**Key Takeaways**

**Data Science and Artificial Intelligence disciplines together are predicted to create 11 million jobs by 2026.****Though a great career option, learning data science demands strategic planning and a well-planned curriculum. Get a comprehensive overview of the data science syllabus and important subjects.****Discover the must-haves for data science programs and how you can assess if the program is a good fit for you.**

**What Is a Data Science Course?**

Today data has become a critical asset for organizations, thus the need for skilled data professionals who can extract the value out of data is on the rise. Though this is a reality, data science as a discipline requires mastery over multiple components, making it a difficult skill to hone. That’s where the data science professional courses prove helpful. With a data science course, you get a well-planned learning structure and materials to enable individuals to establish careers in the field of data science.

These courses start from the very fundamental blocks like the basics of programming, and statistics for data science, and then take you through the advanced concepts like machine learning, data wrangling, visualization, and analytics. Through data science programs, learners become skilled in handling different data science roles and are given more preference by organizations when it comes to recruitment.

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## Important Data Science Subjects

Data science is a combination of multiple disciplines, hence there are a wide number of important subjects one needs to master to become a data scientist. Some of these subjects will be highly theoretical, whereas some of them will be a blend of technical and practical parts. Below we have compiled the core subjects for any top-tier data science program:

**1. Statistics and Probability**

Understanding statistics and probability is crucial in data science. The major topics that are being covered in this domain that you need to learn are descriptive statistics, inferential statistics, probability distributions, hypothesis testing, and statistical modeling. Mastering these concepts will help you as a data scientist in analyzing data, making predictions, and digging out valuable information from the raw data.

**2. Programming Language**

Python and R are the most common and essential skills for data scientists. They offer some powerful libraries for data analysis, such as NumPy, Pandas, Matplotlib, etc.

**3. Query Language**

Apart from programming languages, you also need to master query languages like SQL. This helps data scientists retrieve, manipulate, and extract insight from the databases. They help in data extraction, transformation, and loading, which is an ETL process for data analysis.

**4. Machine Learning**

Machine learning is the domain that deals with the teaching of machines from data for making decisions and predictions. If we talk about the core topics of machine learning, then it covers topics such as supervised learning (which includes regression and classification) and unsupervised learning (which includes clustering, and dimensionality reduction). Apart from these, it also includes deep learning, neural networks, reinforcement learning, and the practical use of these algorithms.

**5. Data Visualization**

Data visualization is a visual representation of the information and data. It is an important tool for data scientists to showcase their findings effectively. This involves various graphical representations such as bar charts, scatter plots, line graphs, and heat maps. This graphical representation helps the end user visualize and interpret the findings.

**6. Data Modeling**

Data modeling is the process of creating a logical representation of data structures and relationships. It helps in database design, performance optimization, and data integrity protection in data-driven systems.

**7. Data Mining and Data Wrangling**

Data mining is the process of extracting information from large data sets, whereas data wrangling is the process of transforming and translating raw data into a more appropriate format for analysis. This subject includes topics such as data preprocessing, data cleaning, data exploration, and the use of algorithms to find patterns and valuable insights.

**8. Business Intelligence**

Business intelligence refers to the process of transforming raw data into valuable insights that help the business make data-driven decisions. This course covers the skills, which is required to handle various Business Intelligence methods and technologies.

**9. Databases and Big Data Technologies**

When it comes to managing data, databases play a crucial role. So, you need to understand subjects such as relational databases like SQL, non-relational databases like NoSQL, and big data technologies such as Spark, Hadoop, and cloud storage solutions. Hence, when it comes to storing, retrieving, and processing large volumes of data, then these tools help effectively.

Above are the course subjects that are critical components of data science courses. However, besides them, you will also come across special mentions such as Electives, and capstones. Let’s understand what these two things depict quickly:

**Electives and Specializations**

Generally, the top program offers you the specialization in the area of your interest through electives. The elective course may include advanced machine learning, computer vision, artificial intelligence, robotics, or natural language processing.

**Projects and Capstones**

Learning through practical implementation can be the best way to learn any technology. So, a top-notch program will provide you with hands-on projects and capstone courses, allowing you to apply learned concepts to real-world problems.

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## Key Data Science Topics in Detail

It doesn’t matter which kind of program you choose to learn data science discipline, be it on on-campus degree programs or online professional training, the critical concepts that you need to learn remain the same. You need to have a strong understanding of 4 major pillars of data science: Statistics, Python Programming, Machine Learning, and Data Analysis. Of course, the above 4 components are critical, but besides them below are some more topics that every good data science program must cover:

SQL | Database Management | Big Data |

Probability | Linear Algebra | Statistics |

Data Collection | Programming | OOPS |

Data Mining | Data Warehousing | Data Cleaning |

Exploratory Data Analysis | Business Intelligence | Reinforcement Learning |

Natural Language Processing | Computer Vision | Ensemble Learning |

Classification | Regression | Clustering |

## Data Science Course Syllabus: Structured Overview

The Data Science course syllabus usually outlines the topics highlighted in the table below. To become a data science professional having theoretical and practical exposure to all these concepts is a must for every aspirant. Hence, we recommend you to make note of all the subjects and their internal topics.

Subject Name | Topics |

Data Science Fundamentals (Preparatory Sessions) | Python and R Programming Languages NumPy, Pandas, Seaborn, and Matplotlib Libraries Statistics for Data Science Linux |

Data Transformation Using SQL | SQL Basics Advanced SQL Deep Dive into User-Defined Functions SQL Optimization and Performance |

Inferential Analytics | Statistics and Descriptive Analytics using MS Excel Python for Descriptive, Diagnostic, and Inferential Statistics |

Machine Learning | Python Linear Algebra & Advanced Statistics Machine Learning Supervised and Unsupervised Learning in ML |

Data Visualization | Introduction to Data Visualization Acquiring and Visualizing Data Applications of Data Visualization Data Visualization tools and techniques |

Advanced Machine Learning Algorithms | Bagging And Boosting Algorithms Predictive Analytics And Machine Learning Cognitive Science and Analytics |

Data Science at Scale with Spark | Introduction to Big Data And Spark RDDs Advanced Concepts & Spark-Hive |

Natural Language Processing | Overview of Natural Language Processing and Text Mining Text Mining, Cleaning, and Pre-processing Text Classification Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling Introduction to Semantics and Vector Space ModelsDialog Systems |

Computer Vision | RBM and DBNs & Variational AutoEncoder Object Detection using Convolutional Neural Net Distributed & Parallel Computing for Deep Learning Models Reinforcement Learning Deploying Deep Learning Models and Beyond |

*Check out our blog on **Data Science Tutorial** to learn more about Data Science.*

## Data Science Syllabus PDF

Suppose you want to have a detailed overview of the ideal data science course curriculum and the learning trajectory. In that case, we highly recommend you download the data science course syllabus pdf attached below. This syllabus has been created by the top instructors associated with us and can help you comprehend the ideal curriculum for data science programs.

**Download a Printable Data Science Syllabus PDF!!**

## Comparison of Data Science Programs

In this section, we will try to compare the different data science programs, such as the B.Tech Data Science program, B.Sc Data Science program, professional certifications, etc.

**B.Tech Data Science Program**

The B.Tech program is considered an undergraduate program providing students with a gateway to enter into the world of engineering and technology. This program starts with fundamentals and provides just the necessary skills for one to make it into the IT field. The B.Tech in data science is a good option for those who have just cleared the higher secondary education. Here’s an overview of the typical curriculum structure for this program:

**Year 1**

- Computer Science Fundamentals: Introduction to Software Engineering, Data Structures and Algorithms
- Maths for Data Science: Calculus, Linear Algebra, Discrete Mathematics
- Python Programming Language for Data Science
- Statistics and Probability
- Communication Skills: Developing Written Communication Skills

**Year 2**

- Programming Fundamentals: OOPS, Problem Solving using Programming
- Database Management Systems: SQL, NoSQL & Database Design
- Data Analysis and Visualization: Learning about tools for data analysis and visualization
- Statistical Modelling: Learning about advanced statistics concepts and models for statistical inference
- Machine Learning Fundamentals: Classification, Regression, Clustering

**Year 3**

- Introduction to Big Data: Discovering tools like Hadoop & Spark
- Advanced Machine Learning: Reinforcement Learning, Time-Series Analysis, Dimensionality Reduction, Hyperparameter Tuning, etc.
- Natural Language Processing: Introduction to NLTK Framework, Sentiment Analysis, Learning the Skill of Processing Human Level Language
- Deep Learning with Tensorflow or Pytortch

**Year 4**

- Advanced Electives (Students can choose advanced data science concepts to learn from the options provided by the institute)
- Capstone Project

### M.Tech Data Science Program:

M.Tech in data science is a two-year post-graduate degree program designed for candidates who are technically sound and want to master further advanced data science concepts related to data science in detail. . Below is the simplified and commonly observed year-wise segregation of the curriculum for the M.Tech data science program:

**Year 1**

- Python for Data Science & Essentials of Programming: Learn OOPS Concepts, Numpy, Pandas, MatplotLib, Seaborn, etc.
- Statistical Foundation for Machine Learning: Descriptive Statistics, Normal Distribution, Statistical Tests, etc.
- Artificial and Computational Intelligence: Fundamentals of Neural Networks, Artificial Neural Networks, Computational Intelligence and Relevance to Mathematics
- Machine Learning: Discover Classification, Regression, Clustering, and Ensemble Learning Techniques
- Pattern Recognition: Master Descriptive Statistics, Visualization, and Exploratory Data Analysis to Understand Trends and Patterns in Data
- Video and Image Processing
- Deep Learning: Master Advanced Algorithms like Autoencoders, RNN, GAN, LLM, etc. to hone foundational AI skills

#### Year 2

- Natural Language Processing: Learn Techniques and Tools for Driving Insights from Text Data
- Data Pipeline: Understanding the mechanism of Data Pipelines. Learn how to Extract, Transform, and Load the Data.
- Capstone Projects: Work on Research-Backed Projects for Data Science
- Electives (Students can Choose Advanced Electives Provided by the University to Further Enhance their Knowledge

**B.Sc in Data Science Program**

The Bachelor of Science is an undergraduate program designed to equip individuals with the foundational knowledge about any discipline. There are very few colleges that offer B.Sc in Data Science programs, and their curriculum somewhat generalizes around the following program structure:

**Year 1**

- Linear Algebra: Discover Concepts like Vectors, Vector Operations, Matrix, Kernel PCA, etc.
- Basic Statistics: Correlation, Variability, Descriptive Statistics, Probability Distribution, etc.
- Introduction to Data Science: Overview of Data Science, and Fundamental Ideology of the Discipline
- Database Management System: Basics of Database Management, SQL, and NoSQL.

**Year 2**

- Data Structures and Algorithms: Understanding Data Structures and Algorithms to Build Problem-Solving Skills and Figuring Out Less Complex (Time & Space Complexity) Solutions
- Data Wrangling & Visualization: Learn How to Prepare Data for Analysis and Create Visualizations to Communicate Patterns.
- Introduction to Machine Learning: Overview of Data Science, and Fundamental Ideology of the Discipline
- Introduction to Big Data: Understanding Big Data Concepts, and How to Utilize Big Data Tools to Analyze Massive Datasets.

**Year 3**

- Exploratory Data Analytics: Learn How to Figure Out Patterns From the Datasets
- Hands-on Machine Learning: Work on Machine Learning Algorithms and Create Real-time Models for Datasets
- Business Intelligence and Data Analytics: Learn How Business Problem is Solved Using Data.
- Introduction to Deep Learning and AI: Overview of Deep Learning and How AI is Changing the World Around Us.
- Major Project

**M.Sc Data Science Program:**

M.Sc in Data Science is a master’s program that will extensively teach you advanced data science concepts and how you can apply them to create research-backed projects of your own. To enroll in this program, you need a B.Sc degree in computer science and relevant fields. We have highlighted the general course structure for MSc programs below:

**Year 1**

- Preparatory Sessions – Python and Linux: Python Basics, OOPS, Numpy, Pandas, Matplotlib, Seaborn, Linux installation, Linux Commands for File Handling and Data Extraction, etc.
- Data Wrangling with SQL: SQL Basics, Advanced SQL, SQL Optimization and Performance, etc.
- Advanced Statistics: Hypothesis Testing, ANOVA, Joint Probabilities, Bayes Theorem, Regression, etc.
- Machine Learning & Prediction Algorithms: Supervised, Unsupervised, Dimensionality Reduction, Time-series Forecasting
- Introduction to Deep Learning: Tensorflow, Neural Networks, Multi-Layered Neural Networks, ANN, etc.
- MLOps: AI Pipelines, Training, Tuning, and Serving on AI Platform, CI/CD for Kuberflow Pipelines
- Data Science Capstone Project

**Year 2**

**Big Data Technologies**– Database, Modern Storage Frameworks, Data Formats, Distributed Computing, etc.- Data Visualization with Power BI: Power BI Basics, DAX, How to Create Power BI Dashboards, Visualizations with Power BI.
- Model Engineering: Data Quality, Future Engineering, Future Selection, Avoiding Common Fallacies.
- Software Engineering and Data Science: Agile Project Management, From Model to Production, API, DevOps.
- Capstone Project: Research-Backed Data Science Project
- Electives (Advanced Data Science and AI Topics that Universities are offering as Electives can be taken up by the Learner)

**Data Science Course by Intellipaat:**

Intellipaat offers state-of-the-art technical certification programs. All these programs are designed by keeping current industry trends and job requirements in mind. The Data Science Program offered by Intellipaat in collaboration with esteemed iHub, IIT Roorkee is ranked as the #1 program in the data science category by India today.

Whether you are an experienced professional looking to switch to a data science career stream or a fresher looking to enter a lucrative IT field, this program could prove to be an asset. Below are some of the key features of this program:

**Key Features of Intellipaat’s Data Science Program**

- Curriculum Designed by Industry Experts Keeping Industry Demands in Consideration: Major Intellipaat programs are developed in collaboration with top institutions across the world. The Subject Matter Experts ensure that the new job requirements are also taught in these programs, to produce industry-ready professionals.
- Mentorship From Industry Experts: Intellipaat offers instructor-led live training. This means, you directly learn from the pros in the field.
- Hands-On Assignments: At Intellipaat practical exposure is given more preference over theoretical learning. Thus, you will go through multiple hands-on sessions, and with every module you get the chance to solve hands-on assignments.
- Capstone Project: Projects are critical for any professional to build their portfolio. Capstone projects are usually industry-oriented, requiring thorough research and an adept amount of practical skill demonstration.
- Query Resolution from Subject Matter Experts: Intellipaat believes in continuous learning, thus if you come across any technical query, subject matter experts will step in and guide you with the resolution.

#### Curriculum Overview

The detailed curriculum for Intellipaat’s data science and AI program in collaboration with iHub IIT Roorkee is depicted in the image below:

## What Are the Pre-requisites for the Data Science Program?

The prerequisites for a data science program typically include:

- For a data science program, one must have pursued high school. It doesn’t matter whether your stream is humanities or commerce, one can pursue a data science program from scratch.
- Along with this, basic concepts like mathematics, statistics, and computer science are essential.
- Proficiency in languages like Python or R would definitely add value.
- Additionally, a curiosity for exploring and visualizing data, coupled with problem-solving skills, greatly enhances learning

*Go through these **Data Science Interview Questions** and Answers to excel in your interview.*

## Is Coding Needed in Data Science?

Yes! Coding is one of the fundamental building blocks of data science. Without coding, collecting & processing the data alongside creating machine learning models will become impossible. Mostly, programming languages like Python or R are must-haves for one to solve business problems using machine learning algorithms and programming.

## Cracking Data Science With Intellipaat

Intellipaat offers a range of data science training programs such as Bootcamps, Executive Post Graduate Programs, and Advanced Training Certifications, alongside degree programs like M.Tech and M.Sc for data science discipline. Below we have provided a brief description of these programs to make an informed decision about opting for the right data science program.

## Choosing the Right Data Science Program

Intellipaat’s data science courses are curated by assessing industry demands for the data scientist job role, thus once the training comes to an end, learners become skilled at key data science concepts, establish deep practical exposure, and become employable professionals. Here’s the list of different data science programs offered by Intellipaat alongside their details:

Program Name | Data Science Bootcamp | Advanced Certification in Data Science and AI | Executive Post Graduate Certification in Data Science | Executive MTech in AI and ML | M.Sc in Data Science |

University | Intellipaat | IIT Roorkee | IIT Roorkee | IIT Jammu | IU Germany |

Work Experience Required | No | No | Yes (Need not to be related to Tech) | No | |

Coding Expertise Required | No | No | No | No | No |

Course Duration | 6 Months | 7 Months | 11 Months | 2 Years | 12 Months |

Benefits | Learn From Top Industry Experts Receive Job Assistance from Intellipaat 153 Hrs Self-paced Videos Get certified from IBM and Microsoft You will get to learn Python, Data Wrangling, Data Visualization, and much more. | Latest Curriculum with ChatGPT and Generative AI Models Learn from the IIT Roorkee faculty and industry practitioners 200 Hrs. projects & exercises.1:1 with industry mentors and 24*7 support | Learn from IIT Faculty & Industry Practitioners iHUB DivyaSampark, IIT Roorkee Certification620 Hrs of Applied Learning 90+ Live Sessions Across 11 months 218 Hrs of Self-Paced Learning50+ Industry Projects & Case Studies | GATE Score Not Required Receive Alumni Status From IIT Jammu Get M.Tech Degree From IIT Jammu Job Assistance From Intellipaat and IIT Jammu Python and SQL Bootcamp Multiple Case Studies and Project Work | No IELTS Required WES Recognised Program Global M.Sc. Degree from IU Alumni Status by IU Germany Free German Classes by Intellipaat 3 Guaranteed Interviews |

Cost | Explore Program | Explore Program | Explore Program | Explore Program | Explore Program |

## Conclusion

Knowing the Data Science Course Syllabus and Subjects will help a learner determine where to start. No matter if you are a beginner or a seasoned professional, having a deep understanding of the course curriculum is critical. This guide has covered all the essential topics and subjects that come under data science. Also, a proper pattern is followed for the curriculum so that one can start by learning basic concepts and then gradually move toward the more advanced topics.

We also provided a comparison between different data science programs available to help you make an informed decision. Consider all the details provided in this blog, and make a successful career decision.

*Reach out to us on our **Community Page** and get rid of all your doubts!*

**FAQs on Data Science Course Syllabus**

Data science is a domain that is based on 4 major components: Mathematics, Programming, Machine Learning, and Business Acumen. Hence, the course syllabus for data science will include a blend of all the above-mentioned components.

Data science is a difficult field to get into since it requires mastery of mathematics, programming, machine learning, and strong business know-how. However, if you get into a data science course with a well-designed curriculum, and performance-tracking mechanism, the learning journey will become easier. Also, throughout the course duration, you have to be ready to learn, and dedicated to putting in extra effort to attain success.

Yes, data science has a bright and promising future. Nowadays, companies are looking for highly skilled data science professionals and also offering them high salaries. Advancements in AI, machine learning, and big data technologies ensure a continued demand for skilled data scientists.

Yes, coding is required for data science. Knowing programming languages like Python, R, and SQL is essential for tasks such as data cleaning, manipulation, analysis, and model building. To achieve these tasks, one should know how to code.

No, data science is not hard. With dedication, consistency, and hard work, one can learn data science. Having prior basic knowledge will definitely help.