Define and prepare the development environment
Select development environment
assess the deployment environment constraints
analyze and recommend tools that meet system requirements
select the development environment
Set up development environment
create an Azure data science environment
configure data science work environments
Quantify the business problem
define technical success metrics
quantify risks
Prepare data for modeling
Transform data into usable datasets
develop data structures
design a data sampling strategy
design the data preparation flow
Perform Exploratory Data Analysis (EDA)
review visual analytics data to discover patterns and determine next steps
identify anomalies, outliers, and other data inconsistencies
create descriptive statistics for a dataset
Cleanse and transform data
resolve anomalies, outliers, and other data inconsistencies
standardize data formats
set the granularity for data
Perform feature engineering
Perform feature extraction
perform feature extraction algorithms on numerical data
perform feature extraction algorithms on non-numerical data
scale features
Perform feature selection
define the optimality criteria
apply feature selection algorithms
Develop models
Select an algorithmic approach
determine appropriate performance metrics
implement appropriate algorithms
consider data preparation steps that are specific to the selected algorithms
Split datasets
determine ideal split based on the nature of the data
determine number of splits
determine relative size of splits
ensure splits are balanced
Identify data imbalances
resample a dataset to impose balance
adjust performance metric to resolve imbalances
implement penalization
Train the model
select early stopping criteria
tune hyper-parameters
Evaluate model performance
score models against evaluation metrics
implement cross-validation
identify and address overfitting
identify root cause of performance results