Books & Videos

Table of Contents

Chapter: Introduction


04m 33s

Chapter: Lesson 1: Open Data Science for Everyone

Learning objectives

00m 37s

1.1 Use Anaconda Repository for data science artifacts

03m 30s

1.2 Use Anaconda Navigator to open and run Jupyter Notebooks

01m 58s

1.3 Perform fundamental Jupyter operations

02m 51s

1.4 Ingest, analyze and clean data with Pandas

08m 59s

1.5 Visualize data with Bokeh

04m 34s

1.6 Create machine learning and predictive modeling with Scikit-Learn

11m 49s

Chapter: Lesson 2: Background Concepts for Open Data Science

Learning objectives

00m 29s

2.1 Understand the concept of Open Data Science

02m 40s

2.2 Identify the different personas on an Open Data Science team

06m 46s

2.3 Understand Open Data Science workflows

09m 13s

Chapter: Lesson 3: Data Wrangling with Pandas

Learning objectives

00m 55s

3.1 Load, view and plot Pandas DataFrames

12m 30s

3.2 Modify content and create new columns

12m 49s

3.3 Use boolean masks for data selection

11m 50s

3.4 Read data from disk

14m 45s

3.5 Group data

12m 59s

3.6 Connect to a database

21m 44s

3.7 Work with time series data

09m 29s

3.8 Read and write Excel files

14m 48s

3.9 Publish notebooks to Anaconda Cloud

04m 21s

Chapter: Lesson 4: Anaconda Platform Overview

Learning objectives

01m 6s

4.1 Describe the Anaconda Distribution

05m 5s

4.2 Identify what Conda is used for

03m 49s

4.3 Relate Anaconda Enterprise components

12m 4s

4.4 Identify core technology components

05m 19s

4.5 Describe typical data science workflows

02m 59s

4.6 Create projects in Anaconda enterprise with a team

12m 22s

Chapter: Lesson 5: Creating Interactive Visualizations with Bokeh

Learning objectives

00m 48s

5.1 Describe Bokeh

06m 17s

5.2 Plot Pandas DataFrames with bokeh.charts

09m 3s

5.3 Manage plot construction with bokeh.plotting

15m 1s

5.4 Use widgets and plot linking for interactivity

19m 6s

5.5 Create web plots

03m 36s

5.6 Create data apps using Bokeh Server

07m 39s

Chapter: Lesson 6: Conda Package Management

Learning objectives

01m 18s

6.1 Install packages from Navigator

12m 0s

6.2 Add channels from Navigator

05m 34s

6.3 Upgrade, downgrade and remove packages from Navigator

04m 41s

6.4 Create a new environment from Navigator

07m 10s

6.5 Select Conda environments and Jupyter kernels

10m 34s

6.6 Use Conda from the command line

16m 47s

6.7 Understand the difference between pip and conda

17m 13s

6.8 Keep pip and conda up to date

02m 17s

6.9 Export, save, and share Conda environments

13m 2s

6.10 Find packages on Anaconda Cloud and from Conda-Forge

09m 47s

Chapter: Lesson 7: Data Processing and Visualization in R

Learning objectives

00m 48s

7.1 Configure an R analytics environment

06m 22s

7.2 Access and process data with dplyr and tidyr

15m 10s

7.3 Create visualizations with ggplot

28m 31s

7.4 Use linear models for predictive analytics

17m 21s

7.5 Create interactive visualizations with rBokeh and Shiny

12m 50s

7.6 Bridge between R and Python with rpy2

16m 6s

Chapter: Lesson 8: Build Statistical and Predictive Models

Learning objectives

00m 33s

8.1 Use Scikit-Learn to create a predictive model

08m 36s

8.2 Generate predictions with a model

05m 35s

8.3 Score a model

10m 37s

8.4 Visualize model performance

03m 31s

Chapter: Lesson 9: Excel and Python with Anaconda Fusion

Learning objectives

00m 36s

9.1 Understand which problems Fusion solves

02m 35s

9.2 Install and start Fusion

05m 31s

9.3 Connect spreadsheets to codesheets

06m 20s

Chapter: Lesson 10: Databases and Distributed Data with Mosaic

Learning objectives

00m 32s

10.1 Understand which problems Mosaic solves

01m 46s

10.2 Install and start Mosaic

01m 6s

10.3 Use Mosaic to register datasets and create data views

06m 51s

Chapter: Lesson 11: Distributed and Parallel Computing with Dask

Learning objectives

00m 36s

11.1 Describe Dask in relation to Pandas

07m 16s

11.2 Profile the creation of Dask dataframes

13m 16s

11.3 Analyze and plot Dask data

06m 33s

Chapter: Summary


02m 40s