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CDS Video Series | Sec 03. Exploratory Data Analysis

  • Basic understanding of programming concepts in Python.
  • Familiarity with fundamental statistical concepts such as mean, median, variance, and standard deviation.
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What I will learn?

  • Comprehensive understanding of Exploratory Data Analysis (EDA) techniques and methodologies.
  • Proficiency in utilizing various data visualization tools and libraries for EDA, such as Matplotlib, Seaborn, and Plotly.
  • Mastery in identifying patterns, trends, and outliers within datasets to derive meaningful insights.

Course Curriculum

Exploratory Data Analysis: Video Lesson
Get an detailed understanding of the Exploratory Data Analysis. In these video tutorials.

  • Exploratory Data Analysis
    04:39
  • Exploratory Data Analysis in Python
    10:19
  • Descriptive Statistics
    04:20
  • Descriptive Statistics in Python
    10:33

Exploratory Data Analysis: Handout Notes

Hands-on Implementations
Find the python notebooks here with hands-on implementations of popular exploratory data analysis techniques.

Assessment
Self-assess what knowledge you gained in this course.

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Material Includes

  • Video Lessons
  • Handout Notes
  • Python Codes
  • MCQs

Requirements

  • Basic understanding of programming concepts in Python.
  • Familiarity with fundamental statistical concepts such as mean, median, variance, and standard deviation.

Who Should Take this course?

  • Beginners in Data Analysis

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