Python for Data Analysis & Visualization

Master the main data analysis and visualization libraries in Python: Numpy, Pandas, Matplotlib, Seaborn, Plotly + more
4.57 (2098 reviews)
Udemy
platform
English
language
Programming Languages
category
Malvik Vaghadia
instructor
Python for Data Analysis & Visualization
38 222
students
10 hours
content
Mar 2025
last update
$89.99
regular price

What you will learn

Python, we will be using Python3 in this course

Data Analysis Libraries in Python such as NumPy and Pandas

Data Visualization Libraries in Python such as Matplotlib and Seaborn

How to analyse data

Data Visualization

Jupyter Notebooks IDE / Anaconda Distribution

Course Gallery

Python for Data Analysis & Visualization – Screenshot 1
Screenshot 1 – Python for Data Analysis & Visualization
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Screenshot 2 – Python for Data Analysis & Visualization
Python for Data Analysis & Visualization – Screenshot 3
Screenshot 3 – Python for Data Analysis & Visualization
Python for Data Analysis & Visualization – Screenshot 4
Screenshot 4 – Python for Data Analysis & Visualization

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Our Verdict

This Python for Data Analysis & Visualization course on Udemy offers comprehensive coverage of key data analysis and visualization libraries in Python, making it a valuable resource for those wanting to hone their skills in this domain. While the clear teaching style is refreshing, deeper conceptual understanding could be aided through more thorough explanations and an even distribution of challenges throughout lectures. Adjusting volume settings and seeking further clarification when needed ensures the best outcome for learners.

What We Liked

  • Covers a wide range of data analysis and visualization libraries in Python including Numpy, Pandas, Matplotlib, Seaborn, Plotly among others
  • Instructor is concise, clear and to-the-point, making it easy to follow along
  • Real-world examples and exercises help reinforce concepts and make them more tangible
  • Includes Coding Challenges which enable deeper learning of topics

Potential Drawbacks

  • Lacks depth in explaining certain concepts or theory, making it difficult for beginners to grasp ideas without prior knowledge
  • Coding challenges could be more evenly distributed throughout the course
  • Volume of lectures can sometimes be too low and requires adjusting external settings
  • Occasionally, lacks proper explanation, leaving students confused (e.g., Seaborn section)
Related Topics
4471124
udemy ID
01/01/2022
course created date
09/02/2022
course indexed date
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course submited by
Python for Data Analysis & Visualization - Coupon | Comidoc