Colab, Python, Let’s start!
I just started my journey learning Python. To be honest, it is not the first trial. Years ago I tried, and I started reading about the Anaconda project. I installed a lot of things on my computer, I can’t even remember how many hours doing that. For some destiny reason, it never worked, and my motivation went really down.
Last week I first heard about Colab. Colab is the Collaboratory tool from Google, and it is just amazing how easy it is to start. You just search Colab on Google and that’s it! So let’s leave all the frustrations behind and give it a new try. Let’s hope it’s a long way :)
And here is my first project. An analysis of a fictional company was made. The spreadsheet presented 3 sheets: Stock, Sales, and Employees.
I had advice from my mentor Carlos Melo, and I am pretty amazed at how it went quickly to start writing the language and having results on the screen.
After importing and visualizing the data, a few checks were made. The total amount of sales of the company, average value per sale, and the standard deviation per sale.
A line graphic was made to give some color about the sales behave. On the X-axis is provided the dates, and on Y-axis, the total sales on the date. This visualization looks a bit chaotic. At first, one could say that the sales are volatile.
Treating the data in a different way might provide more information about the company. In a histogram chart, one can observe the different amounts per sale. Here it is possible to visualize the average amount of a sale, which is in accordance with the average total per sale calculated previously.
Further, by arranging the sales by total sales per day of the week, one can start making useful insights. For instance: Mondays and Wednesdays are the days that the company sold more. If a marketing manager was to decide about which day of the week a campaign should happen, it should definitely be one of those days.
This exercise was made with the Panda library. It is known for its statistical power and it is widely used in data analysis.
Programming is a big ally for decision-making based on data. It can make the analytical process more Lean and provide powerful insights. This process presents many benefits for the companies. From better understanding of the company situation to forecasting and preparing for trends.
I am very excited to move forward on this journey, I hope this space can be a channel for meeting and sharing insights with others with the same interest.