## Professional Certificate – A 9-course course series to prepare you for the in-demand field of data analytics. In this program, you will learn valuable skills like Excel, Cognos Analytics, and the R programming language to **be job-ready in less than 3 months**. Data analytics is a strategy-based science where data is analyzed to find trends, answer questions, design business processes, and aid decision-making. This professional certificate focuses on data analysis **using Microsoft Excel and the R programming language**. If you are interested in Python, please check out the IBM Analyst Certificate. This program will teach you the fundamental data skills that employers are looking for in entry-level data analytics roles and provide you with a **project portfolio and professional certificate** from IBM to showcase your expertise to potential employers. You will learn the latest skills and tools used by professional data analysts, and upon successful completion of the program, you will be able to work with **Excel Sheets, Jupyter Notebooks, and R Studio** to analyze data and create visualizations. Additionally, you will use the R language to complete the entire data analysis process, including data preparation, statistical analysis, data visualizations, predictive modeling, and interactive dashboard creation. Finally, you will learn how to **communicate your data findings** and prepare a summary report. This program is recommended by ACE® and FIBAA—when you graduate, **you can earn up to 15 academic credits and 4 ECTS credits**. ### Hands-on Learning Project You will complete hands-on labs to build your portfolio and gain hands-on experience with Excel, Cognos Analytics, SQL, and the R language and data science-related libraries, including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest. Projects include: – Analyzing fleet inventory data using pivot tables. – Using KPI data from vehicle sales to create an interactive dashboard. – Identifying patterns in COVID-19 testing rates across countries using R. – Using SQL with the RODBC R package to analyze foreign grain markets. – Creating linear and polynomial regression models and comparing them with weather station data to predict precipitation. – Using the R Shiny package to create a dashboard that explores trends in census data. – Using hypothesis testing and predictive modeling skills to build an interactive dashboard with the R Shiny package and Leaflet’s dynamic map widget to explore how weather affects bike share demand.
