IBM Data Analytics with Excel and R

Understand the key steps in the data analytics process and differentiate between various data roles in the industry.

• Explain different data structures, file formats, and sources of data.
• Describe the data analysis process involving collecting, wrangling, mining, and visualizing data.

Develop working knowledge of Excel for data analysis and learn to perform basic spreadsheet tasks and data quality techniques.

• Perform basic spreadsheet tasks including navigation, data entry, and using formulas.
• Analyze data in spreadsheets using filter, sort, look-up functions, and pivot tables.

Learn to create basic and advanced visualizations using Excel spreadsheets and construct interactive dashboards using Excel and Cognos Analytics.

• Explain the role of charts in data storytelling and construct advanced visualizations.
• Build and share interactive dashboards using Excel and Cognos Analytics.

Demonstrate readiness for foundational data analysis and visualization tasks, and differentiate between the roles of data professionals in the modern data ecosystem.

• Perform basic Excel tasks for data analysis and exhibit abilities in visualizing data using Excel and creating dashboards using Excel and Cognos Analytics.

Gain proficiency in R programming for data science, including manipulating data types and structures, controlling program flow, and data file handling.

• Manipulate primitive data types in the R programming language and control program flow with conditions and loops.
• Construct and manipulate R data structures and read, write, and save data files using R.

Learn SQL for data science with R, including creating and accessing a database instance on the cloud, composing and executing basic SQL statements, and analyzing data from Jupyter using R and SQL.

• Create and access a database instance on the cloud and compose and execute basic SQL statements.
• Analyze data from Jupyter using R and SQL by combining SQL and R skills to query real-world datasets.

Prepare data for analysis, compare predictive models, examine data using descriptive statistics, and evaluate model performance using R programming language.

• Prepare data for analysis by handling missing values, formatting, and normalizing data.
• Compare and contrast predictive models and examine data using descriptive statistics and correlation statistics.

Create various visualizations using R, design customized charts and plots, and build interactive dashboards using the Shiny package for R.

• Create bar charts, histograms, pie charts, scatter plots, line graphs, and maps using R and related packages.
• Design customized charts and plots using annotations, axis titles, and themes, and create interactive dashboards using the Shiny package for R.

Conduct a capstone project involving web scraping, data preparation, exploratory data analysis, and building a Shiny app containing a Leaflet map and an interactive dashboard.

• Write a web scraping program to extract data from an HTML file and prepare data for modeling.
• Interpret data with exploratory data analysis techniques and build a Shiny app containing a Leaflet map and an interactive dashboard.