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It’s now commonly known that many businesses across the world utilise vast amounts of data to enhance their offerings and streamline business processes.
Due to this, it’s equally unsurprising that more employees want to have a greater understanding of how to work with large data sets – including how they can utilise these to benefit their organisation.
With enough dedication, anyone willing to learn programming can eventually forge a successful career in the field. While data analytics can seem intimidating, there are a range of instruments available to practitioners that can assist them in their work.
As a student of our online MSc Data Analytics course, you’ll be familiarised with a number of these tools. Our course will also help future-proof your career by teaching you how to assess new programs as they emerge, so that you can choose the right one to meet your company’s needs.
Read on for more details of software and tools often used by data analytics professionals:
KNIME
The Konstanz Information Miner, or KNIME, is an open-source data analytics and reporting platform. KNIME utilises the concept of a modular data pipeline to enable both machine learning and datamining within an intuitive user interface.
One of the main reasons for KNIME’s popularity is its ability to easily create visual data flows. This allows data analytics professionals to review their output in a more comprehensible, interactive manner.
Scalability is another key benefit of the platform. Thanks to the numerous extensions that can be added to KNIME, it can be customised substantially to work alongside a company’s evolving needs.
Tableau
Tableau is an interactive data visualisation tool that helps translate raw data into easily understandable formats. Tableau can pull data stored in a variety of places, from cloud systems to Excel documents. Data analytics professionals can then utilise the platform to create graphs, maps, tables, and similar.
Accessibility is one of the platform’s strongpoints. Its straightforward visualisations help teams across an organisation easily identify or investigate any trends in data collected.
In fact, a part of Tableau’s popularity can be attributed to the fact that it can be employed by people from non-technical backgrounds as well. As it doesn’t require any programming expertise, anyone can use the software to build their own data dashboards.
Apache Hadoop
Typically referred to as simply “Hadoop”, this open-source software framework facilitates using a network of computers to provide immense levels of data storage and processing. The mammoth degree of computational power provided by Hadoop gives servers the capacity to handle a virtually limitless number of concurrent tasks.
Companies often turn to Hadoop as a way of securing relatively inexpensive storage and processing capabilities. With the framework, non-specialist computers to be purchased and linked together to form powerful networks that can serve most organisations well.
Plus, the nature of this set up also means that it is highly scalable. A network can grow to include thousands of machines, making it an easily adaptable approach to handling data.
Apache Spark
Like Hadoop, Spark is a part of the Apache Software Foundation. It is an open-source data processing framework. Spark specialises in processing tasks on large data sets at a high speed.
In a similar fashion to Hadoop, it can also distribute these tasks across multiple computers. Its efficiency is accomplished by transferring data from physical storage into electronic memory, which allows processing to be carried out at far high speeds.
Data analytics professionals will often utilise the framework to perform tasks such as creating data pipelines, ingesting data, running machine learning algorithms, and so on.
Are you ready to become a data analytics professional? Our part-time, online MSc in Data Analytics allows you to study from anywhere, at any time – all while still honouring your current commitments. Connect remotely to Portsmouth’s own computer clusters and supercomputers, and analyse real data to answer real-world challenges.
