Customer Analytics in Python
Why take this course?
🎓 Beginner and Advanced Customer Analytics in Python: PCA, K-means Clustering, Elasticity Modeling & Deep Neural Network
Course Overview:
Introduction:
Welcome to the crossroads of Marketing and Data Science! This course is your gateway to mastering Customer Analytics in Python, a skill set that combines the power of data with the art of marketing. 📊✨
What You'll Learn:
This comprehensive course covers advanced topics in customer analytics, all implemented through the powerful and versatile programming language Python. We've broken down the learning journey into five major parts:
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Theoretical Foundations
- We kick off with an essential introduction to the marketing theory that underpins customer analytics. This is a brief section designed to get you up to speed without overwhelming you with information.
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Cluster Analysis & Dimensionality Reduction
- Dive into the world of Python, leveraging libraries like NumPy, SciPy, and scikit-learn to perform cluster analysis.
- Focus on K-means clustering techniques and visualize your data effectively.
- Learn how to apply Principal Components Analysis (PCA) to reduce dimensions and enhance insights into your customer data.
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Descriptive Statistics
- Explore the descriptive analytics of your customers with hands-on examples, helping you understand their behavior and preferences.
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Machine Learning & Artificial Intelligence
- Step into the realm of machine learning with TensorFlow 2.0 to build a feedforward neural network.
- Aim for high accuracy predictions regarding customer behavior by mastering this cutting-edge technology.
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Real-World Application
- The course culminates with practical application, where you'll see how these skills translate into real-world scenarios and job opportunities. 🌐💼
Your Instructors:
Led by Nikolay Georgiev, a Ph.D. in marketing analytics with extensive consulting experience, alongside Elitsa and Iliya, this teaching collective brings together a wealth of knowledge from both academic and practical standpoints. 🏫🚀
Why This Course?
- Salary/Income: Data science roles are highly sought after, offering competitive salaries across various industries.
- Promotions: Expand your skill set to open doors for professional growth within the field of data science.
- Secure Future: Prepare for a future where automation is commonplace, and understanding data is paramount.
Course Highlights:
- Engaging animations and high-quality course materials.
- Quizzes, handouts, and course notes to solidify your learning.
- Notebook files with comments to accompany your coding journey.
Join Us Today!
Don't miss out on the opportunity to enhance your career with these in-demand skills. Click "Buy Now" and embark on this transformative learning adventure with us! 🚀🎓
Enroll now and let's unlock the secrets of customer analytics together with Python as our guide!
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Comidoc Review
Our Verdict
Customer Analytics in Python is a comprehensive course that covers beginner and advanced topics with in-depth explanations of marketing modeling theory and techniques. However, the code can be buggy and fast-paced coding may require a lot of revision, especially for newcomers to Machine Learning and deep learning exercises. Overall, this 5-hour course delivers value far beyond its length but requires previous knowledge in Machine Learning or Data Science to fully grasp the concepts.
What We Liked
- Covers both beginner and advanced topics in customer analytics using Python
- In-depth explanations of marketing modeling theory and techniques such as PCA, K-means clustering, and elasticity modeling
- Unique and interesting dataset with animations that illustrate practical challenges
- Concise and direct content that is equivalent to 20 hours of other Udemy courses
Potential Drawbacks
- Code can be buggy and messy, not adhering to coding best practices, which makes it difficult for reapplication
- Coding pace is fast and some field names are confusing; code may not work as expected without troubleshooting
- Lack of explanation on the choice of libraries used in the course
- Price elasticity section needs more clear explanations, deep learning section requires basic knowledge of Neural Networks