Explore

Data Science for Business Innovation

Approx. 7 hours to complete

Save Course

Go to Course

Course Summary

Learn how to leverage data science to drive innovation in business. This course will teach you the key concepts and techniques needed to analyze and interpret data, and use it to make informed business decisions.

Key Learning Points

Learn to collect, analyze, and interpret data to inform business decisions
Understand the basics of statistics, data visualization, and machine learning
Apply data science techniques to solve real-world business problems

Job Positions & Salaries of people who have taken this course might have

Data Analyst
- USA: $63,000 - $105,000
- India: ₹350,000 - ₹1,200,000
- Spain: €24,000 - €45,000
Business Intelligence Analyst
- USA: $68,000 - $115,000
- India: ₹450,000 - ₹1,500,000
- Spain: €25,000 - €50,000
Data Scientist
- USA: $87,000 - $140,000
- India: ₹600,000 - ₹2,500,000
- Spain: €30,000 - €60,000

Learning Outcomes

Understand how to collect, analyze, and interpret data to inform business decisions
Apply data science techniques to solve real-world business problems
Use statistics, data visualization, and machine learning to gain insights from data

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics
Familiarity with a programming language like Python or R

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Data Science Essentials
Applied Data Science
Data Science Fundamentals

Related Education Paths

Related Books

Description

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. From a more technical perspective, the course covers supervised, unsupervised and semi-supervised methods, and explains what can be obtained with classification, clustering, and regression techniques. It discusses the role of NoSQL data models and technologies, and the role and impact of scalable cloud-based computation platforms. All topics are covered with example-based lectures, discussing use cases, success stories and realistic examples.

Knowledge

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

Outline

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?
Data-driven Decision Making for Data-centric Organizations
What's Big Data? How Does It Relate to Data Science?
Data-driven Decision Making for Data-centric Organizations
Big Data and Data Science

Terminology and Foundational Concepts
Success Story: Data Science at Netflix
Machine Learning
Solving Problems: Programming vs. Machine Learning
Success Story: Data Science at Netflix
Machine Learning slides
Solving Problems: Programming vs. Machine Learning
Success Story: Data Science at Netflix
Machine Learning
Solving Problems: Programming vs. Machine Learning

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
Linear Regression for Product Price Prediction
Classification User-generated Content to Recommend Restaurants
Product Recommendation Using Decision Trees and Random Forests
Hiring Employees Using Logistic Regression
Using k-means for Clustering
Linear Regression
Naive Bayes
Decision Trees and Random Forests
Logistic Regression
K-means Clustering

Challenges and Conclusions
Data Science Challenges
Conclusions
Data Science Challenges
Data Science Challenges

Summary of User Reviews

Key Aspect Users Liked About This Course

Many users appreciated the practical approach to learning data science skills and applying them to real-world business problems.

Pros from User Reviews

Great introduction to data science for business applications
Hands-on projects with real data sets
In-depth explanations of statistical concepts
Excellent support from the instructor and community
Flexible schedule and self-paced learning

Cons from User Reviews

Some users felt that the course was too basic and lacked advanced topics
The workload can be overwhelming for beginners
The quizzes and exams can be challenging
The course may require additional resources and time commitment outside of the lectures
The platform can be glitchy at times

Recommended for you

Deep Learning with TensorFlow 2.0 [2021]

Build Deep Learning Algorithms with TensorFlow 2. 0, Dive into Neural Networks and Apply Your Skills in a Business Case They are all masters of deep learning....

Save Course

Natural Language Processing (NLP) with NLTK and Scikit-learn

Learn to build expert NLP applications and machine learning projects using NLTK and Python library- scikit-learn Natural Language Processing (NLP) is the most interesting subfield of data science....

Save Course

[2021] Data Science: A-Z Bootcamp + Real Cases (Version 8.1)

A-Z Data Science Training: Advanced Statistics & Machine Learning with R, Excel + Tableau, Power BI from Best Seller Do you want a rewarding career?...

Save Course