Time Series can be useful to analyze financial data in different frequencies based on data distribution.
We can reload/repeat the data analysis process in Python and export the result to Excel or CSV on daily, weekly, monthly, quarterly or yearly basis.

A data dashboard is an information management tool that visually tracks, analyzes and displays key performance indicators, metrics and key data points. Behind the scenes, a dashboard connects to your files, attachments, services and API’s, but on the surface displays all this data in the form of tables, line charts, bar charts and gauges.

Plotly provides a web-service for hosting graphs and we can view the dashboard offline as well.

Data visualizations and static or interactive visualizations are one of the data analysis tools to helping identify outliers or data transformations and ideals for financial models.

The maximum number of data series per chart in Excel is 255, so using Python to plot a huge dataset is very useful.

MongoDB is a document-oriented database.

Instead of storing your data in tables made out of individual rows, like a relational database does, it stores your data in collections made out of individual documents. In MongoDB, a document is a big JSON blob with no particular format or schema.

You can have all your data in one single collection.

After merging and reshaping the datasets, we can use SQLite to store, organize and manipulate data in smaller environments.
SQLite is a public-domain software package that provides a relational database management system.

Sometimes the way that data is stored in files or databases is not the way you need it for a data processing application.
Pandas along with the Python standard library provide us with a high-level, flexible, and high-performance set of core manipulations
and algorithms to enable us to wrangle data into the right form without much trouble.

A favorite old saying of mine with respect to data quality is "Garbage In and Garbage Out". Your analysis is only ever as good as your dataset.
Pandas is a popular Python library used for data science and analysis. Used in conjunction with other data science toolsets like SciPy, NumPy, and Matplotlib, a modeler can create end-to-end analytic workflows to solve business problems.
My objective here is to go through some examples for how to perform basic data validation.

When we computed the mean, minimum and maximum of the data in Python, we are employing a technique to get information about the data distribution.
This can be an effective tool when we need to summarize datasets for analysis.

Goal: Use Python to load data into a dataframe and get basic statistics.