Скачать или смотреть Exploratory Data Analysis in Python | Exploratory Data Analysis Using Python | Python Data Analysis

Exploratory Data Analysis in Python | Exploratory Data Analysis Using Python | Python Data Analysis
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In this EDA Tutorial, you will learn the introduction to Exploratory Data Analysis in Python and what the important steps that should be included in it with some EDA Case study. Unlock the potential of your data with our comprehensive tutorial on "Exploratory Data Analysis in Python." Presented by upGrad.

1. What is Exploratory Data Analysis?:
The analysis method known as exploratory data analysis, or EDA, finds broad patterns in the data. Outliers and potentially surprising data elements are included in these patterns. In any data analysis process, EDA is a crucial initial step. This section provides a conceptual foundation for the entire tutorial.

2. Exploratory Data Analysis Process:
One analysis method that finds broad patterns in the data is called exploratory data analysis (EDA). Outliers and maybe surprising characteristics of the data are included in these patterns. Any data analysis process must begin with EDA. Explore the key steps involved, including data collection, data cleaning, data transformation, and data visualization. Understand how each step contributes to gaining a deeper understanding of your dataset.

3. Data Quality Check:
Learn how to assess and ensure the quality of your data. This section covers techniques for identifying and handling missing values, detecting outliers, and dealing with inconsistent data. Ensuring data quality is critical for accurate and reliable analysis.

4. Data Type Check:
Understand the importance of checking data types in your dataset. Learn how to identify and correct data type mismatches, ensuring that each column is in the correct format for analysis. Proper data type handling is essential for effective data manipulation and analysis.

5. Import Libraries:
Get hands-on by importing the necessary Python libraries for EDA. This section introduces key libraries such as pandas, NumPy, matplotlib, and seaborn, which are essential tools for data manipulation, statistical analysis, and visualization.

6. Load Data :
Learn how to load your dataset into Python for analysis. This section covers various methods for reading data from different sources, including CSV files, Excel files, and databases, using pandas.

7. Data Analysis:
Delve into deeper data analysis techniques. This section covers various methods for exploring relationships between variables, including correlation analysis and cross-tabulation. Learn how to uncover hidden patterns and insights in your data.

8. Plot Churn and Non-Churn :
Visualize your data to gain deeper insights. Learn how to plot churn and non-churn rates using various plotting techniques. Understand how visualization helps in identifying trends.

Key Moments :
00:00 - Exploratory Data Analysis in Python
01:16 - What is Exploratory Data analysis
05:33 - Exploratory Data Analysis Process
08:09 - Data Quality Check
13:42 - Data Type Check
22:58 - Import Libraries
23:37 - Load Data
25:36 - Descriptive Analytics
29:59 - Data Analysis
33:13 - Plot churn and non-churn
42:30 - Plot churn

By the end of this Exploratory Data Analysis in Python tutorial, you'll be proficient in performing Exploratory Data Analysis using Python, capable of transforming raw data into actionable insights. Join us on this data analysis journey with upGrad and take your data science skills to the next level. Watch now and master EDA in Python!

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