5 Must-Know Data Science Terms and their Simple Definition

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If you are new to Data Science, you are most likely to come across tons of new words, technologies and applications. From Relational Data Modeling to Augmented Intelligence, things can get very complex and murky as you advance to higher plains in Data Science industry.

To help you glide past the complex world of data science glossary, we have compiled a list of influential words that are part of best data science certification programs.

Data Science

In its core essence, Data Science is defined as the science of working with data and analyzing data using Computation, Statistics, Data Mining and Programming to derive useful and relevant information for decision making and problem solving.

Big Data

Like it’s worded, Big Data simply means troves of data pooled into a storage virtual container for current or future analytics using a structured or semi-structured programming technique. All modern Data-based companies are churning, leveraging and analyzing tons of Big Data to tell a story, mobbing away from the noise and clutter of traditional business operations.

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In schools, you would have learned a flowchart of information and steps needed to solve any problem. In Data Science, algorithms are no different! An algorithm can be defined as a series of logical steps expressed rationally and mathematically to accomplish a specific task or solve a problem using techniques such as Linear, Logistic Regression or KNN, etc.

In any data science project, you will be required to learn these three steps of designing an Algorithm.

  • Data Preparation
  • Data Optimization
  • Machine Learning/ Cognitive Approach to Algorithms

In their current machine learning stage, data science algorithms are used to predict, classify, analyze, supervise and polarize complex Big Data with lesser number of conflicts, errors and redundancy. In data science certification courses, you would be designing hundreds of Algorithms to press in charge complex data science problems.

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Data Visualization

Ok, so now that you have learned about Big Data and Algorithms, it’s time to check how Data Scientists report their outcomes to teams. It’s done using a technique called Data Visualization.

Data Visualization is the graphical representation of information extracted from complex data sets. The graphical representation could be in the form of spreadsheets, charts, bars, pie-graphs, heat-maps, pinned maps, dotted legends or in video formats.

As we dive deeper in to the world of Big Data and the era of AI kicks into a higher gear, you can expect data science certification programs to come handy in dealing with Data Visualization techniques.

Data Purge

In data management, Operational techniques take the bulk of time and slow down the system. It largely occurs due to streaming and storing of large volumes of non-resourceful data. Sometimes, it’s necessary to eliminate old data sets to make way for new ones. That’s where, purging comes into the play.

Data Purging is a series of QC-related steps taken to erase and remove data from the storage space. It should not be confused with its temporary alibi- Deletion. Purging is permanent in its consequences.

If you are gearing towards the next stage of Data Science learning, make sure you understand the ‘value chain’ of data management very well.

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