Yuvraj S. D.
Before 365:
Student Research Assistant
After 365:
Machine Learning Engineer | PERFACCT GmbH
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Online Course
top-rated
Customer Analytics in Python
with
Nikolay Georgiev
and
Elitsa Kaloyanova
Blend retail marketing understanding with data analytics skills: Master customer segmentation and purchase behaviour modeling in Python
8,309 students already enrolled
Skill level:
Advanced
Duration:
10 hours
-
Lessons (5 hours)
-
Projects (5 hours)
- Lessons (5 hours)
- Projects (5 hours)
CPE credits:
7.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited
certificate
What you learn
- Master K-means clustering to identify distinct customer groups accurately.
- Combine PCA and K-means for improved customer segmentation.
- Analyse purchase quantity elasticity to assess customer buying decisions.
- Model purchase incidence using the probability of purchase elasticity.
- Leverage deep learning to predict future customer behavior precisely.
Topics & tools
PythonFinancial AnalysisProgrammingData ProcessingMachine LearningData AnalysisCustomer AnalyticsK-Means ClusteringHierarchical ClusteringPrincipal Component Analysis (PCA)Deep LearningTensorflowIndustry SpecializationMachine and Deep Learning
Your instructor
Data Science Leader | Business Impact with AI & Behavioral Insights
Track record
Bringing real-world expertise from leading global companies
Academic background
Doctorate (PhD), Economics & Business Administration
Media and recognition
- Author of 11 scientific publications
- Presenter at international conferences and colloquiums
- Silver medalist in multiple academic competitions (Mathematics & Econometrics)
Senior Data Scientist at 365 Data Science | Computational Biologist | Data Visualization Expert
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Bioinformatics
Course OVERVIEW
Description
CPE Credits: 7.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
Customer Analytics in Python is where marketing and data science meet. These are two of the key driving forces that help companies create value and stay on top in today’s fast-paced economy. In addition, this course is packed with knowledge and includes sections on customer and purchase analytics, as well as a deep-learning model, all implemented in Python.
Prerequisites
- Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
- Basic familiarity with Python programming is required.
Advanced preparation
Curriculum
60 lessons
18 exercises
1 project
1 exam
- 2. Setting up the environment2 minHere you will learn how to set up Python 3 and load up Jupyter. We’ll also show you what the Anaconda Prompt is and how you can use it to download and import new modules.2 minHere you will learn how to set up Python 3 and load up Jupyter. We’ll also show you what the Anaconda Prompt is and how you can use it to download and import new modules.Setting up the environment FreeInstalling the relevant packages Free
- 3. Segmentation Data16 minWe will begin with customer analytics, which is a major part of the course. We will perform some initial exploration of our segmentation data set. Using Python, we’ll visualize our data and standardize it to aid in future analysis.16 minWe will begin with customer analytics, which is a major part of the course. We will perform some initial exploration of our segmentation data set. Using Python, we’ll visualize our data and standardize it to aid in future analysis.Getting to know the Segmentation Dataset FreeImporting and Exploring Segmentation Data FreeStandardizing Segmentation Data Free
- 4. Hierarchical Clustering11 minHere, we discuss the theory behind hierarchical and flat clustering. Throughout the course we’ll implement both approaches. In this section, we’ll focus on hierarchical clustering and use it to determine the number of clusters in our data set.11 minHere, we discuss the theory behind hierarchical and flat clustering. Throughout the course we’ll implement both approaches. In this section, we’ll focus on hierarchical clustering and use it to determine the number of clusters in our data set.Hierarchical Clustering: BackgroundHierarchical Clustering: Implementation and Results
- 5. K-means Clustering18 minWe continue with segmentation, this time focusing on a flat clustering technique, namely K-means. We’ll divide our customers into clusters using K-means and determine the customer segments. This is a crucial part of the customer analysis, as it is where we discover the most important characteristics, which define our customer groups.18 minWe continue with segmentation, this time focusing on a flat clustering technique, namely K-means. We’ll divide our customers into clusters using K-means and determine the customer segments. This is a crucial part of the customer analysis, as it is where we discover the most important characteristics, which define our customer groups.K-Means Clustering: BackgroundK-Means Clustering: ApplicationK-Means Clustering: Results
- 6. K-Means Clustering based on Principal Component Analysis23 minIn our final section of customer analytics, we’ll introduce a dimensionality reduction algorithm: Principal Components Analysis (PCA). We’ll combine K-means and PCA to obtain even better clustering results and gain insight about our customers. In addition, we’ll perform the model deployment, which we’ll need in the following sections of our course23 minIn our final section of customer analytics, we’ll introduce a dimensionality reduction algorithm: Principal Components Analysis (PCA). We’ll combine K-means and PCA to obtain even better clustering results and gain insight about our customers. In addition, we’ll perform the model deployment, which we’ll need in the following sections of our coursePrincipal Component Analysis: BackgroundPrincipal Component Analysis: ApplicationPrincipal Component Analysis: ResultsK-Means Clustering with Principal Components: ApplicationK-Means Clustering with Principal Components: ResultsSaving the ModelsCustomer Segmentation in Marketing with Python Project
- 7. Purchase Data14 minIt’s time for purchase analytics. This is the second major part of the course. We’ll examine our purchase data set and see similarities and differences with the segmentation data. We’ll analyze and preprocess our data and ultimately segment the purchase customers into groups.14 minIt’s time for purchase analytics. This is the second major part of the course. We’ll examine our purchase data set and see similarities and differences with the segmentation data. We’ll analyze and preprocess our data and ultimately segment the purchase customers into groups.Purchase Analytics - IntroductionGetting to know the Purchase DatasetImporting and Exploring Purchase DataApplying the Segmentation Model
- 8. Descriptive Analyses by Segments26 minWe’ll analyze our customers by filtering our data by brand and segment, and we’ll visualize the findings. In addition, we’ll compute the revenues by brand and total revenues for each segment. Through the descriptive analysis, we will form our hypotheses about our segments, thus ultimately setting the ground for subsequent modeling.26 minWe’ll analyze our customers by filtering our data by brand and segment, and we’ll visualize the findings. In addition, we’ll compute the revenues by brand and total revenues for each segment. Through the descriptive analysis, we will form our hypotheses about our segments, thus ultimately setting the ground for subsequent modeling.Purchase Analytics Descriptive Statistics: Segment ProportionsPurchase Analytics Descriptive Statistics: Purchase occasion and Purchase IncidenceBrand ChoiceDissecting the revenue by segment
- 9. Modeling Purchase Incidence36 minWe’re ready to create our first prediction model. We’ll use logistic regression and model purchase probability of our clients. We’ll compute the price elasticity of purchase probability and use it to gain insight into our average customers, as well as each of our customer segments. Furthermore, we’ll explore the effects of promotion on our clients’ purchase behavior.36 minWe’re ready to create our first prediction model. We’ll use logistic regression and model purchase probability of our clients. We’ll compute the price elasticity of purchase probability and use it to gain insight into our average customers, as well as each of our customer segments. Furthermore, we’ll explore the effects of promotion on our clients’ purchase behavior.Purchase Incidence Models. The Model: Binomial Logistic RegressionPrepare the Dataset for Logistic RegressionModel EstimationCalculating Price Elasticity of Purchase ProbabilityPrice Elasticity of Purchase Probability: ResultsExercisePurchase Probability by SegmentsPurchase Probability Model with PromotionCalculating Price Elasticities with PromotionComparing Price Elasticities with and without Promotion
- 10. Modeling Brand Choice34 minOur goal is to understand the brand preferences of our clients. We’ll imagine we work for one of the brands and create marketing strategies for targeting customers to attract them to our own brand. We’ll compute price elasticity of brand choice for our brand, as well as calculate cross price elasticities for a competitor brand to try to stay ahead of the competition.34 minOur goal is to understand the brand preferences of our clients. We’ll imagine we work for one of the brands and create marketing strategies for targeting customers to attract them to our own brand. We’ll compute price elasticity of brand choice for our brand, as well as calculate cross price elasticities for a competitor brand to try to stay ahead of the competition.Brand Choice Models. The Model: Multinomial Logistic RegressionPrepare Data and Fit the ModelInterpreting the CoefficientsOwn Price Brand Choice ElasticityCross Price Brand Choice ElasticityOwn and Cross-Price Elasticity by SegmentOwn and Cross-Price Elasticity by Segment - Comparison
- 11. Modeling Purchase Quantity19 minWe conclude the purchase analytics part of the course with a model for purchase quantity. Here, we’ll use linear regression to determine how many units of our product the customer likes to buy. We’ll examine the effects of promotion on purchase quantity and determine price elasticity of demand.19 minWe conclude the purchase analytics part of the course with a model for purchase quantity. Here, we’ll use linear regression to determine how many units of our product the customer likes to buy. We’ll examine the effects of promotion on purchase quantity and determine price elasticity of demand.Purchase Quantity Models. The Model: Linear RegressionPreparing the Data and Fitting the ModelCalculating Price Elasticity of Purchase QuantityOwn Price Brand Choice Elasticity
- 12. Deep Learning for Conversion Prediction55 minMachine learning and artificial intelligence are at the forefront of the data science revolution. We will take advantage of the TensorFlow 2.0 framework to create a feedforward 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.55 minMachine learning and artificial intelligence are at the forefront of the data science revolution. We will take advantage of the TensorFlow 2.0 framework to create a feedforward 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.Introduction to Deep Learning for Customer AnalyticsExploring the DatasetHow Are We Going to Tackle the Business CaseWhy do We Need to Balance a DatasetPreprocessing the Data for Deep LearningOutlining the Deep Learning ModelTraining the Deep Learning ModelTesting the ModelObtaining the Probability of a Customer to ConvertSaving the Model and Preparing for DeploymentPredicting on New Data
- 13. Course exam75 min75 minCourse exam
Free lessons
1.1 Course Introduction
7 min
1.2 Segmentation, Targeting, Positioning
7 min
1.3 Marketing Mix
8 min
1.4 Physical and Online Retailers: Similarities and Differences.
7 min
1.5 Price Elasticity
8 min
2.1 Setting up the environment
1 min
9 in 10
people walk away career-ready
with practical data and AI skills.
9 in 10
of our graduates landed a new AI & data job
after enrollment
94%
of AI and data science graduates
successfully change
or advance their careers.
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