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Udemy - Data Science_A Practical Guide for Beginners

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Udemy - Data Science_A Practical Guide for Beginners (Size: 1.4 GB)
  1 -Introduction to Data science.mp4 111.3 MB
  1 -Problems facing when handling large volumes of data.mp4 39.5 MB
  10 -Lecture 11EXPLORING DATA USING SERIES AND DATA FRAME.mp4 97.5 MB
  10 -String Manipulation.mp4 51.2 MB
  2 -Facets of data.mp4 85.2 MB
  2 -lecture 13General Techniques for handling large volumes of data.mp4 71.3 MB
  3 -General Programming Tips when handling large volumes of data.mp4 21.4 MB
  3 -Lecture 4Data Science Process.mp4 69.6 MB
  4 -Data Wranglling.mp4 93 MB
  4 -Lecture 5 Introduction to Numpy.mp4 29.3 MB
  5 -Combining and Merging Data Sets.mp4 101.8 MB
  5 -Lecture 6Creating array, attributes and objects.mp4 37.8 MB
  6 -Lecture 7Array basic operations.mp4 66 MB
  6 -Reshape in Data Wrangling.mp4 74.1 MB
  7 -Data Cleaning and Preparation.mp4 61.9 MB
  7 -Lecture 8 ARRAYS JOIN,SPLIT,SEARCH SORT.mp4 93.5 MB
  8 -Handling Missing Values.mp4 65.8 MB
  8 -Lecture 9Array indexing,slicing and iterating.mp4 91.7 MB
  9 -Data Transformation.mp4 82.3 MB
  9 -Lecture 10copying arrays,Array shape manipulation.mp4 48.9 MB
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 22 total files

Description


Data Science_A Practical Guide for Beginners

https://WebToolTip.com

Published 4/2025
Created by Dr Padmini Panneer Selvam
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 20 Lectures ( 3h 24m ) | Size: 1.36 GB

Mastering the Fundamentals Through Real-World Applications and Hands-On Projects in Data Science

What you'll learn
Students will learn the end-to-end workflow of data science, from data collection and exploration to analysis and visualization.
Students will gain proficiency in using Numpy and Pandas to manipulate, transform, and analyze datasets through basic operations and functions.
Students will learn how to clean, handle missing data, and prepare datasets for analysis using techniques like data transformation and summarization.
Students will learn to create and customize various types of visualizations (e.g., scatter, line, bar plots) to communicate data insights effectively.
Students will acquire techniques for efficiently merging, concatenating, and reshaping large datasets, ensuring smooth handling of complex data.

Requirements
Learners should have a basic understanding of mathematics and statistics, familiarity with programming fundamentals (preferably in Python), and a keen interest in data-driven problem solving.

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