Python for Data Analysis
The general-purpose programming language Python has quickly become the go-to south africa phone number data programming language for data analysts. This is partly due to its simple syntax, which makes it easy to learn. However, it is also famous for its large library library, the Python Package Index (PyPI). Python can be used for almost every aspect of data analysis. For example, Pandas is excellent for manipulating time series and other quantitative data. Matploblib is perfect for data visualization. And NumPy is popular for handling a lot of mathematical functionality. These are just three of the thousands of Python packages available.
R for Data Analysis
R, another language, is also common in data analysis. While R is considered a more complex language to learn than Python, it remains popular due to its historical use in statistical programming (which has benefits in fields such as data analysis). While R does not easily perform things like image processing as Python does, R has more data analysis functions; it is also frequently used in scientific fields. Like Python, R also has a software library, CRAN , with many additional packages available.
Databases and data management systems
As the types of data we collect become more complex, the way we manage that data has also evolved. In data analysis, it is vital to have an understanding of how databases and data warehouses work. For example, MySQL is a commonly used, simple type of relational database management system. Whether you use simple databases or complex infrastructures, they are inevitable!
Structured Query Language (SQL)
SQL (sometimes pronounced “Sequel”) is a language designed to communicate with relational databases. In a world where data is the currency, this tool has obvious applications. While relational databases are built using a variety of languages, such as C or C++, SQL allows you to pull, add, and edit data without needing to know the underlying database language. Since most organizations have information stored digitally and online, SQL is becoming an important language to learn, even for non-analysts. It is a language that everyone should learn.
Industry-specific data analysis tools
In addition to the tools already mentioned, industries are starting to produce more sophisticated, industry-specific applications to help with data analysis. These tools range from generalist BI software like Microsoft Power BI to data visualization and dashboarding applications like Tableau . They also include niche products that you might only learn about if you work in a specific field. For example, Definitive Healthcare is an analytics platform specifically designed for managing healthcare-related data.
6. Data Analyst Salary
Despite being a new profession, it is a profession in the IT area and according to recent research and surveys, this is an area that is in high demand at the moment we are living in, but there are very few professionals working in the area, according to Brasscom, the information technology market will be looking for around 420 thousand people by 2024.
The average salary of a Data Analyst in Brazil is R$3,000.00, reaching R$5,517.00 as a full data analyst and finally R$7,850.00 as a Senior.
7. Conclusion
In this article, we cover everything you need to know if you’re just starting out in data analytics. We explore what a data analyst does, what skills are required, what basic tools a beginner should aim to learn, and what the salary is.
Once you have all of these skills at your fingertips, you’re ready to enter this field. Whether you’re interested in data analytics for e-commerce, finance, healthcare, government, science, or any other field of your choosing, one of the great benefits of this field is its versatility. With a little experience, you can break into data science or specialize in areas like data engineering, data modeling, or machine learning.
