Home > What Is Data Science? Know About The Data Science Course Syllabus & More

What Is Data Science? Know About The Data Science Course Syllabus & More

A blend of statistics, mathematics, business acumen, tools, algorithms, and machine learning techniques constitutes data science! The knowledge of all these concepts helps in finding the hidden patterns in big volumes of data that can fetch actionable insights. You must have heard industry-leaders say “Data is the new oil!” which makes it clear that if it’s unrefined it’s barely of any use. It is here that Data Science actually comes into the picture! A data scientist knows how to put the unrefined data to use.

The market is abuzz with the data science jobs and hence, many schools are bringing out courses on this discipline. But while opting for any of the courses, it is pertinent to know Data Science Course Syllabus that the institute is offering.

Here’s a list of key-areas recommended by our expert educators that you should check in the data science course syllabus to decide on whether or not you should take up the course:

  1. Foundations are covered: Knowledge of statistics, finance analytics, and Tableau is very important as the course progresses. Hence look for a data science course that has a foundation module that can strengthen your concepts in these areas.
  2. Machine Learning: Machine Learning is the science of learning to get the computers to act without actually being programmed explicitly.
  3. Big Data Analytics: All the data that is being produced by internet users everyday forms the big data. Big Data Analytics involves making the colossal amount of data structured in order to put it to use.
  4. Live Projects: This should be a must-have in any data science course syllabus as it involves putting all the theory that an individual has learned into practice.

There are three components of Data Science namely Machine Learning, Big Data, and Business Intelligence. Hence, any course that offers full coverage on these three key components is best suited for learning. Let us look at what these are:

  1. Machine Learning - ML involves learning about algorithms and mathematical models that are chiefly employed to make machines learn and prepare them to adapt to everyday advancements. An example of this could be making predictions by studying patterns in data.
  2. Big Data- Each day internet users produce a lot of data in the form of purchase, watch-history, image uploads, clicks, etc. All of this is unstructured data and collectively called Big Data.
  3. Business Intelligence- Each business works on big volumes of data that need to be presented in visual form to augment effective decision making. Business Intelligence helps in making it possible.

Being an industry leader in the education sector, Career Launcher brings to you a comprehensive Post Graduate Program in Data Science & Big Data Analytics covering all the three key components in great detail. The course has been designed in collaboration with faculty from KREA University. Check out the data science course syllabus of this program below.

Data Science Course Syllabus

1. Foundation Module:

The foundation module helps in ensuring a firm grounding in the basic tools required to appreciate both the application & delivery aspects of Data Science. Candidates need no prior experience in programming. The topics that are covered in this module include:

  1. Introduction to Statistics
  2. Introduction to Data Science using R
  3. Financial Analytics 
  4. Data Visualization using Tableau

2. Predictive Modeling  & Machine Learning:

The science of using past data to generate useful future scenarios is called predictive analytics. Candidates need to have a clear and concise understanding of predictive modeling to appreciate the application modules that follow. The topics covered in this module include:

  1. Perspective on Data Science
  2. Python for Data Science
  3. Predictive Analytics & Data Mining
  4. Machine Learning

3. Big Data and Applications:

Big Data is the colossal volume of data that is unstructured. This module gives an insight into the understanding of big data and also introduces real-world applications in Data Science, through a mixture of theory, real-world problem-solving and guest lectures. The topics covered in this module include:

  1. Retail Analytics
  2. Deep Learning & Ensemble Methods
  3. Big Data Analytics
  4. Forecasting

4. Prescriptive Analytics

Prescriptive Analytics focuses on making the participants learn analytics techniques that are useful when no clear past intelligence is available with respect to input-output correlation. The topics covered in this course include:

  1. Optimization Techniques
  2. Supply Chain Analytics
  3. NLP and Sentiment Analysis
  4. Marketing Analytics

5. Capstone Project Ideas:

During this module, the participants will get the opportunity to work on real-world problems across several industries. It will allow the participants to put all the theoretical knowledge that they have gained through the course into practice.

When you’ve decided that you want to make a career in this up-and-coming industry, it is better you choose a course that fits the demand of the industry with its course contents. Get in touch with our expert counselors to get complete guidance on a data science course.