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Data Science Course Syllabus

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Data job roles have been the talk of the town in this decade. Thus, professional data science certification programs have grown in popularity. But how can you determine which program is best suited for you in this crowded online training space? The answer lies in the course syllabus. Read this Intellipaat blog to land an ideal data science course syllabus to make an informed decision about choosing the right data science program.

In this Data Science Course Syllabus blog, we will discuss the following topics in detail:

What Is a Data Science?
Why Data Science Course?
Data Science Course Subjects
Data Science Course Syllabus
Comparison of Data Science Programs
What Are the Pre-requisites for the Data Science Program?
Conclusion

Frequently Asked Questions (FAQs)

What Is Data Science?

Data science as the term itself implies, is the science of dealing with data and finding something meaningful out of it. It just doesn’t stop at discovering patterns, data science also tries to create prediction, recommendation, detection, as well as sentiment analysis models, which can be generalized for the same type of data. 

As a discipline, data science involves programming, statistical modeling, preparing data, data analysis, machine learning, and deep learning. So if you are looking to master this field, you must acquire these mentioned skills. 

Why Data Science Course?

Whether it’s a startup or an MNC, every organization nowadays follows a data-driven decision-making strategy. According to a recent survey, almost 48% of organizations have adopted data-driven strategies. Due to the rise in the adoption of data-driven strategies, the demand for jobs in data science is also at its peak.

Did you know that on LinkedIn, more than 1,16,000+ data scientist jobs are available in India right now?

Why Data Science Course?-Data Science Course Syllabus and Subjects-Intellipaat

Also if you consider the USA, there are more than 218,000+ data scientist jobs available.

Data Scientist Jobs Available in United States - Data Science Course Syllabus and Subjects -Intellipaat

This is just the LinkedIn job board, if you consider the other job boards, the number of jobs available right now would be even more substantial. This fact itself proves that data science is one of the ultimate career choices right now. As we have understood why data science, moving forward let’s try to break down the key concepts you need to master in order to become a professional in this particular discipline.

Data Science Course Subjects

It is essential to understand the course subjects of any field before getting into it. The reason is that if you have an idea about the subjects, you can easily identify what are the critical concepts you will have to master. Not only that researching the course syllabus would also help you make a judgemental call whether a certain professional training program is good enough to help you become a data scientist. After a careful analysis, we have listed the course subjects that every data science program must cover.

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 assignments, allowing you to apply learned concepts to real-world problems.  

Data Science Course Syllabus

The Data Science course syllabus usually outlines the topics and assignments for courses in data science. Below is the data science syllabus of various programs.

Data Science Course Syllabus-Data Science Course Syllabus and Subjects-Intellipaat
Subject NameSyllabus
Data Science Fundamentals (Preparatory Sessions)Python and R Programming Languages
NumPy, Pandas, Seaborn, and Matplotlib Libraries
Statistics for Data Science
Linux
Data Transformation Using SQLSQL Basics
Advanced SQL 
Deep Dive into User-Defined Functions
SQL Optimization and Performance
Inferential AnalyticsStatistics and Descriptive Analytics using MS Excel
Python for Descriptive, Diagnostic, and Inferential Statistics
Machine LearningPython
Linear Algebra & Advanced Statistics
Machine Learning
Supervised and Unsupervised Learning in ML
Data VisualizationIntroduction to Data Visualization
Acquiring and Visualizing Data
Applications of Data Visualization
Data Visualization tools and techniques
Advanced Machine Learning AlgorithmsBagging And Boosting Algorithms
Predictive Analytics And Machine Learning
Cognitive Science and Analytics
Data Science at Scale with SparkIntroduction to Big Data And Spark
RDDs
Advanced Concepts & Spark-Hive
Natural Language ProcessingOverview 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 Models
Dialog Systems
Computer VisionRBM 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

Now that we have a clear picture of data science course subjects and syllabi, we will now look into the comparison of the best data science programs.

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: 

It is a four-year program. It starts with the basics of computer science and programming. In the third year, you will be presented with an opportunity to choose your minor. Many universities these days offer data science as a minor. However, the learning journey will not be extensive.

With such a program, you can just learn the basics of data science along with fundamental skills like data wrangling, SQL, EDA, machine learning algorithms, etc.

  • M.Tech Data Science Program: 

M.Tech in data science is a two-year post-graduate program that is designed to teach you various techniques and theories related to data science which includes statistics, data mining, data warehousing, and data visualization. This program teaches students how to apply their understanding of computer techniques and technologies in the field of Big Data to solve real-world problems.

This program is beneficial for students who want to improve and apply their skills to gain a complete understanding of data science and engineering. Some of the characteristics include the ability to collect, understand, and manage vast amounts of raw data to gain valuable information. Students undertake research projects, gaining expertise in developing innovative solutions to complex data problems.

  • M.Sc Data Science Program: 

M.Sc in Data Science is a master’s program that will extensively teach you everything that you need to know about data science. Generally, this program is spanned for a duration of 2 years and covers everything from statistics, data warehousing, wrangling, data analysis, data mining, predictive analytics, machine learning, deep learning, etc. 

To get into this program, you at least need a BSc degree in relevant domains like statistics or computer science. If you are interested in doing an MSc in Data science, check out the global MSc degree program offered by Intellipaat in collaboration with Germany’s #1 university, IU Germany. 

  • Professional Data Science Certifications: 

Professional data science certifications offer a more flexible and focused approach to acquiring data science skills. For those who belong to a non-tech background such as commerce or humanities, these certifications are best suited for them. They are often designed for working professionals or career changers seeking to upskill or specialize in areas such as data engineering, business analytics, or machine learning. 

Certifications offer targeted learning paths that can be completed at one’s own pace, making them suitable for those looking to enter or advance within the data science field with a specific focus.

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

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, knowing what is the curriculum is important. 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. 

Frequently Asked Questions (FAQs)

What exactly is data science?

Data science is the field of study that deals with the data to extract meaningful insights for business. It is a multidisciplinary technique that uses principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze vast amounts of data.

What is a data scientist's salary?

According to Glassdoor, the salary of a data scientist ranges from ₹7L – ₹19L per annum in India, whereas in the USA it ranges from $92,000 – $2,00,000 per annum.

Does data science have a future?

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.

Is coding required for data science?

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.

Is data science hard?

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

Course Schedule

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