IBM - Data Analytics with Excel and R Professional Certificate

Curriculum

Module 1: Introduction to Data Analytics

• Explain what Data Analytics is and the key steps in the Data Analytics process
• Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst
• Describe the different types of data structures, file formats, and sources of data
• Describe the data analysis process involving collecting, wrangling, mining, and visualizing data

Module 2: Excel Basics for Data Analysis

• Display working knowledge of Excel for Data Analysis.
• Perform basic spreadsheet tasks including navigation, data entry, and using formulas.
• Employ data quality techniques to import and clean data in Excel.
• Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables.

Module 3: Data Visualization and Dashboards with Excel and Cognos

• Create basic visualizations such as line graphs, bar graphs, and pie charts using Excel spreadsheets.
• Explain the important role charts play in telling a data-driven story.
• Construct advanced charts and visualizations such as Treemaps, Sparklines, Histogram, Scatter Plots, and Filled Map Charts.
• Build and share interactive dashboards using Excel and Cognos Analytics.

Module 4: Assessment for Data Analysis and Visualization Foundations

• Demonstrate readiness for performing foundational data analysis and data visualization tasks and key steps in the Data Analytics process.
• Differentiate between the roles different data professionals play in a modern data ecosystem.
• Perform basic Excel tasks for Data Analysis including data quality and data preparation skills.
• Exhibit abilities in visualizing data using Excel and proficiency in creating dashboards using Excel and Cognos Analytics.

Module 5: Introduction to R Programming for Data Science

• Manipulate primitive data types in the R programming language using RStudio or Jupyter Notebooks.
• Control program flow with conditions and loops, write functions, perform character string operations, write regular expressions, handle errors.
• Construct and manipulate R data structures, including vectors, factors, lists, and data frames.
• Read, write, and save data files and scrape web pages using R.

Module 6: SQL for Data Science with R

• Create and access a database instance on the cloud
• Compose and execute basic SQL statements - SELECT, INSERT, UPDATE, DELETE, CREATE, DROP
• Construct SQL statements to filter, sort, group results, use built-in functions, compose nested queries, access multiple tables
• Analyze data from Jupyter using R and SQL by combining SQL and R skills to query real-world datasets

Module 7: Data Analysis with R

• Prepare data for analysis by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.
• Compare and contrast predictive models using simple linear, multiple linear, and polynomial regression methods.
• Examine data using descriptive statistics, data grouping, analysis of variance (ANOVA), and correlation statistics.
• Evaluate a model for overfitting and underfitting conditions and tune its performance using regularization and grid search.

Module 8: Data Visualization with R

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

Module 9: Data Science with R - Capstone Project

• Write a web scraping program to extract data from an HTML file using HTTP requests and convert the data to a data frame.
• Prepare data for modelling by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.
• Interpret datawithexploratory data analysis techniques by calculating descriptive statistics, graphing data, and generating correlation statistics.
• Build a Shiny app containing a Leaflet map and an interactive dashboard then create a presentation on the project to share with your peers.