Mit mehr als 10 Jahren Erfahrung in der Softwareentwicklung und mehr als 6 Jahren Erfahrung in der Datenwissenschaft bin ich davon begeistert, Data Wissenschaft zu nutzen, um Wirkung zu erzielen.
Als begeisterter Community-Mitglied bin ich Mitbegründer / Mitorganisator vieler regionaler und internationaler Veranstaltungen und Communities wie des Cairo Open Data Day, des Data Science Meetup in Kairo, der Initiative Data Science in Arabic und der MUFIX Community. Ich bin auch ein TedX-Sprecher!
Ich Gastdozent für Master- und Doktoranden an die regionalen Universitäten bin.
With 10+ Years of experience in Software Engineering and 6+ Years of experience in Data Science, I’m passionate about using Data Science for making Impact!
Being a Community Enthusiastic, I’m Co-Founding/Co-Organizing many regional and international events and communities like Cairo Open Data Day, Cairo Data Science Meetup, Data Science in Arabic Initiative, MUFIX Community. I’m also a TedX speaker!
I’m transferring Data Science Industry Insights to Academia by being a guest lecturer for Masters and PhD Students at regional Universities.
My Mentoring Topics
- Starting of the career in data
- Career Shift into Data Science
- Crafting Data strategy
- Building Data Products
- Information Retrieval
- Natural Language Processing
- Machine Learning In Production
Deep Work - Rules for Focused Success in a Distracted World
Master one of our economy’s most rare skills and achieve groundbreaking results with this “exciting” book (Daniel H. Pink) from an “exceptional” author (New York Times Book Review). Deep work is the ability to focus without distraction on a cognitively demanding task. It's a skill that allows you to quickly master complicated information and produce better results in less time. Deep Work will make you better at what you do and provide the sense of true fulfillment that comes from craftsmanship. In short, deep work is like a super power in our increasingly competitive twenty-first century economy. And yet, most people have lost the ability to go deep-spending their days instead in a frantic blur of e-mail and social media, not even realizing there's a better way. In Deep Work, author and professor Cal Newport flips the narrative on impact in a connected age. Instead of arguing distraction is bad, he instead celebrates the power of its opposite. Dividing this book into two parts, he first makes the case that in almost any profession, cultivating a deep work ethic will produce massive benefits. He then presents a rigorous training regimen, presented as a series of four "rules," for transforming your mind and habits to support this skill. 1. Work Deeply 2. Embrace Boredom 3. Quit Social Media 4. Drain the Shallows A mix of cultural criticism and actionable advice, Deep Work takes the reader on a journey through memorable stories-from Carl Jung building a stone tower in the woods to focus his mind, to a social media pioneer buying a round-trip business class ticket to Tokyo to write a book free from distraction in the air-and no-nonsense advice, such as the claim that most serious professionals should quit social media and that you should practice being bored. Deep Work is an indispensable guide to anyone seeking focused success in a distracted world. An Amazon Best Book of 2016 Pick in Business & Leadership Wall Street Journal Business Bestseller A Business Book of the Week at 800-CEO-READView
Atomic Habits - the life-changing million-copy #1 bestseller
THE PHENOMENAL INTERNATIONAL BESTSELLER: 1 MILLION COPIES SOLD Transform your life with tiny changes in behaviour, starting now. People think that when you want to change your life, you need to think big. But world-renowned habits expert James Clear has discovered another way. He knows that real change comes from the compound effect of hundreds of small decisions: doing two push-ups a day, waking up five minutes early, or holding a single short phone call. He calls them atomic habits. In this ground-breaking book, Clears reveals exactly how these minuscule changes can grow into such life-altering outcomes. He uncovers a handful of simple life hacks (the forgotten art of Habit Stacking, the unexpected power of the Two Minute Rule, or the trick to entering the Goldilocks Zone), and delves into cutting-edge psychology and neuroscience to explain why they matter. Along the way, he tells inspiring stories of Olympic gold medalists, leading CEOs, and distinguished scientists who have used the science of tiny habits to stay productive, motivated, and happy. These small changes will have a revolutionary effect on your career, your relationships, and your life. ________________________________ A NEW YORK TIMES AND SUNDAY TIMES BESTSELLER 'A supremely practical and useful book.' Mark Manson, author of The Subtle Art of Not Giving A F*ck 'James Clear has spent years honing the art and studying the science of habits. This engaging, hands-on book is the guide you need to break bad routines and make good ones.' Adam Grant, author of Originals 'Atomic Habits is a step-by-step manual for changing routines.' Books of the Month, Financial Times 'A special book that will change how you approach your day and live your life.' Ryan Holiday, author of The Obstacle is the WayView
Radical Candor: Fully Revised & Updated Edition - Be a Kick-Ass Boss Without Losing Your Humanity
* New York Times and Wall Street Journal bestseller multiple years running * Translated into 20 languages, with more than half a million copies sold worldwide * A Hudson and Indigo Best Book of the Year * Recommended by Shona Brown, Rachel Hollis, Jeff Kinney, Daniel Pink, Sheryl Sandberg, and Gretchen Rubin Radical Candor has been embraced around the world by leaders of every stripe at companies of all sizes. Now a cultural touchstone, the concept has come to be applied to a wide range of human relationships. The idea is simple: You don't have to choose between being a pushover and a jerk. Using Radical Candor—avoiding the perils of Obnoxious Aggression, Manipulative Insincerity, and Ruinous Empathy—you can be kind and clear at the same time. Kim Scott was a highly successful leader at Google before decamping to Apple, where she developed and taught a management class. Since the original publication of Radical Candor in 2017, Scott has earned international fame with her vital approach to effective leadership and co-founded the Radical Candor executive education company, which helps companies put the book's philosophy into practice. Radical Candor is about caring personally and challenging directly, about soliciting criticism to improve your leadership and also providing guidance that helps others grow. It focuses on praise but doesn't shy away from criticism—to help you love your work and the people you work with. Radically Candid relationships with team members enable bosses to fulfill their three core responsibilities: 1. Create a culture of Compassionate Candor 2. Build a cohesive team 3. Achieve results collaboratively Required reading for the most successful organizations, Radical Candor has raised the bar for management practices worldwide.View
The Five Dysfunctions of a Team - A Leadership Fable
Patrick M. Lencioni
A leadership fable that is as compelling and enthralling as it is realistic, relevant, and practical In The Five Dysfunctions of a Team, Patrick Lencioni once again offers a leadership fable that is as captivating and instructive as his first two best-selling books, The Five Temptations of a CEO and The Four Obsessions of an Extraordinary Executive. This time, he turns his keen intellect and storytelling power to the fascinating, complex world of teams. Kathryn Petersen, Decision Tech's CEO, faces the ultimate leadership crisis: Uniting a team in such disarray that it threatens to bring down the entire company. Will she succeed? Will she be fired? Will the company fail? Lencioni's utterly gripping tale serves as a timeless reminder that leadership requires as much courage as it does insight. Throughout the story, Lencioni reveals the five dysfunctions which go to the very heart of why teams even the best ones-often struggle. He outlines a powerful model and actionable steps that can be used to overcome these common hurdles and build a cohesive, effective team. Just as with his other books, Lencioni has written a compelling fable with a powerful yet deceptively simple message for all those who strive to be exceptional team leaders.View
Introduction to Information Retrieval
Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.View
The Elements of Statistical Learning - Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, Jerome Friedman
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.View
Pattern Recognition and Machine Learning
Christopher M. Bishop
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.View
Speech and Language Processing - An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Dan Jurafsky, James H. Martin
For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing. An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology - at all levels and with all modern technologies - this text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material. Supplements: Click on the "Resources" tab to View Downloadable Files: Solutions Power Point Lecture Slides - Chapters 1-5, 8-10, 12-13 and 24 Now Available! For additional resourcse visit the author website: http://www.cs.colorado.edu/~martin/slp.htmlView
Ian Goodfellow, Yoshua Bengio, Aaron Courville
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.View