Data Analytics and Data Science

Data Analytics and Data Science An analyst who understands and interprets data and a scientist who comes up with innovative ways to capture and analyse data add value to organisations.

Candidates in this field need to excel in math and statistics as well as computer programming.

Data analysts crunch numbers to figure out why their companies are not performing as well as they should; or why they are not achieving their key performance objectives. Through data modelling, analytics and visualisation, analysts can identify trends that can help team heads work on successful plans to drive businesses. For data querying they use SQL. Excel is required for data analysis and forecasting.

Data scientists, on the other hand, have to work on developing better and more efficient ways to gather and interpret data. Most of them use Python, Java and machine learning to read and manipulate data. They mine data by using application programme interface or building extract, transform, load (Or ETL) pipelines, which means using processes to move data from a source or multiple sources into a database such as a data warehouse. For statistical analysis they need to know machine learning algorithms such as natural language processing, logistic regression, kNN, Random Forest or gradient boosting.

An interesting part of the data scientist’s work involves setting up ‘libraries’ by creating programming and automation techniques for developing machine learning models.

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