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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تولید کننده: Udemy