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.
In the last few years, big data has become central to the tech landscape. You can consider big data as a collection of massive and complex datasets that are difficult to store and process utilizing traditional database management tools and traditional data processing applications. The key challenges include capturing, storing, managing, analyzing, and visualization of that data.
In recent years, machine learning has been one of the most talked about tech topics and is being applied to businesses widely. Put simply, this application of artificial intelligence allows computers to learn and improve without being programmed directly. The revolutionary technology presently forms a highly crucial aspect of countless established, as well as, burgeoning industries. Let’s have a look at the key reasons why you should start preparing now to become a machine learning professional.
If you’re interested in learning artificial intelligence or machine learning or deep learning to be specific and doing some research on the subject, probably you’ve come across the term “neural network” in various resources. In this post, we’re going to explore which neural network model should be the best for temporal data.
Being a data analyst would mean you’ll have several skill-sets that one needs to work in the domain of data science. However, it doesn’t mean you can easily jump from your data analyst career into the role of a data scientist. Before discussing if being a data analyst could act as a step to becoming a data scientist, let’s take a look at what each of these professionals do.
In today’s business landscape, data science has become almost ubiquitous and is steadily gaining control
over the tech field. With a massive amount of data being generated on a regular basis, a huge number of
organizations are trying to leverage the power of data science. With the help of data scientists,
businesses can come up with effective solutions for their problems and predict present and future
trends, which directly lead to success. But as with any other field, data science also comes with its fair
share of difficulties.
Though the blockchain technology was conceptualized as a core element of the cryptocurrency Bitcoin,
today we can see a significant number of blockchain applications and businesses across the globe are
steadily adopting the technology to leverage its full potential. If you’re an aspiring blockchain
enthusiast, keep on reading this post as we’re going to take a detailed look at the blockchain technology
and its working method.
You’re probably aware of the fact that data science and machine learning together with Python as the
preferred programming language of data scientists and developers alike are all being considered the
hottest career paths in the IT field. Businesses across the globe are increasingly becoming reliant on
these fields and are hiring trained professionals who’ll help them rise above the competition.
Magnimind Academey, one of the leading names in the field of data science training provider, might be
best known for its full-stack data science bootcamp in Silicon Valley but it also offers a broad range of
mini bootcamps on various related fields including machine learning and Python.
In this post, we’re going to discuss why any of the certificates obtained from Magnimind Academy
would help you make a big leap forward in your career.
These days, terms like data science, machine learning and artificial intelligence are sometimes mentioned interchangeably, albeit incorrectly. Even an organization offering a new technology powered by any of these may talk about their high-end data science techniques without having much knowledge about them.
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.