Data Analysis Tools in Python

This course is meticulously designed to equip participants with the requisite skills to proficiently utilize Python for data analysis. It caters to both beginners and experienced programmers, providing a comprehensive curriculum that encompasses the essential tools and methodologies for loading, extracting, storing, analyzing, and visualizing data.

In the initial section on data loading, participants will develop expertise in retrieving data from web APIs and online resources using the requests library, as well as engaging in web scraping with Scrapy to collect data from websites.

The data extraction segment will encompass the parsing and manipulation of JSON data, extraction of data from HTML documents using BeautifulSoup, handling of XML data with ElementTree, extraction of text and data from PDF files using PyMuPDF, management of Excel files with openpyxl, and reading and writing of CSV files using Python's built-in csv module.

The data storage section will instruct participants on storing data in CSV format for ease of access as well as manipulation, and managing data in relational databases using SQLite.

The data analysis section will introduce participants to performing numerical operations and handling large datasets efficiently with NumPy, as well as utilizing pandas for data manipulation, cleaning, and analysis.

The final section on data visualization will guide participants in creating web applications to display data visualizations using Flask and generating a variety of plots and charts to visualize data with Matplotlib.

Participants will acquire the skills to retrieve and scrape data from various sources, extract and manipulate data in diverse formats, store data efficiently in CSV files and relational databases, perform advanced data analysis using NumPy and pandas, and create interactive visualizations and web applications to present their findings.

• Helps trainees acquire competencies required for data collection, math processing, and analysis of data by using Python.

Introduction. Brief introduction to the course (theory 1h)

Data loading (theory 2h + practice 3h)

• HTTP request libraries
• Requests library
• Scrappy framework

Data extraction (theory 2h + practice 3h)

• JSON
• XML/HTML
• CSV
• PDF
• XLS

Data storing (theory 2h + practice 2h)

• Storing data to CSV
• Storing data to RDBMS

Data analysis (theory 2h + practice 3h)

• Numpy library
• Pandas library
• Visualization with matplotlib

Data visualization (theory 2h + practice 3h) 

• Web framwork Flask