Data Science
Designing and Implementing a Data Science Solution on Azure

1 - 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

2 – 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

3 – 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

4 – 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

Who Should Attend This Data Science Training?

The course is ideal for anyone who wants to start their career in the data science domain or is already working in the following roles:

• Data architects
• Data scientists
• Data analysts

Pre-requisites

Participant should have a basic understanding of:

• Mathematics
• Statistics
• Programming fundamentals

Remarks:

COMAT is a business centre of e-Services, Electronics, ST Engineering.

Terms & Conditions

• All prices stated above are in Singapore Dollars (SGD).
• e-Services, Electronics, ST Engineering reserves the right to change the date or venue without prior notice.
• e-Services, Electronics, ST Engineering reserves the right to cancel or reschedule the course due to class size or unforeseen circumstances.