Data science is an exciting area to work in, with skilled data scientists much in demand. Using advanced analytic techniques and scientific principles, data scientists can discover valuable information relevant to virtually all aspects of business. That includes customer information for marketing campaigns, identifying and blocking cyber-attacks and fraud, managing equipment and managing financial risks.
These roles require an array of skill sets, often drawing people from mathematics and statistics backgrounds. But good software skills in data science roles are essential to success, especially with the range of software systems commonly used. Gaining these skills can help if you wish to advance your career in this field.
Knowing how to write code is required for a data scientist. Python is the most common computer language in data science, but it is not the only one, with R, Java, Scala, Octave and Clojure also common. Good coding skills help with flexible data transformation to speed up workflow and give greater control over the data, so time spent practicing and learning to code is well spent.
Increasingly tools are being used in data science, replacing the more traditional manual calculations. When using these tools and machine learning libraries, a knowledge of coding is necessary. For some businesses, an understanding of coding is considered so essential that if their data analyst does not have adequate coding skills, they will pair him or her up with a coder to streamline the work for maximum efficiency.
A key task for those working in data science is preparing the data for processing. It is, therefore, essential for the data scientist to know how to effectively process the information. Database management software usually includes a group of programs that can be used to manipulate, edit or index the data. In large systems it might be a multi-user system, providing access to the data in parallel. Data scientists need to be confident in using common database management software including SQL Server, Oracle and IBM DB2.
Windows Azure, IBM Cloud, Google Cloud and Amazon Web services are popular cloud platforms for use in data science. These platforms provide access to operational tools, frameworks, programming languages and databases that help data scientists manage the vast amount of data. Using cloud computing, data scientists can carry out tasks including data acquisition, data mining, testing predictive models and tune data variables. Understanding the concept of cloud computing is one of the key computing skills a good data scientist will need.
Machine Learning uses a type of artificial intelligence (AI), which looks at the use of data and algorithms to mimic how people learn. Machine learning and AI are skills that are particularly in demand in companies that uses a data-centric approach to decision making. Areas of data science that are likely to use machine learning include airline route planning, healthcare, fraud detection and facial and voice recognition systems, so anyone considering a career in these areas should consider developing their understanding of machine learning.
The amount of data produced on a daily basis is phenomenal thanks to the internet and social media, with recent statistics revealing we create 2.5 quintillion bytes of data each day. This data is high in volume, velocity and veracity, something that is known as the 3vs of Big Data. To store, use and manage all this data effectively, many companies are turning to Big Data Technology, such as Spark, Hadoop and Apache Storm. Proficiency in these technologies is fast becoming essential to prevent organizations becoming overwhelmed with the sheer amount of data they need to manage.
Gaining the skills
For those wanting to start or advance in a data science career, there is training available to fill any gaps in your skillset. If there is just one area, such as coding that you feel uncertain about, a course to target this specific area can help you gain skills valuable for employment. Building software skills in data science disciplines can be done a la carte or more comprehensively. If you feel you have several areas you need to build on or are completely new to the career, a more comprehensive course in data science would be a worthwhile move to provide you with all the skills and knowledge you need.
Many universities offer degrees in data science and these are proving popular with students. However, it is not always practical with other work or family commitments to attend such an establishment full time, or you may find there is not a suitable course close enough for you to attend.
Fortunately you also have the option of studying data science online. For those who already hold a bachelor of science degree, a good option is an Online Master’s in Data Science at Worcester Polytechnic Institute. Providing a high level of support, core course elements covering all aspects of data science and a choice of specializations in either AI & Machine Learning or Big Data Analytics, this course provides a thorough grounding to prepare you for a data science career.
It is not always necessary to get a Backelor’s or Master’s degree in order to start and entry level career in data science. Online courses and bootcamps can provide a great start for people not yet ready to pay for a college degree. A data science course can also let you know if this kind of work is enjoyable and meant for you.
Good data science graduates are likely to be in high demand in this growing field and can command lucrative salaries. Careers that data scientists opt for include data analyst, data scientist, database administrator, business analyst, data architect and data engineer.
What other skills are required?
It can sometimes seem that a data scientist needs to be a jack of all trades and a master of them all. Along with good software, mathematical, and statistical skills, data scientists require good communication, structured thinking, and storytelling skills, along with the ability to carry out data visualization, including proficiency in python for data science. Thus, software skills in data science fields may be only one aspect of what you need to have a successful career.
Above all a data scientist needs to be endlessly curious, always keen to see what the data reveals or what might happen if the data is tweaked in any way. This desire for new learning will serve them well, as data science is an ever developing discipline, with new tools and software emerging all the time.