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.

4.70 (1647 reviews)

Udemy

platform

English

language

Data & Analytics

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.

Course Gallery

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Comidoc Review

Our Verdict

This Udemy bestseller on Data Science for Business offers valuable, industry-specific case studies that are well-explained and accompanied by useful visual aids. While the course requires prior knowledge in Python and TensorFlow, it does provide engaging content using real-world examples and techniques. Some inconsistencies may arise regarding video quality, missing information, and lack of support on certain algorithms and hyperparameters. Nonetheless, this training is still an excellent starting point for professionals willing to dive deeper into data science applications.

What We Liked

The course offers a practical, hands-on approach to data science by presenting real-world case studies, which helps learners better understand its applications in various business scenarios.
Instructor explains the steps clearly and showcases a good structure for someone who wants to analyze data and map out correct actions, providing valuable insights into presenting final results and interpreting data.
The course focuses on showcasing multiple techniques and tricks that can help learners become more adept in their data science career paths. Many real-world examples help illustrate various use cases.
Covers a wide range of topics including AI, DL, NLP, time series forecasting, defect detection, customer segmentation, and social media sentiment analysis.

Potential Drawbacks

Some improvements are needed regarding video quality and updates as there were midway corrections in videos that had missing or incorrect information. Some critical details are not fully explained, requiring external research.
There can be some inconsistency concerning hyperparameters of models and parameters for specific algorithms; additional explanations and support might help clarify these aspects.
Not suitable for beginners in Python as certain concepts were left unexplained, assuming prior knowledge. This may lead to confusion for those who are new to data science or programming.
Some students mentioned that the course could benefit from including actual datasets for real-world projects instead of just displaying hypothetical examples.