Pallav BAKSHI

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.

Intro to SQL for Data Science

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

Intermediate Python for Data Science

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

pandas Foundations

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

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.

Supervised Learning with scikit-learn

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

Intermediate R

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

Importing Data in Python (Part 1)

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

Python Data Science Toolbox (Part 2)

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

Deep Learning in Python

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

Cleaning Data in Python

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

Manipulating DataFrames with pandas

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

Importing Data in R (Part 1)

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

Data Visualization with ggplot2 (Part 1)

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

Introduction to Data Visualization with Python

Learn complex data visualization techniques using Matplotlib and seaborn.

Statistical Thinking in Python (Part 1)

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

Introduction to PySpark

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

Cleaning Data in R

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

Importing Data in Python (Part 2)

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

Introduction to Data

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

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.

Correlation and Regression in R

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

Merging DataFrames with pandas

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

Writing Functions in R

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

Network Analysis in Python (Part 1)

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

Importing Data in R (Part 2)

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

Statistical Thinking in Python (Part 2)

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

Exploratory Data Analysis

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

Introduction to Machine Learning

Learn to train and assess models performing common machine learning tasks such as classification and clustering.

Interactive Data Visualization with Bokeh

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

Reporting with R Markdown

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

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.

Introduction to Databases in Python

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

Machine Learning with the Experts: School Budgets

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

Joining Data in R with dplyr

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

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...

Data Visualization with ggplot2 (Part 2)

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

Unsupervised Learning in R

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

Data Visualization in R

This course provides a comprehensive introduction to working with base graphics in R.

Exploratory Data Analysis in R: Case Study

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

Machine Learning Toolbox

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

Intermediate R: Practice

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

Data Analysis in R, the data.table Way

Master core concepts in data manipulation such as subsetting, updating, indexing and joining your data using data.table.

Convolutional Neural Networks for Image Processing

Convolutional neural networks are deep learning algorithms that are particularly powerful for analysis of images.

Foundations of Inference

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Data Types for Data Science

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them t...

Machine Learning with Tree-Based Models in R

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

Text Mining: Bag of Words

Learn the bag of words technique for text mining with R.

Financial Trading in R

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

Data Visualization with ggplot2 (Part 3)

This course covers some advanced topics including strategies for handling large data sets and specialty plots.

Building Recommendation Engines with PySpark

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

Spatial Statistics in R

Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.