Watch Data Science with Python and R LiveLessons (Anaconda Video Series)

2017
1 Season

Data Science with Python and R LiveLessons is an informative and comprehensive online course designed for individuals who want to learn the basics of data science using Python and R programming languages. The course is presented by Anaconda - a leading provider of data science and machine learning software solutions.

The course is divided into 4 modules, with each module focusing on different aspects of data science. The modules are designed to be logical and sequential, so each module builds on the concepts and skills introduced in the previous module.

Module 1 is an introduction to data science and covers the basics of Python and R, including data types, variables, functions, and control structures. It also provides an introduction to data visualization using the Matplotlib library in Python and the ggplot2 library in R.

Module 2 focuses on data manipulation and cleaning. It covers topics such as importing, cleaning, and transforming data, and introduces participants to the powerful Pandas library in Python and the dplyr library in R.

Module 3 is where participants will learn about data analysis and statistical modeling techniques. They will learn how to perform exploratory data analysis in Python and R, as well as how to create statistical models using linear and logistic regression.

Finally, Module 4 covers machine learning concepts and techniques. Participants will learn how to apply supervised and unsupervised machine learning algorithms using the popular Scikit-learn library in Python and the caret package in R.

Throughout the course, participants will work with Jupyter Notebooks, which provide an interactive environment for working with data, accessing helpful documentation, and sharing code and results.

Data Science with Python and R LiveLessons provides an excellent introduction to the world of data science and machine learning, and is suitable for anyone with a basic understanding of programming concepts. The course is taught by experienced instructors who provide clear and concise explanations of complex topics, and who use real-world examples to demonstrate how to apply the techniques in practice.

Overall, Data Science with Python and R LiveLessons is an ideal course for anyone who wants to learn essential data science skills quickly and effectively, and who wants to start using Python and/or R for data science and machine learning projects.

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11. Lesson 11: Distributed and Parallel Computing with Dask

February 10, 2017

In this lesson you learn how to leverage Dask to perform parallel computations on a single computer or a cluster. You use the Dask version of a Pandas data frame, which is able to load data in parallel, and provides a similar API. Dask includes some great profiling tools, so you understand how your distributed Dask task graph executes.

11. Lesson 11: Distributed and Parallel Computing with Dask

February 10, 2017

10. Lesson 10: Databases and Distributed Data with Mosaic

February 10, 2017

In this lesson, you learn how to use Mosaic to connect databases and file sources into an integrated browser in order to create synthetic views of merged data sources. You start with getting Mosaic installed from either the command line or Navigator. You see how to register data sources, create transforms, browse data, and generate visualizationsthrough the graphical interface.

10. Lesson 10: Databases and Distributed Data with Mosaic

February 10, 2017

9. Lesson 9: Excel and Python with Anaconda Fusion

February 10, 2017

In this lesson, you learn how to use Fusion to leverage the Anaconda open data science ecosystem from within Excel. You start with getting Fusion installed, configured, and running on your system. Then, you learn how to connect codesheets to an Excel worksheet.

9. Lesson 9: Excel and Python with Anaconda Fusion

February 10, 2017

8. Lesson 8: Build Statistical and Predictive Models

February 10, 2017

In this lesson, we focus on the scikit-learn Python library to understand the basics of model building, training,operation, and evaluation. You start by creating a linear regression model and random forest classifier, which you then score to evaluate its performance, and examine some model visualization techniques that help gain insights into model behavior and results.

8. Lesson 8: Build Statistical and Predictive Models

February 10, 2017

7. Lesson 7: Data Processing and Visualization in R

February 10, 2017

In this lesson, you're introduced to working with R scripts from the Anaconda ecosystem starting off with creating an R environment. You use dplyr and tidyrto process R dataframes and create visualizations with ggplot. Data modeling and model fitting are key features of R, so you're introduced to the basics of linear models.

7. Lesson 7: Data Processing and Visualization in R

February 10, 2017

6. Lesson 6: Conda Package Management

February 10, 2017

Conda underpins the entire Anaconda Platform, so learning how to use it is an essential skill to be able to leverage the power of open data science. You start by learning how to install packages from Navigator.

6. Lesson 6: Conda Package Management

February 10, 2017

5. Lesson 5: Creating Interactive Visualizations with Bokeh

February 10, 2017

You learn how Bokeh is organized as a visualization framework that follows the grammar for graphics model and lets you create web-oriented,interactive visualizations without any custom Java script. You use Bokeh to render Pandas Data Frames using the charts interface and then look at finer grain control of canvas layout with the plotting interface.

5. Lesson 5: Creating Interactive Visualizations with Bokeh

February 10, 2017

4. Lesson 4: Anaconda Platform Overview

February 10, 2017

The Anaconda Platform and wider ecosystem provide a foundation for open data science, so, in this lesson, you learn what is available in the core Anaconda Distribution and how it is built from the language and platform independent Conda package management system.

4. Lesson 4: Anaconda Platform Overview

February 10, 2017

3. Lesson 3: Data Wrangling with Pandas

February 10, 2017

In this lesson you're introduced to the Pandas library for data processing. You start learning how to load, view and plot Pandas Dataframes. Followed by the standard data manipulation techniques, including boolean masks for sophisticated data selection.

3. Lesson 3: Data Wrangling with Pandas

February 10, 2017

2. Lesson 2: Background Concepts for Open Data Science

February 10, 2017

In this lesson, you gain a perspective on the growing demand of open data science and see how it's a team sport, bringing together a cross-functional team. By looking at typical data science workflows, you gain an understanding of where you fit in to the larger process of data analysis and production data science systems.

2. Lesson 2: Background Concepts for Open Data Science

February 10, 2017

1. Lesson 1: Open Data Science for Everyone

February 10, 2017

In this lesson, you use Anaconda Cloud, to fetch template notebooks, allowing you to follow along. You're introduced to Anaconda Navigator, and Jupyter Notebooks, which are mainstays through this course. Following that, you track a beginning to end data science workflow, starting with Pandas to process a dataset, followed by interactive visualizations with Bokeh.