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Python for Data Science: Python Programming & Data Analysis

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Transform data into insights using Python and its powerful Libraries such as Numpy, Pandas, MatplotLib, Seaborn etc.


1. Introduction
  • 1. Introduction
  • 2.1 Python training.zip
  • 2. Course resources.html

  • 2. Getting Started with Python
  • 1. Introduction to Python
  • 2. First code to Python Programming.html
  • 3. Variables in Python Declaration and Use
  • 4. Basic Arithmetic Operations in Python.html
  • 5. Data types in Python
  • 6. Python String
  • 7. Quiz.html
  • 8. String methods
  • 9. Calculating Body Mass Index (BMI) in Python.html
  • 10. Practice Assignment.html

  • 3. Data Structures in Python
  • 1. List in Python
  • 2. Working with Lists in Python.html
  • 3. Tuples in Python
  • 4. Dictionaries in Python
  • 5. Working with Dictionaries in Python.html
  • 6. Sets in Python
  • 7. Practice Assignment.html

  • 4. Conditional Statements in Python
  • 1. Python Conditional Expressions
  • 2. Conditional Statements in Python.html
  • 3. Exploring Operators and Conditional Expressions in Python
  • 4. Practice Assignment.html

  • 5. Loops in Python
  • 1. For loops in Python
  • 2. Generating Square Numbers in Python.html
  • 3. While loops in Python
  • 4. Practice Assignment.html

  • 6. Python Functions
  • 1. Function in Python
  • 2. Checking Even Numbers Using Functions in Python.html
  • 3. Recursion in Python
  • 4. Lambda function
  • 5. Lambda Functions for Exponentiation.html
  • 6. Practice Assignment.html

  • 7. File handling in Python
  • 1. File IO in Python
  • 2. Writing to a File in Python.html
  • 3. Quiz.html

  • 8. NumPy Library
  • 1. Introduction to NumPy arrays
  • 2. Accessing the elements of NumPy arrays
  • 3. Creating and Accessing Elements in NumPy Arrays.html
  • 4. Leveraging Data Types, Shapes, and Array Stacking in NumPy
  • 5. Exploring Diverse Approaches to Creating NumPy Arrays
  • 6. Creating NumPy Arrays with Evenly Spaced Numbers.html
  • 7. Mathematical operations on arrays
  • 8. Array Operations and Median Calculation in Python.html
  • 9. Practice Assignment.html

  • 9. Pandas Library
  • 1. Introduction to Pandas Library
  • 2. Creating DataFrame from Dictionary in Pandas.html
  • 3. Exploring Series and DataFrame in Python
  • 4. Essential Data Analysis Methods in Python
  • 5. Importing Data from CSV File using Pandas.html
  • 6. Missing Data Handling in Python
  • 7. Manipulating DataFrame in Python
  • 8. Practice Assignment Analyzing and Visualizing the Titanic Dataset with Pandas.html

  • 10. Matplotlib Library
  • 1. Introduction to Matplotlib Library
  • 2. Data Visualization with Matplotlib Plotting Essentials and Customization
  • 3. Visualizing Average Session Length per Day with a Line Chart.html
  • 4. Exploring Subplots, Scatter Plots, and Customization
  • 5. Creating a Scatter Plot of Random Data Points.html
  • 6. Crafting Bar Plots, Histograms, Pie Charts with Customization Using Matplotlib
  • 7. Practice Assignment Visualizing Olympic Medals Data with Matplotlib.html

  • 11. Seaborn Library
  • 1. Introduction to Seaborn Library
  • 2. Exploring Seaborn Univariate and Bivariate Analysis for Data Visualization
  • 3. Advanced Data Visualization with Seaborn Pairplot and Barplot Customization
  • 4. Advanced Visualizations with Countplot and Heatmap Using Seaborn
  • 5. Practice Assignment Data Visualization with Seaborn and the Iris Dataset.html

  • 12. Conclusion
  • 1. About your certificate.html
  • 2. Bonus lecture.html
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    تولید کننده:
    شناسه: 38449
    حجم: 2212 مگابایت
    مدت زمان: 365 دقیقه
    تاریخ انتشار: ۲۲ تیر ۱۴۰۳
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