Five Things to Know About Data Science
Data science has evolved from a traditional analysis-based industry into an intricate field filled with different complexities. Businesses are demanding data scientists more than ever to help them understand their growing databases and uncover useful intelligence.
Investing in data science allows organizations to analyze and use data in creative ways to generate business value. But what exactly is it, and is it the right career for you?
What Is Data Science?
Data science combines business, technology, statistics and communication into one role to gain knowledge and insights from big data (i.e., structured and unstructured data). The field uses the scientific method alongside algorithms and probability to organize and analyze information.
The terms “data science” and “data scientist” came about around 2008 after companies like LinkedIn and Facebook realized that they had access to massive amounts of data and that they could do something with that information. But because the datasets were so large, it was necessary to place someone in a specific role dedicated to sifting through and analyzing it.
Data scientists ask the right questions, collect the information needed to answer those questions, organize data and extract insights that they can present to company leaders. Because of the influx of information companies are now gaining due to technology, every industry can benefit from these skills, making the data scientist role incredibly valuable.
Goals of Data Science
Data scientists are hired to help businesses translate data into new potential revenue streams. As such, their main goal is to clean up and analyze large amounts of big data. Using software designed specifically for big data and analytics, data scientists must be able to extract insights and present their findings in a way that business leaders and other stakeholders can understand.
Data scientists are required to deliver data-based deliverables, such as pattern detection analysis, optimization algorithms, prediction engines, etc.
Fields Within Data Science
There are a few important roles that branch out from the field of data science:
- Data Scientist
As we’ve already noted, data scientists combine business knowledge, data analysis and communications to help companies find and extract knowledge from big data. Once they present their findings to business leaders and stakeholders, the insights are used to make decisions about moving the organization forward.
Data scientists should have some programming skills for data science-specific languages (such as Python, R, SAS, etc.), a background in statistics and mathematics, the ability to tell stories using data visualization and knowledge of Hadoop, SQL and machine learning. You can find more information and some recommended course titles on our Analytics and Data Management Job Role page
- Data Engineer
Data engineers develop, deploy, optimize and manage data pipelines and infrastructure. This role is required to manage the influx of rapidly changing data and prepare information to be sent to data scientists.
Data engineers need skills in programming, such as Java and Scala. They must also have a background in NoSQL databases and frameworks such as Apache Hadoop.
- Data Analyst
Data analysts and data scientists are often mistaken for one another. Data analysts, however, often act as the middleman between data scientists and business analysts.
Analysts sift through the data to find the answers that align with high-level business strategy, rather than generating the questions themselves. Another difference is that data analysts don’t usually have a computer programming background, and they aren’t typically involved with machine learning or statistical modeling.
While data analysts still need programming skills in Python, S and R, they don’t need as advanced programming skills as data scientists. They also need to be able to map out and visualize data to make it easy to understand, and they still need statistical and mathematical skills. You can find more information and some recommended course titles on our Analytics and Data Management Job Role page.
Common Data Science Tools
Programming Languages: As you’ve read above, data scientists and analysts have to know a handful of programming languages, including R, Python, Scala, Julia, SQL, Java, etc. You don’t have to be an expert in all of these languages, but many are helpful for breaking down data.
Data Modeling and Visualization Tools: Tools include Scikit-learn, Pandas, TensorFlow, Numpy, e1071, Mat plotlib, Shiny, D3 and ggplot2. These pre-existing packages and libraries all help with the statistics, mathematics, data visualization, algorithms and modeling needed to organize data.
Database Tools: Data engineers also have backgrounds in NoSQL databases such as MongoDB and Cassandra DB. Scientists and analysts must be able to access and query data, so they also need to be able to use NoSQL, NewSQL and relational database management systems (i.e., MySQL, Redshift, Hadoop, HBase, etc.)
Big Data Tools: Hadoop, Spark, Pig, Drill, Hive, Presto and other big data technologies are used to analyze data and provide a framework for processing and distributing big data.
Responsibilities and Career Opportunities for Data Science Professionals
A Data Scientist’s typical job duties may include:
Researching and developing statistical learning models for data analysis.
Collaborating with operations, business teams and engineering departments to understand company needs and devise solutions.
Optimizing joint development efforts through database use and project design.
Communicating results and ideas to key decision-makers.
Implementing new statistical methodologies as needed for specific models or analysis.
Career opportunities are excellent as data science roles exist within every industry imaginable, from world-class IT companies and big-box retailers to healthcare organizations and even marketing agencies.
There are currently over 30,000 data science positions in the US, according to Glassdoor. Data analysts start around $81,000 per year in the United States. Entry-level data scientists make about $100,000 on average, with the rate increasing based on experience. And big data engineers start at $126,250.
The Bureau of Labor Statistics also predicts that jobs in big data are some of the fastest growing careers in the US. The field expected to grow by 11 percent by 2024. Anyone who chooses a career in this field can expect to have plenty of options for their future.
Become a Data Science Expert with New Horizons
If you’re interested in working with big data, then a career in data science is the right path for you. Once you choose between data scientist, data analyst and data engineer, all you have to do is start learning.
New Horizons, the world’s largest IT training company, is here to help. We have the data science training courses you need to start working toward a certification, or you can talk to one of our training specialists to determine the right path for you. Click here to view our Data Science page.
At New Horizons, we’re talking about IT everyday—and not just with a variety of clients, but with leading vendors—about industry trends and real-life challenges. And because of these close partnerships, New Horizons is positioned to help businesses like yours leverage our knowledge experts to discuss strategies, implementation and troubleshooting.
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