In the modern world, professional data analysts are in great demand. This field not only offers job security but opens up fascinating career opportunities as well.
With the ease and comfort of sitting at home, data analysts can work anywhere around the world, just like how you can access online betting at bet777 at any time or place. They just need their electronic gadgets and internet.
To become a data analyst, you need to be well-versed in a few analytics tools such as Microsoft Excel, Python, and SQL. All of these tools are used for their own specific functions to help you navigate the process of data analysis.
Some tools are better at storing data, while some are better at reporting data. This article will discuss the best data analysis tools you need to help you out with your career as a data analyst.
- Microsoft Excel or Google Sheets
Both Microsoft Excel and Google Sheets are considered to be the best tools for storing or editing data and also for converting pdf to png. You also have the option to perform basic analysis on the data, and manipulate it according to your requirements.
Furthermore, you can visualize data on Excel and Google Sheets through graphs and charts. Whether you want to represent your data through bar charts, whisker plots, or pie charts, these tools offer them all.
These tools also offer a wide array of functions to manipulate any kind of data you want. By using the pivot table and other functions, you can also perform basic analysis on data.
Additionally, having a command on VBA, the programming language of Excel, you can automate tasks according to your ease and convenience.
However, these tools are not suitable to perform complex data manipulations or perform advanced statistical analysis.
Structured-Query Language, or SQL, is the best tool for querying your data. Although you can also perform data manipulation in excel, SQL can handle vast amounts of data much more efficiently. What may take hours in Excel, only take seconds in SQL.
With SQL, you can compress large files by storing them as plain text files which makes it easy to send them. Moreover, you can use SQL to combine the data of multiple datasets together for querying or manipulation.
Python is considered to be the best tool for Machine Learning and Automation purposes. It is the most popular open-source programming tool amongst expert data analysts. It contains a lot of libraries that makes it easier for you to handle large files of datasets.
Many people use Python all over the world and there is a great deal of community support on the internet, where you can find help. In these communities, you can explore more libraries to make your code as simple as possible.
Python is best known for how fast it is at handling big datasets. This saves the big organizations a lot of time, which ultimately keeps it in demand.
Some data analysts consider R and Python as alternatives to each other, but R is more efficient when you have to perform statistical analysis. R is especially used for exploratory data analysis (EDA), as some of R’s analysis libraries are more handy as compared to those of Python.
Even though Python and R almost perform the same functions, Python is preferred to design production grade apps while R is preferred for advanced statistical analysis.
Tableau is a popularly known data analysis tool for creating interactive dashboards and aesthetic graphs. This is very helpful when a data analyst has to communicate the results to non-technical people and interact with them through interactive dashboards to keep them in the loop.
Although you can perform basic data analysis on Tableau, it could be difficult to clean the data in it. So, Python and R are better suited for data analysis as compared to Tableau.