Final Exam: Business Analyst to Data Analyst

Intermediate
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Final Exam: Data Analyst will test your knowledge and application of the topics presented throughout the Data Analyst track of the Skillsoft Aspire Business Analyst to Data Analyst Journey.

WHAT YOU WILL LEARN

  • recognize the properties of ensemble models which can be configured in BigML
    import data from a CSV file
    wire up a complex user form to store data in an Excel workbook
    create macros
    identify and deal with duplicate records
    filter data using the iloc function
    load data from a variety of sources into BigML to train
    use BigML to build an ensemble of decision trees to solve a classification problem
    perform basic operations on Series objects
    create models from your cluster instances to identify the factors which affect cluster membership
    perform common grouping and aggregation operations
    recall the various metrics used to evaluate the quality of a machine learning model
    summarize records into bins or categories
    train a logistic regression model to predict an output based on the probability of occurrence
    describe the features and use cases of linear regression
    create pandas Series objects
    describe the process in which a machine learning model is constructed using training data
    cast data types within Series objects
    lookup data using different techniques
    import and export data in CSV files
    apply basic data manipulation operations on DataFrames
    import and export data using HTML and JSON files
    perform basic data manipulation operations on DataFrames
    use the loc and iloc functions to access specific rows and columns
    apply the loc and iloc functions to access specific rows and columns
    serialize data to Excel and Pickle files
    introduce user forms as a way to add complex UIs to an Excel workbook and use the VBA forms control toolbox to add elements, such as buttons, to a user form
    navigate Visual Basic editor
    apply a brute-force approach to find the optimal model for your dataset
    recognize the purpose of clustering algorithms and list some of its use cases
  • edit the contents of a range of cells using direct cell references
    recognize the features available in BigML to load data and to train a machine learning model
    generate clusters in your input data and analyze the properties of each cluster
    use relative references while recording a macro and recognize how it affects the output, accept user input using the InputBox function, and insert sheets into a Workbook from VBA
    wire up a button to VBA code so that whenever that button is clicked, a message is displayed, design a fully-fledged user form to accept complex user input using input boxes, and configure buttons to submit or reset that user input
    use shortcut keys to navigate Visual Basic Editor
    add a button to a workbook to display a complex user form and demonstrate the resulting fully-fledged user application from Excel
    perform inner join operations using the merge() method
    use VBA's support for sending emails from within macros
    organize your BigML resources such as data sources, datasets and models into projects
    identify the features of clustering models which can be configured
    describe the features of clustering models which can be configured
    customize Excel menus to display developer features
    compute aggregations on data
    use VBA macros to autofit rows and columns
    filter data using the loc, iloc, at, and iat functions
    use inner join operations using the merge() method
    create a pandas Series object
    specify the rows in a table as well as a primary key, import data from a CSV file into this table, and recognize how SQL queries work in MS Access
    run SQL queries, such as SELECT-FROM-WHERE queries, to analyze data in MS Access
    use SQL INSERT statements to add rows to the Access database based on user input to a form
    compare the performance of a small ensemble with a larger one
    load data from a variety of sources into BigML to train and evaluate machine learning models
    describe the process of preparing a dataset for logistic regression
    create a dataset out of a data source and analyze the different fields in the data
    perform a brute-force approach to find the optimal model for your dataset
    recognize the types of machine learning algorithms and their applications
    demonstrate the use of MS-Access, a lightweight relational database, and create a sample database (.acc) file and a table
    illustrate the use of MS-Access, a lightweight relational database, and create a sample database (.acc) file and a table
    add user forms to an Access database and configure various user controls

IN THIS COURSE

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    Data Analyst
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