The Most Likely Problems In Data Analysis?

As a growing number of businesses and organizations rush to unlock the value of massive amounts of data to derive high-value, actionable business insights via data analysis, they are also facing certain problems. Here are the most common problems that you’re likely to face when performing data analysis:



Will You Be A Part Of Future Big Data Analytics?

Big data as a concept may not be something new, but in the past few years, it has gained a huge amount of interest and media attention. It’s the volume of a dataset that primarily defines big data. In general, big datasets are huge, crossing the threshold of petabytes sometimes. Traditional data analysis methods fail to deal with this amount of data.



What Tools Do You Use To Perform Data Analysis?

Data analysis comes with the goal of deriving useful information from data, suggesting conclusions, and supporting critical business decision making. There’re lots of data analysis tools that can be utilized to help a business to get a competitive edge. If you’re trying to step into the field of data analysis, it’s extremely important to have a good working knowledge of the most commonly used data analysis tools. In this post, we’re going to discuss five such tools by learning which you’d be able to propel your career in data analysis.



Why Python is Essential for Data Analysis?

In the U.S., over 36,000 weather forecasts are issued every day that cover 800 different areas and cities. Though some people may complain about the inaccuracy of such forecasts when a sudden spell of rain messes with their picnic or outdoor sports plan, not many spare a thought about how accurate such forecasts often are. That’s exactly what the people at Forecastwatch.com (a leader in climate intelligence and business-critical weather) did.  They assembled all 36,000 forecasts, placed them in a database, and compared them to the actual conditions that existed on that particular day in that specific location. Forecasters around the country then take advantage of these results to improve their forecast models for the subsequent round. Those at Forecastwatch used Python to write a parser for collecting forecasts from other websites, an aggregation engine to assemble the data, and the website code to show the results. Though the company originally used PHP to build the website, it soon realized that it was much easier to only deal with a solitary language throughout. And there lies the beauty of Python, which has become essential for data analysis. Let’s delve deeper to understand what makes Python so popular in the field of data analysis.