Publisher: 8
Data Science - zawód przyszłości. Kurs video. Jak rozpocząć karierę w pracy z danymi
Anna Kotarba
Obierz kurs na karierę mistrza danych Data science, big data - odkąd naszą wirtualną rzeczywistość opanowały wiedzące o nas wszystko ciasteczka, wyszukiwarka Google i media społecznościowe, te słowa budzą emocje i nadzieję u wielu osób zajmujących się biznesem. Kto ma dostęp do danych, ten trafił na żyłę złota. A kto potrafi je odpowiednio sortować, filtrować i - co najważniejsze - wyciągać z nich wnioski, ten może naprawdę dużo! Nic dziwnego, że specjaliści w tej dziedzinie są sowicie wynagradzani - w 2020 roku data scientist był trzecim najlepiej opłacanym zawodem w branży IT w Stanach Zjednoczonych. Siłą rzeczy zawód data scientist (po polsku mówi się czasem „mistrz danych”) zyskuje na popularności. Coraz więcej firm i organizacji dostrzega wagę zbierania i analizowania pozyskanych danych w kontekście przyszłości i strategicznego planowania działalności. Stąd ogłoszeń z propozycjami zatrudnienia dla specjalistów z tej dziedziny nie brakuje, a fraza „data scientist zarobki” jest szalenie popularna! Liczba miejsc pracy rośnie proporcjonalnie do tego, jak zwiększa się ilość danych. Czyli naprawdę szybko! Dlatego przy wyborze informatycznej specjalizacji warto rozważyć karierę „mistrza danych”. Nie tylko dla pieniędzy. Także dlatego, że daje ona możliwość ciągłego rozwoju i nauki nowych aspektów. Co Cię czeka podczas naszego profesjonalnego szkolenia video z Data Science? Szkoląc się w ramach tego kursu online Data Science: Poznasz specyfikę pracy na stanowisku data scientist Zdobędziesz umiejętność rozróżniania obszarów pracy z danymi Opanujesz podstawy pracy z językami: Python, R, SQL Zaznajomisz się ze środowiskami big data Co więcej... W trakcie szkolenia dowiesz się też, jak stworzyć atrakcyjne dla przyszłych pracodawców CV i portfolio data scientist Data Science - zawód przyszłości. Kurs video. Jak rozpocząć karierę w pracy z danymi jest wprowadzeniem do pracy w dziedzinie data science, kończy się więc na poziomie podstawowym. Wyposaży Cię jednak w wiedzę niezbędną do dalszego, samodzielnego rozwoju i podpowie, po jakie narzędzia sięgać, aby doskonalić warsztat profesjonalisty. Nauka o danych Data science to dziedzina prawdziwie interdyscyplinarna, w której używa się naukowych metod, procesów, algorytmów i systemów do wydobywania wiedzy, a także spostrzeżeń ustrukturyzowanych i nieustrukturyzowanych danych. Z wiedzy zdobytej na podstawie analizy danych zaś korzysta się w szerokim przekroju zastosowań. Data science blisko wiąże się z takimi pojęciami jak data mining (eksploracja danych), machine learning (samouczenie się maszyn) i big data (praca na dużych zbiorach danych). To dziedzina niezwykle rozległa, oferująca naprawdę duże możliwości, jeśli chodzi o karierę i rozwój. Idealna dla kogoś, kto czuje, że jego powołaniem jest analiza. Jeśli wiesz, że to właśnie Ty, nie czekaj, tylko działaj! Uruchom kurs video Data Science - zawód przyszłości i w krótkim czasie (bez setek godzin poświęconych na poszukiwanie rozmaitych materiałów) zanurz się w fascynujący świat danych. I wreszcie zacznij poruszać się wśród nich jak profesjonalista! Zobacz także kursy video MySQL dostępne w naszej ofercie.
David Natingga
Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well.Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis.By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Data Science for Decision Makers. Enhance your leadership skills with data science and AI expertise
Jon Howells
As data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI.This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently. By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements.By the end of this book, you’ll be able to characterize the data within your organization and frame business problems as data science problems.
Mercury Learning and Information, P. G. Madhavan
This book introduces data science to professionals in engineering, physics, mathematics, and related fields. It serves as a workbook with MATLAB code, linking subject knowledge to data science, machine learning, and analytics, with applications in IoT. Part One integrates machine learning, systems theory, linear algebra, digital signal processing, and probability theory. Part Two develops a nonlinear, time-varying machine learning solution for modeling real-life business problems.Understanding data science is crucial for modern applications, particularly in IoT. This book presents a dynamic machine learning solution to handle these complexities. Topics include machine learning, systems theory, linear algebra, digital signal processing, probability theory, state-space formulation, Bayesian estimation, Kalman filter, causality, and digital twins.The journey begins with data science and machine learning, covering systems theory and linear algebra. Advanced concepts like the Kalman filter and Bayesian estimation lead to developing a dynamic machine learning model. The book ends with practical applications using digital twins.
Shane Molinari, Jim Packer
In today's world full of online threats, the complexity of harmful software presents a significant challenge for detection and analysis. This insightful guide will teach you how to apply the principles of data science to online security, acting as both an educational resource and a practical manual for everyday use.Data Science for Malware Analysis starts by explaining the nuances of malware, from its lifecycle to its technological aspects before introducing you to the capabilities of data science in malware detection by leveraging machine learning, statistical analytics, and social network analysis. As you progress through the chapters, you’ll explore the analytical methods of reverse engineering, machine language, dynamic scrutiny, and behavioral assessments of malicious software. You’ll also develop an understanding of the evolving cybersecurity compliance landscape with regulations such as GDPR and CCPA, and gain insights into the global efforts in curbing cyber threats.By the end of this book, you’ll have a firm grasp on the modern malware lifecycle and how you can employ data science within cybersecurity to ward off new and evolving threats.
Mirza Rahim Baig , Gururajan Govindan ,...
Unleash the power of data to reach your marketing goals with this practical guide to data science for business.This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects.You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions.As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior.By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.
Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar
Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments.The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.
Gabriela Castillo Areco
Data is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3.You’ll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You’ll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data.The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you’ll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients.By the end of this book, you’ll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.