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Data Science for Beginners – Python, Azure ML and Tableau

سرفصل های دوره

Practical Data Science: Machine Learning, AI, Cloud and Data Analysis in Python, Tableau and Azure ML with Real Projects


1 - Introduction
  • 1 -Introduction
  • 2 -The Data Scientist Role

  • 2 - Introduction to Python for Data Science
  • 1 -Setting Up Your Python Environment with Anaconda and Jupyter Notebook
  • 2 -Jupyter Notebook Overview
  • 3 -Notebooks.zip
  • 3 -Understanding Variables in Python
  • 3 - Download All Notebooks Here.html
  • 4 -Data Types and Their Importance
  • 5 -Working with Lists
  • 6 -Exploring Dictionaries
  • 7 -Tuples and Sets
  • 8 -Introduction to Arithmetic and Comparison Operators
  • 9 -Conditional Statements in Python
  • 10 -Using For Loops
  • 11 -Combining For Loops with Conditional Statements
  • 12 -Defining Functions in Python
  • 13 -Test your Knowledge Python Basics Q & A

  • 3 - Statistics Fundamentals for Data Science
  • 1 -Descriptive Statistics Mean, Median, and Mode Explained
  • 2 -Measuring Spread Standard Deviation and Variance
  • 3 -Understanding Sampling Techniques in Data Science
  • 4 -Understanding Variables
  • 5 -Frequency Distribution Organizing Data for Insights

  • 4 - Pandas for Data Science
  • 1 -DataScience salaries 2024.csv
  • 1 -Reading CSV Files with Pandas
  • 1 -titanic.csv
  • 2 -Using Describe to Summarize Data
  • 3 -Algebraic Operations in Pandas
  • 4 -Renaming Columns
  • 5 -Handling Missing Values
  • 6 -Counting Values Understanding Data Distribution
  • 7 -Grouping Data Aggregating Insights
  • 8 -Filtering Data in Pandas
  • 9 -Applying Functions to Data
  • 10 -Converting Dates in Pandas
  • 10 -web sales.zip
  • 11 -Plotting Data with Pandas
  • 12 -Test your Knowledge Pandas Q & A

  • 5 - Using ChatGPT as an Assistant in Data Science
  • 1 -Introduction Signing Up for ChatGPT
  • 2 -Assigning a Role for ChatGPT
  • 3 -Crafting Effective Instructions for ChatGPT
  • 4 -Enhancing Responses by Providing Context
  • 5 -Improving Responses with Few-Shot Examples
  • 6 -Limitations and Considerations When Using ChatGPT
  • 7 -Practical Data Analysis with ChatGPT (Part 1)
  • 8 -Practical Data Analysis with ChatGPT (Part 2)

  • 6 - Data Visualization in Python for Data Science
  • 1 -Introduction to Line Plots
  • 2 -Creating Histograms
  • 3 -Customizing Plot Size (Figsize)
  • 4 -Formatting Your Plots
  • 5 -Correlation Explained
  • 6 -2018-2019 Happiness.csv
  • 6 -Building Basic Scatter Plots
  • 7 -Creating Subplots
  • 8 -Box Plots for Data Spread and Outliers
  • 9 -Using Violin Plots for Distribution
  • 10 -Visualizing Categorical Data with Bar Plots
  • 11 -Advanced Scatter Plots with Seaborn
  • 12 -Correlation Heatmaps
  • 13 -Using Pair Plots for Multi-Variable Relationships

  • 7 - Introduction to Machine Learning
  • 1 -Understanding the Machine Learning Lifecycle
  • 2 -Supervised and Unsupervised Learning
  • 3 -Supervised Learning Explained
  • 4 -Unsupervised Learning Explained
  • 5 -Practical Example of Linear Regression in Python - Part 1
  • 5 -house data.csv
  • 6 -Practical Example of Linear Regression in Python - Part 2

  • 8 - Hands-On Python Project California Housing Data Analysis and Modeling
  • 1 -Data Import and Initial Analysis
  • 1 -housing.csv
  • 2 -Preparing Categorical Data with One-Hot Encoding
  • 3 -Mapping Geographic Data with Longitude and Latitude
  • 4 -Scaling Data with Log Transformation
  • 5 -Feature Engineering
  • 6 -Understanding Multicollinearity
  • 7 -Detecting Multicollinearity with a Heatmap
  • 8 -Training the Regression Model
  • 9 -Evaluating Model Performance with R-Squared
  • 10 -Understanding Mean Squared Error (MSE)
  • 11 -Introduction to Random Forests
  • 12 -Applying Random Forest to the Housing Project
  • 13 -Exploring Feature Importance in Random Forests

  • 9 - Hypothesis Testing in Data Science
  • 1 -Introduction to Hypothesis Testing
  • 2 -Understanding Null and Alternative Hypotheses
  • 3 -Exploring t-Tests and z-Tests
  • 4 -Understanding the P-Value
  • 5 -Practical Example of Hypothesis Testing with Python

  • 10 - Azure Machine Learning Project Build a Loan Approval Classification Model
  • 1 -Signing Up and Getting Started with Azure
  • 2 -Optimizing and Managing Azure Costs
  • 3 -Setting Up Your Workspace and Compute Environment
  • 4 -Creating and Importing Data Assets
  • 4 -Loan.csv
  • 5 -Design the Model in Azure Machine Learning Designer
  • 6 -Interpreting the Confusion Matrix for Model Evaluation
  • 7 -Measuring Model Accuracy and AUC
  • 8 -Evaluating Model Precision, Recall, and F1 Score
  • 9 -Final Model Evaluation and Insights
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    مدت زمان: 530 دقیقه
    تاریخ انتشار: ۸ اسفند ۱۴۰۳
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