IBM Data Analytics with Excel and R

Get job-ready skills, tools and a portfolio for an entry-level data analyst or data scientist position. In this IBM Data Analyst Certification, you will explore the role of a data analyst or data scientist and work with a range of data sources, applying powerful tools like Excel, Cognos Analytics, and the R programming language, to become a data driven practitioner. Gain a competitive edge in the job market. No prior data analysis, statistics or programming experience needed to enroll.

Data Analysis

Use Excel to perform a variety of data analysis tasks like data wrangling, using pivot tables, data mining, and creating charts

Create Relational Databases

Use tables, query data, sort, filter and aggregate result sets using SQL and R from JupyerLab, use predictive modeling using R and R Studio

Data Visualization Techniques

Communicate data findings using various techniques including charts, plots and interactive dashboards with Cognos and R Shiny

Professional Certificate Programs enable you to become empowered and successful in every phase of your job!

Dana Baker

Dana Baker, Executive Director of Regional Campuses

"We are committed to developing current and relevant coursework to help transform our next generation of leaders."

IBM Data Analytics with Excel and R

100% Online

Learn on your own schedule

Flexible Schedule

Set and maintain flexible deadlines

Entry Level

No previous experience required

6-Months to Complete

Suggested pace of 10 hours/week; 8 Courses

IBM Data Analytics with Excel and R Professional Certificate Courses

Introduction to Data Analytics

This course introduces you to the concepts of data analysis, the role of a data analyst and the tools that are used to perform daily functions. Learn to differentiate between the roles of a data analyst, data scientist and data engineer. Gain an understanding of the data ecosystem and the fundamentals of data analysis, acquiring the soft skills required to effectively communicate your data to stakeholders. Uncover the major vendors within the data ecosystem and explore the various tools on-premise and in the cloud.

By the end of this course, you will be able to:

  • Visualize the daily life of a data analyst.
  • Understand the different career paths that are available for data analytics.
  • Identify the many resources available for mastering this profession.
  • Learn the key aspects to data analysis.
  • Explore the fundamentals of gathering data and learning to identify data sources.
  • Learn to clean, analyze and share your data with the use of visualizations and dashboard tools.
  • Produce a final project that exhibits your knowledge of the course material and what it means to be a data analyst and provide a real-world scenario of data analysis.

Excel Basics for Data Analysis

This course provides basic working knowledge for using Excel spreadsheets. Excel is an essential tool for working with data – whether for business, marketing, data analytics or research. There is a strong focus on practice and applied learning in this course. With each lab, you will gain hands-on experience in manipulating data and begin to understand the important role of spreadsheets.

By the end of this course, you will learn to:

  • Work with several data sets and spreadsheets and demonstrate the basics of cleaning and analyzing data all without having to learn any code.
  • Cleanse and wrangle data using functions and then analyze your data using techniques like filtering, sorting and creating pivot tables.
  • Gain an understanding of how spreadsheets can be used as a data analysis tool and understand the limitations.
  • Produce a final project enabling you to show off your newly acquired data analysis skills.

Data Visualization and Dashboards with Excel and Cognos

This course covers first steps in the development of data visualizations using spreadsheets and dashboards. Effectively create data visualizations, such as charts or graphs, and see how they play a key role in communicating your data analysis findings. All of this can be accomplished by learning the basics of data analysis with Excel and IBM Cognos Analytics, without having to write any code.

By the end of this course, you will be able to:

  • Describe common dashboarding tools used by a data analyst.
  • Design and create a dashboard in a cloud platform.
  • Create intermediate- level data visualizations with IBM Cognos Analytics
  • Create an interactive dashboard that can be shared with peers, professional communities or prospective employers.

Introduction to R Programming for Data Science

This course introduces you to the basics of the R language such as data types, techniques for manipulation and how to implement fundamental programming tasks. If you work in the data science field, you will need to become acquainted with the R language and the role it plays in data analysis. This course emphasizes hands-on and practical learning .

By the end of this course, you will:

  • Understand common data structures, programming fundamentals and how to manipulate data with the help of the R programming language.
  • Write a simple program using RStudio.
  • Manipulate data in a data frame or matrix.
  • Complete a final project as a data analyst using Watson Studio and Jupyter notebooks to acquire and analyze data-driven insights.

SQL for Data Science with R

This course introduces relational database concepts and teaches you to apply foundational knowledge of the SQL and R languages to perform SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning .

Much of the world’s data resides in databases. SQL (Structured Query Language) is a powerful language used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist.

By the end of this course, you will:

  • Work with real databases, real data science tools and real-world datasets.
  • Create a database instance in the cloud.
  • Through a series of hands-on labs, practice building and running SQL queries.
  • Learn to access databases from Jupyter notebooks using SQL and R.

Data Analysis with R

In this course, you will put your basic understanding of R programming language fundamentals to work. The R programming language takes the problems you want to solve with data and provides the answers you need to meet your objectives.

By the end of this course, you will:

  • Learn important techniques for preparing (or wrangling) your data for analysis.
  • Gain a better understanding of your data through exploratory data analysis.
  • Summarize your data and identify relevant relationships between variables that can lead to insights. Learn to develop your model and evaluate and tune its performance.
  • Build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays.
  • Use an Airline Reporting Carrier On-Time Performance Dataset to practice reading data files, preprocessing data, creating models, improving models and evaluating them to ultimately choose the best model.

Data Visualization with R

You have completed your data analysis, but do you really have a full picture of the results? Sometimes trends, patterns or anomalies are not obvious when looking at raw data, so you can use data visualization techniques to help you see this information and communicate its story to others.

By the end of this course, you will:

  • Learn the Grammar of Graphics, a system for describing and building graphs.
  • Learn how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots.
  • Learn to further customize your charts and plots using themes and other techniques.
  • Learn to use another data visualization package for R called Leaflet to create map plots, a unique way to plot data based on geolocation data.
  • Create interactive dashboards using the R Shiny package.
  • Learn to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components and deploy your interactive data apps on the web.

Data Science with R – Capstone Project

In this capstone course you will apply all the data science skills and techniques you learned in previous courses.

For this project, you will:

  • Assume the role of a data scientist who has recently joined an organization.
  • Be presented with a challenge that requires data collection, analysis, basic hypothesis testing, visualization and modelling to be performed on real-world datasets.
  • Collect and understand data from multiple sources.
  • Conduct data wrangling and preparation with Tidyverse.
  • Perform exploratory data analysis with SQL, Tidyverse and ggplot2.
  • Model data with linear regression,.
  • Create charts and plots to visualize the data.
  • Build an interactive dashboard.
  • Present your data analysis report, with an executive summary for the various stakeholders in the organization.

Skills you will gain: