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Data Science Course Syllabus for Beginners

Data Science Course Syllabus for Beginners
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Data gets more complicated as it grows. Hence, data science is becoming very important for the future of business, society, and science. In this blog, we’ll explore what data science is, the important associated syllabus to learn, and finally, why it’s important in our lives today.

Table of Contents:

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What is a Data Science Program?

Data science is the study of analyzing and visualizing raw and large data to find insights, meaningful data, and hidden patterns and to make predictions. It is a domain of study where first the data is collected or recorded on a large scale from heterogeneous resources. Then the data cleaning is done with the help of some cleansing techniques, also known as data preprocessing. After this, some EDA functions are performed, and then data visualization is done. From the visualization, we can make predictions and find hidden patterns.

flow chart of data science subjects

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Data Science Program Subject

Learning data science in this era of technology is quite easy. There are many in-built libraries through which one can learn this technology in a very effective way. For learning data science from beginner to advanced level, there are some main subjects or technologies that an enthusiast has to learn. Here are some important subjects to learn that are given below:


Data science is that domain of study where mathematics plays a vital role in the analysis part. Mathematical concepts like linear algebra and calculus, probability, and statistics will help in understanding the algorithms more easily and efficiently.

Mathematics, at its core, is the language through which scientific data communicates and interprets the complex explanations hidden in the data. This makes it indispensable for success and excellence in the field. Some of the mathematical concepts are mentioned below:

  • Linear Algebra and Calculus: It is commonly used for data manipulation, while on the other hand, calculus is vital for optimizing techniques for the data. For regression techniques like Singular Value Decomposition (SVD), calculus matrix operations are beneficial, and for algorithms like Principal Component Analysis (PCA), linear algebra is useful. Linear algebra and calculus are also implemented in machine learning models, which are based on neural networks.
  • Probability and Statistics: Probability and Statistics are important concepts in scientific literature, and their importance cannot be overstated. These concepts are used in hypothesis testing, decision-making, and predictions. As probability is the chance of an event occurring, whereas statistics is the study of mathematical data, while working with a large population of datasets, we do sampling and conclude. We can also find out the uncertainty and variability of the data, which will further help in better model-making.

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Computer Science

Computer science in data science has huge importance. It is used in almost every technique in data science. It is used in model making, predictions, data preprocessing, etc. All the programming languages, big data, and collections of data all fall under computer science. We can also do real-time data processing. Below are some of the computer science technologies that are beneficial for taking data science as a career:

  • Programming languages: The programming languages one needs to have hands-on experience with are as follows:
  1. Python: For data science, learning Python is the best alternative. It is one of the most popular programming languages. Python is the simplest language, and it has inbuilt libraries like Numpy, Pandas, Matplotlib, Seaborn, etc., all of which are used for performing mathematical operations, data manipulation, and data visualization.
  2. R: R programming language has a rich collection of packages that are used for data science tasks. Along with this, R also helps in data visualization, data exploration, statistical modeling, and statistical analysis.
  3. Ruby: It is not widely used but can be used for tasks like web scraping due to its simplicity and readability. 
  • Basic DSA: Having a basic understanding of data structure and algorithms is a must, as this is the fundamental concept of programming. As a data scientist, one has to deal with large datasets. So, knowledge of algorithms (searching, sorting) and data structures (arrays, linked lists, heaps, stacks, and queues) is needed as they help in better data processing.
  • SQL, Database: SQL is also a fundamental core of data science. It will help you to create a database first and then perform data retrieval, data cleaning, aggregation, join, merge, etc. One must have basic knowledge of databases like Relational Database Management Systems, NoSQL, and Data Warehouse.
  • Machine Learning: It is a field of artificial intelligence that focuses on training machines in such a way that they can perform tasks that humans can do. Machine learning also aims to develop algorithms and statistical models so that machines can improve their accuracy. It is divided into three major categories, mentioned below:
machine learning flowchart
  1. Supervised Machine Learning: This algorithm uses a labeled dataset, which means that each input data has its own predefined output with which it is paired. This algorithm takes an independent variable and a dependent variable and maps input to output by finding patterns.
  2. Unsupervised Machine Learning: This algorithm deals with an unlabeled data set, which means that it will predict output on its own on the basis of classification. This algorithm is not provided with target values. It tries to discover structures, patterns, or relationships in data on its own.
  3. Reinforcement Learning: It focuses on training agents to make a sequence of decisions in an environment to maximize reward signals. The agent takes action, observes the outcome, and learns over time which actions lead to better outcomes. The common applications of reinforcement learning include game playing, robotics, etc. 


Visualization is one of the most important steps and techniques in data science. It helps in the better representation of raw data into meaningful insights for better decision-making. It represents data in the form of charts, such as pie charts and graphs. It conveys complex information in an easy and effective way.

For visualization, we have several tools, which are as follows:

  •  Tableau: Tableau is one of the most important visualization tools used in data science. It is used for better data storytelling, optimization, data preparation, real-time data analysis, and also for collaboration and sharing.
  • Power BI: It is ready-to-use source in data science. It helps in data preprocessing, integration, data exploration, and advanced analytics. Even though Power BI is not a data science tool, it is still an important asset to data science.

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How much Time Does it take to Learn Data Science?

Data science is a huge field of technology and comprises subjects like mathematics, computer science, and visualization, so it will take some time to start from scratch.

Being a beginner with a mathematics background will be easy, as mathematics is the core foundation and first step towards the journey of becoming a data scientist.

Whereas, if someone has prior knowledge of coding and some libraries like Pandas and Numpy, then it will be a little easier for them.

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The journey to becoming a data scientist is very interesting. Here you can learn about multiple technologies, as discussed above. The scope of job opportunities as a data scientist or data analyst is increasing day by day. One can earn a good package in this particular field, as the job market is booming in India as well as abroad.

Choosing data science as a career is one of the best alternatives, as it helps not only in financial growth but also builds your critical thinking, decision-making, and problem-solving skills.

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About the Author

Principal Data Scientist

Meet Akash, a Principal Data Scientist who worked as a Supply Chain professional with expertise in demand planning, inventory management, and network optimization. With a master’s degree from IIT Kanpur, his areas of interest include machine learning and operations research.