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Complete Guide to NumPy, Pandas, SciPy, Matplotlib & Seaborn
Rating: 3.9 out of 5(78 ratings)
12,073 students

Complete Guide to NumPy, Pandas, SciPy, Matplotlib & Seaborn

Boost your data science skills by mastering NumPy, Pandas, SciPy, and powerful visualization tools in Python.
Created bySheikh Coding Institute
Last updated 7/2025
English

What you'll learn

  • Introduction to Python for Data Science
  • Overview of NumPy, Pandas, Matplotlib, and SciPy
  • Creating NumPy Arrays
  • Mathematical Operations with NumPy Arrays
  • Working with Random Numbers and Simulations
  • Advanced Array Manipulation and Linear Algebra
  • NumPy for Statistical Computations (Mean, Median, Standard Deviation)
  • Performance Optimization with NumPy
  • Loading and Saving Data with Pandas (CSV, Excel, SQL, etc.)
  • Indexing, Selecting, and Filtering Data in DataFrames
  • Advanced Pandas Techniques
  • Matplotlib Data Visualization
  • Seaborn Advanced Visualization Techniques
  • SciPy Scientific Computing
  • Combining Libraries for Real World Data Science
  • And more........

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Course content

8 sections • 29 lectures • 4h 28m total length

  • Creating NumPy Arrays6:05

    Create NumPy arrays from Python lists with np.array, print both, and use np.arange(start, stop, step) to generate values.

  • Array Indexing, Slicing, and Reshaping9:10

    Explore array indexing, slicing, and reshaping with NumPy in Python, learning how to access elements, create two-dimensional arrays, and reshape a one-dimensional array into two rows and three columns.

  • Mathematical Operations with NumPy Arrays8:37
  • Broadcasting and Vectorized Operations7:25
  • Working with Random Numbers and Simulations7:51

Requirements

  • Basic understanding of Python programming (variables, data types, loops, functions).
  • No prior experience with NumPy, Pandas, SciPy, Matplotlib, or Seaborn is required.

Description

Are you ready to unlock the full potential of Python for data science, analytics, and scientific computing? Whether you're a beginner eager to enter the world of data or an experienced programmer looking to deepen your skills, this course is your complete resource for mastering the core Python libraries: NumPy, Pandas, SciPy, and Matplotlib/Seaborn.


This hands-on, project-driven course is designed to take you from the basics all the way to advanced techniques in data analysis, numerical computing, and data visualization. You'll learn how to work with real-world datasets, perform complex data operations, and create stunning, publication-quality visualizations.


What You’ll Learn:

  • NumPy – Work with multidimensional arrays, broadcasting, indexing, and performance optimization

  • Pandas – Master dataframes, series, grouping, filtering, merging, and time series data

  • SciPy – Dive into scientific computing with optimization, statistics, interpolation, signal processing, and more

  • Matplotlib & Seaborn – Create insightful and beautiful visualizations, from basic plots to advanced charts

  • Data Workflow – Clean, transform, and prepare data for analysis and modeling


Why Take This Course?

  • Taught by experienced data professionals

  • Practical, hands-on learning with real-world datasets

  • Covers both the theory and the application

  • Builds a solid foundation for advanced data science and machine learning


By the end of this course, you'll be confident in your ability to manipulate, analyze, and visualize data using Python’s most essential libraries — a skill set that's in high demand across industries.


Enroll now and start your journey into data mastery today!

Who this course is for:

  • Anyone interested in mastering the core Python libraries for data manipulation, analysis, and visualization.
  • Students and professionals looking to enhance their data driven skills.
  • Machine Learning Engineers who need to manipulate and understand data effectively.
  • Python developers looking to transition into data science.

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