Skip to main content
MindLeap AI MindLeap AI
Intermediate Level

Microsoft Certified Azure Data Scientist Associate (DP-100)

★ 4.8 (300 learners) 60 hrs total 6 modules
Microsoft Azure AKS Databricks Apache Spark Python
Only 5 seats left · Next batch starts 15 Aug 2026
Download Curriculum
60 hrs
of content
6 modules
in curriculum
16 projects
hands-on builds
Intermediate
difficulty

Curriculum covers

Frameworks you'll master

5 tools & frameworks
Microsoft Azure AKS Databricks Apache Spark Python

Course Curriculum

6 modules · 60 hours · 24 live sessions

Basics of Machine Learning 1 sessions
  • Why Machine Learning is the Future?
  • What is Machine Learning?
  • Understanding various aspects of data Type, Variables, Category
  • Common Machine Learning Terms Probability, Mean, Mode, Median, Range
  • Types of Machine Learning Models Classification, Regression, Clustering etc
  • Basics of Machine Learning
Getting Started with Azure ML 1 sessions
  • What is Azure ML and highlevel architecture.
  • Azure ML Experiment Workflow
  • Azure ML Cheat Sheet for Model Selection
DP100 – SetUp Azure Machine Learning Workspace 4 sessions
  • Understand the AzureMLService Architecture
  • Create the AzureML Workspace
  • View and Manage Workspace Settings
  • Overview of New AzureML Studio
  • What is AzureML Datastore and Dataset?
  • Create and Register a Datastore
  • Create a Dataset
  • Explore the AzureML Dataset
  • Understanding the AzureML Compute Resources
  • Create a Compute Cluster and Compute Instance
  • DP100 – Run Experiments and Train Models
  • What is an AzureML Pipeline?
  • Create a Pipeline using AzureML Designer
  • Submit the Designer Pipeline run
  • DP100 – Deploy and consume the models
  • Create an Inference Pipeline
  • Deploy a realtime endpoint using Designer
  • Create a batch inference pipeline using Designer
  • Run a Batch Inference Pipeline from Designer
  • Data Processing using AzureML Designer
  • Get Data to the workspace
  • Import Data to the workspace from external sources
  • Edit Metadata Column Names
  • Understanding the Run
  • Edit Metadata Data Type
  • Export Data to the Blob Storage
  • Add Columns to the Dataset
  • Add Rows to the Dataset
  • Normalization of Data Part 1
  • Normalization of Data Part 2
  • Clean Missing Data
  • Partition and Sample Data Part 1
  • Partition and Sample Data Part 2
Classification 2 sessions
  • What is Logistic Regression
  • Two Class Logistic Regression Problem Statement
  • Data Preparation for Two Class Classification
  • Train the Model for Logistic Regression
  • Evaluate the Model Part 1
  • Evaluate the Model Confusion Matrix
  • Evaluate the Model AUC ROC
  • Parameters of Two Class Logistics Regression
  • What is Decision Tree?
  • Ensemble Learning in Decision Tree
  • Bagging and Boosting in Decision Tree
  • Hands On Train the Two Class Boosted Decision Tree
  • Evaluate and Compare Decision Tree output
Regression using AzureML Designer 2 sessions
  • What is Linear Regression?
  • Ordinary Least Square and Common Errors
  • Hands On Automobile Price Predictions Data Analysis
  • Hands On Automobile Price Predictions Data Processing
  • Hands On Automobile Price Predictions Train Model
  • Hands On Automobile Price Predictions Evaluate
  • RSquared or Coefficient of Determination
  • Math Behind Gradient Descent
  • Gradient Descent Explained
  • Online or Stochastic Gradient Descent
  • Experiment Linear Regression using Online Gradient Descent
  • Evaluate Linear Regression using Online Gradient Descent
Designer/Classic Studio vs Pandas and ScikitLearn 14 sessions
  • Pandas Import Data for Experiments
  • Pandas Import Data Part 2
  • Select Columns using Pandas
  • Select Columns By drop method
  • Add columns and rows
  • Clean Missing Data
  • Edit Metadata of columns using Pandas
  • Create Summary Statistics using describe
  • Clip Values Remove Outliers using Constants
  • Clip Values Remove Outliers with Percentiles
  • Convert and save a delimited file using Pandas
  • Data Normalization
  • Label Encoding of String Categorical data
  • Why Hot encoding is required?
  • Hot Encoding using Pandas get_dummies
  • Split The Data for training and testing
  • Build Logistic Regression using Python Part 1
  • Build Logistic Regression using Python Part 2
  • Azure Machine Learning with AzureML SDK
  • Introduction to AzureML SDK
  • DP100 – SetUp Azure Machine Learning Workspace
  • Create AzureML Workspace using SDK
  • Verify the Workspace and Write the Workspace Config File
  • Create and Register a Datastore using AzureML SDK
  • Create and Register a Dataset using SDK
  • Access Workspace, Datastore and Datasets using SDK
  • Pandas DataFrame and AzureML Dataset conversions
  • Upload local data to storage account via datastore
  • DP100 – Run Experiments and Train Models
  • Problem Statement Run a sample experiment and log values
  • Run a sample experiment using AzureML SDK Part 1
  • Run a sample experiment using AzureML SDK Part 2
  • Run a script in AzureML environment Part 1
  • Run a script in AzureML environment Part 2
  • Run a script in AzureML environment Part 3
  • Run a script in AzureML environment Part 4
  • Run a script in AzureML environment Part 5
  • Train and Run a Model Script in AzureML Part 1
  • Train and Run a Model Script in AzureML Part 2
  • Train and Run a Model Script in AzureML Part 3
  • Train and Run a Model Script in AzureML Part 4
  • Train and Run a Model Script in AzureML Part 5
  • Provisioning Compute Cluster using SDK
  • Automate Model Training using AzureML SDK
  • Automate Model Training Define Pipeline Steps
  • Automate Model Training Define Run Configuration
  • Automate Model Training Define Build and Run
  • Detour Command Line Arguments
  • Automate Model Training Create Dataprep Step
  • Automate Model Training Create Training Step
  • Run the pipeline and see the results
  • Using Python Scripts in AzureML Designer
  • Simple Python Script in Designer
  • Execute Python Script using Zip Bundle
  • Execute Python Script using Zip Bundle Hands on
  • Use Automated ML to create Optimal Models
  • What is Azure AutoML?
  • Use the Automated ML interface in Azure Machine Learning studio
  • View the AutoML Run Result
  • Note on Normalized Macro Recall
  • Use Automated ML from the Azure Machine Learning SDK
  • Retrieve the Best Model and View results
  • Use Azure Hyperdrive to Tune Hyperparameters
  • Introduction to Azure Hyperdrive
  • Define the Hyperparameter Search Space
  • Select a Sampling method
  • Define Early Termination Options
  • Configure the Hyperdrive run
  • Create the Training Script for Hyperdrive run
  • Retrieve the Best Model
  • Use model explainers to interpret models
  • Why model explanation is important?
  • Understanding Shapley Value
  • Interpretability Techniques in Azure
  • Implement Interpretability Initial Setup
  • Implement Interpretability Global Explanations
  • Implement Interpretability Local Explanations
  • Implement Interpretability Local Explanations Part 2
  • Run Interpret Model Script in Azure Workspace
  • Visualize Explanations in AzureML Studio
  • Retrieve/Download Feature Importance Values.
  • Model Registration and Deployment using AzureML SDK
  • Model Deployment Steps
  • Understanding Model/Object Serialization
  • Hands on Serialization using Joblib
  • Handling OneHotEncoding/Dummy Values in Production
  • Hands on Dummy Variables in Production
  • Train the model for webservice deployment
  • Register the Model using Run_ID
  • Register the Model using local pkl file
  • Retrieve all the registered models from the workspace
  • Provisioning AKS Production Cluster using SDK
  • Create the Inference and Deployment Configuration for Webservice
  • Entry Script Init Function
  • Understanding Data processing using JSON, Dictionary and DataFrame
  • Entry Script run Function
  • Create webservice deployment object
  • Deploy a realtime endpoint using SDK
  • Consume the web service from Python program
  • Consume the web service as an End Point.
  • Using Databricks with Azure Machine Learning
  • Databricks Update to DP100
  • (Optional) What is Big Data?
  • (Optional) What is Hadoop?
  • What is Spark and Databricks?
  • Create an Azure Databricks workspace
  • Note on Deleting Databricks resource in Azure Portal
  • Note on Increasing vCPU Quota Limits
  • Create an Azure Databricks cluster
  • Link AzureML Workspace with the Databricks Workspace
  • Create and run notebooks in Azure Databricks Part1
  • Create and run notebooks in Azure Databricks Part2
  • Mount Blob storage to Databricks using dbutils Part1
  • Mount Blob storage to Databricks using dbutils Part2
  • Run an Sklearn experiment with Databricks Notebook
  • Overview to Run a Training script using DatabricksStep in a pipeline
  • Saving data to Azure Blob storage from Databricks
  • Passing parameters between Azure Databricks notebooks
  • Attach a Databricks Cluster as an Attached Compute Target
  • Verify Databricks Cluster as Attached Compute
  • Databricks Pipeline Initial Setup
  • Databricks Pipeline Build DatabricksStep
  • Databricks Pipeline Databricks and Python notebook
  • Databricks Pipeline Submit the pipeline and verify the output
Download Full Curriculum

Batch Schedule

⚡ Seats filling fast — only 5 remaining across all batches

Available Batches
Batch Type Schedule Duration Next Start Date Availability
Weekend Sat & Sun, 10am–1pm 10 weeks 15 Aug 2026 Only 5 seats left
Weekday Mon–Fri, 7–9pm 8 weeks 17 Aug 2026 Available
Fast-track Mon–Sun, 6 hours/day 2 weeks 24 Aug 2026 Available

Ready to Get Started?

Download the curriculum to see every module, batch dates and fees. Book a free 30-minute session with our advisors to get your questions answered.

Book Free Demo

Download the Full Curriculum

See every module, capstone project, and tool covered — free, instant access.

Similar Courses You Might Like

View Course →
Microsoft Azure
microsoft azure

Azure Kubernetes Service with Azure DevOps and Terraform

★ 4.8 · 300 learners · Intermediate
Starts 8 Aug 2026 · ~1–2 months
View Course →
Microsoft Azure
microsoft azure

Microsoft Certified - Security, Compliance & Identity Fundamentals - SC-900

★ 4.8 · 300 learners · Beginner
Starts 15 Aug 2026 · ~1–2 months
View Course →
Microsoft Azure
microsoft azure

Microsoft Certified Azure Administrator Associate (AZ-104)

★ 4.8 · 300 learners · Intermediate
Starts 22 Aug 2026 · ~1–2 months
View Course →
Microsoft Azure
microsoft azure

Microsoft Certified Azure AI Engineer Associate (AI-102)

★ 4.8 · 300 learners · Intermediate
Starts 29 Aug 2026 · ~1–2 months
Book Free Demo