Data Analysis Tools in Python
Description
This training reviews the capabilities of Python programming language for data gathering and analysis. Python is a
modern, powerful programming language that enables you to develop complex software using traditional imperative
programming, utilize the capabilities of functional programming, and create reusable code based on an objectoriented approach. It will be useful for both beginning (Junior) programmers and more experienced (Regular)
developers who move to Python from other programming languages (and want to understand its ecosystem).
The training covers the following topics:
1. Introduction: Short Python overview, information about software programs used during the training.
2. Project description: Provides a description and architecture of the project to be developed.
3. Regular expressions: Provides a detailed overview of using regular expressions in Python; regular
expressions are used for searching data by templates, text parsing, and replacement.
4. HTTP-client: Examines how to communicate via the HTTP to web services and microservices.
5. Graphical user interface in Python - wxPython: Fully dedicated to the development of a graphical user
interface based on the wxWidgets cross-platform library for Python.
6. Maths in Python - NumPy: Basics of using the powerful math library for working with vectors and
multidimensional matrices.
7. Data analysis with Pandas: Closing module which focuses on data analysis by using the Pandas library,
relevant for anyone who wants to analyze and make decisions based on a large amount of data. It also
provides an overview of building charts based on collected data.This training course will cover various applications of Python for data analysis: regular expressions, data collection via
HTTP, using NumPy and Pandas libraries for data analysis, and matplotlib for visualization. During the training, you
will learn to write a full-fledged GUI application.
is issued on the Luxoft Training form
Objectives
Helps trainees acquire competencies required for data collection, math processing, and analysis of data by using
Python.
Target Audience
Junior and Regular Python Developers
Prerequisites
• Understanding the principles of object-oriented programming
• Understanding the principles of HTTP
• Passing courses SCRIPT-002, SCRIPT-003 or practical experience of using Python (not less than six
months)
Roadmap
1. Introduction. Brief introduction to the course (theory + practice)
1.1. Theory:
1.1.1. Main features of Python
1.1.2. Getting the project source code
1.2. Practice:
1.2.1. Preparing the virtual environment for future project
2. Project description
2.1. Theory:
2.1.1. Project description
3. Regular expressions (theory + practice)
3.1. Theory:
3.1.1. Basic constructions of regular expressions
3.1.2. Unions and groups
3.1.3. Match-objects and grouping brackets
3.1.4. Complex position templates (lookarounds)
3.2. Practice:
3.2.1. Developing the first prototype application for working with currency rates
3.2.2. Searching and grouping data received from the html file, using regular expressions
4. Working with the HTTP-client (theory + practice)
4.1. Theory:
4.1.1. Basics of Requests. Available REST-API methods
4.1.2. Response content
4.1.3. Redirection
4.1.4. Data transfer in a request
4.2. Practice:
4.2.1. Creating an HTTP-client based on Requests
4.2.2. Getting html pages with data from a remote resource
5. Graphical user interface wxPython (theory + practice)
5.1. Theory:
5.1.1. Introduction to wxPython. A simple application
5.1.2. GUI base elements
5.1.3. A binding mechanism
5.1.4. Events
5.1.5. Absolute and relative positioning of elements
5.2. Practice:
5.2.1. Developing the application graphical interface with a set of required elements
6. Maths in Python. NumPy (theory + practice)
6.1. Theory:
6.1.1. Introduction to NumPy. Basics
6.1.2. Working with matrices and n-dimensional arrays
6.1.3. Math operations and algorithms
6.1.4. Statistic metrics. Moving average (MA), simple moving average (SMA)
6.2. Practice:
6.2.1. Creating a matrix with a set of statistical data
6.2.2. Calculating a moving average
7. Data analysis with Pandas / Charts (theory + practice)
7.1. Theory:
7.1.1. Pandas basics
7.1.2. Data collection and storage in Pandas
7.1.3. Calculations in Pandas
7.1.4. Data visualization, charts
7.2. Practice:
7.2.1. Using Pandas data structure to store statistic data
7.2.2. Calculations and charts