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.
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