Ans. Data analysis refers to the process of collecting data, cleaning, interpreting and visualizing it to derive insights or conclusions and generate reports to help make business decisions..

Ans. While analyzing data, a Data Analyst can encounter the following issues:
1) Duplicate entries and spelling errors due to which data quality can be hampered
2) Incomplete data. Leads to errors and faulty analysis
3) Dirty data which leads to a lot of time getting wasted in cleaning
4) Business stakeholders’ unrealistic timelines and expectations
5) Data integration from multiple sources
6) Insufficient data architecture and tools to achieve the analytics goals on time.

Ans. Data cleaning is a process of identifying and then modifying, replacing, or deleting the incorrect, incomplete, inaccurate, irrelevant, or missing portions of the data as the need arises. This fundamental element of data science ensures data is correct, consistent, and usable.

Ans. In a dataset, Outliers are values that differ significantly from the mean of characteristic features of a dataset. With the help of an outlier, we can determine either variability or an experimental error. There are two kinds of outliers i.e., Univariate and Multivariate.

Ans. It allows you to divide code modules into logical groups to make it flexible to change
To ease the website administration, it provides auto-generated web admin
It provides pre-packaged API for common user tasks
It gives you template system to define HTML template for your web page to avoid code duplication
It enables you to define what URL be for a given function
It enables you to separate business logic from the HTML
Everything is in python