Corporate Training Analytics

People or organizations carefully following the trends and expert opinions in data science and predictive analysts will know that it is best to start from machine learning.   Experts often advise that it is best to take one step at a time. Start with the proverbial’ low hanging fruit’ and then move on to bigger and more complex operations as you gain relevant experience along the way. Machine-learning-as-a-service (MLaaS) platform Current trends and indications clearly point towards the value of machine-learning-as-a-service (MLaaS) platforms. Machine Learning is fast turning into a commodity thus making it well within the reach and resources of small and mid size organizations.   Leading vendors such as Microsoft, Amazon and Google provide Application Process Interfaces (APIs) and platforms to run basic ML operations without the elaborate need to invest in building complex infrastructure and hire professionals with deep knowledge and expertise in data analytics. It...

No matter what sort of business you have, your employees may become bored and stale doing the same routine tasks every single day. In order to foster an engaged workforce or set up employees for success, you may consider offering opportunities to cross-train your employees that will broaden their horizons and make them a valuable asset to the organization. Cross-training is not only helpful in improving productivity, but it also allows employees to develop new skills in a specific field that will heighten their professional development and career growth. Here are the reasons to consider cross-training employees: 1. Maintain the same productivity even when employees are absent.   There are certain things that can’t be avoided, such as family emergency or a sudden injury. But when one employee is absent for a couple of days, the other members can easily cover up for that absence if they are well-versed with that employee’s key tasks.   For short-term absences, cross-t...

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...

In the next few years, big data technology will continue to remain the ‘Big thing’ for large enterprises. Tech experts have estimated that the amount of data large enterprises produce every day could be worth trillions of dollars or even more. Access to such a huge amount of data can be a blessing or a curse for businesses. If churned well, it is not only helpful in deciphering information, but also in reducing decision-making times. On the other hand, access to large and widely disparate sets of data increases the need to hire big data analysts to draw meaningful business insights from the data. To exploit big data, business organizations will have to mend the big data skill gap. Listed below are some of the best ways to bridge the big data skill gap in 2018. #1 Scout for the right resources with the right skill set A good big data scientist should have an analytical mind along with a strong background in statistics and mathematics, particularly, linear algebra and calc...

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...