Course: Getting Started with Data Sciences

Getting Started with Data Sciences

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
  • Self-Paced
About this Course

In the age of Data Revolution and rapid technological advancement, do not be left behind. Data Sciences has come to the fore as a must have knowledge whether you are a Businessman wanting to invest in new Products and Services OR whether you are working on any particular domain. With so much data available from almost any kind of device we use in our daily lives, the application of Data Sciences is growing at an exponential pace.

This course provides an introduction to Data Sciences. The goal of this short course is to expose as many areas of Data Sciences as possible within 1 hour. Once you are aware of these topics, you can study further any specific topic or all of the topics.

The course is designed for CxO and other Decision Makers who want to invest their money in taking advantage of this Data Revolution. This course is also meant for Students, Researchers and almost everyone from any work of life so that they are able to understand what is the upcoming world going to be.

Basic knowledge
  • No Prerequisite required
What you will learn
  • Introduction to Data Sciences
  • Trends in Data Sciences
  • Examples of applications of Data Sciences
  • Introduction to Machine Learning
  • Introduction to Predictive Analytics
Number of Lectures: 20
Total Duration: 01:06:31
  • Introduction  
Examples of Application of Data Sciences
  • Examples: Introduction  

    We start the course by discussing some examples where Data Sciences have been applied by our Company. These are problems we have provided solutions for or are working on providing solutions. We discuss solutions we have worked on because this will provide a view of what are the Industry requirements like for application of Data Sciences. If anyone browses the Internet, one can find a plethora of applications of Data Sciences. And the number of application are only growing everyday.

  • Example 1: Predictive Analytics for a Gym  

    In this video, we discuss a solution we formulated for a Chain of Gyms. The need was to predict the outcome of usage of the Gym facilities for the purpose a Customer would have joined the Gym for.

  • Example 2: Prescriptive Analytics for a Telecom Company  

    This this video, we discuss the solution for a Telecom Operator. Every Telecom Operator provides Interconnection Services to other Telecom Operators. Now, Telcos have systems for optimising the way they route calls of other Telecom Operators so that they revenues for the Telecom Operator is the maximum. However, this is a huge area for application of Data Sciences and Artificial Intelligence so that this optimisation can be improved to generate maximum revenue for the Telecom Operator.

  • Example 3: Fraud Detection for an Insurance Company  

    In this video, we discuss the application of Artificial Intelligence for detection of Fraud in an Insurance Company. Insurance Company suffer from huge amount of revenue loss due to fraudulent claims. This loss can be minimised through the application of Data Sciences and Artificial Intelligence.

Data Analysis
  • Data Analysis: Introduction  

    In this video, we introduce Data Analysis and/or Data Analytics.

  • Social Network AnalysisL Introduction  

    Social Networks are a modern phenomenon which is being absorbed by most people. This is making the world more connected. This connectivity means that these networks can be analysed for many business purposes. The amount of data generated by Social Networks is enormous. Analysing this data provides a lot of insights which is very useful for business.

  • Network Visualisation  

    Network Visualisation is a very important aspect of Social Network Analysis (SNA). Through Network Visualisation, we can make very key business decisions in situations like Marketing Campaigns, etc.

  • Network Simulation  

    Network Simulation is a very powerful application in Social Network Analysis (SNA). Simulating a network, we can determine how our program might behave before we have launched the same. It is very useful in situations like when an epidemic strikes a town or city. We can plan contingency measures based on network simulations.

Predictive Analysis
  • Introduction to Predictive Analysis  

    Predictive Analysis is a huge area of Data Sciences. Predictive Analysis is an area of Statistics where we try to predict the outcome or trends in the past, present or in the future. Predictive Analysis is primarily of 3 types - Predictive Modelling, Descriptive Modelling and Decision Modelling.

  • Example of Predictive Analysis: Next Word Predictor  

    In this video, we see an example of a Predictive Analytics application. We discuss briefly how these algorithms are developed.

  • How to find problems to solve through Predictive Analysis?  

    Though Predictive Analysis has been around for a very long time, the use of Artificial Intelligence in conducting Predictive Analysis is not very prevalent in the Industry across the World so far. This is an area where Businessmen could provide a lot of solutions.

Machine Learning
  • Introduction to Machine Learning  

    Machine Learning is an aspect of Artificial Intelligence. Machine Learning is required where a Computer cannot be provided explicit set of instructions for performing a task; instead the Computer needs performing the tasks and making decisions based on patterns and inferences provided to it from the past.

  • Categories of Machine Learning  

    Machine Learning can be broadly categorised into Supervised Learning, Unsupervised Learning and Reinforcement Learning. We discuss each of these briefly in this video.

  • Common Algorithms for Machine Learning  

    In this video, we discuss the different types of Algorithms we generally program for Machine Learning.

  • Basic Steps in creating Data Products  
Upcoming Areas of Data Sciences
  • Upcoming Areas  

    In almost every Industry, work is being conducted for finding solutions based on Data Sciences. We discuss 2 areas of Data Sciences where a massive amount of Research and Development effort is being expended in the Modern World.

  • Introduction to Neural Networks  

    These artificial networks may be used for predictive modelling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from experience can occur within networks, which can derive conclusions from a complex and seemingly unrelated set of information.

Essentials for venturing into Data Sciences
  • Essential Skills and Knowledge  

    In this video, we discuss what are the skills required for developing and/or doing business with Data Products.

Course Closing
  • About Me  
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