The primary audience for this course is individuals who need to master the basics of data science and who plan an implementation of data science solution. The secondary audience of this course is individuals who develop data related solutions and keen to learn data science, its concepts and technologies related.
Working knowledge of data related solutions
Working knowledge of programming
Basic knowledge on relational data and business intelligence
Basic knowledge on programming concepts
After completing this course, you will be able to:
- Describe Data Science and Data Science methodologies.
- Describe essentials statistics for Data Science.
- Describe how to use Python for exploring and transforming data, and creating, validating and deploying Machine Learning models.
- Describe how to use Azure Machine Learning Studio for exploring and transforming data, and creating, validating and deploying Machine Learning models.
- Describe supervised and unsupervised Machine Learning techniques.
- Describe how to implement a predictive model and consume it.
The following items describe the outline of the course:
- Introduction to Data Science
- Introduction to Python for Data Science
- Essentials statistics for Data Science
- Exploring, cleaning and preparing data
- Introduction to Azure Machine Learning Studio
- Supervised Machine Learning
- Unsupervised Machine Learning
- Creating a machine learning model using Python
- Creating a machine learning model using Azure Machine Learning Studio
The following materials are included as part of the course.
- Printed slides with hands-on instructions
- Sample datasources for hands-on
Founder / Principal Architect at dinesQL
Dinesh is an experienced professional and database enthusiast with skills in database management systems and business intelligence, especially on the Microsoft SQL Server product suite. Possessing over 16 years of experience on data related technologies, he does training, consulting, and is a top contributor to the local SQL Server community.