“Data Science for Business | 6 Real-world Case Studies” will teach you important concepts related to commercial applications of data science in a straightforward, enjoyable, and practical manner. Students gain practical, hands-on experience in the course using datasets from the real world. These days, data science is widely used in a variety of industries, including finance, healthcare, transportation, and technology. Data science is used in business to streamline operations, increase profits, and cut costs. The following six departments have been assigned to the students: (1) Human Resources, (2) Marketing, (3) Sales, (4) Operations, (5) Public Relations, and (6) Production/Maintenance.
Datasets from each of these departments will be given to the students, and they will be required to complete the following tasks: Develop an AI model to lower hiring and training costs for employees by foretelling which individuals may leave the organization. Currently, udemy is offering the Data Science for Business | 6 Real-world Case Studies course for up to 87 % off i.e. INR 449 (INR 3,500).
Who all can opt for this course?
- Experienced consultants looking to revolutionize firms through the use of AI and data science
- Business leaders with a vision who want to use AI and data science to optimize their operations and increase profits
- Practitioners of data science who wish to develop their portfolios and progress their careers
- Data science and AI aficionados who are eager to obtain real-world, hands-on experience
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 11 Hours |
| Rating | 4.4/5 |
| Student Enrollment | 9,061 students |
| Instructor | Dr. Ryan Ahmed, Ph.D., MBA https://www.linkedin.com/in/dr.ryanahmed,ph.d.,mba |
| Topics Covered |
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| Course Level | N.A |
| Total Student Reviews | 1,148 |
Learning Outcomes
- Create an AI model to lower employee hiring and training expenditures by foreseeing who would depart the organization
- Create a deep learning model to automate and improve the hospital’s disease detection procedures
- Create time series forecasting models to forecast product pricing in the future
- Create models for defect localization, categorization, and detection
- Customer segmentation can help you optimize your marketing strategy
- To assess online customer reviews and determine consumer sentiment, create natural language, processing models
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Course Introduction and Welcome Message (28 minutes) | Updates on Udemy Reviews |
| Introduction | ||
| Key Tips and Best Practices | ||
| Course Outline and Key Learning Outcomes | ||
| Get the Materials | ||
| 2. | Human Resources Department (02 hours 05 minutes) | Introduction to Case Study and Key Learning Outcomes |
| Task #1: Problem Statement and Business Case | ||
| Task #2: Import Libraries and Datasets | ||
| Task #3: Explore Dataset – Part 1 | ||
| Task #3: Explore Dataset – Part 2 | ||
| Task #3: Explore Dataset – Part 3 | ||
| Task #3: Explore Dataset – Part 4 | ||
| Task #4: Perform Data Cleaning | ||
| Task #5: Understand intuition of Random Forest, Logistic Regression, and ANNs | ||
| Task #6: Understand Classification KPIs | ||
| Task #7: Build and Train Logistic Regression Classifier | ||
| Task #8: Build and Train Random Forest Classifier Model | ||
| Task #9: Build and Train Artificial Neural Network Classifier Model | ||
| 3. | Marketing Department (02 hours 04 minutes) | Introduction to Case Study and Key Learning Outcomes |
| Task #1: Understand the Problem Statement and Business Case | ||
| Task #2: Import Libraries and Datasets | ||
| Task #3: Perform Data Visualization | ||
| Task #4: Understand the Theory and Intuition behind K-Mean Algorithm | ||
| Task #5: Obtain the Optimal Number of Clusters “K” | ||
| Task #6: Apply K-Means Clustering to Perform Market Segmentation | ||
| Task #7: Understand the Intuition Behind Principal Component Analysis (PCA) | ||
| Task #8: Understand the Intuition Behind Autoencoders | ||
| Task #9: Build and Train Autoencoder – Part #1 | ||
| Build and Train Autoencoder – Part #2 | ||
| 4. | Sales Department (01 hour 44 minutes) | Introduction to Case Study and Key Learning Outcomes |
| Task #1: Understand the Problem Statement and Business Case | ||
| Task #2: Import Datasets – Part #1 | ||
| Task #2: Import Datasets – Part #2 | ||
| Task #3: Explore Data – Part #1 | ||
| Task #3: Explore Data – Part #2 | ||
| Task #3: Explore Data – Part #3 | ||
| Task #3: Explore Data – Part #4 | ||
| Task #4: Understand Facebook Prophet’s intuition | ||
| Task #5: Train The Model – Part #1 | ||
| Task #6: Train The Model – Part #2 | ||
| 5. | Operations Department (01 hour 37 minutes) | Introduction to Case Study and Key Learning Outcomes |
| Task #1: Understand the Business Case and Problem Statement | ||
| Task #2: Load and Explore Dataset | ||
| Task #3: Visualize Datasets | ||
| Task #4: Understand the Intuition Behind Convolutional Neural Networks (CNNs) | ||
| Task #5: Understand Intuition Behind Transfer Learning | ||
| Task #6: Load Model with Pretrained Weights | ||
| Task #7: Build and Train ResNet | ||
| Task #8: Evaluate Trained Model Performance | ||
| 6. | Public Relations Department (01 hour 58 minutes) | Introduction to Case Study and Key Learning Outcomes |
| Task #1: Understand the Problem Statement and Business Case | ||
| Task #2: Import Libraries and Datasets | ||
| Task #3: Explore Dataset – Part #1 | ||
| Task #3: Explore Dataset – Part #2 | ||
| Task #4: Perform Data Cleaning | ||
| Task #5: Remove Punctuation | ||
| Task #6: Remove Stopwords | ||
| Task #7: Perform Tokenization/Count Vectorization | ||
| Task #8: Perform Text Cleaning pipeline | ||
| Task #9: Naive Bayes Intuition | ||
| Task #10: Train a Naive Bayes Classifier | ||
| Task #11: Evaluate Trained Naive Bayes Classifier | ||
| Task #12: Train and Evaluate a Logistic Regression Classifier | ||
| 7. | Production/Manufacturing/Maintenance Department (01 hour 42 minutes) | Introduction and Welcome Message |
| Task #1 – Understand the Problem Statement & Business Case | ||
| Task #2 – Import Libraries and Datasets | ||
| Task #3 – Visualize and Explore Dataset | ||
| Task #4 – Understand the Intuition behind ResNet, CNNs, and Transfer Learning | ||
| Task #5 – Build & Train ResNet Classifiers | ||
| Task #6 – Assess Trained ResNet Model Performance | ||
| Task #7 – Understand the Intuition behind ResUnet Segmentation Models | ||
| Task #8 – Build & Train a ResUnet Segmentation Model | ||
| Task #9 – Assess Trained ResUnet Model |
Resources Required
- It’s advised to have some programming skills
- The course has no prerequisites and is open to anyone with some familiarity with programming because these topics will be thoroughly covered in the first few lectures
- Students who take this course will become proficient in the principles of data science and use these abilities to tackle difficult business problems in the real world
Featured Review
Emmanuel Angel Cabrera Navarrete (5/5) : Excellent course! I learned a lot about data science using the practical examples.
Pros
- Magorzata Poturalska (5/5) : Comprehensive course, excellent quality, projects are performed with details, great job.
- Prasad Yendamuri (4/5) : Great Job but Some of concepts are skipped in Marketting Department project Task 6.
- Dipanjan Ghosh (5/5) : i think the projects are a great way to learn the in and outs of data science and machine learning.
- Mo aly (5/5) : The code is structured into mini tasks which is very nice.
Cons
- John Joachim (2/5): And, I’m sorry to make such a personal critique about the Instructor (though it’s also a factor, since it’s affecting the presentation of the content), but his jerky-voice is quite exhausting and distracting, and ultimately not conducive to education.
About the Author
The instructor of this course is Dr. Ryan Ahmed, Ph.D., MBA who is a Professor & Best-selling Instructor, 300K+ students. With 4.5 Instructor Rating and 31,591 Reviews on Udemy, he/she offers 46 Courses and has taught 319,979 Students so far.
- Professor and best-selling online educator Dr. Ryan Ahmed has a strong interest in both education and technology
- Ryan has a huge knowledge of both finance and technology
- Ryan has a Ph.D. in mechanical engineering with a concentration on mechatronics and electric vehicles from McMaster University
- In addition, Dr. Ryan Ahmed earned an MBA in Finance from the DeGroote School of Business and a Master of Applied Science from McMaster, with a concentration on artificial intelligence (AI) and fault detection at Fortune 500 organizations around the world, including Samsung America and Fiat-Chrysler Automobiles (FCA) Canada, Ryan held a number of engineering positions
- He has taught 46+ courses on science, technology, engineering, and mathematics to more than 300,000 students from 160 different countries
Comparison Table
| Parameters | Data Science for Business | 6 Real-world Case Studies | Machine Learning Practical: 6 Real-World Applications | Machine Learning Practical Workout | 8 Real-World Projects |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 11.5 hours | 8.5 hours | 14 hours |
| Rating | 4.4 /5 | 4.5 /5 | 4.4 /5 |
| Student Enrollments | 9,060 | 19,610 | 15,038 |
| Instructors | Dr. Ryan Ahmed, Ph.D., MBA | Dr. Ryan Ahmed, Ph.D., MBA | Dr. Ryan Ahmed, Ph.D., MBA |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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