Introducing DataCamp Workspace – Your AI-Powered Data Science Notebook

DataCamp · Beginner ·📊 Data Analytics & Business Intelligence ·2y ago

Key Takeaways

DataCamp Workspace is an AI-powered data science notebook that provides a pre-configured Python and R environment with built-in SQL support, intelligent suggestions, and real-time collaboration features.

Original Description

Try Workspace now: https://www.datacamp.com/workspace Enter Workspace, the AI-powered data notebook that welcomes all data-skill levels. 🚀 Kickstart your work in seconds with a pre-configured Python and R environment, complete with built-in SQL support! 🤖Experience intelligent suggestions based on your existing code and context. The AI Assistant generates code and debugs them for you. 🤝 Effortlessly collaborate with peers using real-time editing and built-in commenting.
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Playlist

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1 SQL Server Tutorial: Date manipulation
SQL Server Tutorial: Date manipulation
DataCamp
 2 R Tutorial: Intermediate Interactive Data Visualization with plotly in R
R Tutorial: Intermediate Interactive Data Visualization with plotly in R
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 3 R Tutorial: Adding aesthetics to represent a variable
R Tutorial: Adding aesthetics to represent a variable
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 4 R Tutorial: Moving Beyond Simple Interactivity
R Tutorial: Moving Beyond Simple Interactivity
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 5 Python Tutorial: Why use ML for marketing? Strategies and use cases
Python Tutorial: Why use ML for marketing? Strategies and use cases
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 6 Python Tutorial: Preparation for modeling
Python Tutorial: Preparation for modeling
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 7 Python Tutorial: Machine Learning modeling steps
Python Tutorial: Machine Learning modeling steps
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 8 R Tutorial: The prior model
R Tutorial: The prior model
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 9 R Tutorial: Data & the likelihood
R Tutorial: Data & the likelihood
DataCamp
 10 R Tutorial: The posterior model
R Tutorial: The posterior model
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 11 R Tutorial: An Introduction to plotly
R Tutorial: An Introduction to plotly
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 12 R Tutorial: Plotting a single variable
R Tutorial: Plotting a single variable
DataCamp
 13 R Tutorial: Bivariate graphics
R Tutorial: Bivariate graphics
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 14 Python Tutorial: Customer Segmentation in Python
Python Tutorial: Customer Segmentation in Python
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 15 Python Tutorial: Time cohorts
Python Tutorial: Time cohorts
DataCamp
 16 Python Tutorial: Calculate cohort metrics
Python Tutorial: Calculate cohort metrics
DataCamp
 17 Python Tutorial: Cohort analysis visualization
Python Tutorial: Cohort analysis visualization
DataCamp
 18 R Tutorial: Building Dashboards with flexdashboard
R Tutorial: Building Dashboards with flexdashboard
DataCamp
 19 R Tutorial: Anatomy of a flexdashboard
R Tutorial: Anatomy of a flexdashboard
DataCamp
 20 R Tutorial: Layout basics
R Tutorial: Layout basics
DataCamp
 21 R Tutorial: Advanced layouts
R Tutorial: Advanced layouts
DataCamp
 22 Python Tutorial: Time Series Analysis in Python
Python Tutorial: Time Series Analysis in Python
DataCamp
 23 Python Tutorial: Correlation of Two Time Series
Python Tutorial: Correlation of Two Time Series
DataCamp
 24 Python Tutorial: Simple Linear Regressions
Python Tutorial: Simple Linear Regressions
DataCamp
 25 Python Tutorial: Autocorrelation
Python Tutorial: Autocorrelation
DataCamp
 26 R Tutorial: The gapminder dataset
R Tutorial: The gapminder dataset
DataCamp
 27 R Tutorial: The filter verb
R Tutorial: The filter verb
DataCamp
 28 R Tutorial: The arrange verb
R Tutorial: The arrange verb
DataCamp
 29 R Tutorial: The mutate verb
R Tutorial: The mutate verb
DataCamp
 30 R Tutorial: What is cluster analysis?
R Tutorial: What is cluster analysis?
DataCamp
 31 R Tutorial: Distance between two observations
R Tutorial: Distance between two observations
DataCamp
 32 R Tutorial: The importance of scale
R Tutorial: The importance of scale
DataCamp
 33 R Tutorial: Measuring distance for categorical data
R Tutorial: Measuring distance for categorical data
DataCamp
 34 Python Tutorial: Plotting multiple graphs
Python Tutorial: Plotting multiple graphs
DataCamp
 35 Python Tutorial: Customizing axes
Python Tutorial: Customizing axes
DataCamp
 36 Python Tutorial: Legends, annotations, & styles
Python Tutorial: Legends, annotations, & styles
DataCamp
 37 Python Tutorial: Introduction to iterators
Python Tutorial: Introduction to iterators
DataCamp
 38 Python Tutorial: Playing with iterators
Python Tutorial: Playing with iterators
DataCamp
 39 Python Tutorial: Using iterators to load large files into memory
Python Tutorial: Using iterators to load large files into memory
DataCamp
 40 SQL Tutorial: Introduction to Relational Databases in SQL
SQL Tutorial: Introduction to Relational Databases in SQL
DataCamp
 41 SQL Tutorial: Tables: At the core of every database
SQL Tutorial: Tables: At the core of every database
DataCamp
 42 SQL Tutorial: Update your database as the structure changes
SQL Tutorial: Update your database as the structure changes
DataCamp
 43 Python Tutorial: Classification-Tree Learning
Python Tutorial: Classification-Tree Learning
DataCamp
 44 Python Tutorial: Decision-Tree for Classification
Python Tutorial: Decision-Tree for Classification
DataCamp
 45 Python Tutorial: Decision-Tree for Regression
Python Tutorial: Decision-Tree for Regression
DataCamp
 46 Python Tutorial: Census Subject Tables
Python Tutorial: Census Subject Tables
DataCamp
 47 Python Tutorial: Census Geography
Python Tutorial: Census Geography
DataCamp
 48 Python Tutorial: Using the Census API
Python Tutorial: Using the Census API
DataCamp
 49 R Tutorial: A/B Testing in R
R Tutorial: A/B Testing in R
DataCamp
 50 R Tutorial: Baseline Conversion Rates
R Tutorial: Baseline Conversion Rates
DataCamp
 51 R Tutorial: Designing an Experiment - Power Analysis
R Tutorial: Designing an Experiment - Power Analysis
DataCamp
 52 R Tutorial: Introduction to qualitative data
R Tutorial: Introduction to qualitative data
DataCamp
 53 R Tutorial: Understanding your qualitative variables
R Tutorial: Understanding your qualitative variables
DataCamp
 54 R Tutorial: Making Better Plots
R Tutorial: Making Better Plots
DataCamp
 55 SQL Tutorial: OLTP and OLAP
SQL Tutorial: OLTP and OLAP
DataCamp
 56 SQL Tutorial: Storing data
SQL Tutorial: Storing data
DataCamp
 57 SQL Tutorial: Database design
SQL Tutorial: Database design
DataCamp
 58 Python Tutorial: Introduction to spaCy
Python Tutorial: Introduction to spaCy
DataCamp
 59 Python Tutorial: Statistical Models
Python Tutorial: Statistical Models
DataCamp
 60 Python Tutorial: Rule-based Matching
Python Tutorial: Rule-based Matching
DataCamp

DataCamp Workspace is a powerful tool for data scientists and analysts, providing a pre-configured environment for Python, R, and SQL, along with intelligent suggestions and collaboration features. This tool can help users streamline their workflow and improve productivity. By using DataCamp Workspace, users can focus on insights and decision-making rather than setup and configuration.

Key Takeaways
  1. Sign up for DataCamp Workspace
  2. Launch a new project with a pre-configured Python and R environment
  3. Use the AI Assistant to generate code and debug existing code
  4. Collaborate with peers in real-time using built-in commenting and editing features
💡 The AI Assistant in DataCamp Workspace can generate code and debug existing code, making it easier for users to focus on data analysis and insights.

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