Jupyter Notebook for Data Science

Jupyter Notebook for Data Science, available at $39.99, has an average rating of 3.85, with 20 lectures, based on 270 reviews, and has 2129 subscribers.

You will learn about Learn how to efficiently use Jupyter Notebook for data manipulation and visualisation Perform interactive data analysis and visualisation using Jupyter Notebook on real data Analyse time series data using Pandas Create interactive widgets where non-technical users can also get involved in the data exploration using the notebooks you create Scrape websites to build datasets and deal with common challenges like unstructured or missing data Combine different datasets in a single graph to enable people to compare them visually and gain new insights Analyse and visualise geographic datasets to create stunning information-rich maps This course is ideal for individuals who are This course is for developers with a basic understanding of Python and Jupyter Notebook. It is particularly useful for This course is for developers with a basic understanding of Python and Jupyter Notebook.

Enroll now: Jupyter Notebook for Data Science

Summary

Title: Jupyter Notebook for Data Science

Price: $39.99

Average Rating: 3.85

Number of Lectures: 20

Number of Published Lectures: 20

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how to efficiently use Jupyter Notebook for data manipulation and visualisation
Perform interactive data analysis and visualisation using Jupyter Notebook on real data
Analyse time series data using Pandas
Create interactive widgets where non-technical users can also get involved in the data exploration using the notebooks you create
Scrape websites to build datasets and deal with common challenges like unstructured or missing data
Combine different datasets in a single graph to enable people to compare them visually and gain new insights
Analyse and visualise geographic datasets to create stunning information-rich maps

Who Should Attend

This course is for developers with a basic understanding of Python and Jupyter Notebook.

Target Audiences

This course is for developers with a basic understanding of Python and Jupyter Notebook.

This 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.

About the Author

Dražen Lucanin is a developer, data analyst, and the founder of Punk Rock Dev, an indie web development studio. He’s been building web applications and doing data analysis in Python, JavaScript, and other technologies professionally since 2009. In the past, Dražen worked as a research assistant and did a PhD in computer science at the Vienna University of Technology. There he studied the energy efficiency of geographically distributed datacenters and worked on optimizing VM scheduling based on real-time electricity prices and weather conditions. He also worked as an external associate at the Ruder Boškovic Institute, researching machine learning methods for forecasting financial crises. During Dražen’s scientific work Python, Jupyter Notebook (back then still IPython Notebook), Matplotlib, and Pandas were his best friends over many nights of interactive manipulation of all sorts of time series and spatial data. Dražen also did a Master’s degree in computer science at the University of Zagreb.

Course Curriculum

Chapter 1: Jupyter Notebook Introduction

Lecture 1: The Course Overview

Lecture 2: Setting Up Jupyter Notebook

Lecture 3: Using Jupyter Notebook

Lecture 4: Publishing Notebooks

Chapter 2: Data Analysis Using Pandas

Lecture 1: Parsing the Crime Dataset

Lecture 2: Pandas Data Structures

Lecture 3: Exploring and Visualising the Data

Lecture 4: Creating an Interactive Widget

Chapter 3: Scraping Data

Lecture 1: Introduction to Data Scraping

Lecture 2: Fetching Data from a REST API Using Requests

Lecture 3: Importing API Data into Pandas

Lecture 4: Scraping Websites Using BeautifulSoup

Chapter 4: Advanced Visualization

Lecture 1: Introduction to Information-Dense Visualisations

Lecture 2: Vizualising Data Correlation

Lecture 3: Linear Regression

Lecture 4: Correlation Matrix

Chapter 5: Analyzing Geographic Data

Lecture 1: Maps in Data Science

Lecture 2: Plotting Crime Locations

Lecture 3: Interactive Maps Using Plotly

Lecture 4: Final Remarks

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 9 votes
2 stars: 12 votes
3 stars: 44 votes
4 stars: 108 votes
5 stars: 97 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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