Overview

This Professional Certificate program is intended for anyone who is seeking to develop the job-ready skills, tools, and portfolio for an entry-level data analyst or data scientist position. No prior knowledge of R, or programming is required to get you started!

In this Data Analytics and Visualization with Excel and R Professional Certificate Program, you will dive into the role of a data analyst or data scientist and develop the essential skills you need work with a range of data sources and apply powerful tools, including Excel, Cognos Analytics, and the R programming language (including: ggplot2, Leaflet and R Shiny), towards becoming a data driven practitioner, and gaining a competitive edge in the job market.

By the end of this program, you will be able to explain the data analyst and data scientist roles. Skills you will developer and tools you will be exposed to in this program include:

Excel spreadsheets to create charts and plots.
Cognos Analytics to create interactive dashboards.
Relational databases and query data using SQL statements.
R programming language to complete the entire data analysis process - including data preparation, statistical analysis, data visualization, predictive modeling, and creating interactive data applications.
Various methods to communicate your data findings and learn to prepare a report for stakeholders. This program is suitable for anyone with a passion for learning and does not require any prior data analysis, statistics, or programming experience.

Applied Learning Project
Throughout this Professional Certificate, you will also complete hands-on labs and projects to help you 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.

In the final course in this Professional Certificate, you will complete a capstone project that applies what you have learned to a challenge that requires data collection, analysis, basic hypothesis testing, visualization, and modelling to be performed on real-world datasets.

Syllabus

Courses under this program:
Course 1: Data Analytics Basics for Everyone

Learn the fundamentals of Data Analytics and gain an understanding of the data ecosystem, the process and lifecycle of data analytics, career opportunities, and the different learning paths you can take to be a Data Analyst.

Course 2: Analyzing Data with Excel

Build the fundamental knowledge required to use Excel spreadsheets to perform basic data analysis. The course covers the basic workings and key features of Excel to help students analyze their data.

Course 3: Data Visualization and Building Dashboards with Excel and Cognos

Build the fundamental knowledge necessary to use Excel and IBM Cognos Analytics to create data visualizations and to create dashboards containing those visualizations to help tell a lucid story about data.

Course 4: R Programming Basics for Data Science

This course introduces you to R language fundamentals and covers common data structures, programming techniques, and how to manipulate data all with the help of the R programming language.

Course 5: SQL for Data Science with R

Build a baseline understanding about relational database concepts and learn how to apply foundational knowledge of the SQL and R languages through a series of hands-on labs to practice building and running SQL queries.

Course 6: Analyzing Data with R

R is the key that opens the door between the problems you want to solve with data and the answers you need. This course walks you through the process of answering questions through data.

Course 7: Visualizing Data with R

Learn about the ggplot2 data visualization package for R, creating map plots with geolocation data using Leaflet, and interactive dashboards to display your work on the web using the R Shiny package!

Course 8: R Data Science Capstone Project

Apply various data analysis and visualization skills and techniques you have learned by taking on the role of a data scientist working with real-world data sets.