There are several tools you need to learn about to help you in your career as a data analyst. At the basic level, you should at least have a working knowledge of web development, SQL, math, and Microsoft Excel. It also follows that you should be good at PHP, HTML, JavaScript, and know how to work with basic programming commands, libraries, and syntaxes. As an advanced user, you should also be adept in the following fields:
● R Programming
One of the challenges many data analysts experience is choosing the right programming language. Essentially, it is wise to learn as many languages as you can, because you never know what the next project you work on will demand. You might not fully understand all the programming languages, but having working knowledge is a great idea.
While there are lots of programming languages you can choose from, R programming is one that any data analyst should master. It is preferred because it is unique and versatile, particularly when dealing with statistical data. Since R is an open-source platform, you have access to several data analysts who can help you.
R is a simple, yet articulately developed program. In R programming, you will use recursive functions, loops, conditionals, and support for I/O features. R also has storage features, which is good for data handling as you proceed with your tasks. You will also find the GUI effective, which is ideal for data display.
● Python
The basics of Python programming have been discussed in the earlier books in this series. However, we can recap by highlighting the power behind this opensource programming language. Python is simple, yet it packs quite the punch in as far as other programming languages are concerned. Programmers and developers alike enjoy coding in Python because of the wide library support, which helps you in data management, manipulation, and analysis. It is one of the easiest languages to learn, especially if you have experience with other languages. The list of projects you can build in Python is
endless, especially because there are many new projects that are still being built today, which we are yet to experience. In terms of existing projects that were built through Python, think about YouTube.
● Database management
You will be working with lots of data, so data management is a skill you should master or polish up. Some of the tools you must learn include MySQL, MongoDB, MS Access, and SQL Server. These tools are mandatory for data collection, processing, and storage. More importantly, you should understand how to use commands like order by, having, group by, where, from, and select .
● MatLab
MatLab is another simple, flexible, and powerful programming language that is necessary for data analysis. Through MatLab, you can manipulate and analyze data using the native libraries. Given that the MatLab syntax is almost similar to C++ and C, prior knowledge of these programming languages will help you progress faster in MatLab.
Over the years, the use of data analysis has become important in different environments. Companies and organizations use data to gain insight into their business performance by studying how their customers interact with their brands at different data collection points. Having understood the basics of data analysis, let’s move on to data analysis with one of the most amazing programming languages, Python.
These are some of the common tools used in Data analysis. Although Excel is still widely used in data analysis but now a days Python is preferred over Excel. This will be our topic of discussion for our next post.
● R Programming
One of the challenges many data analysts experience is choosing the right programming language. Essentially, it is wise to learn as many languages as you can, because you never know what the next project you work on will demand. You might not fully understand all the programming languages, but having working knowledge is a great idea.
While there are lots of programming languages you can choose from, R programming is one that any data analyst should master. It is preferred because it is unique and versatile, particularly when dealing with statistical data. Since R is an open-source platform, you have access to several data analysts who can help you.
R is a simple, yet articulately developed program. In R programming, you will use recursive functions, loops, conditionals, and support for I/O features. R also has storage features, which is good for data handling as you proceed with your tasks. You will also find the GUI effective, which is ideal for data display.
● Python
The basics of Python programming have been discussed in the earlier books in this series. However, we can recap by highlighting the power behind this opensource programming language. Python is simple, yet it packs quite the punch in as far as other programming languages are concerned. Programmers and developers alike enjoy coding in Python because of the wide library support, which helps you in data management, manipulation, and analysis. It is one of the easiest languages to learn, especially if you have experience with other languages. The list of projects you can build in Python is
endless, especially because there are many new projects that are still being built today, which we are yet to experience. In terms of existing projects that were built through Python, think about YouTube.
● Database management
You will be working with lots of data, so data management is a skill you should master or polish up. Some of the tools you must learn include MySQL, MongoDB, MS Access, and SQL Server. These tools are mandatory for data collection, processing, and storage. More importantly, you should understand how to use commands like order by, having, group by, where, from, and select .
● MatLab
MatLab is another simple, flexible, and powerful programming language that is necessary for data analysis. Through MatLab, you can manipulate and analyze data using the native libraries. Given that the MatLab syntax is almost similar to C++ and C, prior knowledge of these programming languages will help you progress faster in MatLab.
Over the years, the use of data analysis has become important in different environments. Companies and organizations use data to gain insight into their business performance by studying how their customers interact with their brands at different data collection points. Having understood the basics of data analysis, let’s move on to data analysis with one of the most amazing programming languages, Python.
These are some of the common tools used in Data analysis. Although Excel is still widely used in data analysis but now a days Python is preferred over Excel. This will be our topic of discussion for our next post.
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