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Data Science Bundle: 180 Hands-On Projects - Course 1 of 3

Name: Data Science Bundle: 180 Hands-On Projects - Course 1 of 3
Rating: 4.1 (100 reviews)

Build & Deploy 180 Projects - Data Science, Machine Learning, Deep Learning (Python, Flask, Django, AWS, Azure Cloud)

Created byPianalytix • 75,000+ Students Worldwide

Last updated 10/2025

English

What you'll learn

Master the essentials of Machine Learning using Python, the go-to language for Data Science
Learn to build robust Machine Learning models that can withstand real-world uncertainties
Acquire the skills needed to deploy Machine Learning models, making them functional in a live environment
Develop a strong intuition for choosing the right Machine Learning models for different tasks.
Implement popular Machine Learning algorithms from scratch, providing you with a deep understanding of their functionalities.
Use SciKit-Learn, the Python library for Machine Learning, to efficiently complete various tasks.
Explore Matplotlib, the Python plotting library, to visualize your data effectively.
Get exposure to Deep Learning, Transfer Learning, and Neural Networks, expanding your skill set.

Course content

61 sections • 425 lectures • 73h 39m total length

Course introduction video0:31
Introduce the data science bundle and course 1 of 3 through the course introduction video, highlighting 180 hands-on projects.
Outline of the course0:40
Course Bonuses: Cheat Sheets, Downloads, Mind maps, Guides.0:47

Introduction - Forecasting Renewable Energy Generation2:43
Data processing14:40
Execute data processing in a Jupyter notebook by importing numpy, pandas, matplotlib and seaborn, cleaning and scaling data, and selecting wind speed as the main predictor for active power.
Model training and Testing6:12
Explore extreme gradient boosting, an ensemble of decision trees for regression, trained on wind speed to predict active power, with early stopping and an r-squared of 0.97.
Predicting Future Values8:23
Flask App6:56
Build a Flask web app with static and templates folders, submit start and end dates, generate predictions via model.py, plot with matplotlib, save to static/output.png, and render index.html.
Download the project files0:30

Introduction - Predicting Diamond Sales Price2:38
Data Reading and Processing7:56
Load and preprocess data with pandas and numpy, drop invalid columns, encode categorical features with label encoding, remove outliers and zeros, then perform feature selection and an 80/20 train-test split.
Model4:04
Flask App6:37
Create a Flask web app structure and a form to input test values. Load the saved TensorFlow/Keras model with Joblib encoders, preprocess inputs, and display the predicted price.
Download the project files0:28

Introduction - Ethereum Price Prediction using GRU/LSTMs for Forecasting2:17
Forecast Ethereum prices using gru and lstm based recurrent neural networks to analyze time series data, build training data, and deploy a Flask web app.
Data pre processing3:58
Learn practical data preprocessing for time series: import libraries, clean and sort data, drop nonessential columns, engineer date-time features, visualize columns, and explore correlations with the close price.
Feature Creation and processing4:07
Learn to build rolling 24-hour windows to predict the 25th hour, using a 240-feature input with min-max scaling and a time-aware 80/20 train-test split.
Model9:11
Flask App7:41
Learn to build a Flask web app with a static and templates structure, power the index.html form for time-series predictions, and save results as static/output.png.
Download the project files0:29

Introduction3:59
Classify stress levels from photoplethysmography data using heart rate variability features, while cleaning data, selecting features, and building an artificial neural network to deploy as a flask web app.
Reading the Dataset2:42
Import libraries, load the csv, and identify features like mean rr and hf/lf. Scale features, split data 70/30, and one-hot encode labels for the Keras model.
Model5:24
Flask App5:58
Build a Flask web app that serves a model, organize files (model.py, scalar.joblib, tf model.h5, templates), handle post data, and render predictions like no stress, low stress, or high stress.
Download the project files0:27

Introduction2:49
Learn to classify brain scans with convolutional neural networks, clean and process images, and deploy a flask web app for brain tumor detection.
Data Reading and processing3:12
Import and preprocess images with Keras and TensorFlow, using an image data generator flow from directory with rescale and validation split, grayscale 150 by 150 brain tumor versus healthy classifier.
Model9:21
Flask App7:31
Build and run a Flask web app that uploads images, loads a trained Keras model, and predicts brain cancer, rendering results via an HTML template.
Download the project files0:23

Introduction - Age and Gender Prediction from Chest X-Ray Scans using CNN3:14
Predict age and gender from chest x-ray scans using a CNN-based classifier and regressor, learn image cleaning and feature extraction, and deploy a Flask web app.
Data pre processing2:38
Model13:41
Explore convolutional neural networks (CNNs) and their 3D input–output volumes, including convolutional, ReLU, pooling, and dense layers, using TensorFlow and Keras for gender and age prediction.
Flask App7:47
Build a flask web app with static and templates directories, an index.html form to upload png images, and model.py that pre-processes and predicts age and gender.
Download the project files0:23

Introduction3:04
Explore image classification on chest x-rays to detect Covid 19, learn convolutional neural networks, clean images, extract features, and deploy the model as a Flask web app.
Data Preprocessing3:18
Import libraries for image pre-processing and reading, configure a grayscale data generator with rescale, zoom, and flip, and split data for training and validation for COVID-19 and non COVID-19 classes.
Model9:31
Flask App7:16
Download the project files0:21

Introduction - Detecting DeepFakes with ResNet and CNN2:47
Data Preprocessing5:42
import libraries for image pre-processing and model building, set up grayscale image data generators, and organize train, valid, and test directories into a data frame with real versus fake labels.
Model10:31
Flask App7:09
Build a Flask web app to upload images and display model predictions. The tutorial covers project structure, HTML templates, and a cv2 preprocessing pipeline for real or fake face classification.
Download the project files0:22

Introduction - Automatic Number Plate Recognition using ResNet and CNN2:31
Explore automatic number plate recognition by using Inception ResNet and CNNs to detect number plates, process images, and deploy a Flask web app with bounding box coordinates stored in XML.
Data preprocessing5:49
Model11:16
Explore convolutional neural networks and their layers—input, convolution, relu, pooling, and fully connected—and train a bounding-box regression model with mse using inception ResNet and Keras.
Flask App8:38
Build a flask web app with a defined project structure, upload and display input and output jpeg images, and render bounding boxes using a trained h5 model.
Download the project files0:22

Requirements

Basic knowledge of data science

Description

Unleash your data science mastery in this dynamic course! Learn to build and deploy machine learning, AI, NLP models, and more using Python and web frameworks like Flask and Django . Elevate your projects to the cloud with Heroku, AWS, Azure, GCP, IBM Watson, and Streamlit . Get ready to turn data into powerful solutions!

Embark on a dynamic learning experience with our comprehensive course, "Applied Data Science: From Theory to Real-World Impact . " Dive deep into the world of practical machine learning and data-driven projects, where you'll gain the skills to transform theoretical concepts into tangible solutions .

This hands-on program empowers you to tackle complex problems using cutting-edge techniques, guiding you through the entire project lifecycle . From the inception of ideas to data collection, preprocessing, modeling, and deployment, you'll navigate every stage, honing your skills in real-world settings .

Develop proficiency in deploying models across diverse environments, from interactive web applications to critical business systems . Gain insights into the challenges of model deployment and learn to address them effectively . With a strong emphasis on experiential learning, you'll work on actual industry-inspired projects, implementing strategies that yield measurable results .

"Data-Driven Projects" goes beyond the technical aspects, highlighting the integration of data-driven decision-making into various business landscapes . Witness the fusion of data analytics and strategic thinking, driving business impact through informed insights . Whether you're a seasoned data practitioner or a newcomer, this course equips you with the knowledge and confidence to excel in real-world scenarios .

Elevate your data science journey today and become a proficient problem solver, capable of leveraging data for transformative outcomes that make a difference in today's data-rich world .

In This Course, We Are Going To Work On 60 Real World Projects Listed Below:

Project-1: Forecasting Renewable Energy Generation: Time Series and Regression Analysis

Project-2: Predicting Diamond Sales Price with Multiple Regression Methods

Project-3: Ethereum Price Prediction using GRU/LSTMs for Forecasting

Project-4: Detecting Stress Levels from PPG Sensor Data using Neural Networks

Project-5: Classification of Brain Tumors with CNN and OpenCV

Project-6: Age and Gender Prediction from Chest X-Ray Scans using CNN and OpenCV

Project-7: COVID-19 Detection from CT Scans using ResNet, DenseNet, and VGG Models

Project-8: Detecting DeepFakes with ResNet and CNN

Project-9: Automatic Number Plate Recognition using ResNet and CNN

Project-10: Land Segmentation using U-Net Architecture

Project-11: LingoLinx: Unleashing Multilingual Magic - Language Translator App on Heroku

Project-12: AdView Pro: Cracking the Code of Ad View Predictions with IBM Watson on Heroku

Project-13: LappyPricer: Decoding Laptop Prices with Heroku's Predictive Powers

Project-14: TextWise: Unveiling Insights from WhatsApp Text with Heroku's Analytical Arsenal

Project-15: SmartCourse: Guiding Your Academic Journey - Course Recommendation System on Heroku

Project-16: IPL Prophets: Predicting IPL Match Wins with a Touch of Heroku Magic

Project-17: BodyFit: Sculpting Your Body Fat Estimator App on Microsoft Azure

Project-18: CareerPath: Paving the Way to Campus Placement Success on Microsoft Azure

Project-19: AutoCar: Driving the Future of Car Acceptability Prediction on Google Cloud

Project-20: GenreGenius: A Journey into Book Genres with Amazon Web Services

Project-21: DNA Seeker: Unraveling Genetic Clues - E . Coli Classification Adventure on AWS

Project-22: WordWizard: Unleashing Sentence Sorcery - Predicting the Next Word on AWS

Project-23: SeqMaster: Journey into Sequence Prediction - LSTM Adventures on AWS

Project-24: KeywordGenie: Unlocking Textual Treasures - Keyword Extraction using NLP on Azure

Project-25: SpellCheck Plus: Vanishing Typos - Spelling Correction Wizardry on Azure

Project-26: MusicTrends: Dancing with Popularity - Music Popularity Classification on Google App Engine

Project-27: AdClassify: Decoding Advertisements - Advertisement Classification on Google App Engine

Project-28: DigitDetect: Cracking the Code of Image Digits - Image Digit Classification on AWS

Project-29: EmoSense: Delving into Emotions - Emotion Recognition with Neural Networks on AWS

Project-30: CancerGuard: Fighting Against Breast Cancer - Breast Cancer Classification on AWS

Project-31: Unsupervised Clustering of COVID Nucleotide Sequences using K-Means

Project-32: Weed Detection in Soybean Crops using Computer Vision

Project-33: PixelPal: Transforming Images with OpenCV and Tkinter - Image Editor Application

Project-34: BrandQuest: Unveiling Brand Identifications with Tkinter and SQLite - Brand Identification Game

Project-35: TransactionTracker: Monitoring Financial Flows with Tkinter and SQLite - Transaction Application

Project-36: LearnEase: Nurturing Knowledge with Django - Learning Management System

Project-37: NewsWave: Riding the Waves of News - Create A News Portal with Django

Project-38: StudentVerse: Journey into Student Life - Create A Student Portal with Django

Project-39: ProductivityPro: Tracking Progress with Django and Plotly - Productivity Tracker

Project-40: StudyConnect: Forging Study Bonds - Create A Study Group with Django

Power BI Projects:

Project-41: Global Data Professionals Benchmarking Dashboard

Project-42: Beijing Air Quality Dashboard: DAX and Visualizations

Project-43: Real Estate in Daegu: Apartment Pros and Cons Analysis

Project-44: Super Market Sales Analysis: Power Query and DAX

Project-45: COVID-19 WHO Dataset Insights: Power Query and DAX

Project-46: Credit Card Defaulters Analysis: Power Query and DAX

Project-47: Crime in Chicago: 3-Year Analysis with Visualization

Project-48: Customer Churn Analysis: Real-World Business Problem

Project-49: Customer Churn Analysis (Advanced Features): Data Modeling

Project-50: Attrition Analysis: HR Data Transformation and Visualization

Tableau Projects:

Project-51: Revenue Analysis Dashboard: Business Insights and Trends

Project-52: AirBnbs in Seattle: Rental Market Analysis

Project-53: New Year Resolution Tweets: Social Media Analysis

Project-54: Road Accident in the UK: Safety Analysis

Project-55: Ecommerce Sales Dashboard: Sales Optimization

Project-56: Super Store Sales Dashboard: Retail Analysis

Project-57: Credit Card Complaints: Customer Feedback Analysis

Project-58: Data Science Career Dashboard: Job Market Trends

Project-59: Amazon Prime Video Dashboard: Streaming Insights

Project-60: Traffic Collision in Seattle: Safety and Traffic Analysis

Tip: Create A 60 Days Study Plan , Spend 1-3hrs Per Day, Build 60 Projects In 60 Days .

The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career

Note: This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires .

Who this course is for:

Beginners in data science

Data Science Bundle: 180 Hands-On Projects - Course 1 of 3

What you'll learn

Explore related topics

Course content

Introduction to the course3 lectures • 2min

Project-1: Forecasting Renewable Energy Generation: Time Series and Regression6 lectures • 39min

Project-2: Predicting Diamond Sales Price with Multiple Regression Methods5 lectures • 22min

Project-3: Ethereum Price Prediction using GRU/LSTMs for Forecasting6 lectures • 28min

Project-4: Detecting Stress Levels from PPG Sensor Data using Neural Networks5 lectures • 19min

Project-5: Classification of Brain Tumors with CNN and OpenCV5 lectures • 23min

Project-6: Age and Gender Prediction from Chest X-Ray Scans using CNN and OpenCV5 lectures • 28min

Project-7: COVID-19 Detection from CT Scans using ResNet, DenseNet, and VGG Mode5 lectures • 24min

Project-8: Detecting DeepFakes with ResNet and CNN5 lectures • 27min

Project-9: Automatic Number Plate Recognition using ResNet and CNN5 lectures • 29min

Requirements

Description

Who this course is for: