Data Scientist with Python

1. basics of data analytics with Python

• Working with the Data Lab.
• Basics and concepts in Python.
• Presentation of the tools pandas, matplotlib and seaborn.
• Database queries with SQL Alchemy.

2. linear algebra

• Mathematical background.
• Basic concepts of linear algebra.
• Calculation with vectors and matrices.
• Use of the Python library numpy.

3. probability distribution

• Statistics in data science algorithms.
• Discrete and continuous distributions.
• Versioning of code in Git.

4. supervised learning (regression)

• Use linear regression.
• Use of the Python package sklearn.
• Understanding regression models.
• Evaluation of the forecasts.
• Bias variance trade-off and regularization.
• Measurement of model quality.

5. supervised learning (classification)

• Concepts of supervised learning.
• Introduction to classification algorithms.
• The k-Nearest Neighbors algorithm.
• Assessment of the classification performance.
• Optimization of the parameters.
• Division of the data into training and evaluation sets.

6. unsupervised learning (clustering)

• Concepts of Unsupervised Learning.
• The k-Means algorithm.
• Evaluation of performance metrics.
• Alternatives to k-means clustering.

7. unsupervised learning (dimension reduction)

• dimensions in the data analysis.
• Principal Component Analysis (PCA).
• Generate uncorrelated features from original data.
• Introduction to feature engineering.

8. identify and exclude outliers

• Methods for detecting outliers.
• Criteria of unusual data points.
• Robust measurements and reduction of influences due to outliers.

9. collect and merge data

• Read data from web pages and PDF documents.
• Use of regular expressions.
• Structure text data before processing.

10. logistic regression

• Concepts of logistic regression.
• Performance metrics for evaluation.
• Use non-numerical data in models.

11. decision trees and random forests

• The concept of Decision Trees.
• Combine several styles to create ensembles.
• Methods for improving the prediction quality.

12. support vector machines

• Use of Support Vector Machines (SVM).
• Introduction to Natural Language Processing (NLP).
• Text classification with bag-of-words models.

13. neural networks

• Basics of artificial neural networks (ANN).
• Basics of deep learning.
• Deeper understanding of the layers in KNN.

14. visualization and model interpretation

• Derive and illustrate how models work.
• Methods for interpretation and visualization.
• Apply model-agnostic methods.

15. use distributed databases

• Use the Python package PySpark.
• Read data from distributed databases.
• Basics of big data analysis.
• Using machine learning algorithms in distributed systems.

16. exercise project

• Work independently on a comprehensive exercise project.
• Solve prediction problem using a larger data set.
• Preparation for the final project.

17. final project

• Independent analysis of the data project.
• Presentation of results and 1:1 feedback meeting with mentor team.
• Receipt of the certificate for Data Scientist with Python.