Analiza danych

Analiza danych jest ekscytującą dyscypliną, która umożliwia zrozumienie pewnych zjawisk, uzyskanie wglądu i wiedzy na podstawie surowych danych. Pojęcie to oznacza dokładnie przetwarzanie danych za pomocą technik matematycznych i statystycznych w celu uzyskania cennych wniosków, podjęcia ważnych decyzji i opracowania przydatnych produktów. Termin ten wywodzi się od angielskiego data science, często traktowanego jako synonim takich terminów, jak analityka biznesowa, badania operacyjne, business intelligence, wywiad konkurencyjny, analiza i modelowanie danych, a także pozyskiwanie wiedzy. Dzięki takim technologiom, jak języki Python czy R, platformy Hadoop i Spark masz szansę wyciągnąć maksimum wniosków, dostrzec szanse na rozwój swojej organizacji albo przewidzieć i zapobiec zagrożeniom.

siatka lista

Practical Deep Learning at Scale with MLflow. Bridge the gap between offline experimentation and online production

Yong Liu, Dr. Matei Zaharia

Practical GIS. Learn novice to advanced topics such as QGIS, Spatial data analysis, and more

Gábor Farkas

Practical Machine Learning Cookbook. Supervised and unsupervised machine learning simplified

Atul Tripathi

Practical Predictive Analytics. Analyse current and historical data to predict future trends using R, Spark, and more

Ralph Winters

Practical Real-time Data Processing and Analytics. Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

Shilpi Saxena, Saurabh Gupta

Practical Site Reliability Engineering. Automate the process of designing, developing, and delivering highly reliable apps and services with SRE

Pethuru Raj Chelliah, Shreyash Naithani, Shailender Singh

Practical Threat Intelligence and Data-Driven Threat Hunting. A hands-on guide to threat hunting with the ATT&CK™ Framework and open source tools

Valentina Costa-Gazcón

Practical Time Series Analysis. Master Time Series Data Processing, Visualization, and Modeling using Python

Avishek Pal, PKS Prakash

Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition

Sinan Ozdemir

Principles of Data Science. Mathematical techniques and theory to succeed in data-driven industries

Sinan Ozdemir

Principles of Data Science. Understand, analyze, and predict data using Machine Learning concepts and tools - Second Edition

Sinan Ozdemir, Sunil Kakade, Marco Tibaldeschi

Principles of Strategic Data Science. Creating value from data, big and small

Peter Prevos

Programming PowerPoint With VBA Straight to the Point. Harness VBA to Customize, Automate, and Revolutionize PowerPoint Presentations

MrExcel's Holy Macro! Books, Eduardo N Sanchez

Puppet 3 Cookbook. An essential book if you have responsibility for servers. Real-world examples and code will give you Puppet expertise, allowing more control over servers, cloud computing, and desktops. A time-saving, career-enhancing tutorial - Second Edition

John Arundel

PySpark Cookbook. Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

Denny Lee, Tomasz Drabas

Python 3 and Data Analytics Pocket Primer. A Quick Guide to NumPy, Pandas, and Data Visualization

Mercury Learning and Information, Oswald Campesato