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So to start with the question, How to do the best preparation? Is taking the class enough? Is the practice exam or other prep materials worth the cost? Will I pass if I do this or that? Ultimately, how can you improve your chances for success on a SAS Advanced Programming certification exam? Many time candidates are in search of the silver bullet that will guarantee they pass an exam. But, there is no silver bullet, in fact, the only way is to come up with a plan to give your best to succeed. The preparation required depends on what is the status of yours with SAS Software related knowledge and your experience on the technologies. However, if we want to draw a plan which can be applied as a generic plan, that can be as follow.

Start with the First thing first, is this Certification for you?

As some questions to self, first. Is this certification right for you? Do you have the recommended experience? Are you ready to spare time required for preparations and training? Most of the time, the SAS training are costly, so that planning also very important. Is this credential for me? Should I try to gain the knowledge and experience to earn this credential and does it help me in my career path? This is the time that some people like to go ahead and schedule the exam. Draw a line in the sand. A deadline helps some people take action and develop a plan.

When I questioned myself, does certification really important in my career with Qlik? My answer is yes, and this is almost every time! However, certification does not replace years of experience and both complement each other just well. When it comes to passing the certification exam, I feel I should try to answer one generic topic and I feel this should be here on top, so it is, if you only do these certifications because you need to do it, you will fail it in 80% cases! Remember, it is not just a tag, but it actually validate your skills on the QLIK technology and one of the true value of having these certifications is to allow the company to stay as an elite partner. For consultants or developers that looking to start their data adventure with Qlik, this is the drills.

About the QLIK Certifications.

There are a total of six exams available for Qlik products at the moment. Three for Qlikview and three for Qlik Sense and they are pretty much identical to each other in term of the objectives for the exam.

As we all know the time is of DATA, there is no doubt in the mind when I say the boom in the data industry has driven and taken the demand for data science, data Analyst and Data engineers to the next level, across all industry verticals. There are job openings for data scientists, data engineers, and data analysts. And there seems to be a lot of confusion and lot many different opinions among people regarding the roles and skillsets driving this field. Here we will mostly discuss two major job titles, Data Scientist and Data Analyst.

Let us Understand the Main Difference Between Data Scientist and Data Analyst

I know many of you may not convince with this but yes, there are no defined skill-sets that can distinguish between the role of a ‘Data Scientist’ and ‘Data Analyst’. This is mainly because most of the companies have their own definition and role descriptions. In fact, different companies have different definitions for both these roles, and there is a lot of grey area in between the two job titles. Broadly analyzing, a Data Scientist is a professional who combines data handling and data visualization with sound business understanding to make smart business decisions.

We’re living in a time of marvelous opportunity, especially for those who work in the tech industry. With so many evolving technologies, skilled professionals can extend their knowledge to become specialists in several fields or transition into a new field. This is particularly true of Big Data, where the types of jobs are increasing as the rate of data generated each day increases at an amazing pace. Even as we generate unprecedented amounts of data in 2018, IDC predicts we will generate approximately 440 times more information by 2020. All that data must be stored in scalable and cost-effective data platforms, while still being accessed, analyzed, and put to use. And it’s the Big Data professionals who make all of that happen, meaning today’s organizations have a dire need for Big Data professionals, including data scientists, data analysts, data engineers, and developers, and systems administrators and managers. For those with the right skill set and certifications, the market offers plenty of job opportunities and attractive salaries packages. According to Glassdoor, there are almost 49,000 Big Data jobs that need to be filled, isn’t this superbly amazing!

We all know that data scientists are in high demand. In fact, Amazon says that job opportunities for these experts have grown by 505% over the last 5 years. With the Internet of things producing more data than ever before, it’s easy to see why all organisations want people who can make sense of big data to drive them to success. But what does this mean for market researchers? Can data science really replace them all together? Per Statista, the big data market is growing rapidly, with $42 B USD revenues in 2018 and a massive increase to an expected $1.89 B in 2019. Corporations are adopting new technologies to help manage integration and draw insights from big data. In the telecommunications industry alone, around 94.5 per cent of respondents representing the telecommunications industry stated that their organization currently used big data technology as of 2018.

The market researchers are still essential in understanding the context of the information provided by data scientists, and to interpret the ‘what’ from the ‘why’. Ben Page, chief executive of Ipsos MORI goes on to explain how observational research is still essential, using a real-life study conducted for a global pet food manufacturer as a case in point.