Corporate Training Analytics

Organizations today need to do a lot more than just acknowledging big data. Today, they need to make data and analytics an integral part of their business. Obviously, this will need building a quality data science team to handle the data and analytics for the organization. Choosing the right team members can be difficult, primarily because the field is new and most companies are still trying to identify exactly what a good data scientist should be able to do. The following information should help to make the process simpler. The Right People You will need to have data scientists who can work on massive datasets and who know the theory behind the science. Moreover, they should be capable of building predictive models. Data software developers and Data Engineers are significant, too. They need to understand the infrastructure, architecture, and distributed programming. Some of the other positions to fill in a data science team consist of the data platform administrator, da...

With high demand for data scientists and the high salaries that they draw, it’s often not practical for organizations to keep them on staff.   Instead, various organizations work to ramp up their prevailing staff’s analytics skills, comprising predictive analytics. But companies need to advance with caution. Predictive analytics is particularly easy to get wrong. Here are the first few “don’ts” your team needs to learn, and their parallel cures. 1. Don’t Fall for Buzzwords — Explain Your Objective Data Science doesn’t essentially refer to any specific technology, method, or value proposition. Rather, it indicates a culture — one of smart people doing creative things to discover value in their data. It’s essential for everyone to keep this on the top of the mind when learning to work with data. Under the broad umbrella of data science sits predictive analytics, which provides the most actionable win you can draw from data. In a nutshell, predictive analytics is technolo...

In this digitalized world, the amount of data produced by large business organizations is growing at a rapid pace. Today, every large company is struggling to find ways to store, manage, utilize, and analyze the data. Furthermore, you would be astonished to know that the data produced by large business enterprises is growing at the rate of 40 to 60% per year. However, simply storing this massive amount of data won’t be useful for your business. This might be the reason why business enterprises are looking at options like building data lakes and using the latest tools and technologies that can help them in handling big data analytics challenges to a great extent. With no further ado, let’s take a quick look at some big data analytics challenges faced by large business enterprises and how to overcome them. 1. Handling voluminous data in less time Handling the data of any large organization is a challenge in itself, but when it comes to handling voluminous data in a short span ...

If recent reports are to be believed, there will be a need for an additional 346,000 data scientists in Europe by 2020. This clearly shows the skills gap in the UK in the field.   The demand for data scientists and analysts is increasing, but there is not enough talent in the market. The Annual activity report 2015 of the European Commission highlights the problem more precisely. At that time, 77% of data analyst jobs remained unfilled, and it was predicted that the problem would become worse with a 160% increase in demand by 2020. The reason behind this yawning talent gap is clear – demand has surpassed supply and there is a lack of corporate training in data analytics .   There has been an exponential growth of data sources as well as of organizations’ hunger to utilize this data and make the most of the data collected from the disparate sources. A recent study conducted by the International Data Corporation predicts that the 'digital universe' will reach 40 zettabyte...

Today business enterprises are trying to bring machine learning into their arsenal, but without the right skills they will likely fail. The number of jobs that machine learning could make redundant over the next few decades is a growing cause of concern amongst many of us.   According to a research study conducted by the PwC, 38% of jobs in the United States will be automated by the end of 2030, while in other parts of the world it would be somewhat less. In the United Kingdom, it will be 30%, and in Germany, 35%. While it can’t be denied that hundreds of thousands of jobs will be lost, as with all periods of technological advancement, we will witness the creation of new jobs. Many of these new jobs will be dedicated to developing and supervising machine learning algorithms, helping business enterprises to incorporate and implement the technology and bring in efficiencies in their business operations. To some extent this has already begun.   According to Indeed.com, a p...

Let’s admit the truth that today employees are no longer interested in attending group meetings that result in boring lectures. Many of them have started protesting against daylong seminars. As a result of this, corporate learning is shifting gears once again to meet the needs of young professionals who prefer to take corporate training in data analytics , data science and big data through micro-learning modules. Microlearning is the delivery of corporate training on new technology, processes, and equipment in short bursts. Today, microlearning is a significant aspect of employee development.   Over the past few decades, rapid advancement in technology has made corporate training modules easier to understand, more productive, and cost-effective. These days, paragraphs of instructions have been replaced by interactive illustrations, quality graphics videos, and compact textual content. Large business organizations across the world have deployed their own Learning Manag...

Big data isn’t new for many of us, but with the advancements in technology- businesses, both large and small alike, are presented with an opportunity to make the most of the data generated by every single interaction of ours for improved insight into business operations, finances, customer behavior and many other important elements of an organization. While some business organizations are well-versed with the need to collect, store, access, organize and analyze big data in order to remain at the forefront, many are asking how it can all be done. Here comes in the role of the next-generation big data-skilled professionals. These data savvy talented professionals are expert in transforming big data to create meaningful and business driven insights, enabling businesses to improve their customer relationships, optimize business operations, and provide unmatched services for new business opportunities. So desired are these data savvy professionals in the market, that there is a sharp talen...