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...

From automation to smart office gadgets and chatbots, artificial intelligence has today become increasingly prominent in the workplace. As smaller organizations see their major competitors taking advantage of AI and machine learning, they've understood they require to jump on the bandwagon to keep up -- but they might be thinking how they'll be able to afford it. Luckily for organizations on a budget, you don't require to break the bank to begin integrating AI and machine learning (ML) into your operations. By initializing on a smaller scale with ready-made solutions, you can leverage the power of AI and ML, and enhance your business performance. Here are the few ways through which you can incorporate Machine Learning in your enterprise. 1.Leverage Existing Platforms Creating your own AI is quite complex and expensive- but it doesn’t mean it can’t benefit you. Several big organizations such as Facebook and Google have open-sourced their own AI endeavors, making it pos...

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...

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...

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...

As big data and data analytics have become significant to every business irrespective of the industry, business organizations across the globe are facing a sharp talent gap for these skills. This talent shortage has caused many businesses to turn to online corporate training programs in big data to upskill both existing and new employees quickly. Today, the amount of data produced by businesses is staggering and enterprises across the globe are struggling to find the right talent to collect, store, monitor and analyze it to derive insights for actionable results. In 2014, a study conducted by Wanted Analytics found that the demand for programmers with a background in statistics, or data analytics grew tremendously by 337%, but out of the 332,000 programmers in the United States, hardly 4% of them had the required skillsets. As a result, many business organizations turned to online corporate training programs to help employees upskill with the necessary skills that companies d...

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...