Complete Data Science Bootcamp 2024: Python to Azure ML
Focused View
8:49:09
1 -Introduction.mp4
02:11
2 -The Data Scientist Role.mp4
06:54
1 -Setting Up Your Python Environment with Anaconda and Jupyter Notebook.mp4
04:50
2 -Jupyter Notebook Overview.mp4
08:03
3 -Notebooks.zip
3 -Understanding Variables in Python.mp4
09:02
3 - Download All Notebooks Here.html
4 -Data Types and Their Importance.mp4
03:47
5 -Working with Lists.mp4
03:03
6 -Exploring Dictionaries.mp4
04:12
7 -Tuples and Sets.mp4
05:42
8 -Introduction to Arithmetic and Comparison Operators.mp4
04:35
9 -Conditional Statements in Python.mp4
06:27
10 -Using For Loops.mp4
04:28
11 -Combining For Loops with Conditional Statements.mp4
08:11
12 -Defining Functions in Python.mp4
09:21
13 -Test your Knowledge Python Basics Q & A.mp4
10:23
1 -Descriptive Statistics Mean, Median, and Mode Explained.mp4
05:24
2 -Measuring Spread Standard Deviation and Variance.mp4
03:30
3 -Understanding Sampling Techniques in Data Science.mp4
09:29
4 -Understanding Variables.mp4
05:20
5 -Frequency Distribution Organizing Data for Insights.mp4
03:38
1 -DataScience salaries 2024.csv
1 -Reading CSV Files with Pandas.mp4
06:34
1 -titanic.csv
2 -Using Describe to Summarize Data.mp4
08:02
3 -Algebraic Operations in Pandas.mp4
06:51
4 -Renaming Columns.mp4
06:27
5 -Handling Missing Values.mp4
11:55
6 -Counting Values Understanding Data Distribution.mp4
04:39
7 -Grouping Data Aggregating Insights.mp4
06:25
8 -Filtering Data in Pandas.mp4
09:33
9 -Applying Functions to Data.mp4
08:38
10 -Converting Dates in Pandas.mp4
08:33
10 -web sales.zip
11 -Plotting Data with Pandas.mp4
05:51
12 -Test your Knowledge Pandas Q & A.mp4
08:40
1 -Introduction Signing Up for ChatGPT.mp4
04:00
2 -Assigning a Role for ChatGPT.mp4
04:11
3 -Crafting Effective Instructions for ChatGPT.mp4
04:17
4 -Enhancing Responses by Providing Context.mp4
06:05
5 -Improving Responses with Few-Shot Examples.mp4
07:21
6 -Limitations and Considerations When Using ChatGPT.mp4
02:21
7 -Practical Data Analysis with ChatGPT (Part 1).mp4
10:58
8 -Practical Data Analysis with ChatGPT (Part 2).mp4
05:19
1 -Introduction to Line Plots.mp4
11:51
2 -Creating Histograms.mp4
08:11
3 -Customizing Plot Size (Figsize).mp4
02:37
4 -Formatting Your Plots.mp4
06:31
5 -Correlation Explained.mp4
04:05
6 -2018-2019 Happiness.csv
6 -Building Basic Scatter Plots.mp4
08:13
7 -Creating Subplots.mp4
10:06
8 -Box Plots for Data Spread and Outliers.mp4
05:41
9 -Using Violin Plots for Distribution.mp4
02:44
10 -Visualizing Categorical Data with Bar Plots.mp4
04:58
11 -Advanced Scatter Plots with Seaborn.mp4
05:41
12 -Correlation Heatmaps.mp4
09:09
13 -Using Pair Plots for Multi-Variable Relationships.mp4
08:04
1 -Understanding the Machine Learning Lifecycle.mp4
06:15
2 -Supervised and Unsupervised Learning.mp4
04:42
3 -Supervised Learning Explained.mp4
05:27
4 -Unsupervised Learning Explained.mp4
05:11
5 -Practical Example of Linear Regression in Python - Part 1.mp4
09:18
5 -house data.csv
6 -Practical Example of Linear Regression in Python - Part 2.mp4
13:41
1 -Data Import and Initial Analysis.mp4
08:46
1 -housing.csv
2 -Preparing Categorical Data with One-Hot Encoding.mp4
08:07
3 -Mapping Geographic Data with Longitude and Latitude.mp4
03:09
4 -Scaling Data with Log Transformation.mp4
05:14
5 -Feature Engineering.mp4
01:45
6 -Understanding Multicollinearity.mp4
02:45
7 -Detecting Multicollinearity with a Heatmap.mp4
08:27
8 -Training the Regression Model.mp4
05:29
9 -Evaluating Model Performance with R-Squared.mp4
07:47
10 -Understanding Mean Squared Error (MSE).mp4
04:21
11 -Introduction to Random Forests.mp4
04:41
12 -Applying Random Forest to the Housing Project.mp4
04:56
13 -Exploring Feature Importance in Random Forests.mp4
06:35
1 -Introduction to Hypothesis Testing.mp4
03:08
2 -Understanding Null and Alternative Hypotheses.mp4
02:56
3 -Exploring t-Tests and z-Tests.mp4
02:24
4 -Understanding the P-Value.mp4
02:53
5 -Practical Example of Hypothesis Testing with Python.mp4
06:01
1 -Signing Up and Getting Started with Azure.mp4
02:37
2 -Optimizing and Managing Azure Costs.mp4
03:38
3 -Setting Up Your Workspace and Compute Environment.mp4
04:48
4 -Creating and Importing Data Assets.mp4
06:35
4 -Loan.csv
5 -Design the Model in Azure Machine Learning Designer.mp4
14:15
6 -Interpreting the Confusion Matrix for Model Evaluation.mp4
02:18
7 -Measuring Model Accuracy and AUC.mp4
04:00
8 -Evaluating Model Precision, Recall, and F1 Score.mp4
06:32
9 -Final Model Evaluation and Insights.mp4
07:27
More details
Course Overview
This hands-on bootcamp teaches complete beginners practical data science skills using Python, Tableau, and Azure ML, with real-world projects to build your portfolio.
What You'll Learn
- Master Python for data analysis and visualization
- Build and deploy ML models with Azure Machine Learning
- Create interactive dashboards in Tableau
Who This Is For
- Absolute beginners with no coding experience
- Career changers entering data science
- Professionals adding data skills to their toolkit
Key Benefits
- No prerequisites - start from absolute basics
- Hands-on projects with real datasets
- Cloud computing experience with Azure ML
Curriculum Highlights
- Python fundamentals for data science
- Statistical analysis and visualization
- Machine learning and cloud deployment
Focused display
- language english
- Training sessions 86
- duration 8:49:09
- Release Date 2025/04/19
