Duration: 3 days
Time: 9am to 5pm

What Will Be Taught For This Data Science Course?

This course is designed to help you pass Exam DP-100: Designing and Implementing a Data Science Solution on Azure certification. You will learn to apply the Azure machine learning platform to train, evaluate and deploy data models that solve business problems, including selecting and setting up Azure development environments, performing Exploratory Data Analysis (EDA), selecting the right algorithmic approaches, defining technical success metrics and evaluating the success of your data models.

Module 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

Module 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

Module 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

Module 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


Participant should have a basic understanding of:

  • Mathematics
  • Statistics
  • Programming fundamentals
  w/o GST w GST
Course Fee $1,500 $1,605


Please click on the course date to enrol.

  • CL: Classroom Learning
  • VILT: Virtual Instructor-Led Training
  • GTR: Guaranteed To Run
  • Sat: Saturday
  • Wkn: Weekend
Note: Courses are conducted via classroom unless stated otherwise beside the course dates.