Tools Every Data Analyst Should Know
If you’re looking to break into the data analytics, just like any industry, there’s a set of tools you’ll want to know how to use. The data industry is rapidly changing and it is hard to keep up with the latest tools, but here are some tools and technologies that are here for the long haul.
Microsoft Excel
Microsoft Excel isn’t the sexiest tool to use, but it is the most widely used software for anyone in data analytics. The reason MS Excel is so great is because of it’s vast ability to clean, organize, manipulate, and visualize data. It’s the all-in-one tool for data analyst today. MS Excel also has a very small learning curve to get started and there are tons of tutorials out there to help you get started.
SQL
SQL is the most used database language in the world. With data now in the petabyte levels, you will need to know a database language that will allow you to communicate with the data inside. The reason SQL is the go-to language for data analysis is because many large corporations use some variation of SQL to store their data whether it be Oracle, MySQL, SQL Server, etc. You will need to know how to query a database to retrieve useful data and manipulate the data so you can get it ready for analysis.
Tableau
Visualization and communication is vital in becoming a data analyst. You should be able to choose the appropriate charts and graphs in different situation. Tableau is a visualization tool that helps create beautiful dashboards that tell a compelling story. Unlike Microsoft Excel and SQL, Tableau is relatively new, but has been gaining traction over recent years. Tableau also allows you to easily connect multiple data sources (including MS Excel & SQL), making it a great addition to your tool belt.
Python
Python is a general use programming language that has been very important to the data science community. It is used as a substitution to MS Excel to organize and wrangle large sets of data. The reason Python is used, over other programming languages, is because of it’s ability to perform computational tasks with libraries like NumPy, Pandas, and MatPlotLib. Python is very easy to start learning due to it’s human-friendly syntax. As you work with more and more complex datasets, you’ll see the benefits that Python has to offer.
R
A complementary tool to Python is R, an open-source programming language used for statistical computing. With more than 4,400 add-on packages and 18,000+ members of the LinkedIn R Group, R allows you to store a series of complex data-analysis steps. One thing to know about R is that it is not like most programming languages, and is daunting when you first start off.
There are many more tools used by data analyst in different industries, but if you’re just starting off, make sure you have a solid understanding of these tools before moving on. At K2, our students work with all the tools mentioned in this article and multiple datasets to get a complete understanding of real-world data analytics.
