What tools and software are commonly taught in Data Analytics course?

In a Data Analytics course, students typically learn a variety of tools and software to manipulate data, perform analysis, and visualize results. Some commonly taught tools and software include: Microsoft Excel: Often used for basic data cleaning, manipulation, and some statistical analysis. Python: A versatile programming language with libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization. R: Another programming language commonly used in statistical analysis and data visualization, with packages like dplyr, ggplot2, and tidyr. SQL (Structured Query Language): Essential for querying and managing relational databases, which are foundational in many data analytics tasks. Tableau: A popular data visualization tool that allows users to create interactive and shareable dashboards. Power BI: Microsoft's business analytics service for creating interactive visualizations and business intelligence reports. Jupyter Notebooks: Provides an interactive environment for writing code in Python (and other languages) and displaying the results inline. Google Analytics: Used for analyzing web traffic and user behavior on websites. SAS (Statistical Analysis System): Software suite used for advanced analytics, multivariate analysis, and predictive modeling. SPSS (Statistical Package for the Social Sciences): Statistical software used for data management and analysis. Hadoop: An open-source framework used for distributed storage and processing of large datasets. Apache Spark: Another open-source distributed computing system used for big data processing and analytics. The specific tools and software taught can vary depending on the course focus, institution, and instructor preferences. However, these are some of the most commonly encountered tools in data analytics education today.