Syllabus of Data Science

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Syllabus of Data Science

We live in a world where data is power. With social media having an impact on every aspect of our lives, even brands and organizations rely heavily on data to make decisions. There is a huge difference in collecting raw data and using that data in a comprehensive and structured way. The latter is harder and that’s why there is a growing demand for professionals who have the required knowledge of data assimilation along with the knowledge of tools and algorithms that will help decode patterns. The importance of data scientists in recent years has made it a lucrative option. With any discipline, it is important that you make an informed decision about what you will be getting into. The same holds true for data science so if you are planning on pursuing a data science course it is important that you have a fair knowledge of the course syllabus. Check out this blog to know the syllabus of data science, top online courses, for beginners, introduction to data science and more!

What is Data Science?

Data Science is simply defined as an interdisciplinary field of study which uses scientific processes, approaches, methods, systems and algorithms to extract requisite insights and information from structured and unstructured data.

Video Courtesy: What is Data Science by Coursera

Ever thought how does Netflix recommend videos based on the genre of your choice? How does Facebook automatically tag the faces of recognised individuals? Or how do banks identify the potentially loyal customers and which are most likely to leave for a competitor and how has the drug discovery process simplified? All of this is possible because of the emergence of Data Science! Blend of business acumen, machine learning techniques, algorithms and mathematics, Data Science helps to find out the hidden patterns from raw data. This skill becomes instrumental because this information will help the organization make informed and big decisions relating to their business.

Data Science Syllabus

Want to know about the syllabus of Data Science? The Data Science course syllabus comprises three main components, i.e. Big Data, Machine Learning and Modelling in Data Science. Across these three main components, the subjects are cover varied areas of this sought-after discipline. Here is the complete Data Science Syllabus:

  • Introduction to Data Science
  • Mathematical & Statistical Skills
  • Machine Learning
  • Coding
  • Algorithms used in Machine Learning
  • Statistical Foundations for Data Science
  • Data Structures & Algorithms
  • Scientific Computing
  • Optimization Techniques
  • Data Visualization
  • Matrix Computations
  • Scholastic Models
  • Experimentation, Evaluation and Project Deployment Tools
  • Predictive Analytics and Segmentation using Clustering 
  • Applied Mathematics and Informatics
  • Exploratory Data Analysis
  • Business Acumen & Artificial Intelligence

Note: The aforementioned data science course syllabus summarizes the major offerings under this specialisation and can vary as per the course and university.

Introduction to Data Science Syllabus

If you are a beginner in data science, there are various introductory courses available online which you can take to familiarise yourself with the basics. Here is the Introduction to Data Science Syllabus:

  • Introduction to Data Science
  • Understanding Exploratory Data Analysis
  • Machine Learning
  • Model selection and evaluation
  • Data Warehousing
  • Data Mining
  • Data Visualization
  • Cloud Computing
  • Business Intelligence
  • Storytelling with Data
  • Communication and Presentation

Some of the top online Introduction to Data Science courses are:

  • Introduction to Data Science by Canvas WPI Hub
  • Introduction to Data Science in Python by Coursera
  • Introduction to Data Science by Paul G. Allen School of Computer Science & Engineering, University of Washington
  • Introduction to Data Science by High School of Economics, National Research University
Syllabus of Data Science

Components of Data Science Syllabus

Data Science curriculum is designed in a way to help students gather knowledge in the field of business, besides applying the tools and statistics to meet organizational challenges in the near future. Therefore, the skills acquired during the trajectory of Data Science and Data Analytic courses is indispensable to becoming an asset in the field of Data Science. Whether you are looking for Data Science Syllabus for beginners or experts, here we have curated a general syllabus of Data Science. Following are the 3 most important components of Data Science are which are followed by most of the universities to help you adapt to both the theoretical and practical aspects of the subject:

  • Big Data
  • Machine Learning
  • Business Acumen & Artificial Intelligence
  • Modelling in Data Science

Big Data 

This part of the syllabus of Data Science focuses on engaging students with Big Data tools and techniques so that unstructured data can be converted into structured data. Big Data initially consists of unstructured data collected in the form of clicks, videos, orders, comments, images, RSS fields, articles, etc. In the case of comparing different products with the help of web API’s and RSS feeds you can access data from different websites for that product. This data is then presented after it is structured out of its earlier format.

Machine Learning

This part of the syllabus of Data Science comprises of mathematical models and algorithms that are employed to code machines so that they can adapt to everyday developments and face the challenges of an organization. Machine Learning is also used for predictive analysis and for time series forecasting, as it can be very useful in financial systems. It employs historical data patterns to predict future outcomes over the course of a few months or a year. If you want to gain more knowledge about the topic then do go through some of the best books for Machine Learning!

Business Acumen or Intelligence

After an organization assimilates and collect tons of data daily, it is important that they have professionals who can carefully analyze and present this data in the form of visual reports and graphs so that the data can be used effectively for making good business decisions. The best way to do so is through Artificial Intelligence! Not only will it develop your knowledge about the business aspect of the process but it will also help you make trends and bring about changes.

Modelling Process in Data Science

Data Science Subjects

Data Science Subjects

If you plan to pursue a course in Data Science, it is imperative for you to know know what all are some subjects will be essential to your learning experience and fundamental for your understanding of the course. So, if you want to know that what are the topics under Data Science then here is a list that elucidates the same. Here are the Data Science subjects:

  • Introduction and Importance of Data Science
  • Statistics
  • Information Visualisation
  • Data Mining, Data Structures, and Data Manipulation
  • Algorithms used in Machine Learning
  • Data Scientist Roles and Responsibilities
  • Data Acquisition and Data Science Life Cycle
  • Deploying Recommender Systems on Real-World Data Sets 
  • Experimentation, Evaluation and Project Deployment Tools
  • Predictive Analytics and Segmentation using Clustering 
  • Applied Mathematics and Informatics
  • Working on Data Mining, Data Structures, and Data Manipulation
  • Big Data Fundamentals and Hadoop Integration with R

How does Predictive Analysis Work?

The syllabus of Data Science is not just limited to the structuring of data in a comprehensive manner but can also be extended to analyzing unstructured data. The algorithms and tools taught through the course will help you in understanding the predictive analysis aspect of Data Science, which is used in modelling the business structure. Predictive analysis uses historical data to analyze and predict the upcoming trends in the market. This information can be used to influence the present way of handling business as well as help them make future decisions.

Is Coding Needed in Data Science?

Yes. to establish a successful career in this field, you need to have sound knowledge of programming languages like C, C++, Java, SQL, Python, etc. But why so? Coding/programming languages helps you identify, analyse, and organise unstructured data in an efficient way. These languages thus constitute an integral part of the syllabus of Data Science.

FAQs

What are the eligibility criteria to pursue Data Science?

To pursue a degree in Data Science, it is necessary to have a background in a related field and have an understanding of the basic concepts that are covered in the field.

What is the duration of Data Science courses?

The duration of a data science course can differ considerably based on the level of qualification. The course can be 20 weeks long for a diploma degree and go on for many years if an established program like a bachelor’s degree or masters is pursued in Data Science or a related field.

Is maths required for Data Science?

Knowledge of certain basic concepts of Maths like Algebra, Calculus, and Statistics might be required for Data Science but having a background in maths is not mandatory. 

Does Data Science require coding?

It is important for a prospective student to have an idea of the programming languages like C++, Java, Python as coding is an important aspect of data science. 

The field of Data Science is growing at an unprecedented rate and has a lot of scope for further growth if you decide to dive into it.  While we have given you an insight into what the field holds for you, the syllabus of Data Science can vary in different colleges even if the core subjects stay the same. So if you wish to pursue Data Science courses and are confused about how to go about it, let the counsellors at Leverage Edu help you make the right decision and shortlist the best colleges for you.

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