Python for Data Analysts

Paths

Python for Data Analysts

Authors: Janani Ravi, Xavier Morera, Chris Achard, Kishan Iyer, Ian Ozsvald

One of the most pervasive uses of Python is to analyze data. This skill is for those who want to leverage the power of Python in data treatment and analysis.

What you will learn

  • Installing Python
  • Using Jupyter Notebooks
  • Using IDEs
  • Working with Numbers, Strings, and Lists
  • Defining Functions
  • Simple flow control and language features
  • Reading and Writing files
  • Installing and using common analytics libraries like Numpy, Scipy, Matplotlib, and Pandas

Pre-requisites

Data Analytics Literacy

Beginner

Create your first Python analytics solution, and learn to use common Python environments like IDEs and notebooks.

Building Your First Python Analytics Solution

by Janani Ravi

Oct 29, 2019 / 2h 47m

2h 47m

Start Course
Description

Python has exploded in popularity in recent years, largely because it makes analyzing and working with data so incredibly simple. Despite its great success as a prototyping tool, Python is still relatively unproven for large, enterprise-scale development.   In this course, Building your First Python Analytics Solution you will gain the ability to identify and use the right development and execution environment for your enterprise. First, you will learn how Jupyter notebooks, despite their immense popularity, are not quite as robust as fully-fledged Integrated Development Environments, or IDEs. Next, you will discover how different execution environments offer alternative ways of configuring Python libraries, and specifically how the two most popular, Conda and Pip, stack up against each other. You will also explore several different development environments including IDLE, PyCharm, Eclipse, and Spyder. Finally, you will round out your knowledge by running Python on the major cloud environments, including AWS, Microsoft Azure, and the GCP. When you’re finished with this course, you will have the skills and knowledge to identify the correct development and execution environments for Python in your organizational context.

Table of contents
  1. Course Overview
  2. Getting Started with Python for Analytics
  3. Working with Python Using Anaconda
  4. Working with Python Using Other IDEs
  5. Working with Python on the Cloud

Create and Share Analytics with Jupyter Notebooks

by Janani Ravi

Nov 5, 2019 / 2h 12m

2h 12m

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Description

Python has exploded in popularity in recent years, largely because it makes analyzing and working with data so incredibly simple. Jupyter is an execution environment rather than a fully-fledged IDE, but even so, notebooks have various important features that are worth understanding thoroughly. In this course, Create and Share Analytics with Jupyter Notebooks, you will learn how Jupyter notebooks are a key driver of Python’s popularity, by providing an incredibly intuitive, interactive environment for executing Python programs. First, you will learn how to get up and running with Jupyter notebooks, and how best to leverage features such as markdown to enhance the readability of your code. Next, you will discover how more advanced features such as magic functions work, and how the next generation offering from Jupyter, named JupyterLab goes even further towards a fully-fledged development environment. Finally, you will round out your knowledge by working with cloud-hosted Jupyter notebooks on each of the major cloud platforms. When you’re finished with this course, you will have the skills and knowledge to leverage the full power of Jupyter notebooks and Jupyterlab, particularly in the context of cloud-hosted notebooks for distributed and collaborative use-cases.

Table of contents
  1. Course Overview
  2. Getting Started with Jupyter Notebooks
  3. Understanding Jupyter Notebooks
  4. Creating Shareable Analyses in Jupyter Notebooks
  5. Working with Cloud-hosted Jupyter Notebooks

Python for Data Analysts

by Janani Ravi

Oct 29, 2019 / 3h 30m

3h 30m

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Description

Python has exploded in popularity in recent years and has emerged as the technology of choice for data analysts and data scientists. In this course, Python for Data Analysts, you will gain the ability to write Python programs and utilize fundamental building blocks of programming and data analysis. First, you will learn how programming languages such as Python, spreadsheets such as Microsoft Excel, and SQL-based technologies such as databases differ from each other, and also how they inter-operate. Next, you will plunge into Python programming, installing Python and getting started with simple programs. You will then understand the ways in which variables are used to hold data, and how simple and complex data types in Python differ in their semantics. Finally, you will round out your knowledge by working with conditional evaluation using if statements, loops and functions. You will learn how Python treats functions as first-class entities, a key enabler of functional programming. When you’re finished with this course, you will have the skills and knowledge to identify situations when Python is the right choice for you, and to implement simple but solid programs using Python.

Table of contents
  1. Course Overview - end of the process
  2. Getting Started with Python for Data Analysis
  3. Leveraging Built-in Functions and Complex Data Types
  4. Using Python for Complex Interconnected Calculations
  5. Implementing Code Reuse Using Functions in Python
  6. Loading and Saving Data Using Python

Programming Python Using an IDE

by Xavier Morera

Jun 26, 2019 / 2h 0m

2h 0m

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Description

Learning and becoming proficient with Python is one of the best decisions a coder can make. The simplicity of Python, along with the many libraries available make it one of the most productive languages you can use. This course, Programming Python Using an IDE, will help you use an IDE to take your coding skills one level higher! First, you will explore the selection of popular IDEs and how they can help you improve your productivity. Next, you will learn about the many features that make IDEs great for creating applications including syntax highlighting, refactoring, code checking, and more. You will also discover some other features that help you run, debug, unit test, and source control your code. Finally, you will see how some IDEs have features that are meant for scientific Python and creating data science applications. By the end of this course, you will know and understand how IDEs can help you be a more productive developer.

Table of contents
  1. Course Overview
  2. Programming Python Using an IDE! But Why? And Which One?
  3. Improving Your Productivity Programming in Python with an IDE
  4. Leveraging a Python IDE for Data Science
  5. Final Takeaway

Intermediate

Apply Python to specific parts of the analytics workflow. Learn how to load, clean, and visualize data.

Leveraging Online Resources for Python Analytics

by Janani Ravi

Nov 1, 2019 / 2h 12m

2h 12m

Start Course
Description

As data science and data analytics become ever more popular and more specialized, the number and variety of tools and technologies out there can often seem overwhelming. In this course, Leveraging Online Resources for Python Analytics, you will gain the ability to find resources that can help you to correctly frame and solve your problem. First, you will survey some of the important visualization libraries, machine learning and deep learning frameworks, and cloud-based solutions out there. Next, you will discover the benefits of using a tool like BigML, which is a platform for building ML models that abstracts away much of the underlying complexity. Democratization of ML is an important trend today, and technologies like BigML are at the forefront of that trend. You will see, for instance, how BigML seamlessly integrates visualizations known as partial dependency plots, which combine the results of large numbers of ML predictions into an easily understandable form so that you can understand exactly what your ML model is doing. Finally, you will round out your knowledge by working with Google Colab, a free web-based way to build models. The models are hosted in Jupyter notebooks that reside on Google Drive and run on virtual machines in the cloud. When you’re finished with this course, you will have the skills and knowledge to quickly and efficiently identify valuable online resources and libraries that will help you on your journey as a data science practitioner.

Table of contents
  1. Course Overview
  2. Getting Started with Python Analytics
  3. Leveraging Online Resources for Python Analytics with BigML
  4. Working with Interactive Environment Using Google Colab

Importing Data: Python Data Playbook

by Xavier Morera

Nov 17, 2018 / 1h 36m

1h 36m

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Description

Python is one of the most powerful and widely used languages to work with data. In this course, Importing Data: Python Data Playbook, you will learn foundational knowledge and gain the ability to import data from multiple different file formats, including: text, tabular data, binary formats as well as from databases. First, you will learn how to import text and CSV files. Next, you will discover how to import data from JSON, XML, SAS, Stata, HDF5, Matlab, Pickle files, and more. Finally, you will explore how to import relational data from databases, including: SQLite, MySQL, and PostgreSQL. When you're finished with this course, you will have the skills and knowledge of importing data into Python needed to analyze, visualize, and in general work with data.

Table of contents
  1. Course Overview
  2. Importing Text Data into Python Using NumPy
  3. Importing CSV Data into Python Using csv and pandas
  4. Import Data into Python from JSON and XML Files
  5. Import Data into Python from Excel Files
  6. Import Data into Python from Common Binary Data File Formats
  7. Import Data into Python from Relational Databases

Cleaning Data: Python Data Playbook

by Chris Achard

Dec 10, 2018 / 1h 9m

1h 9m

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Description

At the core of any successful project that involves a real world dataset is a thorough knowledge of how to clean that dataset from missing, bad, or inaccurate data. In this course, Cleaning Data: Python Data Playbook, you'll learn how to use pandas to clean a real world dataset. First, you'll learn how to understand, view, and explore the data you have. Next, you'll explore how to access just the data that you want to keep in your dataset. Finally, you'll discover different ways to handle bad and missing data. When you're finished with this course, you'll have a foundational knowledge of cleaning real world datasets with pandas that will help you as you move forward to working on real world data science or machine learning problems.

Table of contents
  1. Course Overview
  2. Understanding Your Data
  3. Removing and Fixing Columns with pandas
  4. Indexing and Filtering Datasets
  5. Handling Bad, Missing, and Duplicate Data

Pygal: Python Data Playbook

by Kishan Iyer

Apr 12, 2019 / 2h 59m

2h 59m

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Description

Vector image formats such as SVG possess many important advantages over scalar formats such as PNG and JPEG. Using SVG, you can build high-quality, compact visualizations that render on low-resolution devices and that can be scaled, zoomed, and moved without distortion. In this course, Pygal: Python Data Playbook, you will gain the ability to construct an array of visualizations and render them to SVG format using Pygal. First, you will learn the advantages of working with Pygal for building SVGs and understand the niche that Pygal occupies relative to other visualization packages such as Matplotlib, Seaborn, Bokeh, and Plotly. Next, you will discover how to build an array of visualizations in Pygal, from in-memory as well as file data. You will then construct a visualization including simple charts such as Line, Tree, and Bar graphs, as well as specialized types like TreeMaps and Sparklines. You will understand the different types of Styles and Configurations that can be used to govern chart appearance. You will work with built-in, parametric, and custom styles, as well as Chart, Serie, and Value configurations. Finally, you will explore how to render Pygal visualizations to a range of image and non-image formats, including XML element trees and base64 encoded formats for online transfer. You will round out the course by building a web application using the Flask microframework in order to render and serve Pygal charts. When you are finished with this course, you will have the skills and knowledge of building and rendering visualizations in Pygal needed to effectively harness the many advantages of the Scalable Vector Graphics format.

Table of contents
  1. Course Overview
  2. Getting Data into Pygal
  3. Plotting Basic Pygal Charts
  4. Visualizing Complex Data with Advanced Charts
  5. Rendering Out Charts

Advanced

Extend your abilities to scraping data from the web and uses databases with Python.

Understanding Databases with SQLAlchemy: Python Data Playbook

by Xavier Morera

Mar 16, 2019 / 1h 23m

1h 23m

Start Course
Description

Databases are an integral part of data science, and every programmer that interacts with data needs to be able to work with a database. In this course, Understanding Databases with SQLAlchemy: Python Data Playbook, you will learn foundational knowledge to work with databases using SQLAlchemy. First, you will see how to perform queries. Next, you will discover how to create databases and tables and populate them with data. Finally, you will explore how to manipulate the data you inserted and queried. When you are finished with this course, you will have the skills and knowledge of interacting with databases needed to successfully work with your database using Python with SQLAlchemy.

Table of contents
  1. Course Overview
  2. Up and Running with SQLAlchemy
  3. Querying with SQLAlchemy
  4. Creating Your Database
  5. Manipulating Your Database
  6. Final Takeaway

Web Scraping: Python Data Playbook

by Ian Ozsvald

May 2, 2019 / 1h 17m

1h 17m

Start Course
Description

Scrape data from a static web page with BeautifulSoup4 and turn it into a compelling graphical data story in a Jupyter Notebook. In this course, Web Scraping: The Python Data Playbook, you will gain the ability to scrape data and present it graphically. First, you will learn to scrape using the requests module and BeautifulSoup4. Next, you will discover how to write a trustworthy scraping module backed by a unit test. Finally, you will explore how to turn the columns of data in a graphical story that will change the opinions of your colleagues. When you're finished with this course, you will have the skills and knowledge of web scraping needed to create a graphically compelling Jupyter Notebook without the use of an API.

Table of contents
  1. Course Overview
  2. Setting Up BeautifulSoup
  3. Understanding Your Scraped Data
  4. Making Scraped Data Usable
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