Prepare for the in-demand field of data analytics. In this program, you’ll learn high valued skills like Excel, Cognos Analytics, and R programming language to get job-ready in less than 3 months.
Data analytics is a strategy-based science where data is analyzed to find trends, answer questions, shape business processes, and aid decision-making. This Professional Certificate focuses on data analysis using Microsoft Excel and R programming language. If you’re interested in using Python, please explore the IBM Data Analyst PC.
This program will teach you the foundational data skills employers are seeking for entry level data analytics roles and will provide a portfolio of projects and a Professional Certificate from IBM to showcase your expertise to potential employers.
You’ll 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 creating interactive dashboards. Lastly, you’ll learn how to communicate your data findings and prepare a summary report.
This program is ACE® and FIBAA recommended—when you complete, you can earn up to 15 college credits and 4 ECTS credits.
Applied Learning Project
You will complete hands-on labs to build your portfolio and gain practical experience with Excel, Cognos Analytics, SQL, and the R programing language and related libraries for data science, including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.
Projects include:
Analyzing fleet vehicle inventory data using pivot tables.
Using key performance indicator (KPI) data from car sales to create an interactive dashboard.
Identifying patterns in countries’ COVID-19 testing data rates 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 examines trends in census data.
Using hypothesis testing and predictive modeling skills to build an interactive dashboard with the R Shiny package and a dynamic Leaflet map widget to investigate how weather affects bike-sharing demand.
OKLAHOMA RESIDENTS ONLY