Professional Certification 'IBM Data Analytics with Excel and R'

Prepare for a career in the in-demand field of data analytics. This program will teach you valuable skills like Excel, Cognos Analytics, and the R programming language, and get you ready for the job in under 3 months . Data analytics is a strategy-based science in which data is analyzed to find trends, answer questions, inform business processes, and aid in decision making. This professional certification focuses on data analysis using Microsoft Excel and the R programming language . If you are interested in using Python, please review the IBM Data Analyst PC program. This program will teach you the foundational data science skills that employers look for in entry-level data science positions, and provide you with a project portfolio and professional certification from IBM to demonstrate your knowledge to potential employers. You will learn the latest skills and tools used by professional data analysts, and upon successful completion of this program, you will be able to work with Excel spreadsheets, Jupyter notebooks, and R Studio to analyze data and create visualizations. You will also use the R programming language to complete the entire data analysis process, including data preparation, statistical analysis, data visualization, predictive modeling, and the creation of interactive dashboards. Finally, you will learn how to communicate the results of your data analysis and prepare a summary report. This program is recommended by ACE® and FIBAA - upon completion you will earn up to 15 college credits and 4 ECTS credits. **Applied learning project** You will complete hands-on assignments to build your portfolio and gain hands-on experience with Excel, Cognos Analytics, SQL, as well as the R programming language and related data science libraries including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest. **Projects include:** - Analyzing vehicle fleet inventory data using pivot tables. - Using car sales KPI data 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 create an interactive dashboard using the R Shiny package and the Leaflet dynamic map widget to study how weather affects bike sharing demand.