Data Science Masterclass

Explore how data science fuels business decisions by turning information into insights. Learn tools and techniques to optimize processes, understand customer behavior, and market trends.

Data science leverages historical data and machine learning to build models that forecast demand, optimize operations, assess credit risk, and power customer and employee interactions, driving smarter business decisions.

Explore regression, classification, clustering, reinforcement learning, and neural networks as tools to predict values, forecast events, segment customers, and tackle large data tasks.

Explore how data science drives business optimization and revenue growth through machine learning for revenue prediction, customer lifetime value analysis, and demand forecasting with dynamic pricing.

Explore how machine learning enables revenue forecasting across e-commerce, airline, hospitality, and telecom by leveraging historical sales and browsing data, cleaning data, feature selection, and model development to production.

Explore a worked sales forecasting example in Python using Jupyter Notebook, merging sales, product, and customer data, creating time-based features, and training a random forest regressor to forecast ten-day revenue.

Calculate and predict customer lifetime value using average order value, purchase frequency, and average customer lifetime with data science, guiding targeted retention and upselling.

Explore customer behavior and market trends through three lessons in data science for business: machine learning for market basket analysis with Python, and customer segmentation with time series forecasting.

Explore market basket analysis across e-commerce, retail, music streaming, and gaming, using frequent itemsets and association rules to drive product recommendations, bundles, and personalized playlists.

Gain insights into customer behavior and market trends by exploring machine learning for market basket analysis with a Python example, plus customer segmentation using clustering and time series forecasting.

Explore machine learning for market basket analysis across e-commerce, retail, music streaming, and gaming, including frequent itemsets, association rules, personalized recommendations, and a Python code demo.

Explore market basket analysis with a Python notebook: load grocery data, inspect transactions, build baskets, apply apriori to derive association rules and lift, revealing top product combinations.

Use machine learning to perform customer segmentation across automotive, finance, healthcare, and retail with demographic, behavioral, and psychographic insights, from problem definition to segmentation refinement.

Explore time series analysis for trend forecasting across finance, retail, energy, and healthcare, applying models like ARIMA, SARIMA, and exponential smoothing to predict stock prices, sales forecasts, and disease trends.

Build a conversational chat bot for customer engagement using large language models and past ticket data to deliver instant, natural-language responses and reduce ticket resolution time.

Explore large language models like ChatGPT, Google Gemini, and Anthropic Cloud handling natural language prompts. See how retrieval augmented generation uses enterprise data and vector databases to enable memory-aware responses.

Load historical customer tickets and resolutions, convert them to embeddings, upload 100 conversations into a vector database, and power a conversational chat solution with memory and semantic search.

Explore how data science drives marketing decisions, personalizes customer experiences, and boosts engagement and ROI. Learn to analyze data, segment audiences, and apply targeted advertising with hands-on mini projects.

Learn how data science drives marketing strategy and decision making by applying clustering for customer segmentation, predictive analytics, recommender systems, sentiment analysis, and market basket analysis.

Explore the data science lifecycle from framing the sales forecasting problem to deploying models, detailing data understanding, preparation, modeling, evaluation, and maintenance, with regression, classification, clustering, reinforcement, and neural networks.

Explore generative AI and large language models, their NLP capabilities and world knowledge, and how retrieval augmented generation enables enterprise data with chatbots, SEO, and personalized marketing.

Analyze consumer behavior through data analysis and segmentation to tailor offerings, personalize experiences, and optimize marketing strategies that boost engagement and revenue.

Merge customer data, purchase behavior, and engagement metrics, apply k-means clustering to reveal three customer segments, enabling targeted promotions for high spenders, low spenders, and moderate spenders.

Learn to predict customer lifetime value using purchase history, demographics, and engagement metrics with regression models, enabling targeted marketing and better resource allocation.

Map the customer journey by visualizing touchpoints across channels and using data from CRM, website analytics, store, and social media to drive segmentation and data science driven personalization.

Explore techniques for personalized marketing and targeted advertising through predictive lead scoring, real-time personalization, and market mix modeling, with a Python demo.

Explore predictive lead scoring with machine learning models and neural networks across real-world cases from real estate, finance, insurance, and telecom. Learn data gathering, classification, clustering, evaluation, and deployment.

Demonstrates building a predictive lead scoring model in Python using a real dataset, including dummy encoding for location, train-test split, standardization, logistic regression, and accuracy evaluation.

Explore real time personalization with machine learning across Netflix, Booking.com, Google Ads, and Zalando, from data gathering and feature engineering to real time integration and A/B testing.

Explore market mix modeling to optimize marketing spend across channels and geographies using data science, predictive modeling, and simulations to boost marketing efficiency.

Design a retrieval augmented generation chat bot that answers mobile product queries in real time by combining a large language model with your product data and vector embeddings.

Demonstrate a practical data science workflow: upload CSV data, convert to language embeddings, store in a vector database, and power a memory-aware chat with retrieval.