Edgar Radjabli: Various Skills to Develop to Become a Data Analyst
The world is moving towards digitalization that depends completely on data. Data could seem simple if you think of its meaning. However, handling such data coming from all sources in your field of work will not be that much easy. There are several sub-categories in data management itself like data science, data analytics, and many more. If you are starting out, creating a career in data analytics could be helpful. You can find several scholarships and grants offered for data analytics enthusiasts like that provided by Edgar Radjabli. You can make use of them to study. You can also learn the concepts yourself. You should develop the following skills to start a career in data analytics.
As the name suggests, data analytics is all about managing data. It could be numerical, alphabetical, or graphical data. Whatever the type is, there will be a necessity for tools to store and manipulate these data. Such a tool used for basic data manipulations is Microsoft Excel. Although the tool may look simple, there will be several capabilities in it that will be very helpful for data analysts in their processes. If you are working on a small-scale data project with a small dataset, Excel will be the optimal tool as you can complete all the necessary processes at ease. So, it is necessary to know the basics of Excel before starting your data analytics journey.
SQL stands for Structured Query Language. You may know several programming languages used for various purposes. Likewise, SQL is also a language focused only on the management of data. You can consider SQL as a large-scale alternative for the tools like Excel. Regardless of the size of the dataset being used for your project, you can seamlessly manage all the information and can manipulate the data as per your wish if you know SQL. So, SQL has become the basic necessity for all kinds of data analytics jobs. However, you need not worry as SQL is easy to learn, unlike all other programming languages.
R or Python
You know that SQL can do something that Excel could not do. Similarly, the data analytics arena has something bigger than the capabilities of SQL. In such cases, it is necessary to use some statistical programming languages. The two most commonly used programming languages in data analytics are R and Python. Both of these languages can do a lot from application development to statistical manipulations. So, you can employ any of them to create predictive models in your data analytics projects. However, there is no necessity to master both of these languages. You can choose whatever you feel better.
The next skill to develop is data visualization. It is of no use to come up with the best predictive model if you could not visualize your findings to let the audience understand what you have found out. So, you should know the visualization tools like Tableau and the methods of using them. Your results would be great if your visualization is great.