Speaker 1: Tool number four. Arr. Arr. Arr. Oh, it's got me. Hey, in this video, we're going to give you a super fast breakdown of the nine tools that you'll be using as a data analyst. Let's dive in. This is going to be a super fast video, so strap in and all the tools are going to be listed in the description below. Tool number one, Microsoft Excel. Microsoft Excel may be used by data analysts to run basic queries and to create pivot tables, graphs, and charts. Excel also features a macro programming language called Visual Basic for Applications, VBA. Tool number two, Tableau. Tableau is a popular data analytics and business intelligence software, which is primarily used as a tool for data visualization. Data analysts use Tableau to simplify raw data into visual dashboards, worksheets, maps, and charts. This helps to make the data accessible and easy to access for everybody, allowing data analysts to effectively share their insights and recommendations. Tool number three, Python. Python is an open source programming language which is used to organize and wrangle large sets of data. Data wrangling is a term used in the industry to describe the processing of data in various formats. Merging and grouping data, for example, to get it ready for analysis. Python has many built-in features which help with data wrangling, making it a popular alternative to Microsoft Excel, especially when it comes to working with more complicated data sets. Tool number four, R. R is another open source programming language used for statistical computing, often serving as a complementary tool to Python. R is particularly popular among data analysts because of its output. It offers a great variety of tools for presenting and communicating the results of data analysis. Tool number five, SAS. SAS is a command-driven software package used for carrying out advanced statistical analysis and data visualization, offering a wide variety of statistical methods and algorithms, customizable options for analysis and output, and publication quality graphics. SAS is one of the most widely used software packages in the industry. Tool number six, SQL. SQL stands for Structured Query Language and it's a language used to access and manipulate databases. You can think of SQL as a tool that allows you to communicate and access data in a database, which is necessary if you want to retrieve particularly useful data for analysis. Most large businesses use some form of SQL to store their big data, so learning SQL is essential if you want to become a data analyst. I hope you're enjoying our rapid list so far, because I am. Let me know what you think about the tools we've shared so far in the comments below. Have we missed anything? Let us know. Tool number seven, RapidMiner. RapidMiner is a software package used for data mining or uncovering patterns in data, text mining, predictive analytics, and machine learning. Used by data analysts and data scientists alike, RapidMiner comes with a wide range of features, including data modeling, validation, and automation. Now onto our penultimate tool, Power8. Tool number eight, PowerBI. PowerBI is a business analytics solution that lets you visualize your data and share your insights across your whole organization. Similar to Tableau, PowerBI is primarily used for data visualization. While Tableau is built for data analysts, PowerBI is a more general business intelligence tool. And last but not least, tool number nine, FindReport. FindReport is another business intelligence tool used to monitor performance, to identify trends in data and to track trends in performance. This is an especially user-friendly tool, which is popular with both data analysts and non-data experts. And that wraps up our super-fast list of the nine essential data analytics tools. Again, this is just a super-brief introduction to some of the tools and languages which are used in the field. You can get a good idea of what tools are most popular within the industry by browsing through data analysts' job descriptions. Employers will often list the tools and languages that they expect a data analyst to work with. I hope you've enjoyed this super-fast introduction, what tools and languages are you using in your workplace, drop them in the comments below. If you've enjoyed this video, subscribe to the channel for more data content just like this. If you've enjoyed this video, check out this other video which I've done about data analytics. It's a much deeper dive into the field. If you want to know more about the industry, what exactly data analytics is, or what you'll be doing on a day-to-day basis, I strongly recommend you check it out. Thank you so much for watching and I'll see you again soon.