HOT
blank

Data Science for Business | 6 Real-world Case Studies

Business

Rating4.6
Enroll Now

Data Science for Business | 6 Real-world Case Studies, available at $79.99, has an average rating of 4.58, with 77 lectures, based on 1534 reviews, and has 12605 subscribers.

You will learn about Develop an AI model to Reduce hiring and training costs of employees by predicting which employees might leave the company. Develop Deep Learning model to automate and optimize the disease detection processes at a hospital. Develop time series forecasting models to predict future product prices. Develop defect detection, classification and localization models. Optimize marketing strategy by performing customer segmentation Develop Natural Language Processing Models to analyze customer reviews on social media and identify customers sentiment. This course is ideal for individuals who are Seasoned consultants wanting to transform businesses by leveraging data science and AI. or Visionary business owners who want to harness the power of Data science and AI to maximize revenue, reduce costs and optimize their business. or Data Science Practitioners wanting to advance their careers and build their portfolio. or Tech enthusiasts who are passionate about Data science and AI and want to gain real-world practical experience. It is particularly useful for Seasoned consultants wanting to transform businesses by leveraging data science and AI. or Visionary business owners who want to harness the power of Data science and AI to maximize revenue, reduce costs and optimize their business. or Data Science Practitioners wanting to advance their careers and build their portfolio. or Tech enthusiasts who are passionate about Data science and AI and want to gain real-world practical experience.

Enroll now: Data Science for Business | 6 Real-world Case Studies

Summary

Title: Data Science for Business | 6 Real-world Case Studies

Price: $79.99

Average Rating: 4.58

Number of Lectures: 77

Number of Published Lectures: 75

Number of Curriculum Items: 77

Number of Published Curriculum Objects: 75

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Develop an AI model to Reduce hiring and training costs of employees by predicting which employees might leave the company.
  • Develop Deep Learning model to automate and optimize the disease detection processes at a hospital.
  • Develop time series forecasting models to predict future product prices.
  • Develop defect detection, classification and localization models.
  • Optimize marketing strategy by performing customer segmentation
  • Develop Natural Language Processing Models to analyze customer reviews on social media and identify customers sentiment.

Who Should Attend

  • Seasoned consultants wanting to transform businesses by leveraging data science and AI.
  • Visionary business owners who want to harness the power of Data science and AI to maximize revenue, reduce costs and optimize their business.
  • Data Science Practitioners wanting to advance their careers and build their portfolio.
  • Tech enthusiasts who are passionate about Data science and AI and want to gain real-world practical experience.

Target Audiences

  • Seasoned consultants wanting to transform businesses by leveraging data science and AI.
  • Visionary business owners who want to harness the power of Data science and AI to maximize revenue, reduce costs and optimize their business.
  • Data Science Practitioners wanting to advance their careers and build their portfolio.
  • Tech enthusiasts who are passionate about Data science and AI and want to gain real-world practical experience.

Are you looking to land a top-paying job in Data Science?

Or are you a seasoned AI practitioner who want to take your career to the next level?

Or are you an aspiring entrepreneur who wants to maximize business revenue with Data Science and Artificial Intelligence?

If the answer is yes to any of these questions, then this course is for you!

Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, transportation and technology.

In business, Data Science is applied to optimize business processes, maximize revenue and reduce cost. The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.

In this course, we will assume that you are an experienced data scientist who have been recently as a data science consultant to several clients. You have been tasked to apply data science techniques to the following 6 departments: (1) Human Resources, (2) Marketing, (3) Sales, (4) Operations, (5) Public Relations, (6) Production/Maintenance. Your will be provided with datasets from all these departments and you will be asked to achieve the following tasks:

  1. Task #1 @Human Resources Department: Develop an AI model to Reduce hiring and training costs of employees by predicting which employees might leave the company.

  2. Task #2 @Marketing Department:Optimize marketing strategy by performing customer segmentation

  3. Task #3 @Sales Department: Develop time series forecasting models to predict future product prices.

  4. Task #4 @Operations Department:Develop Deep Learning model to automate and optimize the disease detection processes at a hospital.

  5. Task #5 @Public Relations Department:Develop Natural Language Processing Models to analyze customer reviews on social media and identify customers sentiment.

  6. Task #6 @Production/Maintenance Departments: Develop defect detection, classification and localization models.

Course Curriculum

Chapter 1: Course Introduction and Welcome Message

Lecture 1: Updates on Udemy Reviews

Lecture 2: Introduction

Lecture 3: Key Tips and Best Practices

Lecture 4: Course Outline and Key Learning Outcomes

Lecture 5: Learning Paths

Lecture 6: Get the Materials

Chapter 2: Human Resources Department

Lecture 1: Introduction to Case Study and Key Learning Outcomes

Lecture 2: Task #1: Problem Statement and Business Case

Lecture 3: Task #2: Import Libraries and Datasets

Lecture 4: Task #3: Explore Dataset – Part 1

Lecture 5: Task #3: Explore Dataset – Part 2

Lecture 6: Task #3: Explore Dataset – Part 3

Lecture 7: Task #3: Explore Dataset – Part 4

Lecture 8: Task #4: Perform Data Cleaning

Lecture 9: Task #5: Understand intuition of Random Forest, Logistic Regression, and ANNs

Lecture 10: Task #6: Understand Classification KPIs

Lecture 11: Task #7: Build and Train Logistic Regression Classifier

Lecture 12: Task #8: Build and Train Random Forest Classifier Model

Lecture 13: Task #9: Build and Train Artificial Neural Network Classifier Model

Chapter 3: Marketing Department

Lecture 1: Introduction to Case Study and Key Learning Outcomes

Lecture 2: Task #1: Understand Problem Statement and Business Case

Lecture 3: Task #2: Import Libraries and Datasets

Lecture 4: Task #3: Perform Data Visualization

Lecture 5: Task #4: Understand the Theory and Intuition behind K-Mean Algorithm

Lecture 6: Task #5: Obtain Optimal Number of Clusters "K"

Lecture 7: Task #6: Apply K-Means Clustering to Perform Market Segmentation

Lecture 8: Task #7: Understand the Intuition Behind Principal Component Analysis (PCA)

Lecture 9: Task #8: Understand the Intuition Behind Autoencoders

Lecture 10: Task #9: Build and Train Autoencoder – Part #1

Lecture 11: Build and Train Autoencoder – Part #2

Chapter 4: Sales Department

Lecture 1: Introduction to Case Study and Key Learning Outcomes

Lecture 2: Task #1: Understand the Problem Statement and Business Case

Lecture 3: Task #2: Import Datasets – Part #1

Lecture 4: Task #2: Import Datasets – Part #2

Lecture 5: Task #3: Explore Data – Part #1

Lecture 6: Task #3: Explore Data – Part #2

Lecture 7: Task #3: Explore Data – Part #3

Lecture 8: Task #3: Explore Data – Part #4

Lecture 9: Task #4: Understand Facebook Prophet intuition

Lecture 10: Task #5: Train The Model – Part #1

Lecture 11: Task #6: Train The Model – Part #2

Chapter 5: Operations Department

Lecture 1: Introduction to Case Study and Key Learning Outcomes

Lecture 2: Task #1: Understand the Business Case and Problem Statement

Lecture 3: Task #2: Load and Explore Dataset

Lecture 4: Task #3: Visualize Datasets

Lecture 5: Task #4: Understand Intuition Behind Convolutional Neural Networks (CNNs)

Lecture 6: Task #5: Understand Intuition Behind Transfer Learning

Lecture 7: Task #6: Load Model with Pretrained Weights

Lecture 8: Task #7: Build and Train ResNet

Lecture 9: Task #8: Evaluate Trained Model Performance

Chapter 6: Public Relations Department

Lecture 1: Introduction to Case Study and Key Learning Outcomes

Lecture 2: Task #1: Understand Problem Statement and Business Case

Lecture 3: Task #2: Import Libraries and Datasets

Lecture 4: Task #3: Explore Dataset – Part #1

Lecture 5: Task #3: Explore Dataset – Part #2

Lecture 6: Task #4: Perform Data Cleaning

Lecture 7: Task #5: Remove Punctuation

Lecture 8: Task #6: Remove Stopwords

Lecture 9: Task #7: Perform Tokenization/Count Vectorization

Lecture 10: Task #8: Perform Text Cleaning pipeline

Lecture 11: Task #9: Naive Bayes Intuition

Lecture 12: Task #10: Train a Naive Bayes Classifier

Lecture 13: Task #11: Evaluate Trained Naive Bayes Classifier

Lecture 14: Task #12: Train and Evaluate a Logistic Regression Classifier

Chapter 7: Production/Manufacturing/Maintenance Department

Lecture 1: Introduction and Welcome Message

Lecture 2: Task #1 – Understand the Problem Statement & Business Case

Lecture 3: Task #2 – Import Libraries and Datasets

Lecture 4: Task #3 – Visualize and Explore Dataset

Lecture 5: Task #4 – Understand the Intuition behind ResNet, CNNs, and Transfer Learning

Lecture 6: Task #5 – Build & Train ResNet Classifiers

Lecture 7: Task #6 – Assess Trained ResNet Model Performance

Lecture 8: Task #7 – Understand the Intuition behind ResUnet Segmentation Models

Lecture 9: Task #8 – Build & Train a ResUnet Segmentation Model

Lecture 10: Task #9 – Assess Trained ResUnet Model

Chapter 8: Congratulations!! Don't forget your Prize 🙂

Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)

Instructors

Rating Distribution

  • 1 stars: 17 votes
  • 2 stars: 26 votes
  • 3 stars: 135 votes
  • 4 stars: 490 votes
  • 5 stars: 866 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

You may also like

Top 10 Relationship Building Courses to Learn in November 2024

Top 10 Storytelling Courses to Learn in November 2024

Top 10 Resilience Training Courses to Learn in November 2024