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Génétique quantitative --- Quantitative genetics

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Quantitative research --- Quantitative research.

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Recherche quantitative

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In bildungswissenschaftlichen Disziplinen galt die Anwendung quantitativer Forschungsmethoden bislang nicht unbedingt als disziplinärer Standardzugang in der Datensuche, -erhebung und -analyse. Gleichzeitig gewinnt im Zuge besserer Datenverfügbarkeit, u.a. auch infolge zunehmender Digitalisierung, die Arbeit mit quantitativen Daten ebenso wie die individuelle Kompetenz zu ihrer Erhebung und Analyse an Bedeutung. Diese forschungsmethodische "Soft Skill"-Lücke greift dieser Sammelband auf und schließt sie: Die Handreichung des multiprofessionellen Autor:innenteams beleuchtet und diskutiert die Möglichkeiten der Nutzung quantitativer Daten in bildungswissenschaftlichen Disziplinen kritisch und vermittelt hilfreiche Kompetenzen im Bereich des Findens geeigneter (Sekundär-)Daten, der Umsetzung eigener Datenerhebungen, sowie weiterer forschungspraktischer Datenkompetenzen (bspw. Datenschutz).

Quantitative research.

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Step-by-step guide to practising data science techniques with Jupyter notebooks Description Modern businesses are awash with data, making data driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with just enough knowledge of Python in conjunction with skills to use powerful tool such as Jupyter Notebook in order to succeed in the role of a data scientist. The book starts with a brief introduction to the world of data science and the opportunities you may come across along with an overview of the key topics covered in the book. You will learn how to setup Anaconda installation which comes with Jupyter and preinstalled Python packages. Before diving in to several supervised, unsupervised and other machine learning techniques, you'll learn how to use basic data structures, functions, libraries and packages required to import, clean, visualize and process data. Several machine learning techniques such as regression, classification, clustering, time-series etc have been explained with the use of practical examples and by comparing the performance of various models. By the end of the book, you will come across few case studies to put your knowledge to practice and solve real-life business problems such as building a movie recommendation engine, classifying spam messages, predicting the ability of a borrower to repay loan on time and time series forecasting of housing prices. Remember to practice additional examples provided in the code bundle of the book to master these techniques. Audience The book is intended for anyone looking for a career in data science, all aspiring data scientists who want to learn the most powerful programming language in Machine Learning or working professionals who want to switch their career in Data Science. While no prior knowledge of Data Science or related technologies is assumed, it will be helpful to have some programming experience. Key Features Acquire Python skills to do independent data science projects Learn the basics of linear algebra and statistical science in Python way Understand how and when they're used in data science Build predictive models, tune their parameters and analyze performance in few steps Cluster, transform, visualize, and extract insights from unlabelled datasets Learn how to use matplotlib and seaborn for data visualization Implement and save machine learning models for real-world business scenarios Table of Contents Data Science Fundamentals Installing Software and Setting up Lists and Dictionaries Function and Packages NumPy Foundation Pandas and Dataframe Interacting with Databases Thinking Statistically in Data Science How to import data in Python? Cleaning of imported data Data Visualization Data Pre-processing Supervised Machine Learning Unsupervised Machine Learning Handling Time-Series Data Time-Series Methods Case Study - 1 Case Study - 2 Case Study - 3 Case Study - 4 About the Author Prateek is a Data Enthusiast and loves the data driven technologies. Prateek has total 7 years of experience and currently he is working as a Data Scientist in an MNC. He has worked with finance and retail clients and has developed Machine Learning and Deep Learning solutions for their business. His keen area of interest is in natural language processing and in computer vision. In leisure he writes posts about Data Science with Python in his blog.

Quantitative research.