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Excel

You can use Excel as a blank canvas, for anything you need; its flexibility allows us to do just about anything.
Constant updates:
Anyone familiar with Excel knows there are many ways to approach different scenarios. However, with Office 365, constant updates allow us to go from multiple formula combinations to just one. Again, it depends on how you approach the problem.
What you can do with Excel:
(Once again, it’s not always the right tool, but God knows it’s useful).
ETL, dashboards, reports, metrics, KPIs, whatever you can imagine, there’s always a formula for it. The key is problem-solving, and spreadsheet automation is highly appreciated. Whatever your creativity allows, if you don’t know, you research it—there’s definitely a formula for it; if not, you create one.
My personal opinion: To me, it's one of the best software programs. You can do pretty much anything, it's almost like magic. It may not always be the right tool, but if you know how to use it, it works for everything, and you can do a ton of things. When Excel hits its limits, you can move on to PowerQuery, Power BI, or, if necessary, switch to a database tool like Access or a program with SQL or Python.

SQL

Here I’ll be completely honest: the only database engine I've used is BigQuery, and it was only when I learned how to write queries about 6 years ago. My knowledge of SQL is not very technical, but I have practiced query writing in the past.
Overall, I believe that mastering DML (Data Manipulation Language) is enough, as long as you know how to write queries. The commands I have the most experience with are:

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  • SELECT

  • FROM

  • WHERE

  • UPDATE

  • DELETE

  • JOIN

  • GROUP BY

  • HAVING

  • ORDER BY

  • DISTINCT

Python

Why Python?


When it comes to processing millions of data points, there's no comparison to Python, which makes it an essential tool in data analysis. This is why having a basic understanding of this tool is crucial.


Python, being a very complete programming language, is extremely vast. As a technically savvy but non-specialized user, my focus is on data analysis with Python. I know the basics, and here are some topics I consider fundamental before diving into more specific ones.

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Fundamental topics:

  • Importing libraries

  • Loading files (XLSX, CSV)

  • Data exploration:

    • df.head(): View the first few rows of the DataFrame

    • df.tail(): View the last few rows of the DataFrame

    • df.info(): Get basic information about the DataFrame

    • df.describe(): Get statistical description of numerical columns

    • df.columns: View the column names

    • df.shape: Get the size of the DataFrame

 

Data manipulation with Pandas:

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  • Select a column

  • Filter data

  • Drop columns or rows

  • Replace values

  • Rename values

  • Convert data types

 

Data visualization:

 

Data visualization is a crucial aspect of data analysis, as it helps to convey insights in an easily digestible format. With Python, there are several libraries available to create different types of plots and charts. Some of the key visualizations include:

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  • Line charts for trends over time

  • Bar charts for comparisons among categories

  • Histograms for distribution analysis of numerical data

  • Scatter plots to visualize relationships between variables

  • Box plots to identify data spread and outliers

  • Heatmaps to visualize correlation matrices

  • Pie charts for showing proportions or parts of a whole

 

Libraries like Matplotlib, Seaborn, and Plotly are commonly used for visualization. These allow not only for creating static plots but also interactive charts that enable deeper insights, especially with libraries like Plotly for dynamic visual exploration.

Visualizations are particularly useful in the exploratory phase of data analysis to uncover patterns or anomalies, and they can be customized to enhance readability and focus attention on key areas.

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