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
Pre-requisites
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