Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.
Leverage your Python and SQL knowledge to create an ETL pipeline to ingest, transform, and load data into a database.
Build the foundation you need to think statistically and to speak the language of your data.
This introductory course will help you hone the skills to build effective, performant, and reliable data pipelines.
Learn how to write unit tests for your Data Science projects in Python using pytest.
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
Learn about ARIMA models in Python and become an expert in time series analysis.
Learn to create your own Python packages to make your code easier to use and share with others.
Prepare for your next coding interviews in Python.
Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
Create new features to improve the performance of your Machine Learning models.
Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Leverage the power of Python and PuLP to optimize supply chains.
Learn how to identify, analyze, remove and impute missing data in Python.
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Learn how to build and test data engineering pipelines in Python using PySpark and Apache Airflow.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
Learn efficient techniques in pandas to optimize your Python code.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Build multiple-input and multiple-output deep learning models using Keras.
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
This course will show you how to integrate spatial data into your Python Data Science workflow.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Create interactive data visualizations in Python using Plotly.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.
In this course, you'll learn the basics of relational databases and how to interact with them.
Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.
Visualize seasonality, trends and other patterns in your time series data.
Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Gain experience using techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Learn to perform linear and logistic regression with multiple explanatory variables.
Learn to process sensitive information with privacy-preserving techniques.
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
In this course you'll learn to use and present logistic regression models for making predictions.
Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.
Learn to design and run your own Monte Carlo simulations using Python!
Tidy a bank marketing campaign dataset by splitting it into subsets, updating values, converting data types, and storing it as multiple csv files.
Apply your data manipulation skills to time series data on water levels of the River Thames.
Apply your knowledge of data types and categorical data to prepare a big dataset for modeling!
Analyze product data for an online sports retail company to optimize revenue.
Apply your importing and cleaning data and data manipulation skills to explore New York City Airbnb data.
Use DataFrames to read and merge employee data from different sources.
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.
Use data manipulation, cleaning, and feature engineering skills to prepare a payment dataset for fraud prediction modeling.
Check what passwords fail to conform to the National Institute of Standards and Technology password guidelines.
Use joining techniques to discover the oldest businesses in the world.
Apply data importing and cleaning skills to extract insights about the New York City Airbnb market.
Recreate John Snow's famous map of the 1854 cholera outbreak in London.
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
Use coding best practices and functions to improve a script!
Use DataFrames to read and merge employee data from different sources.
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Manipulate and plot time series data from Google Trends to analyze changes in search interest over time.
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
Find out about the evolution of the Linux operating system by exploring its version control system.
In this project, you will use importing and text manipulation skills to find out the main protagonists in Peter Pan!
Check what passwords fail to conform to the National Institute of Standards and Technology password guidelines.
Use a variety of data manipulation techniques to explore different aspects of Lego's history!
Analyze the relative popularity of programming languages over time based on Stack Overflow data.
Analyze the relative popularity of programming languages over time based on Stack Overflow data.