| 01 | Defining Data Science | [Introduction](1-Introduction/README.md) | Learn the basic concepts behind data science and how it’s related to artificial intelligence, machine learning, and big data. | | |
| 02 | Data Science Ethics | [Introduction](1-Introduction/README.md) | Data Ethics Concepts, Challenges & Frameworks. | | [Nitya Narasimhan](https://github.com/nitya) |
| 04 | Introduction to Statistics & Probability | [Introduction](1-Introduction/README.md) | TBD | | |
| 05 | Working with Spreadsheets | [Working With Data](2-Working-With-Data/README.md) | Spreadsheets | | |
| 06 | Working with Relational Databases | [Working With Data](2-Working-With-Data/README.md) | Relational Databases | | |
| 07 | Working with NoSQL Data | [Working With Data](2-Working-With-Data/README.md) | NoSQL | | |
| 08 | Working with Python | [Working With Data](2-Working-With-Data/README.md) | Python Data | | |
| 09 | Cleaning and Transforming Data | [Working With Data](2-Working-With-Data/README.md) | Cleaning Transformations | | |
| 03 | Defining Data | [Introduction](1-Introduction/README.md) | How data is classified, and its common sources. | | |
| 04 | Introduction to Statistics & Probability | [Introduction](1-Introduction/README.md) | The mathematical techniques of probability and statistics to understand data. | | |
| 05 | Working with Spreadsheets | [Working With Data](2-Working-With-Data/README.md) | Using spreadsheets as a source of data for exploration and analysis. | | |
| 06 | Working with Relational Databases | [Working With Data](2-Working-With-Data/README.md) | Introduction to relational data and the basics of exploring and analyzing relational data with the Structured Query Language, also known as SQL (pronounced “see-quell”) | | |
| 07 | Working with NoSQL Data | [Working With Data](2-Working-With-Data/README.md) | Introduction to non-relational data, its various types and the basics of exploring and analyzing document databases. | | |
| 08 | Working with Python | [Working With Data](2-Working-With-Data/README.md) | Basics of using Python for data exploration with libraries such as Pandas. Foundational understanding of Python programming is recommended. | | |
| 09 | Data Preparation | [Working With Data](2-Working-With-Data/README.md) | Topics on data wrangling, which are techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data. | | |
| 10 | Visualizing Quantities | [Data Visualization](3-Data-Visualization/README.md) | Learn how to use Matplotlib to visualize bird data 🦆 | [Quantities](3-Data-Visualization/10-visualization-quantities/README.md) | Jen |
| 11 | Visualizing Distributions of Data | [Data Visualization](3-Data-Visualization/README.md) | Distributions | | Jen |
| 14 | Meaningful Visualizations | [Data Visualization](3-Data-Visualization/README.md) | Making meaningful visualizations | | Jen |
| 15 | The Data Science Lifecycle | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Capturing | | |
| 16 | The Data Science Lifecycle | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Processing | | |
| 17 | The Data Science Lifecycle | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Analyzing | | |
| 18 | The Data Science Lifecycle | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Communication | | |
| 19 | The Data Science Lifecycle | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Maintaining | | |
| 20 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | TBD | | |
| 21 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | TBD | | |
| 22 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | TBD | | |
| 23 | Data Science in the Wild | [In the Wild](6-Data-Science-In-Wild/README.md) | TBD | | |
| 24 | Data Science in the Wild | [In the Wild](6-Data-Science-In-Wild/README.md) | TBD | | |
| 14 | Meaningful Visualizations | [Data Visualization](3-Data-Visualization/README.md) | Techniques and guidance for making your visualizations valuable for effective problem solving and insights. | | Jen |
| 15 | Capturing | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Introduction to the data science lifecycle and its first step of acquiring and extracting data | | |
| 16 | Processing | [Lifecycle](4-Data-Science-Lifecycle/README.md) | This phase of the data science lifecycle focuses on techniques to classify and summarize data. | | |
| 17 | Analyzing | [Lifecycle](4-Data-Science-Lifecycle/README.md) | This phase of the data science lifecycle focuses on exploring and discovering the data and applying techniques such as statistical analysis, predictive analysis and regression. | | |
| 18 | Communication | [Lifecycle](4-Data-Science-Lifecycle/README.md) | This phase of the data science lifecycle focuses on presenting the insights from the data in a way that makes it easier for decision makers to understand. | | |
| 19 | Maintaining | [Lifecycle](4-Data-Science-Lifecycle/README.md) | This phase of the data science lifecycle focuses on preparing the data into a consistent format to be analyzed. | | |
| 20 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | This series of lessons introduces data science in the cloud and its benefits. | | |
| 21 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Training models with Azure Machine Learning Studio | | |
| 22 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Deploying models with Azure Machine Learning Studio | | |
| 23 | Data Science in the Wild | [In the Wild](6-Data-Science-In-Wild/README.md) | Data science driven projects in science and sociology | | |
| 24 | Data Science in the Wild | [In the Wild](6-Data-Science-In-Wild/README.md) | Data science driven projects in science and sociology | | |
Jasmine
4 years ago