您将在本课程中学到什么？
本课程内容丰富，涵盖了公司使用的一些最令人兴奋的方法，全部用 Python 实现。
由于客户分析是一个广泛的主题，我们创建了 5 个不同的部分来探索分析过程的各个方面。他们每个人都会有自己的优点和缺点。我们将为每个部分探索硬币的两面，同时确保为您提供最重要和最相关的信息！
以下是5个主要部分：
1. 我们将向您介绍开始执行客户分析所需的相关理论
为了给大家提供更多的实践经验，我们尽量缩短了这部分的内容。尽管如此，这是营销初学者将学习营销基础知识以及我们在整个课程中利用某些模型的原因的地方。
2. 然后我们会进行聚类分析和降维，帮助您细分客户
因为本课程基于 Python，所以我们将使用几个流行的包 – NumPy、SciPy 和 scikit-learn。在聚类方面，我们将展示层次聚类和平面聚类技术，最终重点介绍 K-means 算法。在此过程中，我们将适当地可视化数据，以进一步加深您对方法的理解。在降维方面，我们将通过 scikit-learn (sklearn) 包再次使用主成分分析 (PCA)。最后，我们将结合这两种模型来更好地了解我们的客户。而且，当然，我们不会忘记我们将通过 pickle 包实现的模型部署。
3. 第三步是应用描述性统计作为分析的探索性部分
一旦细分，客户的行为将需要一些解释。没有什么比按品牌和细分获取描述性统计数据并将结果可视化更直观的了。正是课程的那一部分，您将拥有“啊哈！”的感觉。影响。通过描述性分析，我们将形成我们对细分市场的假设，从而最终为后续建模奠定基础。
4. 之后，我们将准备对购买概率、品牌选择和购买数量进行弹性建模
在大多数教科书中，您会发现弹性计算为静态指标，具体取决于价格和数量。但弹性的概念实际上要广泛得多。我们将通过计算购买概率弹性、品牌选择自身价格弹性、品牌选择交叉价格弹性、购买数量弹性来详细探讨。我们将使用线性回归和逻辑回归，再次通过 sklearn 库实现。我们对该主题进行了最先进的研究，以确保您比同行更具优势。虽然我们专注于大约 20 种不同的模型，但您将有机会练习 100 多种不同的模型，所有这些都会为您提供额外的见解！
5. 最后，我们将利用深度学习的力量来预测未来的行为
机器学习和人工智能处于数据科学革命的前沿。这就是为什么我们忍不住将它包含在本课程中。我们将利用 TensorFlow 2.0 框架创建一个前馈神经网络（也称为人工神经网络）。这是我们将构建黑盒模型的部分，从本质上帮助我们在预测客户未来行为时达到 90% 以上的准确率。
Python 中的客户分析由 3 位讲师密切合作创建，以提供最有益的学习体验。
课程作者 Nikolay Georgiev 是一名博士。他在学术生涯中主要专注于营销分析。后来，他在担任众多世界级项目的顾问期间获得了重要的实践经验。因此，他是帮助您架起理论知识和实际应用之间桥梁的完美专家。
Elitsa 和 Iliya 也在课程开发中发挥了关键作用。所有三位讲师合作提供客户分析可以提供的最有价值的方法和方法。
此外，本课程尽可能吸引人。高质量的动画、精湛的课程材料、测验问题、讲义和课程笔记，以及带有评论的笔记本文件，只是您注册后将获得的一些好处。
为什么需要这些技能？
1. 薪水/收入——数据科学领域的职业是当今企业界最受欢迎的职业之一。所有 B2C 企业都意识到使用可支配的客户数据的优势，以更好地了解和定位客户
2. 晋升——即使你是一名熟练的数据科学家，你职业发展的唯一途径就是扩展你的知识。本课程提供了一项非常难得的技能，适用于许多不同的行业。
3. 安全的未来——对理解数字和数据并能解释它的人的需求呈指数增长；您可能听说过很快就会实现自动化的工作数量，对吗？好吧，公司的营销部门已经被数据科学彻底改变了，乘风破浪是通往安全未来的大门。
Genre: eLearning Video | Duration: 76 lectures (5 hour, 10 mins) | Language: English
Beginner and Advanced Customer Analytics in Python: PCA, K-means Clustering, Elasticity Modeling & Deep Neural Networks.
What you’ll learn
Master beginner and advanced customer analytics
Learn the most important type of analysis applied by mid and large companies
Gain access to a professional team of trainers with exceptional quant skills
Wow interviewers by acquiring a highly desired skill
Understand the fundamental marketing modeling theory: segmentation, targeting, positioning, marketing mix, and price elasticity;
Apply segmentation on your customers, starting from raw data and reaching final customer segments;
Perform K-means clustering with a customer analytics focus;
Apply Principal Components Analysis (PCA) on your data to preprocess your features;
Combine PCA and K-means for even more professional customer segmentation;
Deploy your models on a different dataset;
Learn how to model purchase incidence through probability of purchase elasticity;
Model brand choice by exploring own-price and cross-price elasticity;
Complete the purchasing cycle by predicting purchase quantity elasticity
Carry out a black box deep learning model with TensorFlow 2.0 to predict purchasing behavior with unparalleled accuracy
Be able to optimize your neural networks to enhance results
Requirements
You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course
Basic Python programming
A willingness and enthusiasm to learn and practice
Description
Data science and Marketing are two of the key driving forces that help companies create value and stay on top in today’s fast-paced economy.
Welcome to…
Customer Analytics in Python – the place where marketing and data science meet!
This course is the best way to distinguish yourself with a very rare and extremely valuable skillset.
What will you learn in this course?
This course is packed with knowledge, covering some of the most exciting methods used by companies, all implemented in Python.
Since Customer Analytics is a broad topic, we have created 5 different parts to explore various sides of the analytical process. Each of them will have their strong sides and shortcomings. We will explore both sides of the coin for each part, while making sure to provide you with nothing but the most important and relevant information!
Here are the 5 major parts:
1. We will introduce you to the relevant theory that you need to start performing customer analytics
We have kept this part as short as possible in order to provide you with more practical experience. Nonetheless, this is the place where marketing beginners will learn about the marketing fundamentals and the reasons why we take advantage of certain models throughout the course.
2. Then we will perform cluster analysis and dimensionality reduction to help you segment your customers
Because this course is based in Python, we will be working with several popular packages – NumPy, SciPy, and scikit-learn. In terms of clustering, we will show both hierarchical and flat clustering techniques, ultimately focusing on the K-means algorithm. Along the way, we will visualize the data appropriately to build your understanding of the methods even further. When it comes to dimensionality reduction, we will employ Principal Components Analysis (PCA) once more through the scikit-learn (sklearn) package. Finally, we’ll combine the two models to reach an even better insight about our customers. And, of course, we won’t forget about model deployment which we’ll implement through the pickle package.
3. The third step consists in applying Descriptive statistics as the exploratory part of your analysis
Once segmented, customers’ behavior will require some interpretation. And there is nothing more intuitive than obtaining the descriptive statistics by brand and by segment and visualizing the findings. It is that part of the course, where you will have the ‘Aha!’ effect. Through the descriptive analysis, we will form our hypotheses about our segments, thus ultimately setting the ground for the subsequent modeling.
4. After that, we will be ready to engage with elasticity modeling for purchase probability, brand choice, and purchase quantity
In most textbooks, you will find elasticities calculated as static metrics depending on price and quantity. But the concept of elasticity is in fact much broader. We will explore it in detail by calculating purchase probability elasticity, brand choice own price elasticity, brand choice cross-price elasticity, and purchase quantity elasticity. We will employ linear regressions and logistic regressions, once again implemented through the sklearn library. We implement state-of-the-art research on the topic to make sure that you have an edge over your peers. While we focus on about 20 different models, you will have the chance to practice with more than 100 different variations of them, all providing you with additional insights!
5. Finally, we’ll leverage the power of Deep Learning to predict future behavior
Machine learning and artificial intelligence are at the forefront of the data science revolution. That’s why we could not help but include it in this course. We will take advantage of the TensorFlow 2.0 framework to create a feedforward neural network (also known as artificial neural network). This is the part where we will build a black-box model, essentially helping us reach 90%+ accuracy in our predictions about the future behavior of our customers.
An Extraordinary Teaching Collective
We at 365 Careers have 550,000+ students here on Udemy and believe that the best education requires two key ingredients: a remarkable teaching collective and a practical approach. That’s why we ticked both boxes.
Customer Analytics in Python was created by 3 instructors working closely together to provide the most beneficial learning experience.
The course author, Nikolay Georgiev is a Ph.D. who largely focused on marketing analytics during his academic career. Later he gained significant practical experience while working as a consultant on numerous world-class projects. Therefore, he is the perfect expert to help you build the bridge between theoretical knowledge and practical application.
Elitsa and Iliya also played a key part in developing the course. All three instructors collaborated to provide the most valuable methods and approaches that customer analytics can offer.
In addition, this course is as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts, and course notes, as well as notebook files with comments, are just some of the perks you will get by enrolling.
Why do you need these skills?
1. Salary/Income – careers in the field of data science are some of the most popular in the corporate world today. All B2C businesses are realizing the advantages of working with the customer data at their disposal, to understand and target their clients better
2. Promotions – even if you are a proficient data scientist, the only way for you to grow professionally is to expand your knowledge. This course provides a very rare skill, applicable to many different industries.
3. Secure Future – the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you’ve probably heard of the number of jobs that will be automated soon, right? Well, the marketing department of companies is already being revolutionized by data science and riding that wave is your gateway to a secure future.
Why wait? Every day is a missed opportunity.
Click the “Buy Now” button and let’s start our customer analytics journey together!
Who this course is for:
People who want a career in Data Science
People who want a career in Business Intelligence
Individuals who are passionate about numbers and quant analysis
People working in Data Science looking to expand their knowledge into Marketing analytics
People working in Marketing, looking for career growth in the realms of Data Science
原创文章，作者：steven，如若转载，请注明出处：https://rr3d.cn/12591.html
