Data Science and Machine Learning with Python Training in Germany

Delegates must be existing Python Data users who have attended;

Python for Data Handling (QADHPYTHON)

or have a similar level of knowledge with NumPy and Pandas.

Additionally, we recommend that delegates have attended;

Introduction to Data Science for Data Professionals (QAIDSDP)

in order to understand key data science, machine learning, and AI governance requirements before developing Machine Learning models.

Target Audience

Members of the audience are required to have a some technical expertise such as table structure, working with tabular data in Python, and simple data analysis.

They may be Mid/Senior Leadership seeking a greater understanding of how to implement Data Science within their organization.

They may come from other technical backgrounds such as Data Analysts, Software Developers, and Data Engineers who either work with Data Scientists or are using this course in their journey towards training as a Data Scientist.

In the latter case, audience members may ask for recommendations for their next steps in training towards becoming Data Scientists. We recommend the following refreshed courses which are due to launch in 2023 and 2024 in this suggested sequence:

• Statistics for Data Analysis in Python
• Time Series and Forecasting with Python
• Maths and Statistics for Data Science with Python
• Practical Big Data Analytics (with Python and Spark)
• Generative AI Essentials
• Fundamentals of Deep Learning with Python (followed by selected Python & NVIDIA training)

• Introduction to Data Science & Machine Learning
• Introduction to Python for Data Science
• Descriptive & Inferential Statistics with Python
• Preprocessing Data for Analysis
• Supervised Learning: Regression
• Supervised Learning: Classification
• Model Selection & Evaluation
• Unsupervised Learning
• Ethics for Data Scientists
• Deploying Models & Insights
• Where to Go Next

Introduction to Data Science & Machine Learning

• Explain the role of the Data Scientist and the skillset it requires
• Describe common application areas of Data Science, and examples of its usage in industry
• Outline the Data Science process detailed in the CRISP-DM methodology
• Detail the characteristics of problems which Data Science can be used to solve
• Define how to evaluate the success of a Data Science Project

Introduction to Python for Data Science

• Understand why notebooks are often used in Data Science projects
• Use Python and associated libraries to manipulate datasets.
• Describe why virtual environments are used
• Visualise data using Python

Descriptive & Inferential Statistics with Python

• Understand the role that descriptive and inferential statistics play in Data Science
• Use measures of central tendency, variation, and correlation to understand data
• Use hypothesis tests to establish the significance of effects
• Use statistical visualisations to understand data distributions
• Describe the role of Exploratory Data Analysis in a Data Science project

Preprocessing Data for Analysis

• Appropriately process duplicated data, missing values & outliers
• Understand the importance of scaling, encoding, and feature selection
• Describe the importance of training, testing & validation sets
• Engineer novel features to analyse

Supervised Learning: Regression

• Describe regression in the context of machine learning
• Build simple and multiple linear regression models
• Understand non-linear regression approaches
• Evaluate & compare regression models

Supervised Learning: Classification

• Describe classification in the context of machine learning
• Build simple and multiple logistic regression models for classification
• Build Decision Tree & Random forest models for Classification
• Evaluate and compare classification models

Model Selection & Evaluation

• Understand how to choose the best model for regression and classification problems
• Consider tests & baselines that can be used to evaluate model performance & behaviour
• Evaluate 'how good is good enough'

Unsupervised Learning

• Describe clustering and dimensionality reduction in the context of machine learning
• Apply and evaluate KMeans clustering
• Apply and evaluate dimensionality reduction techniques

Ethics for Data Scientists

• Be aware of the legislation and standards Data Scientists must adhere to
• Discuss the importance of legal, ethical, and moral considerations in Data Analytics projects and identify applicable UK legislation for which employees should receive training
• Discuss ethical considerations for data handling
• Recognise ethical considerations in examples of machine learning, deep learning, and AI

Deploying Models & Insights

• Understand how analytical models can be deployed
• Evaluate how best to deploy a given model
• Define checks which can be used to prevent model failures
• Use Python and associated libraries to deploy a machine learning model
• Describe which metrics can be used to monitor deployed machine learning models

Where to Go Next

• Understand the role of deep learning in modern Artificial Intelligence
• Know which qualifications and professional memberships can benefit data scientists