What is a Data Analysis pipeline?
A pipeline, in generic terms, refers to the series of steps via which a particular data or input passes through to get processed into the final output. Data Analysis pipeline also follows the same definition as it involves all steps starting from pre-processing of data to cleaning of data, and extends till the intended visualization is made, the insights are generated and are communicated to the business stakeholders. The main objective of this is to ease out the entire process, and also make the process more readable and manageable from an implementation perspective.
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Why is data analysis important in business?
Data analysis is important in business to ensure that the same mistakes are not repeated, and the business can tailor in historical performances in designing future strategies and formulating the business plan. Descriptive analysis provides a view of ...
Where can I practice practical Data Analysis problems?
The key factor in finding practical data analysis problems in the online domain is that the data provided in online practical sites such as https://www.kaggle.com etc. are much cleaner and more manageable; whereas the data in real-world business ...
Would it be better to first learn Data Analysis or Data Science.
Data analysis (or a data analyst) and Data Science (or a data scientist) are two different facets of analytics with different objectives, roles & responsibilities. Hence, it is important for the student to first understand the key requirement of each ...
What is a data science pipeline?
Data Science pipeline depends on the particular business or industry as well wherein data science projects are operated. While in some cases, the entire set of steps starting data collection comes under the purview of the data science pipeline; in ...
Which is better in terms of salary and long term growth in data science and machine learning.
Data science is a more generic stream of study which encompasses data analysis, machine learning, data visualization, statistical modeling, data engineering, business intelligence, etc. Therefore, from the perspective of opportunities, data science ...