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

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

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  • Copyright 2017
  • Edition: 1st
  • Online Video
  • ISBN-10: 0-13-467262-3
  • ISBN-13: 978-0-13-467262-5

9+ Hours of Video Training

Data Science with Python and R LiveLessons is tailored to beginner data scientists seeking to use Python or R for data science. This course includes fundamentals of data preparation, data analysis, data visualization, machine learning, and interactive data science applications. Students will learn how to build predictive models and how to create interactive visual applications for their line of business using the Anaconda platform. This course will introduce data scientists to using Python and R for building on an ecosystem of hundreds of high performance open source tools.

The companion Jupyter notebooks for these LiveLessons are available at https://anaconda.org/datasciencepythonr.

Skill Level

  • Beginner level for data scientists

What You Will Learn

  • Use Anaconda and Jupyter notebooks
  • Understand Open Data Science concepts, roles, and workflows
  • Wrangle data with Pandas
  • Understand Anaconda Enterprise and collaboration workflows
  • Create interactive visualizations with Bokeh
  • Use Conda package management
  • Use R for data processing and visualization
  • Build statistical and predictive models
  • Use Excel and Python with Anaconda Fusion
  • Understand and use Mosaic for databases with distributed data
  • Understand distributed and parallel computing with Dask

Course Requirements

  • Basic experience in Python programming
  • Anaconda installed. Python. Anaconda downloads are available for Apple OS X®, Microsoft Windows®, and most Linux distributions. Optionally, Anaconda Trial, which includes features in the paid subscriptions, is available for download. 

About Anaconda Powered by Continuum Analytics

Anaconda is the leading Open Data Science platform powered by Python, the fastest growing data science language with more than 11 million downloads to date. Continuum Analytics is the creator and driving force behind Anaconda, empowering leading businesses across industries worldwide with tools to identify patterns in data, uncover key insights and transform basic data into a goldmine of intelligence to solve the world’s most challenging problems. Anaconda puts superpowers into the hands of people who are changing the world. Learn more at continuum.io.

About LiveLessons Video Training

The LiveLessons Video Training series publishes hundreds of hands-on, expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. This professional and personal technology video series features world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, IBM Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include: IT Certification, Programming, Web Development, Mobile Development, Home and Office Technologies, Business and Management, and more.  View all LiveLessons on InformIT at http://www.informit.com/livelessons.

Sample Content

Table of Contents

Part I: Introduction to Anaconda

Lesson 1: Open Data Science for Everyone

Learning objectives

1.1 Use Anaconda Repository for data science artifacts

1.2 Use Anaconda Navigator to open and run Jupyter Notebooks

1.3 Perform fundamental Jupyter operations

1.4 Ingest, analyze and clean data with Pandas

1.5 Visualize data with Bokeh

1.6 Create machine learning and predictive modeling with Scikit-Learn

Lesson 2: Background Concepts for Open Data Science

Learning objectives

2.1 Understand the concept of Open Data Science

2.2 Identify the different personas on an Open Data Science team

2.3 Understand Open Data Science workflows

Lesson 3: Data Wrangling with Pandas

Learning objectives

3.1 Load, view and plot Pandas DataFrames

3.2 Modify content and create new columns

3.3 Use boolean masks for data selection

3.4 Read data from disk

3.5 Group data

3.6 Connect to a database

3.7 Work with time series data

3.8 Read and write Excel files

3.9 Publish notebooks to Anaconda Cloud


Lesson 4: Anaconda Platform Overview

Learning objectives

4.1 Describe the Anaconda Distribution

4.2 Identify what Conda is used for

4.3 Relate Anaconda Enterprise components

4.4 Identify core technology components

4.5 Describe typical data science workflows

4.6 Create projects in Anaconda enterprise with a team


Lesson 5: Creating Interactive Visualizations with Bokeh

Learning objectives

5.1 Describe Bokeh

5.2 Plot Pandas DataFrames with bokeh.chart

5.3 Manage plot construction with bokeh.plotting 

5.4 Use widgets and plot linking for interactivity 

5.5 Create web plots 

5.6 Create data apps using Bokeh Server


Lesson 6: Conda Package Management

Learning objectives 

6.1 Install packages from Navigator

6.2 Add channels from Navigator

6.3 Upgrade, downgrade and remove packages from Navigator

6.4 Create a new environment from Navigator 

6.5 Select Conda environments and Jupyter kernels 

6.6 Use Conda from the command line 

6.7 Understand the difference between pip and conda 

6.8 Keep pip and conda up to date 

6.9 Export, save, and share Conda environments 

6.10 Find packages on Anaconda Cloud and from Conda-Forge


Part II: Exploring and Analyzing

Lesson 7: Data Processing and Visualization in R

Learning objectives

7.1 Configure an R analytics environment

7.2 Access and process data with dplyr and tidyr

7.3 Create visualizations with ggplot

7.4 Use linear models for predictive analytics

7.5 Create interactive visualizations with rBokeh and Shiny

7.6 Bridge between R and Python with rpy2


Lesson 8: Build Statistical and Predictive Models

Learning objectives

8.1 Use Scikit-Learn to create a predictive model

8.2 Generate predictions with a model

8.3 Score a model

8.4 Visualize model performance

Lesson 9: Excel and Python with Anaconda Fusion

Learning objectives

9.1 Understand which problems Fusion solves

9.2 Install and start Fusion

9.3 Connect spreadsheets to codesheets


Lesson 10: Databases and Distributed Data with Mosaic

Learning objectives

10.1 Understand which problems Mosaic solves

10.2 Install and start Mosaic

10.3 Use Mosaic to register datasets and create data views

Lesson 11: Distributed and Parallel Computing with Dask

Learning objectives

11.1 Describe Dask in relation to Pandas

11.2 Profile the creation of Dask dataframes

11.3 Analyze and plot Dask data



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