Some Things About Data Science And Big Data That You Need To Know

Data Science basically means the same thing. It is to extract knowledge from large quantities of data, meaning you make certain conclusions about the subject and environment the data is collected on.

This can be done with several mathematical and statistical methods, including machine learning. Machine learning is a form of artificial intelligence and basically means that a computer is able to make generalizations based on data and becomes more accurate over time (it learns). Data Science is a catch-all term to describe using those all tools to provide answers in those all areas (and also in others), especially when dealing with Big Data, which is nothing more than a label meaning doing any of the above but when the datasets are huge.

Econometrics is used to analyze and make predictions about economic phenomena such as unemployment, GDP growth, effects of raising/lowering taxes, effects of economic hubs (like Silicon Valley) on regional economics, etc. I'm not sure but I think it is mostly used to analyze economics on a macro level, such as per country or economic region.

Fundamentals. Essentially, the difference lies in the focus. Data scientist is an umbrella term for both wide- and narrow-focused professionals in data analysis and engineering. On the other hand, a machine learning engineer is simply a data scientist, focusing on machine learning (ML) domain of the larger data science field.

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The job of a Data Scientists is to  Investigate, explore, analyze, explain, present data,data Scientists come from highly varied backgrounds, since the field is new and evolving quickly. The most common backgrounds are:

Statistics is a branch of Mathematics providing theoretical and practical support to the above tools.

Data Engineers find themselves more often than not dealing with (big) data—from acquisition over cleaning, conversion, disambiguation, de-duplication—and also developing & deploying solutions. The truth is that the job of an engineer is to Design, build, launch, and troubleshoot the truth is that the job of an engineer is to Design, build, launch, troubleshoot, and support. They work with deal with data architecture, master data management and data quality. All these terms are worth a Google as there are whole practices built around them. On the ground, daily work consists of:

·         Managing data stewardship within the organization:

• managing and maintaining data source systems and staging areas
• performing ETL and data conversion
• facilitating data cleansing and enrichment through data de-duplication and construction
• performing ad-hoc data extraction