Data Science Fields

1.  Big Data Engineer (Data WareHousing): This is the initial step towards Data Science, it is optional although without this there will be no data, dataset. It is actually a database system and procedure in which we get data , store data.

2. Data Analytics: This is a core data Science field in which we explore data as layman which will see, analyze and study the data such that what is in data. But after this we have to study in detail using some tools generally using python language in which numpy libraries are popular. 

3. Feature Engineering/Feature Selection: This is one of toughest jobs in Data Science as it is a variant in each and every project. If this step is OK (further steps will be dependent on this) which will result in OK in further steps. In this step you will prepare data for model training. Machine Learning will get this data and if this step is done carefully then ML will result in good results.

4. Machine Learning Engineer: This is the main job of a Data Science Project in which we want to manipulate Data for prediction, classification or other purposes we want. 

5. Reporting/Evaluating: Here we find some assumptions or results that are the final output of all steps. In this step we find which decision should be taken in the business/organization for which we are doing the project.

6. Deployment: This is also an optional step. If someone wants to show the project above to the concerned party on a specific platform for testing or evaluating, the project source code should be deployed on some container/docker for easy access. 

Conclusion: This is not final words through which you will stick to it. Data Science is flexible field which may vary time to time, project to project. Although theses are the most commonly steps used world wide.

I may be wrong if someone knows better than me kindly help me improve this blog directly @ muhammadaadil150@gmail.com

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#data #datascience #dataengineering #dataanalytics