Developing Python Packages
Learn to create your own Python packages to make your code easier to use and share with others.
Learn to create your own Python packages to make your code easier to use and share with others.
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Learn to process, transform, and manipulate images at your will.
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Prepare for your next coding interviews in Python.
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
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.
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Build the foundation you need to think statistically and to speak the language of your data.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Gain experience using techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.
In this course, you'll learn the basics of relational databases and how to interact with them.
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.
Learn about ARIMA models in Python and become an expert in time series analysis.
Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Learn how to identify, analyze, remove and impute missing data in Python.
This course will show you how to integrate spatial data into your Python Data Science workflow.
Learn efficient techniques in pandas to optimize your Python code.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Learn to design and run your own Monte Carlo simulations using Python!
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Create interactive data visualizations in Python using Plotly.
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Visualize seasonality, trends and other patterns in your time series data.
Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
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 perform linear and logistic regression with multiple explanatory variables.
Learn how to detect fraud using Python.
Discover the fundamental concepts of object-oriented programming (OOP), building custom classes and objects!
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Learn how to segment customers in Python.
Ready to analyze and visualize financial ratios? In this project, you will take on real-world challenges like evaluating the profitability and leverage of companies across industries.
Analyze an A/B test from the popular mobile puzzle game, Cookie Cats.
Test your Data engineering skills by creating a data pipeline to analyze E-commerce business of Walmart!
Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means Clustering
Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.
Perform hypothesis tests to determine if the adverse effects of a pharmaceutical drug are significant!
Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.
Perform a machine learning experiment to find the best model that predicts the temperature in London!
Conduct a supply chain analysis of the ingredients used in an avocado toast to gain an understanding of the complex supply chain involved.
Explore local and global fitness trends to identify product niches. Investigate online interest in gyms, workouts, digital services, and web apps.
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie data.
Clean customer data and use logistic regression to predict whether people will make a claim on their car insurance!
Use web scraping and NLP to find the most frequent words in classic literature: Herman Melville's novel, Moby Dick.
Find the true Scala experts by exploring its development history in Git and GitHub.
Tidy a bank marketing campaign dataset by splitting it into subsets, updating values, converting data types, and storing it as multiple csv files.
Perform a hypothesis test to determine if more goals are scored in women's soccer matches than men's!
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!
Use data manipulation and summary statistics to analyze test scores across New York City's public schools!
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 joining techniques to discover the oldest businesses in the world.
Recreate John Snow's famous map of the 1854 cholera outbreak in London.
Use coding best practices and functions to improve a script!
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Find out about the evolution of the Linux operating system by exploring its version control system.
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.