Course Summary

Data Science course it is important that you have a fair knowledge of the course syllabus or Outline : -

Introduction - Introduction to Data Science
Use for the Industry - What does Data Science Syllabus Consist of?
Sub Module1 - Big Data
Sub Module2 - Machine Learning
Sub Module3 - Business Acumen or Intelligence
Topics - What are the Subjects in Data Science?
Assignment - How does Predictive Analysis Work?
Concept Programming - Is Coding Needed in Data Science?

Introduction to Data Science

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.

What does Data Science Syllabus Consist of?

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. 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
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.