Master Data Science with 10 Real-World Projects

What you will learn:

Master data preprocessing, visualization, and insightful analysis of complex datasets.
Develop proficiency in training, evaluating, and deploying machine learning models for accurate predictions.
Gain expertise in sentiment analysis using natural language processing (NLP) techniques.
Become proficient in time series analysis, including data preprocessing, visualization, and forecasting model building.
Scale your skills to handle big data using Apache Spark in a distributed computing environment.
Apply machine learning to real-world scenarios such as customer churn prediction, image classification, fraud detection, and housing price prediction.
Construct a compelling data science portfolio showcasing practical project experience.
Prepare for a successful career transition into the field of data science and machine learning.

Description

Launch your data science career with this intensive, project-based course! Learn by doing with 10 comprehensive projects covering key data science techniques. You'll master crucial skills like exploratory data analysis (EDA), predictive modeling, sentiment analysis, time series forecasting, big data analytics with Apache Spark, and more.

Each project uses real-world datasets, providing valuable experience for your portfolio and future job applications. Build your skills step-by-step with detailed instructions and expert guidance. You'll gain proficiency in popular tools and libraries, including Python and machine learning algorithms. From analyzing customer churn to building image classification models and tackling fraud detection challenges, this course will equip you with practical, in-demand skills to succeed in the field.

Projects Include: Google App Store Data EDA, Sentiment Analysis of Text Data, Titanic Dataset Predictive Modeling, Bitcoin Price Forecasting, Big Data Analytics with Apache Spark, Tabular Playground Series Challenges, Customer Churn Prediction, Cat vs Dog Image Classification, Fraud Detection, and Housing Price Prediction.

Don't just learn the theory – build a portfolio that will impress! Enroll now and transform your data science career.

Curriculum

Introduction

This introductory section sets the stage for the course, providing a brief overview of the program's structure and goals. The lecture provides essential context to ensure a smooth transition into the practical projects that follow. It covers an introduction to the course and how it is structured to be beneficial to the student

Project 1: Exploratory Data Analysis

This project dives into the essential skill of exploratory data analysis (EDA) using the Google App Store dataset. You will learn data cleaning, preprocessing techniques, effective data visualization strategies, statistical analysis, hypothesis testing, and finally data storytelling, concluding with an assignment to solidify your understanding and application of these techniques.

Project 2: Sentiment Analysis

Master natural language processing (NLP) techniques in this project focused on sentiment analysis. You will learn to preprocess text data, extract relevant features, build sentiment analysis models, and rigorously evaluate their performance. This section will cover an introduction to sentiment analysis and NLP, text preprocessing for sentiment analysis, feature extraction for sentiment analysis, building sentiment analysis models and the evaluation of sentiment analysis models.

Project 3: Predictive Modeling

Develop predictive modeling skills using the Titanic dataset. You'll learn to explore and preprocess data, select and evaluate suitable machine learning models, fine-tune hyperparameters for optimal performance, and deploy your model. This includes an introduction to predictive modeling and machine learning, data exploration and preprocessing of the Titanic Dataset, model selection and evaluation of the Titanic Dataset, model training and hyperparameter tuning of the Titanic Dataset, and finally deployment of the predictive models of the Titanic Dataset. This section concludes with an assignment to further reinforce learned concepts.

Project 4: Time Series Analysis

This project explores the intricacies of time series analysis. You'll master data preprocessing, visualization techniques, and build robust forecasting models using real-world examples. This project will delve into the introduction, data preprocessing and cleaning, visualizing time series data, building and evaluating forecasting models and predicting future Bitcoin prices.

Project 5: Big Data Analytics

Scale your skills to handle large datasets with Apache Spark. This project covers big data exploration, preprocessing, transformation, feature engineering, visualization, and analysis techniques. The curriculum is broken down into an introduction to Big Data Analytics and Apache Spark, Big Data Data Exploration and Preprocessing, Big Data Transformation and Feature Engineering, Big Data Visualization and Analysis, and finally Conclusion and Next Steps.

Project 6: Tabular Playground Series Analysis

Tackle real-world datasets from the Tabular Playground Series. You'll practice data reading, preprocessing, transformation, visualization, model selection (e.g., XGBoost), training, prediction, and submission techniques. This project teaches students how to read and preprocess data, data transformation and visualization, train-test split and model selection, model training with XGBoost, and making predictions and submission.

Project 7: Customer Churn Prediction

Learn to predict customer churn using machine learning. This project will cover an introduction to customer churn prediction, feature selection and model building, advanced techniques for churn prediction, ensemble methods and model evaluation, and finally model interpretation, deployment, and next steps. You’ll develop effective retention strategies by analyzing customer behavior and identifying potential churners.

Project 8: Cats vs Dogs Image Classification

Enter the world of computer vision with image classification. You'll learn to download and preprocess image data, build and validate a Convolutional Neural Network (CNN) model to classify images of cats and dogs. This section covers downloading Kaggle data in Google Collab, creating directories and the images data, image data preprocessing and visualization with Python, and finally creating and validating the model using CNN.

Project 9: Fraud Detection

Become a fraud detection expert by building a robust machine learning model. You'll learn anomaly detection techniques, feature engineering, model evaluation, and deployment strategies. The curriculum covers introducing fraud detection and conducting exploratory data analysis, model building for fraud detection, advanced techniques for fraud detection, model evaluation and interpretability, and finally model deployment.

Project 10: House Prices Prediction

Predict housing prices using machine learning. You'll handle data preprocessing, cleaning, exploratory data analysis (EDA), model building, and model validation. This section includes an introduction to house prices prediction, housing data processing & cleaning for ML model, doing EDA (exploratory data analysis) using data visualization, building a model for the housing data and finally validating the model.

Bonus

A concluding section expressing gratitude for student participation and offering additional resources or support.