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Using Data Science Tools in Python

Learn key Python Data Science tools required to work with data and create clear, easy-to-understand visuals.

(DS-TOOLS-PYTHON.AD1) / आईएसबीएन : 978-1-64459-252-6
Lessons
Lab
TestPrep
AI Tutor (ऐड ऑन)
269 समीक्षा
निःशुल्क परीक्षण प्राप्त करें

इस कोर्स के बारे में

This course teaches you how to use the most important tools for data science in Python like NumPy, Pandas, and Matplotlib. You’ll gain hands-on experience with real-world data, learning how to manage, analyze, and visualize it in simple ways. By the end of this data science with Python course, you’ll be able to use Python programming to work with large data sets, create clear charts and graphs, and solve data problems.

कौशल जो आपको प्राप्त होंगे

  • Set up a Python environment for data science using Anaconda and Jupyter Notebook 
  • Create and manage data arrays with NumPy for fast and efficient data analysis 
  • Analyze and transform data using Pandas, enabling easy manipulation of large datasets 
  • Visualize data with clear charts and graphs using Matplotlib and Seaborn
  • Scrape data from websites with Beautiful Soup for real-world data collection 
  • Handle different types of data, from simple arrays to complex data frames 
  • Apply data science techniques to solve problems and make data-driven decisions

पाठ्यक्रम रूपरेखा डाउनलोड करें

1

Introduction

  • Course Description
  • How To Use This Course
  • Course-Specific Technical Requirements
2

Setting Up a Python Data Science Environment

  • Topic A: Select Python Data Science Tools
  • Topic B: Install Python Using Anaconda
  • Topic C: Set Up an Environment Using Jupyter Notebook
  • Summary
3

Managing and Analyzing Data with NumPy

  • Topic A: Create NumPy Arrays
  • Topic B: Load and Save NumPy Data
  • Topic C: Analyze Data in NumPy Arrays
  • Summary
4

Transforming Data with NumPy

  • Topic A: Manipulate Data in NumPy Arrays
  • Topic B: Modify Data in NumPy Arrays
  • Summary
5

Managing and Analyzing Data with pandas

  • Topic A: Create Series and DataFrames
  • Topic B: Load and Save pandas Data
  • Topic C: Analyze Data in DataFrames
  • Topic D: Slice and Filter Data in DataFrames
  • Summary
6

Transforming and Visualizing Data with pandas

  • Topic A: Manipulate Data in DataFrames
  • Topic B: Modify Data in DataFrames
  • Topic C: Plot DataFrame Data
  • Summary
7

Visualizing Data with Matplotlib and Seaborn

  • Topic A: Create and Save Simple Line Plots
  • Topic B: Create Subplots
  • Topic C: Create Common Types of Plots
  • Topic D: Format Plots
  • Topic E: Streamline Plotting with Seaborn
  • Summary
A

Appendix A: Scraping Web Data Using Beautiful Soup

  • Topic A: Scrape Web Pages

1

Setting Up a Python Data Science Environment

  • Setting Up a Jupyter Notebook Environment
2

Managing and Analyzing Data with NumPy

  • एक NumPy ऐरे बनाना
  • NumPy ऐरे विशेषताओं का उपयोग करना
  • Loading and Saving NumPy Data
  • एक NumPy ऐरे में डेटा का विश्लेषण
  • Using Fancy Indexing
  • NumPy सांख्यिकीय सारांश फ़ंक्शंस का उपयोग करना
3

Transforming Data with NumPy

  • एक NumPy ऐरे में डेटा में हेरफेर करना
  • रीशेप फंक्शन का उपयोग करना
  • रेवेल और फ्लिप फ़ंक्शंस का उपयोग करना
  • ट्रांज़ोज़ और कॉन्टेनेट फ़ंक्शंस का उपयोग करना
  • सॉर्ट और अर्गसॉर्ट फ़ंक्शंस का उपयोग करना
  • डालने और हटाने के कार्यों का उपयोग करना
  • Using the Arithmetic Functions and Operators
  • तुलना कार्यों और ऑपरेटरों का उपयोग करना
  • Modifying Data in NumPy Arrays
4

Managing and Analyzing Data with pandas

  • सीरीज और डेटाफ्रेम बनाना
  • Using the Series and DataFrame Attributes
  • Loading and Saving DataFrame Data
  • Analyzing Data in a DataFrame
  • डेटाफ़्रेम में डेटा को स्लाइस करना और फ़िल्टर करना
5

Transforming and Visualizing Data with pandas

  • डेटाफ़्रेम में डेटा में हेरफेर करना
  • Modifying Data in a DataFrame
  • Using the DataFrame Arithmetic Functions and Operators
  • स्कैटर प्लॉट बनाना
6

Visualizing Data with Matplotlib and Seaborn

  • लाइन प्लॉट बनाना
  • सबप्लॉट बनाना
  • बॉक्स प्लॉट बनाना
  • 3-डी स्कैटर प्लॉट बनाना
  • हिस्टोग्राम बनाना
  • स्वरूपण भूखंड
  • एक संयुक्त ग्रिड बनाना
  • एक रेखीय प्रतिगमन प्लॉट बनाना

कोई प्रश्न? FAQ देखें

Find answers to common questions about our Python Data Science course.

हमसे अभी संपर्क करें

Python is widely used in data science for tasks like managing large datasets, analyzing data patterns, and creating visualizations. With its simple syntax and powerful libraries like NumPy, Pandas, and Matplotlib, Python makes data analysis and visualization faster and more efficient.

  Popular Python tools for data science include libraries like NumPy for numerical data, pandas for managing and analyzing datasets, Matplotlib and Seaborn for visualizing data, and Beautiful Soup for web scraping.

This course is perfect for beginners who want to establish a career in data science and Python programming or anyone who is looking for a smart way to upskill.

You can practice working on Python data analysis tools inside our hands-on labs and simulations, where you’ll work with real-world datasets in a risk-free environment and apply your skills to solve data challenges. 

Data Science with Python Made Easy

  Learn, practice, and use Python’s most powerful tools for data science, with step-by-step guidance.

$279.99

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