Kamil Budny

Introduction to Python

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with numpy.

Introduction to R

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

Introduction to SQL

Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.

Intermediate Python

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

Introduction to the Tidyverse

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collect...

Intermediate R

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Python Data Science Toolbox (Part 1)

Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

Joining Data in SQL

Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.

Supervised Learning with scikit-learn

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

Data Engineering for Everyone

Discover how data engineers lay the groundwork that makes data science possible. No coding involved!

Python Data Science Toolbox (Part 2)

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

Introduction to Data Visualization in Python

Learn complex data visualization techniques using Matplotlib and seaborn.

Introduction to Importing Data in R

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

Introduction to Importing Data in Python

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

Introduction to Deep Learning in Python

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.

Statistical Thinking in Python (Part 1)

Build the foundation you need to think statistically and to speak the language of your data.

pandas Foundations

Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.

Data Manipulation in R with dplyr

Master techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr.

Unsupervised Learning in Python

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Intermediate Importing Data in Python

Improve your Python data importing skills and learn to work with web and API data.

Introduction to Databases in Python

In this course, you'll learn the basics of relational databases and how to interact with them.

Introduction to PySpark

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

Introduction to Network Analysis in Python

This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

Introduction to Natural Language Processing in Python

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from ...

Introduction to Data in R

Learn the language of data, study types, sampling strategies, and experimental design.

Data Science for Business

Learn about data science and how can you use it to strengthen your organization.

Correlation and Regression in R

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

Writing Functions in R

Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks.

Exploratory Data Analysis in R

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Machine Learning with Tree-Based Models in Python

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using sciki...

Statistical Thinking in Python (Part 2)

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Data Visualization with ggplot2 (Part 1)

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

Reporting with R Markdown

Learn to create interactive analyses and automated reports with R Markdown.

Case Study: Exploratory Data Analysis in R

Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Cleaning Data in R

Learn to explore your data so you can properly clean and prepare it for analysis.

Supervised Learning in R: Classification

In this course you will learn the basics of machine learning for classification.

Cleaning Data in Python

This course will equip you with all the skills you need to clean your data in Python.

Cluster Analysis in Python

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means c...

Image Processing in Python

Learn to process, transform, and manipulate images at your will.

Manipulating DataFrames with pandas

You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.

Joining Data in R with dplyr

This course will show you how to combine data sets with dplyr's two table verbs.

Linear Classifiers in Python

In this course you will learn the details of linear classifiers like logistic regression and SVM.

Manipulating Time Series Data in Python

In this course you'll learn the basics of working with time series data.

Case Study: School Budgeting with Machine Learning i...

Learn how to build a model to automatically classify items in a school budget.

Introduction to Python for Finance

This course introduces Python for financial analysis.

Introduction to TensorFlow in Python

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Intermediate Importing Data in R

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Supervised Learning in R: Regression

In this course you will learn how to predict future events using linear regression, generalized additive models, rand...

Merging DataFrames with pandas

This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox.

Multiple and Logistic Regression in R

In this course you'll learn to add multiple variables to linear models and to use logistic regression for classificat...

Working with Dates and Times in R

Learn the essentials of parsing, manipulating and computing with dates and times in R.

Preprocessing for Machine Learning in Python

In this course you'll learn how to get your cleaned data ready for modeling.

Extreme Gradient Boosting with XGBoost

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve...

Intermediate R: Practice

Strengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises.

Introduction to R for Finance

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Introduction to Deep Learning with Keras

Learn to start developing deep learning models with Keras.

Time Series Analysis in R

Learn the core techniques necessary to extract meaningful insights from time series data.

Machine Learning for Time Series Data in Python

This course focuses on feature engineering and machine learning for time series data.

Intermediate R for Finance

Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.

Machine Learning with caret in R

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

Manipulating Time Series Data with xts and zoo in R

The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.

Unsupervised Learning in R

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Cluster Analysis in R

Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract in...

Importing and Managing Financial Data in Python

In this course, you'll learn how to import and manage financial data in Python using various tools and sources.

Feature Engineering for Machine Learning in Python

Create new features to improve the performance of your Machine Learning models.

Image Processing with Keras in Python

Learn powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.

Forecasting in R

Learn how to make predictions about the future using time series forecasting in R.

Data Visualization with ggplot2 (Part 2)

Take your data visualization skills to the next level with coordinates, facets, themes, and best practices in ggplot2.

Advanced Deep Learning with Keras

Build multiple-input and multiple-output deep learning models using Keras.

Interactive Data Visualization with Bokeh

Learn how to create versatile and interactive data visualizations using Bokeh.

Feature Engineering for NLP in Python

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

Credit Risk Modeling in R

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Fundamentals of Bayesian Data Analysis in R

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

Dimensionality Reduction in Python

Learn to reduce dimensionality in Python.

Hyperparameter Tuning in Python

Learn to tune hyperparameters in Python.

Winning a Kaggle Competition in Python

Learn how to approach and win competitions on Kaggle.

Introduction to Financial Concepts in Python

Using Python and NumPy, learn the most fundamental financial concepts.

String Manipulation with stringr in R

Learn how to pull character strings apart, put them back together and use the stringr package.

Machine Learning with PySpark

Learn how to make predictions with Apache Spark.

Intermediate Python for Finance

Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.

Model Validation in Python

Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

Tree-Based Models in R

In this course, you'll learn how to use tree-based models and ensembles for regression and classification.

Introduction to Portfolio Analysis in R

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

ARIMA Models in R

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

Introduction to Spark with sparklyr in R

Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.

Bond Valuation and Analysis in R

Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.

Machine Learning in the Tidyverse

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

Financial Trading in R

This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.

Importing and Managing Financial Data in R

Learn how to access financial data from local files as well as from internet sources.

Importing & Cleaning Data in R: Case Studies

In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.

Visualizing Time Series Data in R

Learn how to visualize time series in R, then practice with a stock-picking case study.

Quantitative Risk Management in R

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.

Sentiment Analysis in R: The Tidy Way

In this course, you will the learn principles of sentiment analysis from a tidy data perspective.

Support Vector Machines in R

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

Case Studies: Manipulating Time Series Data in R

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

Hyperparameter Tuning in R

Learn how to tune your model's hyperparameters to get the best predictive results.

Topic Modeling in R

Learn how to fit topic models using the Latent Dirichlet Allocation algorithm.

Object-Oriented Programming with S3 and R6 in R

Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.

Advanced Dimensionality Reduction in R

Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.

Bayesian Regression Modeling with rstanarm

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

Intermediate Portfolio Analysis in R

Advance you R finance skills to backtest, analyze, and optimize financial portfolios.