Data Science.

Chapter 1: Data Analytics with Python

You will brush up on the most important Python basics for data processing with Pandas, data visualization with Matplotlib and Seaborn and database querying with SQL Alchemy.

Chapter 2: Linear algebra

You will familiarize yourself with the mathematical background of data science algorithms and learn the basic concepts of linear algebra. Using the Numpy package, you will calculate with vectors and matrices.

Chapter 3: Probability Distributions

You will learn more about the statistical background of data science algorithms. You will deal with important statistical concepts and learn about discrete and continuous distributions. You will also gain an insight into versioning code with Git.

Chapter 1: Supervised Learning (Regression)

Using linear regression, you will learn how to use the Python package sklearn. You will also deal with the assumptions of the regression model and the evaluation of the forecasts generated. In this context, the bias-variance trade-off, concepts of regularization and various measures of model quality are also clarified.

Chapter 2: Supervised Learning (Classification)

You will be introduced to classification algorithms using the k-Nearest Neighbors algorithm and learn how to evaluate the algorithm and assess the classification performance. You will optimize the parameters of models, taking into account the division of the data into training and evaluation sets.

Chapter 3: Unsupervised Learning (Clustering)

You will learn about the k-Means algorithm as an example of an unsupervised learning algorithm. The assumptions and performance metrics of the algorithm are critically examined and a brief outlook on an alternative to k-Means clustering is given.

Chapter 4: Unsupervised Learning (Dimensionality Reduction)

You will learn how to use Principal Component Analysis (PCA) to reduce the dimension of the data and use PCA to generate uncorrelated features from the original data. In this context, the topic of feature engineering is examined in more detail and new features are created from the old ones.

Chapter 5: Outlier Detection

You will learn about different approaches to identify outliers and understand how to deal with these unusual data points. You will use robust measures and models to minimize the influence of outliers

Chapter 1: Data Gathering

Learn how to collect data with tools like BeautifulSoup for Web Scraping and PyPDF2 for PDF Data Extraction. With the help of Regular Expressions you can structure collected text data so that it can be used together with known algorithms.

Chapter 2: Logistic Regression

You will get to know a second classification algorithm: logistic regression. You will use new performance metrics to evaluate the results and learn how to make non-numerical data usable for your models.

Chapter 3: Decision Trees and Random Forests

You will get to know the decision tree as an easy-to-interpret model. You will combine several models into an ensemble to improve the predictions of your model. You will also be provided with methods for unbalanced categories.

Chapter 4: Support Vector Machines

You will get to know one last classification algorithm - Support Vector Machines (SVM) and examine the behavior of various kernels for the SVM. You will also learn the typical steps of Natural Language Processing (NLP) and work on an NLP scenario using bag-of-words models.

Chapter 5: Neural Networks

You will be introduced to artificial neural networks and learn more about deep learning to create an artificial neural network with multiple layers and apply it to a dataset.

Chapter 1: Visualization and Model Interpretation

You will learn important methods for interpreting and visualizing machine learning models. By using model-agnostic methods for interpretation, you will learn to derive and communicate insights into the functioning of your models.

Chapter 2: Spark

You will learn why working with distributed storage systems is relevant. With the Python package PySpark, you will learn how to read distributed databases, perform big data analyses and use well-known machine learning algorithms on distributed systems.

Chapter 3: Exercise project

You work on a prediction problem with the help of a larger data set and use your data science skills independently, from cleaning the data set to interpreting the model. You will receive feedback on your solution approach in a project meeting with the StackFuel mentoring team.

Chapter 4: Final project

You will receive another larger data set that you will have to analyze independently and solve with less help compared to the practice project. You will receive feedback on your solution approach in an individual project meeting with the mentoring team.