Getting Started with Data Sciences

Welcome 1 lecture 02:39 Introduction Welcome 1 lecture 02:39 Introduction Introduction Introduction Introduction Introduction Examples of Application of Data Sciences 4 lectures 13:28 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. Examples of Application of Data Sciences 4 lectures 13:28 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. 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. 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. 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. 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. 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. 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 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 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 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. 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. 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 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 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 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. 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. 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. 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. 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. 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. 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. 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 4 lectures 08:41 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. Data Analysis. 4 lectures 08:41 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 tegories of Machine...