You would hear this a lot from various sources that Data Science is actually nothing without statistics. Statistical research and data analytics are central to data science projects and, therefore, should be on top of your fingertips. Top data science concepts are built and sustained for results based on these concepts.
Here are the top data science concepts that you can actually learn in less than a quarter of a year.
Data mining is a powerful concept taught religiously in Data Science Institutes in Bangalore. The crux of this concept lies in processing of data and recognizing the various visible and hidden patterns ‘mined’ by processing information for real-time decision-making.
With the advent of Big Data Analytics, the concept has expanded its horizon beyond the basics of traditional data mining techniques. Today, Data Science training course demonstrate various aspects of data mining as a process and how to build an SQL using Document databases and MapReduce.
In Data Mining techniques, you would learn about structuring and implementation of Decision Trees, an intelligent classification-attribution amalgamation. This cool technique mixes historical data with machine learning capabilities to drive the structure of long-term decision trees and build identity classifications for predictive modeling.
A relatively ‘ancient’ concept in Data Science projects, Bucketization is a broad spectrum topic that turns data into a platform agnostic resource. In machine learning, bucketization is often linked to computational signal processing where a wide range of numerical values are replaced by one discrete value, or a quantum of data. Quantum computing is often considered as an offshoot of this powerful concept in data science.
In discrete data modeling, it could solve challenges associated with storage, warehousing and summarization. With the rise of data visualization, bucketization concepts have been revived to better understand and demonstrate the collapsing of unstructured data points.
How could data science concepts be complete without mentioning 5G! As the world moves toward implementing data science for real-time processing of information, telecommunication and mobile devices are making the ‘quantum’ leap to embrace 5G as a technology.
5G-powered applications require powerful database infrastructure to support the increased volume of data traffic and network speed. Backed by the soft errors seen with 4G, today’s data science concepts are looking at 5G with a totally new perspective. 5G and data analytics could solve various adoption pain-points seen during the implementation of newer platforms like the Internet of Things, AI, Connected Health apps, Autonomous vehicles, Smart Cities and Robotic Automation powered by mobile devices.
According to a financial report, online credit fraudsters would siphon off $32 billion by 2020.
What would be the deterrent against fraud? Technology.
With the tokenization of financial resources and growth in stature of crypto and bitcoin, data science projects are getting a big push. Fraud analysis is the fastest-growing and most well-paid aspect of Data Analytics landscape, amply enabled by AI and Machine Learning capabilities.
Data science institutes in Bangalore cover advanced topics in Fraud detection, spam detection and Loan Fraud prediction.