Data Science Projects

Build a deep learning model to predict the listing gains of IPOs in the Indian market.

Apply decision trees and random forests to predict the number of bike rentals.

A guided project to finish the data presentation module.

Practice creating data visualizations in this guided project.

Guided Project — Data Prep in Tableau

Work with retail sales data to explore trends and relationships. Build basic models to confirm the statistical significance of your insights.

In this project, we’ll conduct an Exploratory Data Analysis (EDA) on data from a telecommunications provider to create profiles on customers that are at risk of churn.

In this project, we will apply the knowledge and skills we developed in the preceding lessons to present stock data clearly and help answer important questions about the underlying data.

In this guided project, you’ll import a dataset, organize it, clean it, and then consolidate it into a single table, preparing it for analysis.

Build a business intelligence app in Power BI that visualizes course quality and completion data for Dataquest courses.

Apply what you’ve learned to a real-world database.

Apply data cleaning and data model building to real-world scenarios

Learn how to create and use a machine learning workflow with Kaggle’s ‘Titanic’ Competition

Use machine learning techniques to predict the price of the SP500.

Practice building and improving linear regression models

Practice the machine learning workflow using k-nearest neighbors to predict car prices.

Work on a real-life project using SQL

Create a Shiny app to showcase your personal data projects

Combine the skills you learned in this course to perform linear regression modeling of condominium sale price on the basis of size.

Learn how to analyze text while figuring out strategies to win at Jeopardy.

Learn to use conditional probability and Naive Bayes in a practical setting.

Learn to use probability and combinatorics in a practical setting.

Learn to combine the skills you learned in this course to perform practical data analysis.

Learn to combine the skills you learned in this course to perform practical data analysis.

Learn to combine the skills you learned in this course to perform practical web data extraction.

Practice the SQL data analysis workflow.

Learn how to design, create, and populate a normalized SQL database.

Learn to combine the skills you learned in this course to perform practical data extraction.

Practice your data cleaning and analysis skills and learn to use R Notebooks as you explore survey data.

Use data visualization techniques to explore data on forest fires.

Employ even more programming techniques to create a reusable data workflow.

Apply control flow, loops and functions to create a reusable data workflow.

Learn to combine the skills you learned in this course to perform practical data analysis.

Learn how to install and use RStudio, a free and open-source development environment for R.

Learn how to use a data pipeline to summarize hacker news data.

Use a B-Tree to implement a key-value datastore in Python.

Use the correct data structures to analyze stock prices efficiently

Use stacks to evaluate numerical expressions.

Use MapReduce to analyze Wikipedia pages more quickly.

Practice optimizing DataFrame types and working in chunks.

Practice analyzing data using the pandas SQLite workflow.

Learn how to install PostgreSQL and the Psycopg2 library.

Apply what you have learned to implement an inventory system for a laptop store with efficient queries.

Practice using loops, cleaning strings, and working with dates in python.

Learn the basics of Jupyter Notebook

Learn to combine the skills you learned in this course to perform practical data analysis.

Learn how to set up PySpark and integrate it with Jupyter Notebook.

Learn the basics of image classification to build a handwriting classifier.

In this guided project, we will predict the damage done by forest fires and improve upon an existing model. 

In this project, using the “Productivity Prediction of Garment Employees” dataset from the UCI Machine Learning Repository, we’ll determine the best working conditions to reach the expected productivity thresholds in a garment factory.

In this guided project, you will practice the machine learning workflow and practice creating and optimizing a logistic regression to detect heart disease. 

In this project, you will load, explore, and prepare a dataset to build a stochastic gradient descent regression model (linear regression), and then you will measure the efficiency of the model and visualize the results.

In this guided project, practice linear regression modeling and evaluation.

For this project, we’ll build a clustering model to segment credit card customers into different groups in order to apply different solutions for each type of customer. 

Build a K Nearest Neighbors classifier to predict whether patients might be at risk of heart disease.

Learn how to install Git and authenticate with GitHub.

Apply the skills you’ve learned to determine the creation of data science content.

Practice using SQL to analyze CIA Factbook data.

Perform additional analysis on a clean dataset with maps and visualizations.

Use survey data to better understand Star Wars fans.

Practice data cleaning using pandas.

Combine the skills you’ve learned to create a storytelling data visualization.

Learn to analyze data using exploratory data visualization.

Practice data cleaning and data exploration using pandas

Learn the basics of Jupyter Notebook by analyzing a dataset on helicopter prison escapes