by Dražen Lučanin | Released August 2018 | ISBN: 9781789135411

The easier way to do data scienceAbout This VideoUnderstand how to effectively utilize Jupyter Notebook for interactive data analysis in PythonGet hands-on experience of using popular data science libraries, such as Pandas and matplotlib, to work with real datasetsSpecial focus is placed on addressing typical challenges, such as web scraping, dealing with data that isn't perfectly structured, and missing dataIn DetailThis video course will help you get familiar with Jupyter Notebook and all of its features to perform various data science tasks in Python. Jupyter Notebook is a powerful tool for interactive data exploration and visualization and has become the standard tool among data scientists. In the course, we will start from basic data analysis tasks in Jupyter Notebook and work our way up to learn some common scientific Python tools such as pandas, matplotlib, and plotly. We will work with real datasets, such as crime and traffic accidents in New York City, to explore common issues such as data scraping and cleaning. We will create insightful visualizations, showing time-stamped and spatial data.By the end of the course, you will feel confident about approaching a new dataset, cleaning it up, exploring it, and analyzing it in Jupyter Notebook to extract useful information in the form of interactive reports and information-dense data visualizations.This course uses Jupyter 5.4.1, while not the latest version available, it provides relevant and informative content for data science enthusiasts. Show and hide more Publisher resources Download Example Code

1. Chapter 1 : Jupyter Notebook Introduction
   • The Course Overview 00:03:57
   • Setting Up Jupyter Notebook 00:07:53
   • Using Jupyter Notebook 00:17:14
   • Publishing Notebooks 00:06:50
2. Chapter 2 : Data Analysis Using Pandas
   • Parsing the Crime Dataset 00:10:22
   • Pandas Data Structures 00:18:05
   • Exploring and Visualising the Data 00:15:21
   • Creating an Interactive Widget 00:04:58
3. Chapter 3 : Scraping Data
   • Introduction to Data Scraping 00:03:19
   • Fetching Data from a REST API Using Requests 00:05:00
   • Importing API Data into Pandas 00:13:54
   • Scraping Websites Using BeautifulSoup 00:14:29
4. Chapter 4 : Advanced Visualization
   • Introduction to Information-Dense Visualisations 00:10:32
   • Vizualising Data Correlation 00:13:51
   • Linear Regression 00:05:50
   • Correlation Matrix 00:13:57
5. Chapter 5 : Analyzing Geographic Data
   • Maps in Data Science 00:05:16
   • Plotting Crime Locations 00:10:29
   • Interactive Maps Using Plotly 00:07:13
   • Final Remarks 00:02:47
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