Data Science for Business | 6 Real-world Case Studies
4.5 (57 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
884 students enrolled

Data Science for Business | 6 Real-world Case Studies

Solve 6 real Business Problems. Build Robust AI, DL and NLP models for Sales, Marketing, Operations, HR and PR projects.
Bestseller
4.5 (57 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
884 students enrolled
Price: $199.99
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This course includes
  • 10 hours on-demand video
  • 2 articles
  • 12 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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.
Requirements
  • Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to anyone with basic programming knowledge. Students who enroll in this course will master data science fundamentals and directly apply these skills to solve real world challenging business problems.
Description

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.


Who this course is for:
  • 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.
Course content
Expand all 64 lectures 09:58:30
+ Human Resources Department
13 lectures 02:05:15
Task #2: Import Libraries and Datasets
12:44
Task #3: Explore Dataset - Part 1
13:31
Task #3: Explore Dataset - Part 2
09:44
Task #3: Explore Dataset - Part 3
08:47
Task #3: Explore Dataset - Part 4
09:45
Task #4: Perform Data Cleaning
09:37
Task #5: Understand intuition of Random Forest, Logistic Regression, and ANNs
15:25
Task #8: Build and Train Random Forest Classifier Model
02:57
Task #9: Build and Train Artificial Neural Network Classifier Model
10:25
+ Marketing Department
11 lectures 02:04:30
Task #1: Understand Problem Statement and Business Case
10:41
Task #2: Import Libraries and Datasets
14:42
Task #3: Perform Data Visualization
19:55
Task #5: Obtain Optimal Number of Clusters "K"
08:09
Task #7: Understand the Intuition Behind Principal Component Analysis (PCA)
10:05
Task #9: Build and Train Autoencoder - Part #1
12:07
Build and Train Autoencoder - Part #2
14:02
+ Sales Department
11 lectures 01:44:13
Task #1: Understand the Problem Statement and Business Case
11:50
Task #2: Import Datasets - Part #1
11:17
Task #2: Import Datasets - Part #2
05:39
Task #3: Explore Data - Part #1
12:21
Task #3: Explore Data - Part #2
11:11
Task #3: Explore Data - Part #3
08:28
Task #3: Explore Data - Part #4
12:56
Task #5: Train The Model - Part #1
10:29
Task #6: Train The Model - Part #2
12:23
+ Operations Department
9 lectures 01:37:35
Task #1: Understand the Business Case and Problem Statement
08:05
Task #2: Load and Explore Dataset
16:40
Task #3: Visualize Datasets
06:10
Task #4: Understand Intuition Behind Convolutional Neural Networks (CNNs)
13:56
Task #7: Build and Train ResNet
20:08
Task #8: Evaluate Trained Model Performance
15:21
+ Public Relations Department
14 lectures 01:58:07
Task #1: Understand Problem Statement and Business Case
05:47
Task #2: Import Libraries and Datasets
07:19
Task #3: Explore Dataset - Part #1
09:58
Task #3: Explore Dataset - Part #2
14:40
Task #4: Perform Data Cleaning
06:40
Task #5: Remove Punctuation
05:14
Task #6: Remove Stopwords
07:56
Task #8: Perform Text Cleaning pipeline
13:45
Task #11: Evaluate Trained Naive Bayes Classifier
07:40
Task #12: Train and Evaluate a Logistic Regression Classifier
06:09
+ Production/Maintenance Department
1 lecture 00:01
Introduction and Welcome Message
00:01