Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.
Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
Learn to combine data from multiple tables by joining data together using pandas.
Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
Learn efficient techniques in pandas to optimize your Python code.
Learn how to manipulate and visualize categorical data using pandas and seaborn.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.
In this course you'll learn the basics of working with time series data.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Learn how to explore, visualize, and extract insights from data.
Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.
Learn how to use Python to analyze customer churn and build a model to predict it.
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
Learn how to work with dates and times in Python.
Learn to use the Census API to work with demographic and socioeconomic data.
Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.
Use pandas and Bayesian statistics to see if left-handed people actually die earlier than righties.
Rock or rap? Apply machine learning methods in Python to classify songs into genres.
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.
Explore Disney movie data, then build a linear regression model to predict box office success.
Find out about the evolution of the Linux operating system by exploring its version control system.
Use coding best practices and functions to improve a script!
Analyze an A/B test from the popular mobile puzzle game, Cookie Cats.
Analyze the gender distribution of children's book writers and use sound to match names to gender.