Data Science Training, Data Science - Machine Learning With Python

By Jerome Titus

Blueocean Learning is an IT consulting, Solutions and Services organization based out of Bangalore for the last 2 decades with a bandwidth to train corporate bodies and individuals alike in all niche technologies. We train organizations of all sizes from SME's to Global Corporations. 
DATA SCIENCE: 
The need for its storage also grewas the world entered the era of big data. The main focus of enterprises was on building framework and solutions to store data. when frameworks like Hadoop solved the problem of storage, processing of this data became a challenge.Data science started playing a vital role to solve this problem. Data Science is the future of Artificial Intelligence as It can add value to your business. 
The goal to discover hidden patterns from the raw data, Data Science has a blend of various tools, algorithms, and machine learning principles. Data science course explains how to process history of the data. Data Science does the analysis by using advanced machine learning algorithms to identify the occurrence of a particular event. Data science look at the data from many angles sometimes angles not known earlier. Data Science is used to make decisions and predictions using predictive causal analytics, prescriptive analytics, and machine learning. 
• Predictive causal analytics - This a model is used in predicting the possibilities of a particular event occurring in the future, Say, if you are providing money on credit, then the matter of customers making future credit payments on time is a concern for you. We can build a model to predict if the future payments will be on time or not by using the history of the customer. 
• Prescriptive analytics: This a model which has the intelligence and ability to take its own decisions with dynamic parameters. 
we can run algorithms on data to bring intelligence to it. Using Prescriptive analytics model you can enable your car to take decisions like when to turn, which path to take, when to slow down or speed up. 
• Machine learning for making predictions - You can build a model to determine the future trend of a finance company using the transactional under the paradigm of supervised learning. a fraud detection model can be trained using a historical record of fraudulent purchases by training your machines. 
• Machine learning for pattern discovery-This is the unsupervised model where you don't have any predefined labels for grouping. The most common pattern is Clustering. To establish a network by putting towers in a region we can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.