O1B

Specialized methods for non-IT arts and sciences. The following materials were prepared for the Jupyter system, and are in the form of IPython notebooks. You can access them either on your local Jupyter installation or with Google Colaboratory (account needed).

• Try Jupyter - a few ways to get started with the Jupyter ecosystem.

• How to Use Jupyter Notebooks.

Text/strings manipulation (linguistics, sociology, social sciences, philology)

• source materials maintained on bitbucket

1. Introduction To Strings.

2. Slices.

3. String formatting.

4. Natural Language ToolKit, part 1 - introduction.

5. Natural Language ToolKit, part 2 - POS Tagging, Bag of words, Word frequency, Synonyms & Antonyms.

6. Natural Language ToolKit, part 3 - text-features extraction and creation.

Statistics (economy, social sciences)

• source materials maintained on bitbucket

1. Pandas, an introduction.

2. Input and Output.

3. Manipulation of Data.

4. Graphs.

5. Descriptive Statistics and Parameters.

6. Hypothesis Testing.

7. Linear Correlation and Regression.Linear Correlation and Regression - Additional Notes.

Biostatistics, bioinformatics (biomedical and natural sciences)

• source materials maintained on bitbucket

1. Population and Sample.

2. Centralisation measures.

3. Measures of position.

4. Measures of dispersion.

5. Skewness and kurtosis.

6. Probability.

7. Human Genome I.

8. Human Genome II.

9. Neural Network for Iris Classification.

10. Convolutional Neural Networks.

11. Optimizations in CNNs.

12. Recurrent Neural Network.

Algorithms, theory of programming (mathematics)

• source materials maintained on bitbucket

1. Analysis of algorithms.

2. Recursion.

3. Array-based Sequences.

4. Stacks, queues, and dequeues.

5. Linked lists.

6. Sorting.

Scientific and numerical methods, data analysis (physics)

• source materials maintained on bitbucket

1. Accuracy of numerical calculations.

2. Graphical presentation of data.

3. The numpy package.

4. Math in numpy.

5. Equations and systems of equations.

6. Numerical integration and differentiation.

O2

Programming exercises. For part O1A exercises and questions are included in the manual lessons.

Text/strings manipulation (linguistics, sociology, social sciences, philology)

• source materials maintained on bitbucket

1. Introduction To Strings.

2. Slices.

3. String formatting.

4. Natural Language ToolKit, part 1 - introduction.

5. Natural Language ToolKit, part 2 - POS Tagging, Bag of words, Word frequency, Synonyms & Antonyms.

6. Natural Language ToolKit, part 3 - text-features extraction and creation.

Statistics (economy, social sciences)

• source materials maintained on bitbucket

1. Pandas, an introduction.

2. Input and Output.

3. Manipulation of Data.

4. Graphs.

5. Descriptive Statistics and Parameters.

6. Hypothesis Testing.

7. Linear Correlation and Regression.

Biostatistics, bioinformatics (biomedical and natural sciences)

• source materials maintained on bitbucket

1. Population and Sample.

2. Centralisation measures.

3. Measures of position.

4. Measures of dispersion.

5. Skewness and kurtosis.

6. Probability.

7. Human Genome I.

8. Human Genome II.

9. Neural Network for Iris Classification.

10. Convolutional Neural Networks.

11. Optimizations in CNNs.

12. Recurrent Neural Network.

Algorithms, theory of programming (mathematics)

• source materials maintained on bitbucket

1. Analysis of algorithms.

2. Recursion.

3. Array-based Sequences.

4. Stacks, queues, and dequeues.

5. Linked lists.

6. Sorting.

Scientific and numerical methods, data analysis (physics)

• source materials maintained on bitbucket

1. Accuracy of numerical calculations.

2. Graphical presentation of data.

3. The numpy package.

4. Math in numpy.

5. Equations and systems of equations.

6. Numerical integration and differentiation.

O3

Programming exercises for self-evaluation. You will need working nbgrader server.

Course in programming

1. 1. Variables

   2. Strings

   3. Input and output

   4. Some of the most popular complex types

   5. Branching

   6. While loop

   7. For loop

   8. Subroutines, function

   9. Modules

   10. Object-oriented programming

Text/strings manipulation (linguistics, sociology, social sciences, philology)

• source materials maintained on bitbucket

1. Introduction To Strings.

2. Slices.

3. String formatting.

4. Natural Language ToolKit, part 1 - introduction.

5. Natural Language ToolKit, part 2 - POS Tagging, Bag of words, Word frequency, Synonyms & Antonyms.

6. Natural Language ToolKit, part 3 - text-features extraction and creation.

Statistics (economy, social sciences)

• source materials maintained on bitbucket

1. Input and Output.

2. Manipulation of Data.

3. Graphs.

4. Descriptive Statistics and Parameters.

5. Hypothesis Testing.

6. Linear Correlation and Regression.

Biostatistics, bioinformatics (biomedical and natural sciences)

• source materials maintained on bitbucket

1. Population and Sample.

2. Centralisation measures.

3. Measures of position.

4. Measures of dispersion.

5. Skewness and kurtosis.

6. Probability.

7. Human Genome I.

8. Human Genome II.

9. Neural Network for Iris Classification.

10. Convolutional Neural Networks.

11. Optimizations in CNNs.

12. Recurrent Neural Network.

Algorithms, theory of programming (mathematics)

• source materials maintained on bitbucket

1. Analysis of algorithms.

2. Recursion.

3. Array-based Sequences.

4. Stacks, queues, and dequeues.

5. Linked lists.

6. Sorting.

Scientific and numerical methods, data analysis (physics)

• source materials maintained on bitbucket

1. Accuracy of numerical calculations.

2. Graphical presentation of data.

3. The numpy package.

4. Math in numpy.

5. Equations and systems of equations.

6. Numerical integration and differentiation.