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Step-by-Step Guide to Reading Different Files in Python

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 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
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Data Analytics Tutorial for COBOL Programmers

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Mainframe developers look for an alternative IT course to grow in their careers. I have explained in this post how can they use their business knowledge. Data analytics tutorial is a top an alternative for COBOL programmers.

analytics tutorial for COBOL developers

What is Data Analytics

The field of data science is evolving into one of the fastest-growing and most in-demand fields in the world. 

Organizations across industries are looking to make sense of the data they can now collect from new technologies – from predicting the next hot product to determining the risk of an infectious disease outbreak.

Demand and Opportunity

  • According to The New York Times, data science “promises to revolutionize industries from business to government, health care to academia.”
  • As data accumulates, organizations are hiring individuals with the expertise to find meaning in the numbers and drive positive business decisions based on what they learn.
  • It is estimated that by 2018, 4 million to 5 million jobs in the United States will require data analysis skills, and a recent study from the McKinsey Global Institute found “a shortage of the analytical and managerial talent necessary to make the most of Big Data is a significant and pressing challenge (for the U.S.).”
  • Based on the number of job openings, median base salary and career opportunities, Glassdoor has ranked data scientist as the “Best Job in America”.

Who can opt for Data Analytics Tutorial

  1. Strong interest in data science 
  2. Background in intro level statistics 
  3. Programming experience in Python for Data Science 
  4. Understanding of programming concepts such as variables, functions, loops, and basic python data structures like lists and dictionaries
Start Your Free Data analytics Tutorial here.

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