Explore Data in Weekend Part I
Explore Data in Weekend Part I
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Explore Data in Weekend Part I

Last updated on 2020-04-20

About the Book

About In-weekend Series?

As I have a regular job, I have to go to company and work on solving problem 5 Days in a Week, and as I should increase my knowledge to have a life progress, I need to study and learn more and more, but when I arrive to home everyday, I look like a disaster and I can’t even open my eyes, so the only time I have to study are in the holiday/weekends. In those two days I have to study and have time with my family. So, I got the idea of effectively time management, to manage and keep use of every minute of those two days, in both studying and having family time. So, every week at work I take 10 minute to design and build what I should learn and study on the weekend, and when the weekend comes I start finishing what I have built. And from this the idea came to me to build a series to help you to progress your skill while you have no time to do that.

What to Learn on the Weekend?

  • Getting Started with Python,
  • Managing Data Frames with Pandas package, 
  • Exploratory Data Analysis Checklist, 
  • Principles of Analytic Graphics, 
  • Exploratory Graphs, 
  • Plotting Systems, 
  • Graphics Devices, 
  • The Base Plotting System,

Table of Content of this Weekend.

  • 0. Table of Content
  • 1. Stay in Touch!
  • 2. Preface
  • 2.1 What is Exploratory Data Analysis?
  • 2.2 What does this book cover?
  • 2.3 What you should expect?
  • 2.4 About In-weekend Series?
  • 2.5 What to Learn on the Weekend?
  • 3. Getting Started with Python
  • 3.1 What is Python?
  • 3.1 Installation
  • 3.1.1 Python 2 vs. Python 3
  • 3.1.2 Installation steps   
  • 3.1.3 Python packages   
  • 3.2 IPython and Jupyter
  • 3.2.1 IPython    
  • 3.2.2 Jupyter    
  • 3.2.3 Installation
  • 3.2.4 What is an ipynb File?    
  • 4. Managing Data Frames with the Pandas package   
  • 4.1 Data Frames   
  • 4.2 The Pandas Package
  • 4.3 The Pandas Grammar
  • 4.4 Installing the Pandas Package
  • 4.5 selecting methods    
  • 4.6 filter method    
  • 4.7 sort_values method
  • 4.8 rename method    
  • 4.9 assign method    
  • 4.10 groupby method  
  • 4.11 method chaining
  • 4.12 Summary    
  • 5. Exploratory Data Analysis Checklist
  • 5.1 formulate your question    
  • 5.2 reading data    
  • 5.3 check the shaping
  • 5.4 describe and info
  • 5.5 Look at the head and the tail of your data   
  • 5.6 Check your “n”s    
  • 5.7 Validate with at least one external data source    
  • 5.8 Try the easy solution first    
  • 5.9 Challenge your solution    
  • 5.10 Follow up questions    
  • 6. Principles of Analytic Graphics    
  • 6.1 Show comparisons    
  • 6.2 Show causality, mechanism, explanation and systematic structure    
  • 6.3 Show multivariate data    
  • 6.4 Integrate evidence    
  • 6.5 Describe and document the evidence    
  • 6.6 Content, Content, and Content    
  • 6.7 References    
  • 7. Exploratory Graphs    
  • 7.1 Characteristics of exploratory graphs    
  • 7.2 Survival rate in Titanic dataset    
  • 7.3 Getting the Data    
  • 7.4 Simple Summaries: One Dimension    
  • 7.5 Five Number Summary    
  • 7.6 Boxplot    
  • 7.7 Histogram  
  • 7.8 Overlaying Features    
  • 7.9 Barplot
  • 7.10 Simple Summaries: Two Dimensions and Beyond
  • 7.11 Multiple Boxplots
  • 7.12 Multiple Histograms
  • 7.13 Scatterplots
  • 7.14 Scatterplot - Another Shape
  • 7.15 Multiple Scatterplots
  • 7.16 Summary
  • 8. About the Author

About the Author

Hisham El-Amir
Hisham El-Amir

Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

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