Explore
Browse by Domains
Browse by Degrees
Popular Programs
PG Program in Artificial Intelligence and Machine Learning
6 Months Online Weekend
Applied Data Science Program
12 Weeks Live Virtual Weekdays & Weekend
No Code AI and Machine Learning: Building Data Science Solutions
12 Weeks Online Weekend
PG Program in Data Science and Business Analytics
MIT Data Science and Machine Learning Program
12 weeks Online Weekend
PG Program in Cloud Computing
6 months Online Weekend
Data Science & Business Analytics
Master of Data Science (Global) Program
24 Months Online
Data Analytics Essentials
15 week Online
MS in Data Science Programme
18 months Online
DP-100 - Microsoft Azure Data Scientist Associate Certification Training Program
8 Weeks Online
PL-300 - Microsoft Power BI Data Analyst Certification Training Program
6 Weeks Online
Artificial Intelligence & Machine Learning
PG Program in Artificial Intelligence for Leaders
4 Months Online Weekend
MS in Information Science: Machine Learning
2 Years Online/Hybrid
Generative AI for Business with Microsoft Azure Open AI Program
10 Weeks Online
Microsoft Programs
AZ-900 - Microsoft Azure Fundamentals Training Program
AZ-104 - Microsoft Azure Administrator Training Course
Management
Advanced Digital Marketing and Growth Strategies
12 Weeks Online
NUS Business School Future Leaders Programme
Executive PG Program in Management
12 months Online Weekend
PGP in Strategic Digital Marketing
Cloud Computing
Cyber Security
Post Graduate Program in Cyber Security
16 weeks Online
CompTIA Security+ Bootcamp
6 weeks Online
Software Development
Professional Certificate in Full Stack Software Development: Building Scalable Cloud Applications
6 Months Online
Digital Marketing
Design Thinking
Design Thinking: From Insights to Viability
14 Weeks Online Weekend
Post Graduate Program in User Experience Design
Study Abroad
2 Years Hybrid
MBA
Masters
Know more about
Data Science and Business Analytics
17 programs 48% avg. salary hike
AI & Machine Learning
12 programs 48% avg. salary hike
3 programs 48% avg. salary hike
4 programs 48% avg. salary hike
7 programs 48% avg. salary hike
2 programs 48% avg. salary hike
MBA Courses
1 programs 48% avg. salary hike
Study Abroad Programs
1 programs Earn about 150K USD (in US)
Study in US Programs
Quick Links
GL Excelerate
Get the desired career support
Webinar on Demand
Watch the on-demand webinars
What is your work experience?
This will help us recommend the best programs for you.
Currently in college
0-3 yrs experience
3-8 yrs experience
8+ yrs experience
College Students
Start your career on the right foot, with curated programs, job platforms, and postgraduate programs.
Gain skills in 3-12 months to accelerate career growth and land your first job
GL Live Sessions
Online sessions with industry experts
EXPLORE
Career Path
200+ in-demand careers
Learn for Free
An easy way to get started on your career path with us.
Academy
Get certified with 1000+ Free Courses
START FOR FREE
Unlock the power of data with a comprehensive data science course syllabus designed to equip you with the tools and knowledge to transform raw data into actionable intelligence, propelling your career in this high-demand field. Master essential concepts like Python, R, SQL, and data visualization while honing your skills in big data analytics, statistics, and AI. Delve into the world of data-driven insights, predictive modelling, and machine learning, which are taught by seasoned industry experts.
EXPLORE OUR COURSES
Explore the program syllabus for various data science courses offered by Great Learning, covering essential tools, techniques, and projects.
LEARN MORE
Northwestern University
18 months · Online
MIT IDSS
Ciência de Dados e Machine Learning: Tomada de Decisões Orientadas por Dados
12 weeks · Online
Ciencia de Datos y Aprendizaje Automático: Toma de Decisiones Basada en Datos programa del MIT IDSS
12 Weeks · Online
University of Texas - McCombs
Programa en Ciencia de Datos y Analítica Empresarial
6 months · Online
15 week · Online
12 weeks · Online · Weekend
6 Months · Online · Weekend
We are allocating a suitable domain expert to help you out with program details. Expect to receive a call in the next 4 hours.
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.
Here is in-depth information on the syllabus for each course, designed to support budding data scientists in advancing their careers:
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.
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.
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.
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.
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.
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.
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.
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.
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.
&
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.
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.
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.
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.
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.
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.
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.
It is designed to provide students with a strong foundation in data analysis and visualization. The module covers identifying, preparing, and analyzing data to derive maximum value, as well as visualizing data intelligently to deliver the right message. Additionally, the module focuses on driving success in data analytics projects, highlighting best practices for managing data projects and ensuring that they provide value to stakeholders.
The course is designed to provide students with a solid understanding of making decisions under uncertainty. The module covers Bayesian decision-making, simulations to make decisions under uncertainty, and decision trees and their uses in a variety of situations.
It is designed to provide students with a strong understanding of optimal decision-making. The module covers linear optimization, sensitivity analysis, and shadow price.
It is designed to provide students with a strong foundation in predictive modelling. The module covers linear and logistic regression, classification and regression trees, and cross-validation.
It is designed to provide students with a solid understanding of experimental design and causal inference. The module covers randomized controlled trials, instrumental variables, and differences in differences.
It is designed to provide students with a strong understanding of the business value of data science. The module covers how to drive digital transformation within the organization, aligning organizations and teams for data-driven approaches, and making the business case for data science.
It gives students a practical understanding of ChatGPT, a large language model used in natural language processing. This module covers the fundamentals of ChatGPT, including its architecture and how it works, as well as its applications, such as chatbots, language translation, and text generation. Students will gain hands-on experience in building and deploying these applications, equipping them with the tools they need to solve real-world problems.
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.
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.
This module covers the fundamentals of Python programming, as well as more advanced topics like Pandas, data visualization, exploratory data analysis, and machine learning.
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.
Here is in-depth information on the syllabus for each data science degree course, designed to support budding data scientists in advancing their careers:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The program's first module focuses on foundational concepts and tools required for data analysis.
The second module covers essential topics related to machine learning, mathematical foundations, and data visualization.
The third module covers advanced topics related to machine learning, including classification algorithms, unsupervised algorithms, time series analysis, and natural language processing.
The fourth module covers advanced topics related to data science and machine learning, including deep learning and big data.
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.
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.
The syllabus for the data science courses at Great Learning includes a variety of topics designed to build your proficiency. The topics covered are statistics, programming (in Python or R), data visualization, machine learning, and deep learning, among several others.
Both course syllabuses share commonalities like statistics, data manipulation, and data visualization. However, the data scientist course syllabus goes deeper into machine learning algorithms and deep learning and often includes more complex topics like artificial intelligence.
The data science syllabus at Great Learning is quite comprehensive. It starts with fundamentals like Python programming and statistics. Then, it covers advanced topics such as machine learning, deep learning, big data technologies, and even business intelligence tools. It also includes a capstone project where you can apply your learned skills.
Visit the following data science program pages to learn more about the curriculum:
Post Graduate Program in Data Science and Business Analytics - UT Austin
Post Graduate Program in Data Science and Business Analytics (Classroom) - Great Lakes
Data Science and Machine Learning Online Course - MIT IDSS
Applied Data Science - MIT Professional Education (For Foreign Students)
Data Analysis Courses for Beginners - UT Austin (For Foreign Students)
Data Science Online Certificate Program - Great Lakes
Data Science PG Course (Bootcamp) - Great Lakes
Online Master's Degree in Data Science - Northwestern University
Master of Data Science - 24 Months - Deakin University
Master of Data Science - 12 Months - Deakin University
Data Analytics Course With Placement* - Great Learning Career Academy
The prerequisites vary based on the level of the program. Introductory courses require only foundational math skills, while more advanced programs require prior knowledge of statistics or programming. The program page will mention these prerequisites, if any.
Yes. The data science degree syllabus includes several hands-on projects and a capstone project. These are designed to give you practical exposure and make you industry-ready.
Great Learning constantly updates its data science course syllabus to ensure it includes the most recent tools, methodologies, and best practices in the industry. They work closely with industry experts and incorporate their feedback into the course design.
Yes. Upon successful completion of the data science course, you will receive a professional certificate from Great Learning that can be a valuable addition to your professional profile.
The broad outline of the data science course syllabus is available for free on the program’s website. However, you would need to enroll in the course to access detailed course content, resources, and hands-on projects.
Yes, the syllabus of the data science course at Great Learning is designed to accommodate learners of all levels. It starts with foundational topics and gradually moves to advanced concepts, making it suitable even for beginners. However, some advanced data science courses require a few years of work experience, and the details are available on the specific program page.
Enter your registered email and we'll send you a link to change your password.