Data Scientist - Focus Python

Objective:
Refresh knowledge of Python and mathematical principles

Description:
Participants carry out analysis and data manipulation in Python using the pandas and Matplotlib packages

Chapter 1: Data Analytics with Python

Participants get to know our interactive programming environment - the Data Lab - and brush up on key programming and Python basics for data processing with Pandas, data visualization with Matplotlib and Seaborn, and database querying with SQL Alchemy.

Chapter 2: Linear Algebra

Participants become familiar with the mathematical background of data science algorithms and learn the basic concepts of linear algebra. Participants use the NumPy package to perform calculations with vectors and matrices.

Chapter 3: Probability Distributions

Participants learn more about the statistical background of data science algorithms. They explore important statistical concepts and learn about discrete and continuous distributions.

Objective:
Solving supervised and unsupervised machine learning problems with sklearn

Description:
Participants create data science workflows with sklearn, evaluate their model performance using appropriate metrics, and learn about the overfitting problem

Chapter 1: Supervised Learning (Regression)
Participants learn how to use the Python package sklearn with linear regressions. They also learn about the regression model’s assumptions and evaluating the predictions generated. Participants learn about the bias-variance trade-off, regularization, and various metrics of model quality.

Chapter 2: Supervised Learning (Classification)
Participants are introduced to classification algorithms using the k-Nearest-Neighbors algorithm and learn to evaluate the algorithm and assess classification performance. They optimize the parameters of their model and pay attention to dividing the data into training and evaluation sets.

Chapter 3: Unsupervised Learning (Clustering)
Participants learn about the k-Means algorithm as an example of an unsupervised learning algorithm. They critically examine the algorithm’s assumptions and performance metrics. Then they take a brief look at an alternative to k-Means clustering.

Module 1 Module 2 Machine Learning Basics

Chapter 4: Unsupervised Learning (Dimensionality Reduction)
Participants learn how to reduce the dimension of their data using Principal Component Analysis (PCA) and use PCA to generate uncorrelated features from the original data. In this context, they explore the topic of feature engineering in more detail and new features are generated from the old ones.

Chapter 5: Outlier Detection
Participants learn about different approaches to identifying outliers and understand how to deal with these unusual data points. They use robust measures and models to minimize the impact of outliers.

Objective:
Expanding the data science toolkit

Description:
Participants deepen their knowledge of data classification models. In doing so, they also expand their skills in collecting and preparing data

Chapter 1: Data Gathering

Participants learn to gather data by mining web pages and PDF documents. They structure collected text data using regular expressions so that they can use it together with familiar algorithms.

Chapter 2: Logistic Regression

Participants learn a second classification algorithm: logistic regression. They use new performance metrics to evaluate results and learn how to prepare non-numeric data for their models.

Chapter 3: Decision Trees and Random Forests

Participants learn about the decision tree as an easy-to-interpret model. They combine multiple models in an ensemble to improve the predictions of their model. They also learn methods to deal with unbalanced categories.

Chapter 4: Support Vector Machines

Participants learn about a final classification algorithm - Support Vector Machines (SVMs) and examine the behavior of different kernels for SVMs. They also learn the typical steps of Natural Language Processing (NLP) and work through an NLP scenario using bag-of-words models.

Chapter 5: Neural Networks

Participants are introduced to artificial neural networks and learn more about deep learning, to create a multilayer artificial neural network and apply it to a data set.

Objective:
Independent application of simple and complex modeling

Description:
Participants gain confidence in solving data science problems and learn to communicate results competently

Chapter 1: Visualization and Model Interpretation

Participants learn important methods for interpreting and visualizing machine learning models. By using modelagnostic methods for interpretation, they learn to derive and communicate insights into the functioning of their models.

Chapter 2: Spark

Participants learn why working with distributed storage systems is relevant. Using the Python package PySpark, they learn how to read distributed databases, perform big data analyses and use known machine learning algorithms on distributed systems.

Module 3 Advanced Topics in Data Science

Chapter 3: Exercise Project

Participants work on a prediction problem using a larger data set and independently apply their data science skills from cleaning the data set to interpreting the model. Participants receive feedback on their approach to solving the problem in a project consultation with StackFuel‘s mentoring team.

Chapter 4: Final Project

Participants are given another larger dataset to analyze independently and solve with less assistance than they received for the practice project. Participants receive feedback on their solution approach in an individual project consultation with the StackFuel mentoring team.