O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Hands-On Data Science with Anaconda

Video Description

Develop, deploy, and streamline your data science projects with the most popular end-to-end platform: Anaconda

About This Video

  • Use Anaconda to find solutions for clustering, classification, and linear regression
  • Analyze your data efficiently with the most powerful data science stack
  • Use the Anaconda cloud to store, share, and discover projects and libraries

In Detail

Anaconda is an open-source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting the Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world with ease

Throughout this course, you will learn how to use different packages, with Anaconda to get the best results. You will learn how to efficiently use Conda — the package, dependency, and environment manager for Anaconda. You will also be introduced to several powerful features of Anaconda. You will learn how to build scalable and functionally efficient packages, and how to perform heterogeneous data exploration, distributed computing, and more. You will learn to discover and share packages, notebooks, and environments to increase productivity. You will also learn about Anaconda Accelerate, a feature that can help you to achieve SLAs easily and optimize computational power

The code bundle for this video course is available at- https://github.com/PacktPublishing/Hands-On-Data-Science-with-Anaconda-Video-

Table of Contents

  1. Chapter 1 : Introduction
    1. The Course Overview 00:04:18
    2. Ecosystem of Anaconda 00:04:38
    3. Installing Anaconda 00:01:44
    4. Using IPython 00:02:34
    5. Introducing Spyder 00:01:40
    6. Installing R via Conda 00:00:50
    7. Installing Julia and Linking It to Jupyter 00:01:19
    8. Finding Help 00:01:36
  2. Chapter 2 : Data Basics
    1. UCI Machine Learning 00:04:17
    2. Several Ways to Input Data 00:02:32
    3. Introduction to the Quandl Data Delivery Platform 00:02:47
    4. Dealing with Missing Data 00:02:29
    5. Data Sorting 00:06:11
    6. Python Packages 00:01:00
    7. Generating Python and R Datasets 00:02:04
  3. Chapter 3 : Data Visualization
    1. Data Visualization in R 00:05:36
    2. Data Visualization in Python 00:01:37
    3. Data Visualization in Julia 00:01:05
    4. Drawing Simple Graphs 00:02:51
    5. Dynamic Visualization 00:01:35
  4. Chapter 4 : Statistical Modeling in Anaconda
    1. Running a Linear Regression 00:03:40
    2. F-Test, Critical Value, and Decision Rule 00:04:50
    3. Dealing with Missing Values 00:02:06
    4. Detecting Outliers and Treatments 00:02:19
    5. Several Multivariate Linear Models 00:01:51
    6. Collinearity and Its Solution 00:01:38
  5. Chapter 5 : Managing Packages
    1. Introduction to Packages, Modules, or Toolboxes 00:04:34
    2. Finding all Packages 00:03:09
    3. Package Management 00:03:21
    4. Creating a Set of Programs 00:02:17
  6. Chapter 6 : Optimization in Anaconda
    1. General Issues for Optimization Problems 00:03:43
    2. Quadratic Optimization 00:07:24
    3. Example – Stock Portfolio Optimization 00:01:29
    4. Packages for Optimization 00:02:02
  7. Chapter 7 : Unsupervised Learning in Anaconda
    1. Introduction to Unsupervised Learning 00:02:48
    2. Hierarchical Clustering 00:02:54
    3. Introduction to Packages 00:04:19
  8. Chapter 8 : Optimization in Anaconda
    1. A Glance at Supervised Learning 00:06:39
    2. Classification 00:08:14
    3. Implementation of Supervised Learning via R 00:03:12
    4. Implementation via Python 00:02:26
    5. Implementation via Julia 00:06:00
  9. Chapter 9 : Predictive Data Analytics – Modeling and Validation
    1. A Useful Datasets 00:06:09
    2. Predicting Future Events 00:08:51
    3. Granger Causality Test 00:03:52