One of the most in-demand careers right now is that of a data scientist, and if you are also someone who wants to pursue this field but has no idea what it takes to be one, then you are in the right place.
A Data science course typically covers introductory-level statistics and mathematics, as well as the latest advancements in machine learning and deep learning. With short-term diplomas and degree programs, and executive courses all available, choosing the right one for you can be overwhelming.
This guide aims to simplify your decision. It covers the structure of the data science course, the subjects you will study, the available program formats, and why Futurense offers a practical approach to learning data science.
Data Science Course – What Does the Course Include?
A data science course revolves around understanding data from multiple angles, combining it with analytical thinking, technical skills, and real-world problem solving. It is beyond learning coding and building AI models.
Data science courses are divided into several learning phases, where the foundation begins with mathematics and statistics, as numbers play a major role in this course and skill set. This is then followed by programming and data handling, which helps learners to handle large datasets, write efficient code to perform analysis, and get insights from large sets of data.
In the next phase, learners are introduced to machine learning and predictive modeling, where the primary focus is on building models, evaluating performance, and gaining a deeper understanding of how algorithms make decisions.
The students then transition to solving real-world problems, case studies, and business problems, which help them to understand how data science is applied in day-to-day business and institutional activities.
A data science course is structured in progressive learning phases:
This structure ensures that learners begin with the fundamental core concepts and learn how to apply them in real-world scenarios.
Who Should Do a Data Science Course?
A data science course can be fruitful for students at different levels of their careers, considering that they have interests in developing analytical thinking and problem-solving skills.
Engineering and computer science students are also recommended to pursue a data science course to upskill and gain more knowledge regarding analytics, machine learning, and artificial intelligence. This helps them build a focused career path and strengthen their technical foundation.
Professionals in domains such as marketing, finance, operations, or product management increasingly rely on data to make decisions. Data science skills enable them to work more effectively with data teams and make informed strategic choices.
5 Key Subjects in a Data Science Course
1. Statistics and Probability
Statistics plays the most important role in data science by helping professionals understand patterns, trends, and uncertainty in data. It helps scientists not to assume and make decisions based on numbers and evidence.
Some of the common statistical topics covered are descriptive statistics, probability distributions, hypothesis testing, and confidence intervals. It is highly important to give more time and effort to learn these topics as it helps in evaluating models, interpreting results, and drawing reliable conclusions.
2. Mathematics for Data Science
Mathematics, mostly linear algebra and calculus, underpins many machine learning algorithms. It is not important, and neither is it required to memorize complex formulas, however, understanding core mathematical concepts is highly recommended.
3. Programming
Programming is the backbone of data science workflows. It helps scientists to analyse data and automate monotonous tasks, and build fast and efficient data models. The majority of the courses focus on teaching Python as it is one of the best programming languages for such cases.
The course structure typically comprises data structures, functions, libraries, and best practices for writing clean and reusable code.
To anyone who thinks that programming is not as useful as mathematics in data science is wrong. Programming is highly important in all stages of data science, and its applications, from data analysis to visualizations to model development and deployment.
4. Data Wrangling and Preprocessing
Data wrangling and preprocessing focus on preparing raw data so it can be effectively used for analysis, as raw data is not clean, has missing elements, inconsistencies, or irrelevant information.
This stage involves tasks such as cleaning data, correcting missing or inconsistent values, and transforming the data into a suitable format. It’s the most time-consuming, but it cannot be overstated, as well-prepared data influences the model’s overall accuracy and reliability.
5. Machine Learning
Machine learning is training models to identify patterns in data and use them to make predictive decisions without having to program them for each task. Data science courses typically introduce supervised and unsupervised learning and, at times, reinforcement learning.
Moreover, practical courses give more weightage to implementing algorithms, understanding output, and applying models to real business problems using real datasets.
Top 3 Futurense Data Science Programs You Can Consider
1. Bachelor of Science in Data Science
The B.S/B.Sc in Applied AI & Data Science is an advanced undergraduate degree offered by IIT Jodhpur in association with Futurense Technologies. The course is a 3-4 year live online program with optional campus immersion, designed to bridge India’s growing AI talent requirement.
The program offers:
- A regular IIT degree with full IIT alumni status
- No JEE requirement (entry via a qualifying test)
- Stackable exits (Certificate → Diploma → Degree)
- Strong focus on Applied AI, Data Science, GenAI, LLMs, NLP, ML, and real-world projects
- Teaching by IIT faculty and industry leaders (MAANG, Fortune 500)
- Career pathways include AI roles, M.Tech at IIT Jodhpur, study abroad, entrepreneurship, and government exams.
The eligibility criteria for this course are Class 12 with mathematics and more than 60% marks.
2. MTech in Generative AI Data Science (Executive Program)
The M.Tech in Generative AI & Data Science (Executive) is a 2-year (24 months) AI-native postgraduate program offered by the Indian Institute of Technology Jammu in collaboration with Futurense, curated specially for working professionals.
The primary focus of the course is high-impact skills like Generative AI, Agentic AI, production-grade MLOps, and large-scale data systems, and the best part is that it doesn’t require GATE.
Upon completion, you will receive an official IIT M.Tech Degree and executive alumni status, and gain hands-on experience through AI clinics, live industry projects, and a research-driven dissertation with a real-world use case and deployment.
3. PG Diploma Programs in Data Science
The PG Diploma Programs in Emerging Technologies are residential programs offered by IIT Gandhinagar in collaboration with Futurense Technologies.
These programs are designed as a full-time learning experience aimed at providing industry-level expertise in artificial intelligence and software engineering. The course provides hands-on skills comparable to those found in real-world industry experience.
PG Diploma Programs in Emerging Technologies offer specialization in:
- AI-ML & Agentic AI Engineering
- GenAI-Powered Data Science & Engineering
- AI-Driven Cloud-Based Software Development
It strongly emphasises multi-agent architectures, MLOps pipelines, and cloud native applications. The enrolled learners graduate with 4+ deployed industry projects, a capstone, and Executive Alumni Status from IIT Gandhinagar.
All in all, this course is ideal for roles like AI/ML Engineer, Generative AI Developer, Data Engineer, and Agentic AI Engineer.
Conclusion
Data science is a long-term skill investment that offers career opportunities across various industries. Understanding the data science syllabus and course is essential before pursuing it, as a clear grasp of what is taught sets realistic expectations and helps learners choose what aligns with their personal and professional goals.
Whether you are a student starting your academic journey or a professional looking to strengthen your skills, selecting a course that matches your career stage and goals is necessary. A structured learning pathway, such as the one offered by Futurense, provides a clear path to build data science expertise and long-term growth in a data-driven environment.
FAQ: Data Science Course - Syllabus and Subject
What is the syllabus for data science?
A data science syllabus typically includes statistics, mathematics, Python programming, data preprocessing, machine learning, data visualization, and real-world projects.
Is C++ needed for data science?
No. C++ is not mandatory for data science. Most data science work is done using Python, SQL, and sometimes R. C++ is useful only in performance-heavy or system-level roles.
What are the 4 types of data science?
The four common types are descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Is data science a tough job?
Data science can be challenging at first due to statistics and problem-solving requirements, but it becomes manageable with structured learning and hands-on practice.
Which is better, AI or data science?
Neither is better universally. Data science focuses on data analysis and insights, while AI focuses on building intelligent systems. The better choice depends on career goals and interests.
What is the 80/20 rule in data science?
The 80/20 rule means around 80% of effort goes into data cleaning and preparation, while only 20% is spent on modeling and algorithms.
M.Tech in Generative AI & Data Science (Executive)
IIT Jammu
India's First M.Tech integrating Generative AI with Data Science for working professionals
