Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
This introductory course on data culture provides an essential understanding of data culture concepts and their practical applications.
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Master sampling to get more accurate statistics with less data.
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
This course is an introduction to version control with Git for data scientists.
This course focuses on feature engineering and machine learning for time series data.
Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.
Find tables, store and manage new tables and views, and write maintainable SQL code to answer business questions.
Learn how to use GitHub's various features, navigate the interface and perform everyday collaborative tasks.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
Learn to perform linear and logistic regression with multiple explanatory variables.
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
Learn to start developing deep learning models with Keras.
Learn the fundamentals of cloud computing with AWS.
Take your reporting skills to the next level with Tableau’s built-in statistical functions.
Take your R skills up a notch by learning to write efficient, reusable functions.
Discover how Marketing Analysts use data to understand customers and drive business growth.
Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.
Create new features to improve the performance of your Machine Learning models.
You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.
You will investigate a dataset from a fictitious company called Databel in Excel, and need to figure out why customers are churning.
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
Learn how to build your own SQL reports and dashboards, plus hone your data exploration, cleaning, and validation skills.
Learn to create your own Python packages to make your code easier to use and share with others.
Learn how to build and test data engineering pipelines in Python using PySpark and Apache Airflow.
Learn to create deep learning models with the PyTorch library.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
Learn efficient techniques in pandas to optimize your Python code.
Gain an introduction to data governance, exploring its meaning, purpose, and how to implement a data governance framework.
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
Learn how to identify, analyze, remove and impute missing data in Python.
Learn how to clean data with Apache Spark in Python.
Learn how to use Python to analyze customer churn and build a model to predict it.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
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.
Prepare for your next coding interviews in Python.
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Data visualization is one of the most desired skills for data analysts. This course allows you to present your findings better using Tableau.
Create interactive data visualizations in Python using Plotly.
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Learn the core techniques necessary to extract meaningful insights from time series data.
Build multiple-input and multiple-output deep learning models using Keras.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Learn to write SQL queries to calculate key metrics that businesses use to measure performance.