Written by Pamela Stone

The field of big data can be intimidating, especially if you are new. As big data continues growing even faster than populations, efforts used by organisations to deal with data also deluge. Increasing methods create a continuously expanding set of terminology, including buzzwords, used to describe them. Therefore, this tech field is dynamic, and some people may have a different understanding of the same terms. That said, below are some essential data analytics terms that beginners should know.

An algorithm is a common term in mathematics and computer science. This term describes a categorical process of solving complex problems and performing data analysis. An algorithm is made up of multiple steps that should be followed in a particular order to solve particular problems.

2. Artificial Intelligence

This is a common term in big data and related technologies. Artificial intelligence involves programming machines and computer systems to handle tasks normally done by human intelligence, such as decision making, intelligent data analysis, language translation, speech recognition, and more.

3. Behavioral Analytics

This new business analytics concept involves gathering insights about customer behaviour through eCommerce sites, social media platforms, and mobile or web apps. Information gathered from behavioural analytics enables businesses and digital marketers to target the right customers with the right offers at the right time.

4. Batch Processing

Batch processing has also gained popularity with big data due to the large amounts of data sets involved. The term describes an efficient method used to process or analyse large volumes of data.

Big data forms the basics of data analytics. It refers to large volumes of data collected and stored by businesses and other organisations. Big data may be structured or unstructured. Before the inception of modern technologies, businesses struggled to make sense of or draw meaningful conclusions from this data.

6. Cloud Computing

Cloud computing is a must-know term for anyone learning data analytics. As the name suggests, this new computing system provides virtual services, including IaaS, SaaS, and PaaS. Cloud computing provides virtual IT resources, such as software, storage, database, and infrastructure.

7. Cluster Analysis

This is another term in big data that describes the process of grouping similar objects in clusters. Cluster analysis helps big data analysts to understand differences and similarities between data sets.

8. Data Science

Essentially, data science is a speciality that deals with analysing large amounts of random data to generate meaningful results that can be interpreted for decision making and problem-solving.

9. Data Analyst

A data analyst is a big data career term describing someone responsible for collecting, processing, and analysing big data. Data analysts should also identify various ways that data can be used to help organisations make better decisions.

This is another big-data career term describing someone who practices data science. Often confused with a data analyst, a data scientist should be proficient in statistics, computer science, mathematics, and data visualization.

11. Data Architecture

Data design or architecture describes the models, policies, and rules controlling how data is aggregated, arranged, stored, and used in data systems.

12. Database Administrator

A database administrator is a data analytics role describing someone with the capacity of planning, designing, monitoring performance, troubleshooting, securing, configuring, and offering database backup and data recovery.

13. Data Cleaning

Also called data cleansing or scrubbing, this is the process of rechecking data to remove errors, duplicate entries, add missing data, and incorrect spelling. This is important since using inaccurate data during analysis leads to wrong conclusions.

14. Data Visualisation

Data visualisation describes the act of presenting data in pictorial or graphical formats to communicate results or derive meaningful conclusions. Data visualization makes it easy to derive insights from data.

This is a process where computers or machines learn and adjust behaviours based on data feedback. This usually manifests as a changing but the adaptable algorithm used to predict outcomes of events without human input.

16. Query

This is a method used to derive answers from data sets. That said, query analysis involves analysing a specific search query and optimizing it to produce the best possible results.

17. Python

This is an open-source and object-oriented coding language used in data science to develop various analytics tools. This language has a simple learning curve and can be used by expert programmers and non-programmers. Other programming languages commonly used in data analytics are Ruby and SQL.

18. Server

A server is a physical or virtual computer that receives and sends requests related to software applications over a network. This is a common term used in all big data technologies.

The data science industry is extremely wide and complex. While there are thousands of terms to know, the above terms are a few basics, to begin with. You will encounter more as you scale through the field. Meanwhile, you can check out these available careers in data analytics.