Data Scientists have a blend of skills that make them unique. Mathematicians by nature, they are persistent problem solvers, skilled collaborators and excellent communicators. Data Scientists use feature engineering to prepare data for machine learning and also apply Artificial Intelligence to improve business performance.
Data Scientists filter big data and build processes to help predict consumer habits, in order to create a more reliable business model. Part of the process is to develop artificial intelligence models and tools to reveal patterns in consumers’ behaviors and then automate the process to better anticipate future consumer habits and trends in order to improve business decisions.
After graduation, Data Scientists usually continue to pursue specialized training to enhance their skills in applied statistics, scripting and programming. This is where they learn to customize data to glean more meaningful details that apply to your business needs.
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The HPCC Systems platform is a set of easy-to-use software features, enabling developers and data scientists to process and analyze data at any scale.
While it’s difficult to learn, it’s worth it. 43% of Data Scientists use R programming to solve statistical questions because it’s designed specifically to solve data science issues. Data Scientists are also very likely to understand other data science tools and platforms such as NumPy or MatLab.
Data Scientists are often required to know Python, as well as Java, Perl and C/C++. Python is relatively easy to learn, and it is supported by an active community. Python has been gaining on R in popularity among Data Scientists in recent years, though both of these open-source languages are popular.
Newer Data Scientists in particular are drawn to Python. In a 2018 survey, 48% of Data Scientists, who have been in the field for five years or fewer, ranked it as their favorite programming language. That’s nearly double the number who said the same in 2016.
While SAS continues to see strong support among professionals with 16 or more years’ experience, Python made noticeable gains here as well. Those with 6-15 years’ experience slightly favor R, but the levels of support are within five percentage points among all the tools.”
It’s the backbone of complex queries and universal among Data Scientists to execute complex queries in SQL (structured query language). In a CrowdFlower study of LinkedIn job postings, SQL was mentioned most often, appearing in 57% of the listed desired skills for Data Scientists.
Data Scientists should also be familiar with some NoSQL such as MongoDB or HBase. These systems work quickly with large volumes of data and are easily scalable for a more customized approach.
Many Data Scientists learn the Apache Hadoop platform, and knowing Hive or Pig just adds to their arsenal. In the LinkedIn job postings study mentioned above, Hadoop came in as the second-most important skill for a Data Scientist, appearing in 49% of the job postings.
Apache Spark is also popular because it’s faster than Hadoop – a boon when running extremely complex algorithms.
In addition to platforms such as Hadoop, some data scientists are also experienced in working with cloud-based tools such as Amazon S3.
Data Scientists may be knowledgeable about advanced artificial intelligence and machine learning techniques. Data Scientists working in this area should be intimately familiar with decision trees and logistic regression in order to solve problems and make statistical predictions, which allow businesses to make better decisions.
Machine learning and tools, and the types of algorithms that Data Scientists understand may include Decision Forests, k-NN, SVM, Weka and Naive Bayes among others.
Tableau, ggplot and other software languages allow Data Scientists to work with huge amounts of data. Some of it may even seem useless until they can combine and use that information to reveal specific trends. Together, Data Scientists and your company’s business executives can work to better understand what the data shows, allowing your company to leverage it when making future business decisions.
Customer reviews, blog posts and other unstructured data doesn’t fit neatly into tables, so Data Scientists find new ways to interpret it. This kind of information can add even more details to existing data, honing your decision-making process.
Data Scientists have a blend of skills that make them unique. Often using insights from a variety of fields such as economics, cognitive science or machine learning, they apply data driven systems and then scale them according to your business needs. And they’re naturally mathematicians, with a strong knowledge of vector calculus, linear algebra and statistical computing. They may even have a background in neuroscience!
But Data Scientists are more than human data crunchers. They’re also:
Advanced education is a given for Data Scientists. In general:
There are no specific degrees that are required of Data Scientists, but there are some subjects that they’re drawn to naturally, such as:
One degree may not be enough to satisfy Data Scientists’ curiosity. In order to master big data, some of them have multiple degrees in:
Xperra Data Scientists leverage the latest technology. They apply AI and machine learning to interpret big data for accurate predictions, helping you make more reliable business decisions, faster and more dependable than ever before.
They’re your team of experts who are responsible for:
Xperra’s Data Scientists devise custom answers to your data issues and problems by developing models that achieve ever-increasing accuracy so you can see trends and opportunities. And when you can see the big picture, you’re more equipped to make better marketing and business decisions, and get the edge you need over your competition.