Data Science for Business Innovation
5.0 /5- Offered byCoursera
Data Science for Business Innovation at Coursera Overview
Duration | 6 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Science for Business Innovation at Coursera Highlights
- Offered by EIT Digital & Politecnico di Milano
- Learn from expert instructors of Politecnico di Milano
- Practical learning with example-based lectures, discussing use cases, success stories
- Covers: Supervised, unsupervised and semi-supervised methods, & explains classification, clustering, and regression techniques
Data Science for Business Innovation at Coursera Course details
- What is data science
- How data science, machine learning, and data-driven innovation can benefit business outcomes
- Foundational concepts and intuitions about machine learning techniques
- The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues.
- The course covers terminology and concepts, tools and methods, use cases and success stories of data science applications.
- The course explains what is Data Science and why it is so hyped. It discusses the value that Data Science can create, the main classes of problems that Data Science can solve, the difference is between descriptive, predictive and prescriptive analytics, and the roles of machine learning and artificial intelligence.
Data Science for Business Innovation at Coursera Curriculum
Week 1: Introduction to Data-driven Business
Welcome and Introduction
Data-driven Decision Making for Data-centric Organizations
What's Big Data? How Does It Relate to Data Science?
Week 2: Terminology and Foundational Concepts
Success Story: Data Science at Netflix
Machine Learning
Solving Problems: Programming vs. Machine Learning
Week 3: Data Science Methods for Business
Linear Regression for Price Prediction
Classification of User-generated Content to Recommend Restaurants
Product Recommendation Using Decision Trees and Random Forests
Hiring Employees Using Logistic Regression
K-means Clustering
Week 4: Challenges and Conclusions
Data Science Challenges
Conclusions
Data Science for Business Innovation at Coursera Admission Process
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