Building Recommendation Engines in Python
Learn to build recommendation engines in Python using machine learning techniques.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
Learn to build recommendation engines in Python using machine learning techniques.
In this case study, you’ll use visualization techniques to find out what skills are most in-demand for data scientists, data analysts, and data engineers.
Learn how to segment customers in Python.
Orchestrate data using unions, joins, parsing, and performance optimization in Alteryx.
Analyze text data in R using the tidy framework.
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Learn how to develop deep learning models with Keras.
Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.
Learn how to identify, analyze, remove and impute missing data in Python.
This course covers everything you need to know to build a basic machine learning monitoring system in Python
This Power BI case study follows a real-world business use case on tackling inventory analysis using DAX and visualizations.
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.
Learn to analyze data over time with this practical course on Time Series Analysis in Power BI. Work with real datasets & practice common techniques.
Master data fluency! Learn skills for individuals and organizations, understand behaviors, and build a data-fluent culture.
In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
Learn how to structure your PostgreSQL queries to run in a fraction of the time.
Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Learn to design and run your own Monte Carlo simulations using Python!
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Master Amazon Redshifts SQL, data management, optimization, and security.
This Power BI case study follows a real-world business use case where you will apply the concepts of ETL and visualization.
Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.
Discover how to use the income statement and balance sheet in Power BI