Introduction to Python
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.
Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Learn to combine data from multiple tables by joining data together using pandas.
Learn how to create, customize, and share data visualizations using Matplotlib.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Learn how to explore, visualize, and extract insights from data.
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.
Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Improve your Python data importing skills and learn to work with web and API data.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
Learn to retrieve and parse information from the internet using the Python library scrapy.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Learn how to work with dates and times in Python.
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Learn to use Python for financial analysis using basic skills, including lists, data visualization, and arrays.
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Learn how to implement and schedule data engineering workflows.
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
In this course you'll learn the basics of working with time series data.
Learn the fundamentals of working with big data with PySpark.
Learn to create deep learning models with the PyTorch library.
Learn the fundamentals of AI. No programming experience required!
This course focuses on feature engineering and machine learning for time series data.
Learn how to write unit tests for your Data Science projects in Python using pytest.
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
In this course you'll learn how to get your cleaned data ready for modeling.
Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!
Learn about string manipulation and become a master at using regular expressions.
Learn to start developing deep learning models with Keras.
Create new features to improve the performance of your Machine Learning models.
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.
Learn how to clean data with Apache Spark in Python.
Learn to process, transform, and manipulate images at your will.
Leverage your Python and SQL knowledge to create an ETL pipeline to ingest, transform, and load data into a database.
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Build the foundation you need to think statistically and to speak the language of your data.
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
In this course, you'll learn the basics of relational databases and how to interact with them.
Learn to manipulate and analyze flexibly structured data with MongoDB.
Learn how to build and test data engineering pipelines in Python using PySpark and Apache Airflow.
Learn how to identify, analyze, remove and impute missing data in Python.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Learn efficient techniques in pandas to optimize your Python code.
Build multiple-input and multiple-output deep learning models using Keras.
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
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 to tune hyperparameters in Python.
Prepare for your next coding interviews in Python.
Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
Learn to perform linear and logistic regression with multiple explanatory variables.
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Learn about ARIMA models in Python and become an expert in time series analysis.
Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Learn to create your own Python packages to make your code easier to use and share with others.
Visualize seasonality, trends and other patterns in your time series data.
Leverage the power of Python and PuLP to optimize supply chains.
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Learn how to approach and win competitions on Kaggle.
Learn how to detect fraud using Python.
Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.
Create interactive data visualizations in Python using Plotly.
In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Using Python and NumPy, learn the most fundamental financial concepts.
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
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 how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
In this course you'll learn to use and present logistic regression models for making predictions.
Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.
Learn how to segment customers in Python.
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
This course will show you how to integrate spatial data into your Python Data Science workflow.
Learn to build recommendation engines in Python using machine learning techniques.
Learn to design and run your own Monte Carlo simulations using Python!
Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.
This course is for R users who want to get up to speed with Python!
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Learn how to use Python to analyze customer churn and build a model to predict it.
Learn how to manipulate and visualize categorical data using pandas and seaborn.
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
Learn to build pipelines that stand the test of time.
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Use your knowledge of common spreadsheet functions and techniques to explore Python!
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.
Learn about experimental design, and how to explore your data to ask and answer meaningful questions.
Use survival analysis to work with time-to-event data and predict survival time.
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Imitate Shakespear, translate language and autocomplete sentences using Deep Learning in Python.
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Python's SimPy package.
Learn how to work with streaming data using serverless technologies on AWS.
Learn how to load, transform, and transcribe speech from raw audio files in Python.
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
Learn to use the Census API to work with demographic and socioeconomic data.
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.
Learn to automate many common file system tasks and be able to manage and communicate with processes.
Learn to solve increasingly complex problems using simulations to generate and analyze data.
Learn how to build a model to automatically classify items in a school budget.
Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.
Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.
Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!
Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.
Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.
Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.
Learn the power of deep learning in PyTorch. Build your first neural network, adjust hyperparameters, and tackle classification and regression problems.
Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.
Learn to process sensitive information with privacy-preserving techniques.
In this course you'll learn how to apply machine learning in the HR domain.
Are you curious about the inner workings of the models that are behind products like Google Translate?
Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.
Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.
Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
Learn how to prepare and organize your data for predictive analytics.