IBM Data Analytics with Excel and R Professional Certificate

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.

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.

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.

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.

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

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.

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.

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.