Business Analyst to Data Analyst Proficiency

  • 20m
  • 20 questions
The Business Analyst to Data Analyst Proficiency benchmark will measure your ability to recall, recognize, and apply business analytics concepts and features of Tableau, Power BI, Python, and BigML. You will be evaluated on your ability to recognize and use business analytics techniques in Tableau, Power BI, Maria DB, Python, and Big ML for data visualizations using various charts, including Google charts. You will be evaluated on your ability to create dashboards; load data from a variety of files in Tableau; analyze tables; apply transformations, data validation, and cleaning; use pivot tables; use Python pandas for data analysis; and leverage Maria DB and BigML for machine learning models. A learner who scores high on this benchmark demonstrates that they have strong skills in business analytics and data analytics to build effective dashboards and share reports and insights with business users.

Topics covered

  • add complex controls to an app, including spin buttons and checkboxes, create a close button to unload the form's memory and cease displaying it, and add a submit button to store user input in a workbook
  • apply past data to make future forecasts using a time series model
  • apply regular expressions and other advanced techniques to select and drop columns
  • compute statistical summaries on data stored in DataFrames
  • create and use an update trigger, which is executed after updates to a MariaDB table
  • create a time series model using several years' worth of data
  • edit the page view and page theme of a Power BI page and optimize a Power BI report for display on mobile devices using the mobile phone layout feature
  • extract discussion topics in a collection of text-based product reviews
  • identify the anomalies in a dataset using BigML's anomaly detection model
  • link a matrix with a treemap control so that if a matrix row is clicked, the tile corresponding to that row in the treemap is highlighted, and vice versa
  • manipulate and analyze time series data
  • perform left and right join operations using the merge() method
  • plot a value that falls within a range using a gauge chart
  • recognize what association rules are and state their applications
  • represent multiple dimensions of data in your scatter plot by configuring the points that are rendered
  • set the aesthetics of a dashboard and include an option for the user to download the dashboard to a local file
  • set up a combo chart with multiple visualizations for the same data
  • share a story with the general public by publishing it to Tableau
  • split and sample a dataset, which can then be used to train and test a model
  • use subqueries in their most fundamental application