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Data Science with Python and R

Video Introducing this tutorial

Introduction
Introduction 04m 33s


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



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


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


Lesson 4: Anaconda Platform Overview
Learning objectives 01m 06s
4.1 Describe the Anaconda Distribution 05m 05s
4.2 Identify what Conda is used for 03m 49s
4.3 Relate Anaconda Enterprise components 12m 04s
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


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 03s
5.3 Manage plot construction with bokeh.plotting 15m 01s
5.4 Use widgets and plot linking for interactivity 19m 06s
5.5 Create web plots 03m 36s
5.6 Create data apps using Bokeh Server 07m 39s


Lesson 6: Conda Package Management
Learning objectives 01m 18s
6.1 Install packages from Navigator 12m 00s
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 02s
6.10 Find packages on Anaconda Cloud and from Conda-Forge 09m 47s


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 06s


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


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


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 06s
10.3 Use Mosaic to register datasets and create data views 06m 51s


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


Summary
Summary 02m 40s