ivombi
Microsoft Certified Data Scientist Associate. Impacting lives by enabling data driven decisions.
Belgium
Pinned Repositories
ivombi.github.io
Titanic-Machine-Learning-from-Disaster
use machine learning to create a model that predicts which passengers survived the Titanic shipwreck
Data-visualisation
As data becomes easier and cheaper to generate, we are moving from a hypothesis-driven to data-driven paradigm in scientific research. As a result, we don't only need to find ways to answer any questions we have, but also to identify interesting questions/hypotheses in that data in the first place. In other words: we need to be able to dig through these large and complex datasets in search for unexpected patterns that - once discovered - can be investigated further using regular statistics and machine learning. Interactive data visualization provides a methodology for just that: to allow the user (be they domain expert or lay user) to find those questions, and to give them deep insight in their data. Content Background and context of data visualization and visual data analysis Design as a process: framing the problem, ideation, sketching, design critique, ... Programming visualizations: static and dynamic Project: visualization of expert dataset
Generalized-Linera-Models
At the end of this course, the student should have a profound knowledge of generalized linear models and basic knowledge of some extensions, including Part I Standard descriptive and inferential methods for multiway contingency t ables (odds ratios, conditional independence, Cochran-Mantel-Haenszel procedures,...) Components of a generalized linear model (GLM) GLM for binary data: logistic regression Building and applying logistic regression models Overdispersion and quasi-likelihood Conditional logistic regression and exact distributions Part II Extensions to multinomial responses (baseline category, cumulative link, partial odds ratio,...) Extensions to clustered binary (GEE, random effects) Extensions to clustered & multinomial data Loglinear models Models for matched pairs The student should be able to apply such models and methods using appropriate software (SAS, R).
House-price-prediction
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
NY-Taxi
Visualisation project for the New York Yellow Taxi
NY-Taxi-Forecast
US-Shootings
Time Series Analysis of the number of people shoot dead per week by the police in the USA
ivombi's Repositories
ivombi/ivombi.github.io
ivombi/NY-Taxi
Visualisation project for the New York Yellow Taxi
ivombi/PYthon4DataScience
ivombi/sfguide-data-engineering-with-snowpark-python
ivombi/practical-python
Practical Python Programming (course by @dabeaz)
ivombi/Data-visualisation
As data becomes easier and cheaper to generate, we are moving from a hypothesis-driven to data-driven paradigm in scientific research. As a result, we don't only need to find ways to answer any questions we have, but also to identify interesting questions/hypotheses in that data in the first place. In other words: we need to be able to dig through these large and complex datasets in search for unexpected patterns that - once discovered - can be investigated further using regular statistics and machine learning. Interactive data visualization provides a methodology for just that: to allow the user (be they domain expert or lay user) to find those questions, and to give them deep insight in their data. Content Background and context of data visualization and visual data analysis Design as a process: framing the problem, ideation, sketching, design critique, ... Programming visualizations: static and dynamic Project: visualization of expert dataset
ivombi/House-price-prediction
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
ivombi/US-Shootings
Time Series Analysis of the number of people shoot dead per week by the police in the USA
ivombi/NY-Taxi-Forecast
ivombi/anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
ivombi/Web-Dev-For-Beginners
24 Lessons, 12 Weeks, Get Started as a Web Developer
ivombi/LearningSparkV2
This is the github repo for Learning Spark: Lightning-Fast Data Analytics [2nd Edition]
ivombi/Python4Beginners
These threes series on Channel 9 and YouTube are designed to help get you up to speed on Python. If you're a beginning developer looking to add Python to your quiver of languages, or trying to get started on a data science or web project which uses Python, these videos are here to help show you the foundations necessary to walk through a tutorial or other quick start. We do assume you are familiar with another programming language, and some core programming concepts. For example, we highlight the syntax for boolean expressions and creating classes, but we don't dig into what a boolean is or object oriented design. We show you how to perform the tasks you're familiar with in other languages in Python.
ivombi/github-slideshow
A robot powered training repository :robot:
ivombi/Intro-DataScience
ivombi/Deep-Learning
ivombi/Titanic-Machine-Learning-from-Disaster
use machine learning to create a model that predicts which passengers survived the Titanic shipwreck
ivombi/Azure-Machine-Learning
Python has become a dominant language for doing data analysis with machine learning. Learn how to leverage Python and associated libraries in Jupyter Notebooks run on Azure Notebooks to predict patterns and identify trends.
ivombi/MachineLearningNotebooks
Python notebooks with ML and deep learning examples with Azure Machine Learning | Microsoft
ivombi/Generalized-Linera-Models
At the end of this course, the student should have a profound knowledge of generalized linear models and basic knowledge of some extensions, including Part I Standard descriptive and inferential methods for multiway contingency t ables (odds ratios, conditional independence, Cochran-Mantel-Haenszel procedures,...) Components of a generalized linear model (GLM) GLM for binary data: logistic regression Building and applying logistic regression models Overdispersion and quasi-likelihood Conditional logistic regression and exact distributions Part II Extensions to multinomial responses (baseline category, cumulative link, partial odds ratio,...) Extensions to clustered binary (GEE, random effects) Extensions to clustered & multinomial data Loglinear models Models for matched pairs The student should be able to apply such models and methods using appropriate software (SAS, R).
ivombi/Advance-Modelling-Techniques
ivombi/Multivariate-and-Hierrarchical-Data
Contents "Multivariate and Hierarchical Data": - Repeated measures - Clustered data - Multivariate methods. Contents "Discovering Associations": - Sample size calculations - Statistical research for pharmaceutical research and development - Ethical aspects of consulting, reporting - Statistical consulting training & protocol for the design of experiments
ivombi/Computer-Intensive
Simulations, Monte Carlo methods, Bootstrap techniques, Randomization methods, Permutation methods.
ivombi/Introduction-to-Programming
A program is an algorithm that can be directly executed by a computer. Learning to program therefore encompasses two complementary skills: (1) constructing algorithms; (2) coding an algorithm as a program. This course focuses on both aspects. We will use the programming language Python. In particular, this course has the following goals: - The student can write simple imperative programs in Python. In particular, he/she can utilize primitive types, strings, lists, iteration, conditions, procedures and functions. - The student understands the importance of precise syntax and semantics. - The student is able to reason about programs and can debug programs. - The student is familiar with the notion of an algorithm, can devise algorithms (for simple problems), and can reason over algorithms. - The student is familiar with the principles of computational thinking and can apply these.
ivombi/Project-Learning-From-Data
The aim of this course is to give students the opportunity to collaborate with other students (in a group) and apply, to a real-life dataset, statistical tools and methodology from other courses in the program (Concepts of Probability and Statistics, Linear Models, and Statistical Software and Data Management).
ivombi/Hello-world
Just another repository
ivombi/Bike-Rentel
Machine Learining Project
ivombi/learningPySpark
Code base for the Learning PySpark book (in preparation)