Data Science For Beginners - Python & Azure Ml With Projects

Practical Data Science: Machine Learning, AI, Cloud Computing, and Data Analysis in Python and Azure ML

What you'll learn

Hands On Learning of Data Analysis and Manipulation in Python

Understand and Apply Key Statistical Concepts

Visualize Data to Extract Insights using Matplotlib and Seaborn

Develop and Evaluate Machine Learning Models with Python and Azure Machine Learning Studio

Experience with Cloud Computing and Natural Language Processing

Requirements

There are no prerequisites for this course - it's designed for beginners

All you need is a computer, an internet connection, and a willingness to learn

Description

"Data Science for Beginners - Python & Azure ML with Projects" is a hands-on course that introduces the essential skills needed to work in data science. Designed for beginners, this course covers Python programming, data analysis, statistics, machine learning, and cloud computing with Azure. Each topic is taught through practical examples, real-world datasets, and step-by-step guidance, making it accessible and engaging for anyone starting out in data science.What You Will LearnPython Programming Essentials: Start with a foundation in Python, covering essential programming concepts such as variables, data types, functions, and control flow. Python is a versatile language widely used in data science, and mastering these basics will help you perform data analysis and build machine learning models confidently.Data Cleaning and Analysis with Pandas: Get started with data manipulation and cleaning using Pandas, a powerful data science library. You’ll learn techniques for importing, exploring, and transforming data, enabling you to analyze data effectively and prepare it for modeling.Statistics for Data Science: Build your knowledge of key statistical concepts used in data science. Topics include measures of central tendency (mean, median, mode), measures of variability (standard deviation, variance), and hypothesis testing. These concepts will help you understand and interpret data insights accurately.Data Visualization: Gain hands-on experience creating visualizations with Matplotlib and Seaborn. You’ll learn to make line plots, scatter plots, bar charts, heatmaps, and more, enabling you to communicate data insights clearly and effectively.Practical, Real-World ProjectsThis course emphasizes learning by doing, with two in-depth projects that simulate real-world data science tasks:California Housing Data Analysis: In this project, you’ll work with California housing data to perform data cleaning, feature engineering, and analysis. You’ll build a regression model to predict housing prices and evaluate its performance using metrics like R-squared and Mean Squared Error (MSE). This project provides a full-cycle experience in working with data, from exploration to model evaluation.Loan Approval Model in Azure ML: In the second project, you’ll learn how to create, deploy, and test a machine learning model on the cloud using Azure Machine Learning. You’ll build a classification model to predict loan approval outcomes, mastering concepts like data splitting, accuracy, and model evaluation with metrics such as precision, recall, and F1-score. This project will familiarize you with Azure ML, a powerful tool used in industry for cloud-based machine learning.Customer Churn Analysis and Prediction: In this project, you will analyze customer data to identify patterns and factors contributing to churn in a banking environment. You’ll clean and prepare the dataset, then build a predictive model to classify customers who are likely to leave the bank. By learning techniques such as feature engineering, model training, and evaluation, you will utilize metrics like accuracy, precision, recall, and F1-score to assess your model's performance. This project will provide you with practical experience in data analysis and machine learning, giving you the skills to tackle real-world challenges in customer retentionMachine Learning and Cloud ComputingMachine Learning Techniques: This course covers the foundational machine learning techniques used in data science. You’ll learn to build and apply models like linear regression and random forests, which are among the most widely used models in data science for regression and classification tasks. Each model is explained step-by-step, with practical examples to reinforce your understanding.Cloud Computing with Azure ML: Get introduced to the world of cloud computing and learn how Azure Machine Learning (Azure ML) can simplify model building, deployment, and scaling. You’ll explore how to set up an environment, work with data assets, and run machine learning experiments in Azure. Learning Azure ML will prepare you for a cloud-based data science career and give you skills relevant to modern data science workflows.Additional FeaturesUsing ChatGPT as a Data Science Assistant: Discover how to leverage AI in your data science journey by using ChatGPT. You’ll learn techniques for enhancing productivity, drafting data queries, and brainstorming ideas with AI, making it a valuable assistant for your future projects.Testing and Practice: Each section includes quizzes and practice exercises to reinforce your learning. You’ll have the opportunity to test your understanding of Python, data analysis, and machine learning concepts through hands-on questions and real coding challenges.By the end of this course, you’ll have completed practical projects, gained a strong foundation in Python, and developed skills in data science workflows that are essential in today’s data-driven world. Whether you’re looking to start a career in data science, upskill, or explore a new field, this course offers the knowledge and hands-on experience you need to get started.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 The Data Scientist Role

Section 2: Introduction to Python for Data Science

Lecture 3 Setting Up Your Python Environment with Anaconda and Jupyter Notebook

Lecture 4 Jupyter Notebook Overview

Lecture 5 Download All Notebooks Here

Lecture 6 Understanding Variables in Python

Lecture 7 Data Types and Their Importance

Lecture 8 Working with Lists

Lecture 9 Exploring Dictionaries

Lecture 10 Tuples and Sets

Lecture 11 Introduction to Arithmetic and Comparison Operators

Lecture 12 Conditional Statements in Python

Lecture 13 Using For Loops

Lecture 14 Combining For Loops with Conditional Statements

Lecture 15 Defining Functions in Python

Lecture 16 Test your Knowledge: Python Basics Q & A

Section 3: Statistics Fundamentals for Data Science

Lecture 17 Descriptive Statistics: Mean, Median, and Mode Explained

Lecture 18 Measuring Spread: Standard Deviation and Variance

Lecture 19 Understanding Sampling Techniques in Data Science

Lecture 20 Understanding Variables

Lecture 21 Frequency Distribution: Organizing Data for Insights

Section 4: Pandas for Data Science

Lecture 22 Reading CSV Files with Pandas

Lecture 23 Using Describe to Summarize Data

Lecture 24 Algebraic Operations in Pandas

Lecture 25 Renaming Columns

Lecture 26 Handling Missing Values

Lecture 27 Counting Values: Understanding Data Distribution

Lecture 28 Grouping Data: Aggregating Insights

Lecture 29 Filtering Data in Pandas

Lecture 30 Applying Functions to Data

Lecture 31 Converting Dates in Pandas

Lecture 32 Plotting Data with Pandas

Lecture 33 Test your Knowledge: Pandas Q & A

Section 5: Using ChatGPT as an Assistant in Data Science

Lecture 34 Introduction: Signing Up for ChatGPT

Lecture 35 Assigning a Role for ChatGPT

Lecture 36 Crafting Effective Instructions for ChatGPT

Lecture 37 Enhancing Responses by Providing Context

Lecture 38 Improving Responses with Few-Shot Examples

Lecture 39 Limitations and Considerations When Using ChatGPT

Lecture 40 Practical Data Analysis with ChatGPT (Part 1)

Lecture 41 Practical Data Analysis with ChatGPT (Part 2)

Section 6: Data Visualization in Python for Data Science

Lecture 42 Introduction to Line Plots

Lecture 43 Creating Histograms

Lecture 44 Customizing Plot Size (Figsize)

Lecture 45 Formatting Your Plots

Lecture 46 Correlation Explained

Lecture 47 Building Basic Scatter Plots

Lecture 48 Creating Subplots

Lecture 49 Box Plots for Data Spread and Outliers

Lecture 50 Using Violin Plots for Distribution

Lecture 51 Visualizing Categorical Data with Bar Plots

Lecture 52 Advanced Scatter Plots with Seaborn

Lecture 53 Correlation Heatmaps

Lecture 54 Using Pair Plots for Multi-Variable Relationships

Section 7: Introduction to Machine Learning

Lecture 55 Understanding the Machine Learning Lifecycle

Lecture 56 Supervised and Unsupervised Learning

Lecture 57 Supervised Learning Explained

Lecture 58 Unsupervised Learning Explained

Lecture 59 Practical Example of Linear Regression in Python - Part 1

Lecture 60 Practical Example of Linear Regression in Python - Part 2

Section 8: Hands-On Python Project: California Housing Data Analysis and Modeling

Lecture 61 Data Import and Initial Analysis

Lecture 62 Preparing Categorical Data with One-Hot Encoding

Lecture 63 Mapping Geographic Data with Longitude and Latitude

Lecture 64 Scaling Data with Log Transformation

Lecture 65 Feature Engineering

Lecture 66 Understanding Multicollinearity

Lecture 67 Detecting Multicollinearity with a Heatmap

Lecture 68 Training the Regression Model

Lecture 69 Evaluating Model Performance with R-Squared

Lecture 70 Understanding Mean Squared Error (MSE)

Lecture 71 Introduction to Random Forests

Lecture 72 Applying Random Forest to the Housing Project

Lecture 73 Exploring Feature Importance in Random Forests

Section 9: Hypothesis Testing in Data Science

Lecture 74 Introduction to Hypothesis Testing

Lecture 75 Understanding Null and Alternative Hypotheses

Lecture 76 Exploring t-Tests and z-Tests

Lecture 77 Understanding the P-Value

Lecture 78 Practical Example of Hypothesis Testing with Python

Section 10: Azure Machine Learning Project: Build a Loan Approval Classification Model

Lecture 79 Signing Up and Getting Started with Azure

Lecture 80 Optimizing and Managing Azure Costs

Lecture 81 Setting Up Your Workspace and Compute Environment

Lecture 82 Creating and Importing Data Assets

Lecture 83 Design the Model in Azure Machine Learning Designer

Lecture 84 Interpreting the Confusion Matrix for Model Evaluation

Lecture 85 Measuring Model Accuracy and AUC

Lecture 86 Evaluating Model Precision, Recall, and F1 Score

Lecture 87 Final Model Evaluation and Insights

Section 11: Building and Comparing Models in Azure ML Designer

Lecture 88 Setting Up the Model Pipeline in Azure ML Designer: Part 1

Lecture 89 Setting Up the Regression Model Pipeline in Azure ML Designer: Part 2

Lecture 90 Evaluating and Comparing the Regression Models

Lecture 91 Deploying an Inference Pipeline in Azure ML Designer

Section 12: Automated Machine Learning (AutoML) Project in Azure

Lecture 92 Dataset Overview

Lecture 93 Setting Up an AutoML Job

Lecture 94 Evaluating the Best Performing Algorithm

Section 13: Natural Language Processing with Azure Language Studio

Lecture 95 Creating a Resource Group in Azure

Lecture 96 Setting Up a Language Resource in Azure

Lecture 97 Introduction to Natural Language Processing Concepts

Lecture 98 Extracting Key Information Using Azure Language Studio

Lecture 99 Analyzing Sentiment and Summarizing Text

Lecture 100 Hands-On Project: Training a Text Classification Model with Our Own Data

Lecture 101 Hands-On Project: Testing and Evaluating Our Text Classification Model

Section 14: Final Project: Bank Churn Analysis and Prediction

Lecture 102 Project Introduction and Notebook Setup

Lecture 103 Exploring Features: Investigate the Customer Data

Lecture 104 Exploratory Data Analysis 1: Understanding the Discrete and Categorical Features

Lecture 105 Exploratory Data Analysis 2: Understanding the Continuous Features

Lecture 106 Exploratory Data Analysis 3: Creating Box and Count Plots for Analysis

Lecture 107 Data Preparation: Getting the Data Ready for Modeling

Lecture 108 Building a Logistic Regression Model to Predict Customer Churn

Lecture 109 Building a Logistic Regression Model to Predict Customer Churn and Compare

This course is perfect for beginners who are curious about data science and want a hands-on introduction to this exciting field.,It’s ideal for students, career changers, and professionals from non-technical backgrounds who are looking to build a solid foundation in data science skills, including Python programming, data analysis, statistics, machine learning and cloud computing.