Final Exam: Business Analyst to Data Analyst

WHAT YOU WILL LEARN

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  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