Aspire Journeys

Regular Expression for Data Science

  • 2 Labs | 2h
Rating 5.0 of 2 users Rating 5.0 of 2 users (2)
The Regular Expression for Data Science track takes you on a comprehensive voyage through the crucial aspects of text processing and data manipulation in the world of Data Science. Starting with the basics of Regular Expressions, you will acquire solid grounding in the fundamentals, syntax, and application of these powerful pattern-matching tools. Progressing forward, delve into essential text preprocessing concepts and techniques such as tokenization, stemming, and lemmatization while mastering the NLTK library. As the journey unfolds, explore the fascinating realm of Natural Language Processing (NLP) and learn to proficiently employ Regular Expressions for text parsing and manipulation. Lastly, dive into Data Cleaning with Python and the pandas library, honing your skills in cleaning, shaping, and preparing datasets for optimal analysis. By the end of this learning track, you will be well-equipped to tackle a myriad of challenges related to text processing and data manipulation within the rapidly growing field of Data Science.

Track 1: Learn Regular Expressions

In this track of the Regular Expression for Data Science Aspire Journey, the focus will be on understanding the foundational concepts, syntax, and use of regular expressions.

Track 2: Text Preprocessing

In this track of the Regular Expression for Data Science Aspire Journey, the focus will be on vital programming concepts such as tokenization, stemming, lemmatization, stop words removal, and text normalization using NLTK library.

Track 3: Language Parsing

In this track of the Regular Expression for Data Science Aspire Journey, the focus will be on understanding Natural Language Processing (NLP) and effectively utilizing Regular Expressions (Regex) for text parsing and manipulation.

  • 1 Lab | 1h

Track 4: Data Cleaning

In this track of the Regular Expression for Data Science Aspire Journey, the focus will be on utilizing Python and pandas library for cleaning, manipulating, and preparing datasets effectively for further analysis.

  • 1 Lab | 1h

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