Popular Data Analysis Tools:Introduction

In the current scenario, where the amount of data generated continues to grow exponentially, the use of appropriate tools for analysing data is fundamental to extracting valuable insights and making informed decisions. In this report, I share some of my experience of the main tools used and requested in data analysis, including Python, Power BI, Excel, R and SQL.

Python (with Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn)

I used Python for data analysis in a sales project using Pandas to clean and manipulate a large set of transactional data. I used NumPy to perform numerical calculations, Matplotlib and Seaborn to create clear and informative visualisations that helped identify sales trends over time. Also, although I didn't use it, it is possible to use Scikit-learn to develop a machine learning model that predicts customer buying behaviour patterns.

Power BI or Tableau

Excel

R(with RStudio, dplyr, tidyverse, ggplot2)

SQL (with MySQL, PostgreSQL, Microsoft SQL Server)

SAS

Apache Spark

Research Insights

During the process of applying for 57 data analysis vacancies, I used a variety of approaches, with the majority of applications being made through vacancies found on LinkedIn, both through posted vacancies and direct messages. I also used the Gupy platform on 3 occasions and was contacted by telephone on 2 occasions. One application was made via the company's website.

Featured tecnologies: Based on the vacancies for which I applied, the five technologies most requested by employers were:

• Power BI or Tableau (43 vacancies)

• SQL (42 vacancies)

• Python (27 vacancies)

• Excel (21 vacancies)

• PowerPoint (13 vacancies)

These technologies were the ones I identified that were most sought after by employers in the data analysis vacancies I applied for.

Enphasis on languages required:

I observed a strong demand for professionals with English skills, standing out as the most common requirement among data analysis vacancies.

Location preferences:

With regard to location, I chose to apply for vacancies in Barcelona, Brazil and Spain. However, it's worth noting that the opportunities in Brazil and Spain were for remote work, while in Barcelona there was the option of face-to-face, remote or hybrid work.

Desired levels of experience:

As for the level of experience desired by companies, I observed a significant demand for full professionals, followed by juniors and seniors. It's interesting to note that of the 57 vacancies I applied for, only 6 specified salary information in the job description. This indicates a common tendency for companies not to reveal salary information explicitly in job listings.

Conclusion

I hope this report on the main data analysis tools has been useful and enlightening. As we explore Python, Power BI, Excel, R, SQL, SAS and Apache Spark, it becomes clear how important it is to adapt our skills to the demands of the ever-evolving labour market. Data analysis plays a crucial role in many industries, and mastering these tools can open doors to exciting professional opportunities.

Always remember to keep learning and exploring new techniques, as the journey in data analysis is a continuous search for valuable insights and informed decisions.