🤝Collaborative Data Science

1.2

Replication crisis in science

1.3

Research transparency

1.4

Computational reproducibility

3 min read

1.5

Open data and open methods

1.6

Reproducible workflows

1.7

Reproducibility tools and platforms

Unit 2 – Version Control in Data Science

View all

2.1

Git fundamentals

2.2

GitHub and GitLab

2.3

Branching and merging

2.4

Collaborative development with pull requests

2.5

Conflict resolution

2.6

Version control best practices

2.7

Git for data science projects

Unit 3 – Data Management for Statistical Research

View all

3.1

Data storage formats

3.2

File naming conventions

3.3

Directory structure

3.4

Metadata standards

3.5

Data cleaning and preprocessing

3.6

Data versioning

3.7

Data sharing and archiving

Unit 4 – Statistical Programming Languages

View all

4.1

R programming

4.2

Python for data science

4.3

SQL for data manipulation

4.4

Julia for scientific computing

4.5

SAS and SPSS

4.6

Language interoperability

6 min read

4.7

Choosing the right language for a project

Unit 5 – Collaborative Coding Best Practices

View all

5.1

Code review processes

5.2

Pair programming

5.3

Agile methodologies in data science

5.4

Collaborative platforms and tools

5.5

Code style guides and linting

5.6

Testing and continuous integration

5.7

Managing dependencies and environments

Unit 6 – Data Visualization Essentials

View all

6.1

Principles of effective data visualization

6.2

Static visualizations

6.3

Interactive visualizations

6.4

Geospatial visualizations

6.5

Network and graph visualizations

6.6

Time series visualizations

6.7

Dashboard creation

Unit 7 – Statistical Analysis Techniques

View all

7.1

Descriptive statistics

7.2

Inferential statistics

7.3

Regression analysis

7.4

Analysis of variance (ANOVA)

14 min read

7.5

Time series analysis

7.6

Bayesian statistics

7.7

Multivariate analysis

Unit 8 – Machine Learning Fundamentals

View all

8.1

Supervised learning

8.2

Unsupervised learning

8.3

Deep learning

8.4

Ensemble methods

8.5

Feature selection and engineering

8.6

Model evaluation and validation

8.7

Hyperparameter tuning

Unit 9 – Project Workflow in Data Science

View all

9.1

Project planning and scoping

9.2

Task management and prioritization

9.3

Reproducible analysis pipelines

9.4

Containerization with Docker

9.5

Workflow automation tools

6 min read

9.6

Resource management

9.7

Project delivery and deployment

Unit 10 – Documentation and Literate Programming

View all

10.1

Code documentation best practices

10.2

Markdown and reStructuredText

10.3

Jupyter notebooks

6 min read

10.4

R Markdown

10.5

Automated documentation tools

10.6

API documentation

10.7

Writing reproducible reports

Unit 11 – Open Science: Principles and Practices

View all

11.1

Open access publishing

11.2

Preregistration of studies

11.3

Data sharing platforms

11.4

Open source software development

11.5

Citizen science

11.6

Research ethics in open science

11.7

Impact and metrics of open science

Unit 12 – Reproducibility Across Domains

View all

12.1

Reproducibility in biomedical research

12.2

Reproducibility in social sciences

12.3

Reproducibility in environmental sciences

12.4

Reproducibility in physics and astronomy

12.5

Reproducibility in economics

12.6

Reproducibility in computer science

12.7

Cross-domain reproducibility challenges