Data Science Training, Data Science – Machine Learning with Python

Blueocean Learning is an IT consulting, solutions and services organization based in Bangalore for the past 2 decades and has a bandwidth to train both corporate bodies and individuals across all niche technologies. We train organizations of all sizes, from SMEs to Global Corporations.


The need for storage also grew as the world entered the era of big data. The main focus of companies has been on building frameworks and solutions to store data. When frameworks like Hadoop solved the storage problem, processing this data became a challenge. Data science began to play a critical role in solving this problem. Data Science is the future of artificial intelligence because it can add value to your business.

The purpose of discovering hidden patterns from the raw data, Data Science is a mix of different tools, algorithms and machine learning principles. Data science course explains how to process the history of the data. Data Science performs the analysis using advanced machine learning algorithms to identify the occurrence of a particular event. Data science looks at data from many angles, sometimes unknown angles. Data Science is used to make decisions and predictions with predictive causal analyzes, prescriptive analyzes and machine learning.

• Predictive causal analysis – This model is used to predict the possibilities of a particular event in the future. Suppose you provide money on credit, then the question of customers making future credit payments on time is a concern for you. We can build a model to predict whether future payments will be on time or not, using customer history.

• Prescriptive analytics: this is a model that has the intelligence and ability to make its own decisions with dynamic parameters.

we can run algorithms on data to bring intelligence into it. Using the Prescriptive Analytics model, you can let your car decide when to turn, which path to take, when to slow down or accelerate.

• Machine learning for making predictions – You can build a model to determine the future trend of a financial company using the transactional under the guided learning paradigm. a fraud detection model can be trained using a historical record of fraudulent purchases by training your machines.

• Machine learning for pattern recognition – This is the unattended model where you do not have predefined grouping labels. The most common pattern is Clustering. To build a network by placing towers in a region, we can use the clustering technique to find those tower locations that ensure that all users receive optimal signal strength.