M. - ebooki
Tytuły autora: M. dostępne w księgarni Ebookpoint
-
Power BI. Kurs video. Kompleksowe przygotowanie do pracy Power BI developera
-
Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym
-
AI w Excelu. Kurs video. Automatyzacja zadań w pracy
-
Microsoft Power BI. Jak modelować i wizualizować dane oraz budować narracje cyfrowe. Wydanie III
-
Power BI Desktop. Kurs video. Wykorzystanie narzędzia w analizie i wizualizacji danych
-
Storytelling danych. Poradnik wizualizacji danych dla profesjonalistów
-
Inżynieria danych w praktyce. Kluczowe koncepcje i najlepsze technologie
-
Spark. Błyskawiczna analiza danych. Wydanie II
-
Potoki danych. Leksykon kieszonkowy. Przenoszenie i przetwarzanie danych na potrzeby ich analizy
-
Projektowanie baz danych dla każdego. Przewodnik krok po kroku. Wydanie IV
-
Elasticsearch. Kurs video. Pozyskiwanie i analiza danych
-
Zaawansowana analiza danych. Jak przejść z arkuszy Excela do Pythona i R
-
Siatka danych. Nowoczesna koncepcja samoobsługowej infrastruktury danych
-
Uczenie głębokie od zera. Podstawy implementacji w Pythonie
-
Microsoft Power BI. Jak modelować i wizualizować dane oraz budować narracje cyfrowe. Wydanie II
-
NoSQL. Kompendium wiedzy
-
Google Analytics od podstaw. Analiza wpływu biznesowego i wyznaczanie trendów
-
Wprowadzenie do systemów baz danych. Wydanie VII
-
T-SQL dla zaawansowanych. Przewodnik programisty. Wydanie IV
-
Skazany na sukces. Kariera w Data Science
-
Poznaj Tableau 2022. Wizualizacja danych, interaktywna analiza danych i umiejętność data storytellingu. Wydanie V
-
Deciphering Data Architectures
-
Hadoop. Komplety przewodnik. Analiza i przechowywanie danych
-
Big Data. Krótkie Wprowadzenie 30
-
Hurtownie danych. Od przetwarzania analitycznego do raportowania
-
Hurtownie danych. Od przetwarzania analitycznego do raportowania. Wydanie II
-
Projektowanie baz danych dla każdego. Przewodnik krok po kroku
-
Bazy danych. Pierwsze starcie
-
Inżynieria niezawodnych baz danych. Projektowanie systemów odpornych na błędy
-
MongoDB w akcji
-
Dane grafowe w praktyce. Jak technologie grafowe ułatwiają rozwiązywanie złożonych problemów
-
Data Modeling with Tableau. A practical guide to building data models using Tableau Prep and Tableau Desktop
-
Linux Server. Kurs video. Usługi serwerowe, skrypty i środowisko graficzne
-
Azure Data Factory Cookbook. Build ETL, Hybrid ETL, and ELT pipelines using ADF, Synapse Analytics, Fabric and Databricks - Second Edition
-
Mastering Tableau 2023. Implement advanced business intelligence techniques, analytics, and machine learning models with Tableau - Fourth Edition
-
Cracking the Data Engineering Interview. Land your dream job with the help of resume-building tips, over 100 mock questions, and a unique portfolio
-
The Definitive Guide to Data Integration. Unlock the power of data integration to efficiently manage, transform, and analyze data
-
Microsoft Power BI Performance Best Practices. A comprehensive guide to building consistently fast Power BI solutions
-
Web scraping. Kurs video. Zautomatyzowane pozyskiwanie danych z sieci
-
Delphi 2007 dla WIN32 i bazy danych
-
Responsible AI in the Enterprise. Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI
-
SQL for Data Analytics. Harness the power of SQL to extract insights from data - Third Edition
-
Kompletny przewodnik po DAX. Analiza biznesowa przy użyciu Microsoft Excel, SQL Server Analysis Services i Power BI
-
Struktury danych z przymrużeniem oka. Zabawna przygoda z przykładami pachnącymi kawą
-
Learn SQL using MySQL in One Day and Learn It Well. SQL for beginners with Hands-on Project
-
Data Engineering with Scala and Spark. Build streaming and batch pipelines that process massive amounts of data using Scala
-
Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition
-
Building ETL Pipelines with Python. Create and deploy enterprise-ready ETL pipelines by employing modern methods
-
Practical MongoDB Aggregations. The official guide to developing optimal aggregation pipelines with MongoDB 7.0
-
Building Statistical Models in Python. Develop useful models for regression, classification, time series, and survival analysis
-
Serverless Machine Learning with Amazon Redshift ML. Create, train, and deploy machine learning models using familiar SQL commands
-
Graph Data Modeling in Python. A practical guide to curating, analyzing, and modeling data with graphs
-
Unleashing Your Data with Power BI Machine Learning and OpenAI. Embark on a data adventure and turn your raw data into meaningful insights
-
Platform and Model Design for Responsible AI. Design and build resilient, private, fair, and transparent machine learning models
-
Learning Tableau 2022. Create effective data visualizations, build interactive visual analytics, and improve your data storytelling capabilities - Fifth Edition
-
CompTIA Data+: DAO-001 Certification Guide. Complete coverage of the new CompTIA Data+ (DAO-001) exam to help you pass on the first attempt
-
SQL Query Design Patterns and Best Practices. A practical guide to writing readable and maintainable SQL queries using its design patterns
-
Machine Learning in Microservices. Productionizing microservices architecture for machine learning solutions
-
Azure Machine Learning Engineering. Deploy, fine-tune, and optimize ML models using Microsoft Azure
-
Machine Learning Model Serving Patterns and Best Practices. A definitive guide to deploying, monitoring, and providing accessibility to ML models in production
-
Building Analytics Teams. Harnessing analytics and artificial intelligence for business improvement
-
Real-World Implementation of C# Design Patterns. Overcome daily programming challenges using elements of reusable object-oriented software
-
Azure Data Engineering Cookbook. Get well versed in various data engineering techniques in Azure using this recipe-based guide - Second Edition
-
Production-Ready Applied Deep Learning. Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks
-
Data Cleaning and Exploration with Machine Learning. Get to grips with machine learning techniques to achieve sparkling-clean data quickly
-
Codeless Time Series Analysis with KNIME. A practical guide to implementing forecasting models for time series analysis applications
-
Simplifying Data Engineering and Analytics with Delta. Create analytics-ready data that fuels artificial intelligence and business intelligence
-
Feature Store for Machine Learning. Curate, discover, share and serve ML features at scale
-
Artificial Intelligence with Power BI. Take your data analytics skills to the next level by leveraging the AI capabilities in Power BI
-
Actionable Insights with Amazon QuickSight. Develop stunning data visualizations and machine learning-driven insights with Amazon QuickSight
-
Mastering Kafka Streams and ksqlDB
-
MongoDB Fundamentals. A hands-on guide to using MongoDB and Atlas in the real world
-
Tableau Desktop Certified Associate: Exam Guide. Develop your Tableau skills and prepare for Tableau certification with tips from industry experts
-
Principles of Data Science. Understand, analyze, and predict data using Machine Learning concepts and tools - Second Edition
-
IBM DB2 11.1 Certification Guide. Explore techniques to master database programming and administration tasks in IBM Db2
-
Java: Data Science Made Easy. Data collection, processing, analysis, and more
-
R: Mining spatial, text, web, and social media data. Create and customize data mining algorithms
-
Python: Data Analytics and Visualization. Perform data processing and analysis with the help of python libraries, gain practical insights into predictive modeling and generate effective results in a variety of visually appealing charts using the plotting packages in Python
-
Splunk: Enterprise Operational Intelligence Delivered. Machine data made accessible
-
Scientific Computing with Python 3. Click here to enter text
-
Apache Spark for Data Science Cookbook. Solve real-world analytical problems
-
Practical Business Intelligence. Optimize Business Intelligence for Efficient Data Analysis
-
Principles of Data Science. Mathematical techniques and theory to succeed in data-driven industries
-
MDX with Microsoft SQL Server 2016 Analysis Services Cookbook. Over 70 practical recipes to analyze multi-dimensional data in SQL Server 2016 Analysis Services cubes - Third Edition
-
Bayesian Analysis with Python. Click here to enter text
-
Python: Real World Machine Learning. Take your Python Machine learning skills to the next level
-
Python Data Science Essentials. Learn the fundamentals of Data Science with Python - Second Edition
-
Splunk Best Practices. Operational intelligent made simpler
-
Hadoop: Data Processing and Modelling. Data Processing and Modelling
-
Mastering Data Mining with Python - Find patterns hidden in your data. Find patterns hidden in your data
-
Smarter Decisions - The Intersection of Internet of Things and Decision Science. A comprehensive guide for solving IoT business problems using decision science
-
Mastering Social Media Mining with Python. Unearth deeper insight from your social media data with advanced Python techniques for acquisition and analysis
-
Advanced Machine Learning with Python. Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
-
Mastering Python Data Analysis. Become an expert at using Python for advanced statistical analysis of data using real-world examples
-
R: Data Analysis and Visualization. Click here to enter text
-
Advanced Splunk. Click here to enter text
-
Splunk Operational Intelligence Cookbook. Transform Big Data into business-critical insights and rethink operational Intelligence with Splunk - Second Edition
-
Data Analytics with Hadoop. An Introduction for Data Scientists
-
Mastering Redis. Take your knowledge of Redis to the next level to build enthralling applications with ease
-
Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R
-
Practical Data Analysis Cookbook. Over 60 practical recipes on data exploration and analysis
-
Learning Qlik Sense: The Official Guide. Get the most out of your Qlik Sense investment with the latest insight and guidance direct from the Qlik Sense team - Second Edition
-
SAP Data Services 4.x Cookbook. Delve into the SAP Data Services environment to efficiently prepare, implement, and develop ETL processes
-
Learning PostgreSQL. Create, develop and manage relational databases in real world applications using PostgreSQL
-
Learning Couchbase. Design documents and implement real world e-commerce applications with Couchbase
-
DynamoDB Cookbook. Over 90 hands-on recipes to design Internet scalable web and mobile applications with Amazon DynamoDB
-
Learning Predictive Analytics with R. Get to grips with key data visualization and predictive analytic skills using R
-
Sharing Big Data Safely. Managing Data Security
-
Elasticsearch Blueprints. A practical project-based guide to generating compelling search solutions using the dynamic and powerful features of Elasticsearch
-
Lucene 4 Cookbook. Over 70 hands-on recipes to quickly and effectively integrate Lucene into your search application
-
Apache Solr Search Patterns. Leverage the power of Apache Solr to power up your business by navigating your users to their data quickly and efficiently
-
PostgreSQL Server Programming. Extend PostgreSQL using PostgreSQL server programming to create, test, debug, and optimize a range of user-defined functions in your favorite programming language
-
DynamoDB Applied Design Patterns. Apply efficient DynamoDB design patterns for high performance of applications
-
FileMaker Pro 13: The Missing Manual
-
Pentaho Data Integration Beginner's Guide. Get up and running with the Pentaho Data Integration tool using this hands-on, easy-to-read guide with this book and ebook - Second Edition
-
Transact-SQL. Czarna księga
-
Prywatność danych w praktyce. Skuteczna ochrona prywatności i bezpieczeństwa danych