by Alberto Boschetti, Luca Massaron | Released June 2018 | ISBN: 9781789538526

Become an efficient data science practitioner by understanding Python's key conceptsAbout This VideoQuickly get familiar with data science using Python 3.6Save time (and effort) with all the essential tools explainedCreate effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experienceIn DetailThe Python Data Science Essentials video series takes you through all you need to know to succeed in data science using Python. Get insights into the core of Python data, including the latest versions of Jupyter Notebook, NumPy, Pandas and scikit-learn. In this course, you will delve into building your essential Python 3.6 data science toolbox, using a single-source approach that will allow to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and prepare for machine learning and visualization techniques.The code bundle for the video course is available at - https://github.com/PacktPublishing/Python-Data-Science-Essentials Show and hide more

1. Chapter 1 : First Step
   • The Course Overview 00:03:29
   • Introducing Data Science and Python 00:03:29
   • Getting Ready 00:03:25
   • A Glance at the Essential Packages 00:03:32
   • Introducing the Jupyter Notebook 00:11:28
   • Scikit-learn Toy Datasets 00:13:27
2. Chapter 2 : Data Munging
   • Data Loading and Preprocessing 00:19:39
   • Working with Categorical and Text Data 00:11:54
   • Creating NumPy Arrays 00:12:04
   • NumPy's Fast Operations and Computations 00:10:05
3. Chapter 3 : The Data Pipeline
   • Introducing EDA 00:04:18
   • Building New Features 00:04:22
   • Dimensionality Reduction 00:14:17
   • The Detection and Treatment of Outliers 00:10:59
   • Validation Metrics 00:06:37
   • Testing and Validating 00:07:08
   • Cross-Validation 00:06:58
   • Hyperparameter Optimization 00:09:10
   • Feature Selection 00:09:56
   • Wrapping Everything in a Pipeline 00:06:45
4. Chapter 4 : Machine Learning
   • Preparing Tools and Datasets 00:02:55
   • Linear and Logistic Regression 00:04:04
   • Naive Bayes 00:03:55
   • K-Nearest Neighbors 00:01:57
   • An Overview of Unsupervised Learning 00:09:57
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