End-To-End Data Analytics From Raw Data To Cloud Deployment

[martinstronis65]

End-To-End Data Analytics: From Raw Data To Cloud Deployment
Published 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.91 GB | Duration: 5h 55m​

Build, Analyze, Deploy Real-World Data Analytics Projects -from Data Wrangling to Full-Scale Deployment

What you'll learn
Understand the Roles in Data Analytics: Distinguish between Data Analysts, Data Scientists, and Data Engineers.
Understand the End-to-End Data Analytics Workflow
Develop a Data-Driven Mindset: Make decisions based on data insights, statistical evidence, and predictive models.
Extract and import data
Clean and Preprocess Raw Data: Handle missing values, duplicates, and incorrect data types to prepare data for analysis.
Transform and Normalize Data: Scale and normalize numerical features for machine learning models.
Apply Feature Engineering: Create new features from raw data to improve model accuracy
Create Data Visualizations: Generate bar charts, histograms, scatter plots, heatmaps, and pie charts to visualize patterns and relationships.
Conduct Correlation Analysis: Identify and visualize the relationships between variables using correlation heatmaps.
Use Pandas and NumPy for EDA: Use Pandas for filtering, sorting, and aggregating data, and NumPy for advanced calculations.
Feature Encoding for Categorical Data: Apply techniques like One-Hot Encoding, Label Encoding, and Frequency Encoding for categorical variables.
Apply Feature Scaling and Normalization: Scale features using MinMaxScaler or StandardScaler to prepare data for machine learning.
Create Interaction Features: Generate new features by combining multiple features to create non-linear relationships.
Understand the Basics of Machine Learning: Learn the difference between supervised, unsupervised, and reinforcement learning.
Build and Train Machine Learning Models: Train and evaluate models like Logistic Regression
Split Data into Training and Testing Sets: Use train-test split to ensure models generalize well to new data.
Evaluate Model Performance: Use metrics like accuracy, precision, recall, and F1-score to evaluate models.
Generate Visual Reports Using Python: Use Matplotlib, Seaborn, and Plotly to create interactive visual reports.
Build a Flask Web Application: Use Flask to create web applications that display predictions and visualizations.
Create RESTful APIs: Build simple REST APIs to accept inputs from users and return predictions from machine learning models
Save and Load Trained Machine Learning Models: Save models using Pickle (.pkl files) and load them for use in Flask apps.
Deploy Models as Web Applications: Deploy trained machine learning models to the cloud using Flask.

Requirements
Basic Python Programming Skills
Data Analysis and Visualization Basics
Computer with internet connection.

Description
Are you ready to master the full lifecycle of data analytics and showcase a complete end-to-end project in your portfolio? This comprehensive, project-based course takes you on a journey from data collection to cloud deployment, giving you the technical skills, tools, and confidence to excel in the field of data analytics, machine learning, and model deployment.In this course, you will learn how to collect, clean, analyze, model, and deploy data analytics projects using industry-leading tools like Python, Pandas, NumPy, Scikit-learn, Flask, and SQL. You'll gain hands-on experience with real-world datasets, develop essential machine learning skills, and learn how to create and deploy a live, interactive web application that showcases your work.The course is designed to provide you with a complete, end-to-end experience, enabling you to work on a real-world project from scratch. By the end of the course, you will have a fully deployable web app that allows users to interact with your predictive model - a powerful project to add to your professional portfolio. This project will demonstrate your ability to handle every aspect of the data analytics workflow, from raw data ingestion to live model deployment.This course follows a step-by-step, hands-on approach, making it suitable for beginners, aspiring data analysts, business analysts, and career changers. Each module covers a key phase of the data analytics process, starting with data collection and data wrangling, followed by exploratory data analysis (EDA), and progressing to machine learning model development and model evaluation. You'll also learn Flask web development to transform your model into an interactive web application, and finally, you'll deploy your project to the cloud using platforms like PythonAnywhere, AWS, or Heroku.This course is packed with practical, hands-on exercises that reinforce every concept. You'll clean and analyze raw datasets, visualize patterns using Matplotlib and Seaborn, train predictive models like Logistic Regression, Decision Trees, and Random Forests, and deploy your project to the web. You'll also learn how to manage model files, APIs, and web servers using Flask, enabling you to develop interactive prediction tools that can be accessed by users worldwide.By the end of the course, you'll be able to:Build a portfolio-ready, end-to-end data analytics project.Create a live web app that users can interact with in real-time.Gain hands-on experience with data analysis, machine learning, and web development.Showcase your ability to handle the entire data analytics lifecycle - a key skill that employers value in data analysts, data scientists, and business analysts.If you're ready to move beyond theory and work on real-world projects that showcase your data analytics skills, this course is for you. Sign up now and transform raw data into valuable insights, deploy machine learning models, and create web apps that make your analysis accessible to the world!

Overview
Section 1: . Introduction to End-to-End Data Analytics

Lecture 1 Introduction

Lecture 2 What is an End-to-End Data Analytics Project?

Lecture 3 Key Roles and Responsibilities of a Data Analyst

Lecture 4 Tools and Technologies Used in End-to-End Projects

Lecture 5 Structured vs. Unstructured Data

Section 2: Project Setup and Environment Preparation

Lecture 6 Python Installation

Lecture 7 What are virtual environments

Lecture 8 Creating and activating a virtual environment

Lecture 9 What is Jupyter Notebook

Lecture 10 Installing Pandas and Jupyter Notebook in the Virtual Environment

Lecture 11 Starting Jupyter Notebook

Lecture 12 Exploring Jupyter Notebook Server Dashboard Interface

Lecture 13 Creating a new Notebook

Lecture 14 Exploring Jupyter Notebook Source and Folder Files

Lecture 15 Exploring the Notebook Interface

Section 3: Data Collection and Import

Lecture 16 What is a Dataset

Lecture 17 Download Dataset

Lecture 18 Loading Dataset into a DataFrame

Lecture 19 Inspecting the data

Section 4: Data Cleaning and Transformation

Lecture 20 Data Cleaning

Lecture 21 Data transformation and analysis

Lecture 22 Visualizing data

Section 5: Data Preparation for Machine Learning

Lecture 23 Installing and importing libraries

Lecture 24 Downloading dataset

Lecture 25 Exploring the Dataset

Lecture 26 Handle missing values and drop unnecessary columns.

Lecture 27 Encode categorical variables.

Section 6: Building and Training Machine Learning Models

Lecture 28 What is Feature Engineering

Lecture 29 Create new features.

Lecture 30 Dropping unnecessary columns

Lecture 31 Visualize survival rate by gender

Lecture 32 Visualize survival rate by class

Lecture 33 Visualize numerical features

Lecture 34 Visualize the distribution of Age

Lecture 35 Visualize number of passengers in each passenger class

Lecture 36 Visualize number of passengers that survived

Lecture 37 Visualize the correlation matrix of numerical variables

Lecture 38 Visualize the distribution of Fare.

Lecture 39 Define features and target variable.

Lecture 40 Split data into training and testing sets.

Lecture 41 Standardize features.

Lecture 42 Train logistic regression model.

Lecture 43 Making Predictions

Section 7: Model Evaluation, Saving, and Reusability

Lecture 44 Evaluate the model using accuracy, confusion matrix, and classification report.

Lecture 45 Visualize the confusion matrix.

Lecture 46 Saving the Model

Lecture 47 Loading the model

Lecture 48 Improving Understanding of the model's prediction

Section 8: Predicting real house prices using machine learning

Lecture 49 Importing Libraries and modules

Lecture 50 Loading dataset and creating a dataframe

Lecture 51 Checking for missing values

Lecture 52 Dropping column and splitting data

Lecture 53 Standardize the features for housing dataframe

Lecture 54 Initialize and train the regression model

Lecture 55 Make predictions on the test set.

Lecture 56 Evaluating the model for the housing dataset.

Lecture 57 Predicting a small sample of data

Lecture 58 Creating scatter plot

Lecture 59 Creating a bar plot

Lecture 60 Saving the housing model

Lecture 61 Loading the housing model

Section 9: Building and deploying a Data Analytics Web Application

Lecture 62 Overview of Flask

Lecture 63 Installing Flask

Lecture 64 Installing Visual Studio Code

Lecture 65 Creating a minimal flask app

Lecture 66 How to run a flask app

Lecture 67 Loading the saved model and scaler into Python file

Lecture 68 Define the home route

Lecture 69 Define the prediction route

Lecture 70 Creating the template

Lecture 71 Adding a form to the template

Lecture 72 Displaying predictions and clearing form inputs

Lecture 73 Testing the prediction tool

Lecture 74 Create a new account on pythonanywhere

Lecture 75 Creating a new web app in PythonAnywhere

Lecture 76 Uploading files to Pythonanywhere

Lecture 77 Creating and activating a virtual environment on PythonAnywhere

Lecture 78 Configuring WSGI File

Lecture 79 Running your app in a cloud hosting environment

Lecture 80 Project files

Aspiring Data Analysts,Entry-Level and Junior Data Analysts,Career Changers and Transitioners,Aspiring Data Scientists and Machine Learning Enthusiasts,Data Enthusiasts and Freelancers,Students and Recent Graduates

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