Data Science

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Course Syllabus
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it Gain the career-building skills you need to succeed in the field of data science—from data manipulation to learning curves! Along with Python you will learn how this versatile language allows you to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional. 

With our fantastic practical experience, apply advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning and other uses.

Why to learn data science?

Where oil was the fuel of the last century, data is the fuel for 21st century. Due to a lack of “DataLiteracy” a vacuum has been created in the job market. To take advantage of this, learn with us to get an edge in the market.

By making data-driven decisions, you get a chance to leverage how you shape your workspace – making you a very valuable asset in any firm.

 Why is it worth it?

The average salary for a Data Scientist is way more than any other professional. According to Glassdoor (hiring portal) the average salary, annually for a data scientist is $117,345. Apart from being a lucrative career choice, it the “job future”

Raw data is not useful, till you process it into something meaningful. Learn, analyse and re-transform data into meaningful product with us. Register today!

Data Scientist – Syllabus

Domain 1: Manage Azure resources for machine learning

Module 1: Create an Azure Machine Learning workspace

 create an Azure Machine Learning workspace

 configure workspace settings

 manage a workspace by using Azure Machine Learning studio

Module 2: Manage data in an Azure Machine Learning workspace

 select Azure storage resources

 register and maintain datastores

 create and manage datasets

Module 3: Manage compute for experiments in Azure Machine Learning

 determine the appropriate compute specifications for a training workload

 create compute targets for experiments and training

 configure Attached Compute resources including Azure Databricks

 monitor compute utilization

Module 4: Implement security and access control in Azure Machine Learning

 determine access requirements and map requirements to built-in roles

 create custom roles

 manage role membership

 manage credentials by using Azure Key Vault

Module 5: Set up an Azure Machine Learning development environment

 create compute instances

 share compute instances

 access Azure Machine Learning workspaces from other development environments

Module 6: Set up an Azure Databricks workspace

 create an Azure Databricks workspace

 create an Azure Databricks cluster

 create and run notebooks in Azure Databricks

 link and Azure Databricks workspace to an Azure Machine Learning workspace


Domain 2: Run experiments and train models

Module 1: Create models by using the Azure Machine Learning designer

 create a training pipeline by using Azure Machine Learning designer

 ingest data in a designer pipeline

 use designer modules to define a pipeline data flow

 use custom code modules in designer

Module 2: Run model training scripts

 create and run an experiment by using the Azure Machine Learning SDK

 configure run settings for a script

 consume data from a dataset in an experiment by using the Azure Machine Learning


 run a training script on Azure Databricks compute

 run code to train a model in an Azure Databricks notebook

Module 3: Generate metrics from an experiment run

 log metrics from an experiment run

 retrieve and view experiment outputs

 use logs to troubleshoot experiment run errors

 use MLflow to track experiments

 track experiments running in Azure Databricks

Module 4: Use Automated Machine Learning to create optimal models

 use the Automated ML interface in Azure Machine Learning studio

 use Automated ML from the Azure Machine Learning SDK

 select pre-processing options

 select the algorithms to be searched

 define a primary metric

 get data for an Automated ML run

 retrieve the best model

Module 5: Tune hyper parameters with Azure Machine Learning

 select a sampling method

 define the search space

 define the primary metric

 define early termination options

 find the model that has optimal hyperparameter values

Domain 3: Deploy and operationalize machine learning solutions

Module 1: Select compute for model deployment

 consider security for deployed services

 evaluate compute options for deployment

Module 2: Deploy a model as a service

 configure deployment settings

 deploy a registered model

 deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint

 consume a deployed service

 troubleshoot deployment container issues

Module 3: Manage models in Azure Machine Learning

 register a trained model

 monitor model usage

 monitor data drift

Module 4: Create an Azure Machine Learning pipeline for batch inferencing

 configure a ParallelRunStep

 configure compute for a batch inferencing pipeline

 publish a batch inferencing pipeline

 run a batch inferencing pipeline and obtain outputs

 obtain outputs from a ParallelRunStep

Module 5: Publish an Azure Machine Learning designer pipeline as a web service

 create a target compute resource

 configure an inference pipeline

 consume a deployed endpoint

Module 6: Implement pipelines by using the Azure Machine Learning SDK

 create a pipeline

 pass data between steps in a pipeline

 run a pipeline

 monitor pipeline runs

Module 7: Apply ML Ops practices

 trigger an Azure Machine Learning pipeline from Azure DevOps

 automate model retraining based on new data additions or data changes

 refactor notebooks into scripts

 implement source control for scripts

Domain 4: Implement responsible machine learning

Module 1: Use model explainers to interpret models

 select a model interpreter

 generate feature importance data

Module 2: Describe fairness considerations for models

 evaluate model fairness based on prediction disparity

 mitigate model unfairness

Module 3: Describe privacy considerations for data

 describe principles of differential privacy

 specify acceptable levels of noise in data and the effects on privacy