Video Description
Explore, prepare, clean & visualize your data with Google Colaboratory. Build Jupyter notebooks into Google Drive!
About This Video
- Use Google Colaboratory to solve common problems by filtering and organizing data
- Explore file & database usage with SQLite database for querying your data
- Identify outliers in the data, dealing with dirty data, & common data transformations
In Detail
Google Colaboratory is an online platform to perform data analysis. It enables you to create interactive Jupyter notebooks that mix text with Python code to run queries and display data analysis results. Stored on Google Drive you'll be able to run notebooks and collaborate with peers through Google's cloud services.
In this course, you will learn to solve problems and obtain key results with data. You will begin by building your own Jupyter notebook before you explore and learn the basics of Google Colaboratory. Then you will explore several file formats to store data and use SQLite to query large datasets. Next, you will learn to initialize 1D and 2D data structures with the Numpy and Pandas libraries to help organize and summarize metrics such as the mean, median, and standard deviation of your data.
Moving further, you will learn to identify outliers in your data, eliminate dirty data and perform common data transformations. Finally, you will use qualitative and quantitative data types with Matplotlib to display effective charts and visuals. By the end of this course, you'll have the tools to perform data analysis to tell your own compelling stories with data.
The code bundle for this course is available at https://github.com/PacktPublishing/Hands-On-Data-Analytics-for-Beginners-with-Google-Colaboratory-Video-
Table of Contents
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Chapter 1 : Exploring Data with Google Colaboratory
- The Course Overview 00:03:59
- Getting Started with Jupyter Notebooks and Google Colaboratoy 00:10:36
- Styling with Markdown Language 00:06:45
- Representing Mathematical Formulas Using LaTeX 00:15:48
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Chapter 2 : Loading and Working with Data Sources
- Loading and Processing Common File Formats 00:14:43
- Creating and Modifying SQLite Databases 00:13:13
- Selections and Conditions 00:10:34
- Grouping and Reorganizing Data 00:07:49
- Summarizing Results 00:06:20
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Chapter 3 : Exploring Your Data with NumPy and Pandas
- NumPy Array for 1D Data 00:20:32
- Panda Series for 1D Data 00:16:58
- NumPy Matrix for 2D Data 00:12:57
- Pandas Dataframe for 2D Data 00:29:27
- Pandas Dataframe for 2D Data (Continued) 00:12:53
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Chapter 4 : Preparing and Transforming Data
- Data Quality 00:08:09
- Data Cleaning 00:27:26
- Data Cleaning (Continued) 00:16:06
- Outlier Detection 00:09:22
- Data Transformation 00:08:46
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Chapter 5 : Data Visualization with Matplotlib
- Qualitative Data - 1 QL Variable 00:12:56
- Qualitative Data - 2 QL Variables 00:10:49
- Quantitative Data - 1 QN Variable 00:05:40
- Quantitative Data - 2 QN Variables 00:10:43