Python is a simple yet powerful programming language that’s a must for beginners and advanced programmers alike. Here’s why. High-level programming languages have one goal in mind: to make your life as a programmer easier. Messy syntax and obscure keywords? Forget about it. With languages like Python, you can get away with understanding just the basics of programming, enough to begin writing your own scripts and apps. And since Python developers are high in demand, Python is a great language to learn if you want to pursue a career in software development or big data.
Data Scienceand Big Data are the biggest industry buzzwords in 2018. Experts believe that artificial intelligence (AI) systems will continue to reign supreme in the technology marathon through 2019. “More than 40 percent of data science tasks will be automated by 2020.” –Gartner The data science trends for 2019 are essentially a continuation of some of the key trends of 2018, including areas such as: Machine learning (ML) Artificial intelligence (AI) Big data Edge computing Blockchain Digital twins Serverless computing Because data science is so vast and challenging to navigate, we’ve compiled a list of the most popular data science articles that dominated the industry over the past 12 months.
R is an extremely powerful and lucrative language for data science, and as of 2018, it’s one of the most popular programming language choices for data science professionals. R is an open-source programming language that’s widely used by data miners, statisticians, and data scientists to perform statistical computing as well as data analysis. Given R’s increasing popularity, R professionals are faced with plenty of career options and future possibilities.
Unfortunately, data isn’t always available in the exact structure you prefer. And there’s nothing more frustrating than having inconsistent, untidy data that produces biased results. Let’s take a look at how the Tidyverse can help. What is Tidyverse? Before you can conduct any analyses or draw any conclusions, you often need to reorganize your data. The Tidyverse is a collection of R packages built around the basic concept that data in a table should have one observation per row, one variable per column, and only one value per cell.
Which influential data scientists are leading the charge? To help you keep up with the latest data science trends, we’ve compiled a list of the top data science and big data experts worth following. Putting Your Data to Work! Data science has long been a driving force in modern business, but even more so now with the wealth of data at our fingertips. Data is everywhere, thus, we need to learn how to make the most of it and get ahead in business.
Programmers commonly have many questions about R, a popular programming language in data science and analysis. R is used all over the world by professionals in the fields of data science, data visualization, data mining, and statistical analysis. But what exactly is R? Where did it come from? And why is it being used specifically by data science professionals? This article attempts to answer all these questions, including the most important of them all: Should you be learning R as well?
Are you the type of database administrator who organizes files by folder? Do you have a folder full of sorted folders? If so, then scrum is the right methodology for you. A database administrator (DBA) is an essential member of any IT team. DBAs create, maintain, and secure an organization’s database, in addition to overseeing user rights assignment, backups, querying, and database tuning. You need specialized training and technical expertise to manage the specific RDBMS setup used by your organization (to understand what an RDBMS is, read on), along with a quirk for analytical thinking.
Within organizations, Scrum promotes efficient time and process management along with better team building and leadership. In order to implement Scrum, you’ll need to follow a few simple rules. Introducing Scrum Today, we have the power to collect precise data both quickly and in vast quantities. In fact, 90% of the data available today was collected in the last two years alone. The rise of big data has greatly increased demand for data scientists, but the profession is one where few candidates possess the right skills.
Here’s a reality check: Big Data has hit us like a speeding truck on the highway of business intelligence. In today’s digital age, we’re generating data about ourselves that were once considered private, and we’re doing it willingly! From what we eat and wear to where we are at all times, nearly everything is now public knowledge. The data generated is a potential diamond mine for everyone in business, from one-person companies to Fortune 500 A-Listers alike, all thanks to the Internet.
Our world is bursting with data—the new digital age has dynamically inflated the volumes of data collected by businesses worldwide. By 2020, about 1.7 megabytes of new information will be created every second for every person on the planet. According to Forbes, The White House has already invested more than $200 million in big data projects. Can you imagine that? Seriously! Yet all the data that’s being generated by people, machines, online devices, and other sources doesn’t provide decision-makers with any valuable insights by itself.