Azure Data Scientist

Azure Data Scientist

Azure Data Scientist

An Azure Data Scientist applies scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders. They use machine learning techniques to train, evaluate, and deploy models to build AI solutions that satisfy business objectives. They use applications that involve natural language processing, speech, computer vision, and predictive analytics.

They serve as part of a multi-disciplinary team that incorporates ethical, privacy, and governance considerations into the solution. They typically have background in mathematics, statistics, and computer science.

Steps to get certified as Azure Data Scientist


We are aware that our current online course is NOT covering all the exam topics. Therefore we are working very hard on an Azure Data Scientist (DP-100) exam preparation guide. If you want to keep informed about this guide, please sign up for our newsletter:

Exam Topics for Azure Data Scientist

Define and prepare the development environment (15-20%)

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 (25-30%)

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 (15-20%)

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 (40-45%)

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

About the online prep course

This course teaches about the data science process and how Microsoft Azure services support it. A high level overview of Azure data science related services is provided followed by a deep dive on the premier data science service, Azure Machine Learning service, which supports automation of machine learning model training and deployment.

The course assumes that you already have Azure fundamental skills and understand how to navigate the Azure portal and to create services there. It also assumes that you have some familiarity with the Azure data storage technologies but does not require you to know how to implement these data storage technologies at an expert level.

Although some basic data science concepts are presented, it is assumed the student has an understanding a data science and machine learning prior to taking this course. The course teaches how to bring your data science work to Azure.