Big Data & Business Analytics

Big Data Analytics


Why Business Analytics

1. The future – Industry have so much of data available now generated during the digitization phase in last 2 decades. Now is the time to utilize this data for executive management and strategic decision making.

2.Biggies like Tesco, Wal-mart, American Express, New York Stock Exchange etc have already Big Data implementation on the way.

3. McKinsey Global Institute’s Big data: The next frontier for innovation, competition, and productivity estimates that by 2018, “the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know- how to use the analysis of big data to make effective decisions.

4. 90% of fortune 500 companies will have big data initiatives underway by end 2015.

Benefits of Big Data Analytics Course

1. Analytics using Big Data can be leveraged across all industries including Pharma, Telecom, Manufacturing, Retail, Health care, Public Sector Administration.Big Data & Business Analytics

2. Estimate of over 2.5 lakh big data analytics jobs to be in there by mid 2015 in India alone.

3. Learn from the experts – We have engaged the best brains in the industry with a sea of practical and implementation experience in the field to give you the best available knowledge and share the real life case studies and experiences across different domains to prepare you for the real world.

4. Learn and understand the applicability of Big Data analytics using cross section of tools and technologies that can be used tomorrow.

Course Contents

Module 1 : Basics of Business Analytics & Business Mathematics

Module 2 : Predictive Analytics and Advanced Techniques

Module 3 : Business problem solving using advance analytics

Module 4 : Analytics in the age of Big Data

Module 5 : Big Data implementation across domains

Module 6 :Big Data Architecture

Module 7 : Data management using big data

Module 8:Benefits and business implementation of big data over the conventional

data management

Business analytics has now become so essential to every team, that data analysts are in high demand all over. A ‘full- stack’ data scientist is an elusive target for recruiters, with all seeking someone, expert simultaneously at statistics, analytics and machine learning. On the other hand, those who merely provide solutions on business intelligence or data mining have now been relegated to the periphery. However, what all need to realize is that the three sciences mentioned under the data scientist’s capability are all different to each other in substantial ways. Excellence in statistics requires rigor. Performance is the key to machine learning. For acing analytics, speed is of the essence. There is the major clause of depth versus width when it comes to pitching for all these, or looking for individual specialists. Analytics is thus needed for decision making and running AI, so there exist mass dangers of under- appreciating their adherents- the analysts.


Uploaded Date:23 December 2019

Data scientists have come up with a strategy to build the right model that captures data science. As a first step one needs to think big, yet start with small steps. The KISS principle or Keep It Short and Simple, needs to be followed. The 80- 20 Pareto Principle would also apply here. Another tip is to focus on finding the model, instead of building it. Of course, any such model, would require a through data warehousing operation, before anything could be concretely firmed up. Organizations need not lose sleep over failures. Instead, they must learn quick. The error rate needs to be checked for.


Uploaded Date:21 December 2019

A number of companies are now seeking to ace the competition using a data- centric business strategy. There are however, several barriers to this kind of transformation. In order to become data- driven, the company needs to align its data warehousing efforts, and subsequent analysis to the overall business agenda. For this, a data- driven culture too needs to be curated in place. Data- generated insights need to be made use of. The right technology and infrastructure need to be used, for all this to be fruitful. This includes a suite of analytical tools, for the right purposes. As data is the lifeblood of such digital- driven tasks, companies that have aced this game, will be best able to make us of its business analytics capabilities to generate the right insights.


Uploaded Date: 12 December 2019

There exist several myths surrounding the big data platforms. One of them is that if all the data sources are streamlined together, the most accurate of business insights may be procured. But this isn’t necessarily true, as several data sources just can’t be streamlined together due to the diverse policies, security requirements and related restrictions. Another myth is that with data all at one place, one can focus most on the business analytics. Instead, data needs to be stored on catalogue basis. The third myth is that industry technologies need to be used up. But more than the kind of technology, an agile structure is needed. People also believe in hoarding data, as more will apparently help us answer an increasing number of questions. In reality, it is about the quality of data and the tools being used, and not the extent of inputs available. The final myth is that there aren’t enough of data scientists to perform the tasks necessary for the analysis and intelligence gathering processes. Truth be said, more of capabilities need to be utilized by the data- driven solutions providers.


Uploaded Date:24 October 2019

Predictive modeling and business analytics is of course supposed to be cold and unbiased. But it is an oxymoron to say that there are inherent biases that predictive modeling is laden with. This is because the data warehousing done, followed by the data set understood for analysis, is chosen as per human selection. This selection process is full of biases, unknown to many. A method has now been developed by a statistics professor. It specifically tackles the issue of race- based biases. Criminal justice for instance is one area where the use of algorithms needs some form of extra precaution now. These algorithms must be considered primarily for providing us the inputs, on which we may process out information.


Uploaded Date:14 October 2019

Most organizations these days want to make full use of the data warehousing being done. This is primarily to forecast trends, but also to understand their customers and the markets. That is why the new generation of business analytics makes insights discovery so much more efficient. This branch of analytics comprises of four sub- categories which are- predictive, prescriptive, descriptive and diagnostic. Each have their own sets of advantages. Descriptive is to understand “what happened”, while diagnostic is to assess “why did it happen”. Predictive is to provide foresight, while insights can best be gauged using prescriptive analytics. The greatest power comes from the combo of prescriptive and predictive analytics together.


Uploaded Date:14 October 2019

The platform economy, that we are all a part of now, requires a different mentality to do work in. There are now a number of companies that are actually fake, and use their reach to fraud customers. To be careful, the area of talent recruitment also needs a change in game plan. As several freelancers and part- time workers are part of many companies now, they need to be sourced, but with caution. For this, Upwork is the best platform. It uses big data to connect recruiters with the potential talent. Uber’s success with drivers can well be replicated by Upwork, when it comes to tech and content professionals. Three kinds of platforms are now in vogue. These are for transaction, innovation and a hybrid model. Upwork falls under the first category. The utility of Upwork is best in that it can work across verticals.


Uploaded Date:20 July 2019

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