COURSE INTRODUCTION

This Data Science Capstone course end project will validate your expertise as a job-ready data scientist. You will learn to apply the knowledge, skills and capabilities your learned in the Data Science Master’s Program and build a project on your own from start to finish. You will gain real-world exposure to modern data science challenges and learn to build your own models to solve them and make better data-driven decisions. You will also have the flexibility to choose your own domain and technology stack for your project.

KEY FEATURES

• Flexibility to choose the domain/industry of your choice
• Build on any technology covered within the Data Scientist Master’s program
• Dedicated mentoring sessions to ensure high-quality learning
• Capstone completion certificate

COURSE OBJECTIVES

After finish the course, student will have knowledge and skills to:

• Data Processing - In this step, you will apply various data processing techniques to make raw data meaningful.
• Model Building - You will leverage techniques such as regression and decision trees to build machine learning models that enable accurate and intelligent predictions. You may explore Python, R or SAS to build your model. You will follow the complete model-building exercise from data split to test and training and validating data using the k-fold cross-validation process.
• Model Fine-tuning - You will apply various techniques to improve the accuracy of your model and select the champion model that provides the best accuracy.
• Dashboarding and Representing Results - As the last step, you will be required to export your results into a dashboard with meaningful insights using Tableau

AUDIENCE

Anyone who is interested in upscaling their skillets in data science can take this course to become familiar with real-world industry-specific problems and solutions.

PREREQUISITES

You should complete Simplilearn’s Data Scientist Masters Program in order to take this project

Alternatively, the learner must be proficient in Data Science using R, Python or SAS, Data Visualisation using Tableau, and Machine Learning in order to take up this course.

Below are the recommended courses to take:

Data Science:

• Data Science with SAS training, or
• Data Science with R, or
• Data Science with Python

Data Visualisation:

• Tableau Certification course

Machine Learning:

• Machine Learning course

COURSE CONTENT

Day 1 - Problem and approach overview

Day 2 - Data pre-processing techniques application on data set

Day 3 - Model Building and fine tuning leveraging various techniques

Day 4 - Dashboard problem statement to meet the business objective

Day 5 - Final evaluation