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Hands-on Data Analysis and Visualization with Pandas

It’s time for an upgrade. Learn Pandas once and start commanding those datasets. 

(DA-VISUALIZE.AW1) / ISBN : 978-1-64459-659-3
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Über diesen Kurs

Enroll in our data analysis with Python course and wield JupyterLab, Pandas, and Seaborn to dissect data. 

This course cracks open Python’s data ecosystem: clean messy datasets with Pandas, run statistics with SciPy, and visualize trends with Matplotlib/Seaborn. You’ll optimize memory for large datasets, merge time series, and even automate ETL. 

By the end of this course, you’ll transform raw data into clear, actionable insights. Data waits for no one. Start now. 

Fähigkeiten, die Sie erwerben werden

  • Write and execute Python code in JupyterLab for data analysis.
  • Manipulate numerical data at scale using NumPy arrays and advanced operations.
  • Clean, transform, and merge complex datasets with Pandas DataFrames.
  • Perform time series analysis to identify trends, seasonality, and anomalies.
  • Apply statistical methods for hypothesis testing and data validation.
  • Create publication-quality visualizations using Matplotlib and Seaborn. 
  • Conduct end-to-end exploratory data analysis on real-world datasets. 
Kursübersicht herunterladen

1

Preface

2

Introduction to Data Analysis

  • Inspiration for data analysis
  • Domain expertise
  • Maths and statistics
  • Artificial intelligence
  • Machine learning
  • Data Infrastructure
  • Data Analysis Process
  • Why Python for Data Analysis?
  • Conclusion
3

JupyterLab

  • Introduction to JupyterLab
  • Components
  • Cell modes
  • Menu
  • Magic commands
  • Keyboard shortcuts
  • Conclusion
4

Python Overview

  • Python, Hello World
  • Variables and data types
  • Functions
  • Lambda
  • List comprehensions
  • Functional programming using (map, filter, and reduce)
  • Working with datetime objects
  • Conclusion
5

Introduction to Numpy

  • Ndarray
  • Difference between List and Numpy arrays
  • Storage
  • Type check
  • Speed
  • Copying arrays
  • Mathematical operations
  • Trigonometric functions
  • Statistical operations
  • Reshaping
  • Vertical and horizontal stacking of Numpy arrays
  • Fancy indexing
  • Indexing with Boolean arrays
  • Broadcasting
  • Conclusion
6

Introduction to Pandas

  • Data structures in pandas
  • Series
  • DataFrames
  • Conclusion
7

Data Analysis

  • Handling different file formats
  • Handling rows and columns
  • Groupby
  • Filter
  • Concatenate DataFrames
  • Merge DataFrames
  • Purging duplicate rows
  • Data Transformations
  • Crosstab
  • Cleansing the Data
  • Replacing individual values
  • Pivot and pivot table
  • Grouper
  • Handling large datasets
  • Modin Pandas
  • Conclusion
8

Time Series Analysis

  • Creating time series data
  • Converting string-based dates to datetime objects
  • Unix / Epoch time
  • Time Series Analysis Using a Real-Time Dataset
  • Handling Timezones
  • Shifting or Lagging
  • Handling Holidays
  • Conclusion
9

Introduction to Statistics

  • Population
  • Sample
  • Types of data
  • Levels of Measurement
  • Inferential Statistics
  • Hypothesis Testing
  • Conclusion
10

Matplotlib

  • Why data visualization?
  • Matplotlib architecture
  • Chart properties
  • Controlling xticks, y_ticks, and tick_labels
  • Scatter plot
  • Bar plot
  • Histograms
  • Pie Chart
  • Subplots
  • Conclusion
11

Seaborn

  • Why Seaborn?
  • Matplotlib versus Seaborn
  • About pokemon
  • Importing libraries and dataset
  • Visualizing Statistical Relationships
  • Plotting Categorical Variables
  • Visualizing the Distribution of the Data
  • Conclusion
12

Exploratory Data Analysis

  • A little story, Titanic
  • Importing libraries and dataset
  • Handling missing values
  • Variable identification
  • Categorical nominal
  • Univariate analysis
  • Bivariate analysis
  • HeatMap
  • Multivariate Analysis
  • Handling Outliers
  • Feature Selection
  • Conclusion

Haben Sie Fragen? Schauen Sie sich die FAQs an

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Yes! Pandas is the gold standard for data analysis in Python. It’s optimized for cleaning, transforming, and analyzing large datasets. 

No standalone “Pandas certification” exists but this course includes a Python For Data Science certificate covering Pandas, NumPy, and visualization tools. It’s ideal for resumes. 

  • Basics (1–2 weeks): DataFrames, filtering, basic operations.
  • Intermediate (1 month): Merging datasets, time-series, stats.
  • Advanced (2+ months): Optimization, large datasets, integration with ML.

This course condenses it to 4–6 weeks with focused practice.

This course covers Python Fundamentals, but basic programming knowledge (variables, loops, functions) is recommended for the best experience. 

Absolutely! You’ll gain hands-on skills in data cleaning, analysis, and visualization, which are critical for entry-level data roles.

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