelligent systems capable of learning from data. This phase covers both supervised and unsupervised learning methods, as well as ensemble techniques that help improve the performance and reliability of predictive models.
 

• Linear and Logistic Regression - Understand the fundamentals of linear and logistic regression, modelling relationships between variables and predicting outcomes for classification problems.
   

• Supervised Learning Classification - Learn about popular supervised learning algorithms like k-Nearest Neighbors, Decision Trees, and Support Vector Machines, which leverage labeled data to make predictions.
   

• Unsupervised Learning - Discover unsupervised learning methods like clustering and dimensionality reduction, which identify hidden structures and relationships in data without explicit supervision.
   

• Ensemble Techniques - Explore ensemble methods like bagging, boosting, and stacking, which combine the predictions of multiple models to enhance the overall performance and reduce overfitting.
   

• Module-4: Self-Learning Recorded Videos on the Applications of Data Science

The final module of this program offers self-paced learning through recorded videos, allowing students to explore various applications of data science at their own pace. This phase covers time series analysis, text mining, and data visualization, showcasing the versatility of data science techniques across various problem domains and industries.
 

• Time Series - Learn the fundamentals of time series analysis, including decomposition, forecasting, and advanced techniques like ARIMA and exponential smoothing, for making predictions on temporal data.
   

• Text Mining - Delve into text mining, understanding how to preprocess, analyze, and extract insights from unstructured textual data using techniques like sentiment analysis, topic modeling, and natural language processing.
   

• Data Visualization - Discover the principles and tools of data visualization, such as Tableau, along with popular libraries like Matplotlib and Seaborn, to effectively present complex data and communicate insights in a visually engaging manner.
   

• Capstone Project

It allows students to apply the knowledge and skills acquired throughout the program to a real-world problem, showcasing their ability to develop data-driven solutions and demonstrating their expertise to potential employers.
 

• Career Preparation: Aptitude Skill Training and Development, Resume Review Workshops, Interview Preparation

This comprehensive career support includes aptitude skill training and development, resume review workshops, and interview preparation, ensuring that learners are equipped with the necessary tips and confidence to succeed in their job search and excel in their data science careers.
 

• Exclusive Campus Hiring Drives

As part of the program, students have access to exclusive campus hiring drives, connecting them with top companies and organizations in search of data science talent, opening up a world of exciting career opportunities, and enhancing their chances of securing a rewarding position in the field.

 

• Data Analytics Course With Placement* - Great Learning Career Academy
   

• Module-1: Basics of Excel

It is designed to give students a strong foundation in Excel, a widely used spreadsheet software. The module covers data wrangling, basic visualizations, and data problem-solving using Excel.
 

• Data Wrangling using Excel - This topic covers the fundamentals of data wrangling in Excel, including data cleaning, transformation, and aggregation. 
   

• Basic Visualizations using Excel - The module covers the basics of chart and graph design, as well as data visualization best practices.
   

• Data Problem Solving using Excel - Students will learn to use Excel to analyze data, identify patterns and trends, and make data-driven decisions. The module covers the basics of data analysis, including hypothesis testing, statistical inference, and regression analysis.
   

• Module-2: SQL Programming

It provides students with a strong foundation in SQL programming. The module covers the fundamentals of relational databases, SQL commands, statements, clauses, operators, keywords, and functions, as well as more advanced topics like joins, subqueries, temp tables, views, integrity constraints, and normalization.
 

• Introduction to Relational Database - This topic covers the fundamentals of relational databases, including the concepts of tables, records, and relationships. Students will learn to design and create relational databases using SQL.
   

• SQL Commands - In this topic, students will learn about SQL commands, including data definition language (DDL), data manipulation language (DML), and data control language (DCL). 
   

• Statements, Clauses, Operators, Keywords, and Functions - This topic focuses on SQL statements, clauses, operators, keywords, and functions. Students will learn to use SQL to perform data manipulation tasks, including filtering, sorting, and grouping data. The module covers various types of SQL functions, including date, aggregate, and window functions.
   

• Joins, Subqueries, and Temp Tables - In this topic, students will learn about more advanced SQL topics, including joins, subqueries, and temporary tables. The module covers the basics of these topics and provides hands-on experience in using them to manipulate and query data.
   

• Views, Integrity Constraints, and Normalization - This topic covers the fundamentals of views, integrity constraints, and normalization in SQL. Students will learn to create and manage views, enforce data integrity using constraints, and design normalized databases.
   

• Module-3: Python Programming

This module covers the fundamentals of Python programming, as well as more advanced topics like Pandas, data visualization, exploratory data analysis, and machine learning.
 

• Python Foundations - This topic covers Python programming fundamentals, including variables, data types, functions, and control structures. Students will learn to write and execute basic Python programs.
   

• Pandas – Building Blocks - The module covers the basics of Pandas data structures, including Series and DataFrames, as well as data wrangling tasks like filtering, grouping, and merging data.
   

• Visualization Building Blocks - Students will learn to create basic visualizations using libraries like Matplotlib and Seaborn.
   

• Exploratory Data Analysis - The module covers the basics of EDA, including data cleaning, data transformation, and data visualization.
   

• Introduction to Machine Learning - Students will learn about supervised and unsupervised learning, as well as standard machine learning algorithms like linear regression, logistic regression, k-means clustering, and decision trees.
   

• Module-4: Tableau

The final module covers the basics of Tableau, including importing data, creating calculated fields, creating multiple chart types, designing dashboards and storytelling, and using blends and actions.
 

• Importing Dataset/Connecting to Data Source - This topic covers the fundamentals of connecting Tableau to different data sources, including importing data from files like CSV and Excel and connecting to databases like MySQL and SQL Server.
   

• Measure, Dimensions, and Calculated Fields - In this topic, students will learn about measures and dimensions in Tableau and how to create calculated fields to perform data manipulation tasks.
   

• Multiple Charts (Map Chart, Bar Chart, Line Chart, etc.) - This topic focuses on creating different chart types in Tableau, including maps, bar charts, line charts, and more. Students will learn to create and customize different chart types, as well as how to combine them into a single visualization.
   

• Dashboard and Storytelling - In this topic, students will learn to design and create dashboards and storytelling in Tableau. The module covers the basics of dashboard design, including layout, formatting, interactivity, and best practices for storytelling with data.

 

Data Science Degree Syllabus

Here is in-depth information on the syllabus for each data science degree course, designed to support budding data scientists in advancing their careers:

• Online Master's Degree in Data Science - Northwestern University

• Terms 1-4: Foundation Courses

The first four terms of the MS Data Science Program are dedicated to building a solid foundation in data science, encompassing essential mathematical and statistical concepts, programming languages, and machine learning techniques. This comprehensive curriculum equips learners with the core knowledge and practical skills required to excel in the ever-evolving field of data science.

• Math for Data Scientists - Strengthen your mathematical foundation, focusing on key concepts like linear algebra, calculus, and optimization, which are crucial for data science applications.

• Applied Statistics with R - Learn applied statistics using the R programming language, covering probability distributions, exploratory data analysis, and statistical analysis, among many others.

• Data Science in Practice - Explore real-world examples and case studies, understanding the practical applications of data science across various industries and problem domains.

• Python for Data Science - Master Python programming, learning about libraries like NumPy, Pandas, and Matplotlib, which are essential for data manipulation, analysis, and visualization.

• Supervised Learning Methods - Dive into supervised learning techniques like linear regression, logistic regression, and classification algorithms, which leverage labeled data for predictions.

• Decision Analytics - Gain insights into decision analytics, learning to use data-driven methods to support strategic decision-making in organizations.

• Practical Machine Learning - Develop a hands-on understanding of machine learning algorithms and techniques, applying them to real-world problems.

• Database Systems and Data Preparation - Learn about database systems, data warehousing, and data preprocessing techniques, ensuring the quality and reliability of your data for analysis.

• Data Governance, Ethics, and Law - Understand the ethical, legal, and governance considerations surrounding data science, ensuring responsible and compliant practices in your work.

• Remaining Terms: Artificial Intelligence Coursework

In the remaining terms of the program, students delve into the fascinating realm of artificial intelligence, focusing on advanced topics like natural language processing and deep learning. This phase equips learners with cutting-edge skills and knowledge to design and implement intelligent systems that can learn from data and adapt to complex, real-world challenges.

• Natural Language Processing - Explore the field of natural language processing, learning techniques to analyze, understand, and generate human language, enabling AI systems to interact with textual data effectively.

• Artificial Intelligence & Deep Learning - Master the concepts of artificial intelligence and deep learning, including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning, empowering you to create state-of-the-art AI systems for various applications.

• Capstone Project

It serves as a culmination of the MS Data Science Program, allowing students to apply their acquired skills and knowledge to tackle real-world problems. This hands-on experience demonstrates their proficiency in data science and artificial intelligence techniques, showcasing their ability to develop innovative, data-driven solutions for complex challenges and enhancing their portfolio for potential employers.

 

• Master of Data Science - 24 Months - Deakin University

• First 12 Months

• Pathway - 1: Data Science and Business Analytics Pathway

• Module-1: Data Science Foundations

The first module focuses on building a solid foundation in data science. This phase introduces learners to the fundamentals of the subject and statistical methods that play a crucial role in data-driven decision-making, laying the groundwork for more advanced topics in the curriculum.

• Introduction to Data Science - Acquaint yourself with the essential concepts, tools, and techniques in data science, understanding the role of data scientists and the value they bring to organizations across various industries.

• Statistical Methods for Decision Making - Delve into the critical statistical methods that inform data-driven decision-making, covering concepts such as probability distributions, hypothesis testing, and goodness of fit, equipping you with the analytical tools to make informed, data-backed decisions.

• SQL Programming - Learn Structured Query Language (SQL) to manage and manipulate databases, extract insights, and perform complex data operations.

• Module-2: Data Science Techniques

This module delves deeper into advanced data science methods and techniques, equipping students with the knowledge and tools to tackle complex data-driven challenges. Learners will master a range of powerful approaches to extract valuable insights, make predictions, and optimize decision-making processes.

• Predictive Modeling - Build and deploy predictive models to forecast future outcomes, identify trends, and support strategic planning across various industries.

• Advanced Statistics - Expand your statistical knowledge by exploring multivariate analysis, regression, and other advanced techniques for sophisticated data interpretation.

• Data Mining - Uncover hidden patterns, associations, and trends in large datasets using data mining algorithms and techniques for effective decision-making.

• Time Series Forecasting - Analyze time-dependent data to predict future values and trends, helping businesses adapt and optimize their strategies accordingly.

• Machine Learning - Learn the fundamentals of machine learning, including supervised and unsupervised learning, to create intelligent models that improve over time.

• Module-3: Domain Exposure - Business Analytics

This module focuses on the application of data science and business analytics across various industry domains. Students will gain practical experience leveraging data-driven techniques to solve real-world challenges, enhancing their understanding of the field's impact and relevance.

• Demystifying ChatGPT and Applications - Explore the inner workings of ChatGPT, its architecture, and diverse applications to comprehend the potential of AI-driven language models.

• Marketing and Retail Analytics - Uncover the power of data in marketing and retail, using analytics to optimize pricing, promotions, customer segmentation, and inventory management.

• Finance & Risk Analytics - Learn to apply data science techniques in finance, risk management, and investment, improving prediction accuracy and financial decision-making.

• Module-4: Data Visualization and Insights

The final module emphasizes the importance of effectively communicating data-driven insights through visualization. Students will learn to harness the power of data visualization tools to present complex information in a visually engaging and easily digestible format, allowing for more informed decision-making.

Data Visualization using Tableau - Master Tableau, a leading data visualization tool that creates interactive and shareable dashboards that effectively convey insights and support data-driven decisions. 

• Pathway - 2: Artificial Intelligence and Machine Learning Pathway

• Module-1: Foundations

The first module establishes a solid foundation in AI and ML concepts. This phase introduces learners to Python programming, exploratory data analysis, and applied statistics, equipping them with the tools and techniques to excel in the fascinating world of artificial intelligence and machine learning.

• Introduction to Python - Familiarize yourself with Python programming, one of the most popular languages for AI and ML applications, learning its syntax, data structures, and essential libraries for data manipulation and analysis.

• SELF-PACED MODULE: EDA and Data Processing - Gain insights into exploratory data analysis (EDA) and data processing techniques, enabling you to identify patterns, trends, and relationships in data, as well as prepare it for further analysis and modeling.

• Applied Statistics - Master applied statistics concepts, including probability distributions, hypothesis testing, descriptive statistics, and inferential statistics, which are crucial in developing and evaluating machine learning algorithms and AI systems.

• Module-2: Machine Learning

This module covers a wide range of topics, including supervised and unsupervised learning, ensemble techniques, model selection and tuning, and recommendation systems. By mastering these techniques, students will be well-equipped to develop sophisticated AI and ML models that can learn from data and adapt to complex, real-world challenges.

• Supervised Learning - Explore the fundamentals of supervised learning, focusing on algorithms like linear regression, logistic regression, Naive Bayes algorithm, and support vector machines, which rely on labeled training data to predict outcomes or classify instances.

• Ensemble Techniques - Learn about ensemble methods, such as bagging, boosting, decision trees, and random forests, that combine multiple machine learning models to improve prediction accuracy and overcome individual model limitations.

• Unsupervised Learning - Delve into unsupervised learning techniques, such as clustering and dimensionality reduction, which identify patterns and structures in unlabeled data, enabling data-driven insights and knowledge discovery.

• Featurization, Model Selection & Tuning - Understand the process of featurization, selecting the suitable machine learning model for a specific problem, and tuning hyperparameters to optimize model performance.

• Recommendation Systems - Discover the principles behind recommendation systems, learning collaborative filtering, content-based filtering, and hybrid approaches to provide personalized recommendations and enhance user experiences.

• Module-3: Artificial Intelligence

The third module dives into the world of artificial intelligence, covering a range of topics from neural networks and deep learning to computer vision and natural language processing. Alongside these core AI concepts, the module offers self-paced learning on ChatGPT and its applications, enabling students to broaden their understanding of AI technologies and their practical implementations.

• Self-paced Module: Demystifying ChatGPT and Applications - Gain a comprehensive understanding of ChatGPT, its underlying architecture, and various real-world applications, including chatbots, content generation, and sentiment analysis.

• Self-paced Module: ChatGPT: The Development Stack - Delve into the development stack behind ChatGPT, exploring the tools, frameworks, and libraries that enable the creation and deployment of advanced conversational AI models.

• Introduction to Neural Networks and Deep Learning - Understand the principles of neural networks and deep learning, where you learn how to design, train, and evaluate neural network architectures for solving complex problems.

• Computer Vision - Explore the field of computer vision, mastering techniques for image and video analysis, object detection, and image segmentation, which enable machines to interpret and understand visual information from the world.

• NLP (Natural Language Processing) - Learn the fundamentals of natural language processing, covering techniques like text preprocessing, sentiment analysis, and text summarization, which empower AI systems to process, analyze, and generate human language.

• Module-4: SELF-PACED MODULE: Introduction to Reinforcement

It is designed to introduce students to the concepts of Reinforcement Learning (RL) and Generative Adversarial Networks (GANs). The module is self-paced, allowing students to learn at their own speed.

• Reinforcement Learning (RL) - It covers the fundamentals of RL, including Markov Decision Processes (MDPs) and Bellman Equations. Students will learn about different algorithms used in RL, such as Monte Carlo and Temporal Difference (TD) Learning.

• Introduction to GANs (Generative Adversarial Networks) - It introduces students to the basics of GANs, a popular type of deep learning algorithm used for generating new data. Students will learn about the structure of GANs and how they work and explore real-world applications of GANs, such as image and music generation. Overall, this module provides a solid foundation in RL and GANs, two essential topics in the field of data science.

• PROGRAM CURRICULUM FOR MASTER OF DATA SCIENCE (GLOBAL)

• Next 12 Months

• Master of Data Science - 12 Months - Deakin University

• Module-1: Engineering AI Solutions

It prepares students to develop, deploy, and maintain AI solutions using modern tools, frameworks, and libraries. Students will learn to rigorously apply engineering principles and scientific methods, conduct experiments, and manage stakeholder expectations. The module covers the key characteristics of developing an AI solution, highlighting the differences between traditional software development and AI solution development. By the end of the module, students will be able to advise stakeholders on the process of operationalizing AI solutions from concept inception to deployment and ongoing product maintenance and evolution.

• Module-2: Engineering AI Solutions

Students will learn to identify and summarize mathematical concepts and techniques to solve problems in AI applications. The module covers the role of mathematics in AI and helps students to verify and evaluate results obtained and communicate them to different audiences. Students will also learn to read and interpret mathematical notation and communicate their problem-solving approaches. By the end of the module, students will be equipped with the mathematical skills required to tackle real-world problems in artificial intelligence.

• Module-3: Machine Learning

Students will learn about clustering and dimensionality reduction techniques for unsupervised learning on unlabelled data. The module covers linear and logistic regression/classification and model appraisal techniques to evaluate and develop models. Students will learn about the concept of KNN and SVM for analyzing and developing classification models to solve real-world problems. The module also covers decision tree and random forest models for multi-class classification.

• Module-4: Modern Data Science

The module covers advanced concepts and the theoretical foundation of data science. Students will evaluate modern data analytics and its implications in real-world applications. The module also covers using appropriate platforms to collect and process relatively large datasets. Students will learn to collect, model, and conduct inferential as well as predictive tasks from data.

• Module-5: Real-World Analytics

The module covers applying multivariate functions, data transformations, and data distributions to summarize data sets. Students will learn to analyze data sets by interpreting summary statistics, models, and function parameters. The module also covers game theory, linear programming skills, and models for making optimal decisions. Students will learn to develop software codes to solve computational problems for real-world analytics. The module emphasizes professional ethics and responsibility for working with real-world data.

• Module-6: Data Wrangling

The module covers researching data discovery and extraction methods and tools and applying resulting learning to extract data based on project needs. Students will learn to design, implement, and explain the data model needed to achieve project goals and the processes that can be used to convert data from data sources to both technical and non-technical audiences.

The module emphasizes using statistical and machine learning techniques to perform exploratory analysis on extracted data and communicate results to technical and non-technical audiences. Students will also learn to apply and reflect on practices for maintaining data privacy and exercising ethics in data handling.

 

• M.Tech in Data Science and Machine Learning - PES University

• Module-1:

The program's first module focuses on foundational concepts and tools required for data analysis.

• Python for Data Science - The module covers the basics of Python syntax, data structures, and control structures, as well as how to use Python libraries like Pandas and Numpy for data manipulation and analysis.

• Database and SQL - Students will learn to design and implement relational databases, write SQL queries to extract data from databases, and perform basic data manipulation tasks.

• Statistical Methods for Decision Making - In this topic, students will learn the basics of statistical methods used in data analysis, including probability theory, hypothesis testing, and regression analysis. The module covers the fundamentals of statistical inference and how to apply statistical methods to real-world data problems.

• Module-2:

The second module covers essential topics related to machine learning, mathematical foundations, and data visualization.

• Machine Learning: Regression Algorithms - The module covers popular regression algorithms like linear regression, logistic regression, and feature engineering, as well as how to evaluate and optimize machine learning models.

• Mathematical Foundation - This topic covers the mathematical foundations of data science, including linear algebra, calculus, and probability theory. Students will learn to use mathematical tools and concepts to solve data science problems and build predictive models.

• Data Visualization - The module covers the basics of data visualization theory and design and how to use popular visualization tools like Tableau and PowerBI to create interactive and informative visualizations.

• Module-3:

The third module covers advanced topics related to machine learning, including classification algorithms, unsupervised algorithms, time series analysis, and natural language processing.

• Machine Learning: Classification Algorithms - The module covers popular classification algorithms like decision trees, random forests, and ensemble techniques, as well as how to evaluate and optimize machine learning models.

• Machine Learning: Unsupervised Algorithms - Students will learn popular unsupervised algorithms like k-means clustering, hierarchical clustering, and principal component analysis.

• Time Series - In this topic, students will learn the basics of time series analysis, including forecasting techniques and trend analysis. The module covers popular time series models like ARIMA, exponential smoothing, and state-space models.

• Natural Language Processing - Students will learn about popular NLP techniques like text and sentiment analysis, web scraping, sequential modeling, and transformers.

• Module-4:

The fourth module covers advanced topics related to data science and machine learning, including deep learning and big data.

• Deep Learning - Students will learn how to build and train deep learning models and use them to solve complex problems, such as artificial neural networks and convolutional neural networks.

• Big Data - This module explores the challenges and opportunities presented by big data and teaches students how to extract insights from large datasets using technologies like Hadoop, Spark, and NoSQL databases.

• Capstone Project

Students will apply their data science skills to a real-world project, working in teams to develop a data-driven solution for a business or industry challenge. Throughout the project, they will leverage the knowledge and techniques they have acquired in previous modules, including data acquisition and preprocessing, exploratory data analysis, statistical modeling, and machine learning. Students will also gain experience in project management, communication, and collaboration.

• M.Tech Thesis

Students will undertake an individual research project guided by a faculty mentor. They will identify a research question, design and implement a research plan and analyze and interpret the results. The thesis will be presented in written form and defended orally in front of a panel of faculty members. The M.Tech thesis will allow students to apply their knowledge and skills to an original research problem and contribute to the field of data science.