Uwaga! Ze względu na zagrożenie związane z COVID-19, w trosce o zdrowie i bezpieczeństwo klientów jak i pracowników SAS, wszystkie szkolenia kalendarzowe oraz dedykowane prowadzimy w formule Live Web z instruktorem na żywo, ale w wirtualnej sali. Zachęcamy również do zapoznania się z bezpłatną ofertą szkoleń w postaci e-learning do samodzielnej nauki. Do zobaczenia on-line!



SAS Enterprise Miner

Nazwa szkolenia Poziom szkolenia Rodzaj szkolenia
Analityka praktyczna w SAS Enterprise Miner
Kurs ten obejmuje umiejętności wymagane do tworzenia diagramów przepływu analizy przy użyciu bogatego zestawu narzędzi SAS Enterprise Miner, zarówno w zakresie odkrywania wzorców (segmentacja, asocjacjacja i analiza sekwencji), jak i modelowania predykcyjnego (drzewo decyzyjne, regresja i modele sieci neuronowych).

3 zaawansowany Classroom Live Web Classroom e-Learning
Strategie i koncepcje dla data scientistów i analityków biznesowych Business Knowledge Series
To be effective in a competitive business environment, analytics professionals need to use descriptive, predictive, and prescriptive analytics to translate information into decisions. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends.

In this course, you gain the skills data scientists and statistical business analysts must have to succeed in today's data-driven economy. Learn about visualizing big data, how predictive modeling can help you find hidden nuggets, the importance of experiments in business, and the kind of value you can gain from unstructured data.

This course combines scheduled, instructor-led classroom or Live Web sessions with small-group discussion, readings, and hands-on software demonstrations, for a highly engaging learning experience.

3 zaawansowany Live Web Classroom
Social Network Analytics Business Knowledge Series
This course discusses how to leverage social networks for analytical purposes. Obviously, when we say "social networks," many people think of Facebook, Twitter, Google+, LinkedIn, and so on. These are all examples of networks that connect people using either friendship or professional relationships. In this course, we zoom out and provide a much more general definition of a social network. In fact, we define a social network as a network of nodes that are connected using edges. Both nodes and edges can be defined in various ways, depending on the setting. This course starts by describing the basic concepts of social networks and their applications in marketing, risk, fraud, and HR. It then defines various social metrics and illustrates how they can be used for community mining. The course also discusses how social networks can be used for predictive analytics. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details, and is illustrated by several real-life cases. The instructor extensively reports on both his research and consulting experience in the field. References to background material such as selected papers, tutorials, and guidelines are also provided.

3 zaawansowany e-Learning
Survival Data Mining Using SAS Enterprise Miner Software Business Knowledge Series
This advanced course covers predictive hazard modeling for customer history data. Designed for analysts, the course uses SAS Enterprise Miner to illustrate survival data mining methods and their practical implementation.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.

4 ekspert e-Learning
Fraud Detection Using Descriptive, Predictive, and Social Network Analytics Business Knowledge Series
A typical organization loses an estimated 5 of its yearly revenue to fraud. This course shows how learning fraud patterns from historical data can be used to fight fraud. The course discusses the use of supervised learning (using a labeled data set), unsupervised learning (using an unlabeled data set), and social network learning (using a networked data set). The techniques can be applied across a wide variety of fraud applications, such as insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and counterfeiting. The course provides a mix of both theoretical and technical insights, as well as practical implementation details. During the course, the instructor reports extensively on his recent research insights about the topic. Various real-life case studies and examples are presented for further clarification.

4 ekspert Live Web Classroom e-Learning
Zaawansowana analityka w świecie Big Data Business Knowledge Series
In today's big data world, many companies have gathered huge amounts of customer data about marketing success, use of financial services, online website usage, and even fraud behavior. Given recent trends and needs such as mass customization, personalization, Web 2.0, one-to-one marketing, risk management, and fraud detection, it becomes increasingly important to extract, understand, and exploit analytical patterns of customer behavior and strategic intelligence. This course helps clarify how to successfully adopt recently proposed state-of-the art analytical and data science techniques for advanced customer intelligence applications. This highly interactive course provides a sound mix of both theoretical and technical insights as well as practical implementation details and is illustrated by several real-life cases. The instructor will extensively report on both his research and consulting experience in the field. References to background material such as selected papers, tutorials, and guidelines are also provided.

Note: This course was formerly titled Advanced Analytics for Customer Intelligence Using SAS.

4 ekspert Live Web Classroom e-Learning
Analytics Value Training


Program description
The Analytics Value Training (AVT) is in its essence a software and technology agnostic/independent program, that helps to transform organizations to be more data-driven and obtain business value with analytics. The program objectives are to establish a mindset for analytics, understand the business value and develop critical skills to succeed in the organization’s interdepartmental analytics efforts. Attendees can bring their own cases and benefit from the expertise of an ecosystem of industry thought leaders.
The program will be delivered in 5 session of 2 days each during 12 months. The sessions will be scheduled bi-monthly. The learning consists of learning lessons and hand-on sessions.

Program outcomes and Learning Objectives
Knowledge
  • Define value created by analytics in organizations
  • Identify suitable advanced analytical techniques for their tasks
  • Recognize necessary data for each task
  • Match relevant data for specific analytics
  • Skills
  • Apply analytical techniques for analytics
  • Develop analytical models
  • Perform business analytics
  • Compare different solutions/models
  • Abilities
  • Communicate analytics value in different context
  • Present solutions in a clear manner
  • Define analytics problem clearly
  • 2 średnio zaawansowany Live Web Classroom
    Zarządzanie modelami analitycznymi w SAS Model Manager 14.2
    Szkolenie skupia się na następujących kluczowych obszarach: zarządzanie źródłami danych w SAS Model Manager, tworzenie projektów w SAS Model Manager, importowanie modeli do SAS Model Manager, używanie Query Utility w SAS Model Manager, tworzeniu zadań scoringowych, eksportowanie modeli i projektów do repozytorium SAS oraz tworzenie i konfigurowanie wersji cyklu życia modelu. Szkolenie obejmuje również generowanie raportów porównawczych modeli, publikowanie i wdrażania modeli, tworzenia raportów monitorujących model produkcyjny oraz tworzenie przez użytkownika własnych raportów.

    3 zaawansowany Classroom Live Web Classroom
    SAS Enterprise Miner Integracja z językami Open Source
    Kurs ten wprowadza podstawy do integracji programowania R i skryptów Pythona z SAS i SAS Enterprise Miner. Tematyka kursu jest prezentowana w kontekście eksploracji danych, która obejmuje eksplorację danych, prototypowanie modeli oraz nadzorowane i nienadzorowane techniki uczenia się.

    3 zaawansowany Classroom Live Web Classroom
    Rozszerzenie funkcjonalności SAS Enterprise Miner o własne węzły
    To szkolenie pokazuje jak rozszerzyć funkcjonalności narzędzia SAS Enterprise Miner.

    3 zaawansowany Classroom Live Web Classroom
    Experimentation in Data Science
    This course explores the essentials of experimentation in data science, why experiments are central to any data science efforts, and how to design efficient and effective experiments.

    The e-learning format of this course includes Virtual Lab time to practice.

    3 zaawansowany e-Learning
    Data Mining Techniques: Predictive Analytics on Big Data
    This course introduces applications and techniques for assaying and modeling large data. The course also presents basic and advanced modeling strategies, such as group-by processing for linear models, random forests, generalized linear models, and mixture distribution models. Students perform hands-on exploration and analyses using tools such as SAS Enterprise Miner, SAS Visual Statistics, and SAS In-Memory Statistics.

    3 zaawansowany e-Learning
    Using SAS to Put Open Source Models into Production
    This course introduces the basics for integrating R programming and Python scripts into SAS Enterprise Miner. Topics are presented in the context of data mining, which includes data exploration, model prototyping, and supervised and unsupervised learning techniques.

    3 zaawansowany Live Web Classroom e-Learning
    Credit Risk Modeling Business Knowledge Series
    In this course, students learn how to develop credit risk models in the context of the Basel guidelines. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and exercises.

    Please note: This course is not intended to teach credit risk modeling using SAS. Previous SAS software and SAS Enterprise Miner experience is helpful but not necessary.

    3 zaawansowany Live Web Classroom e-Learning
    Budowa kart scoringowych w środowisku SAS Enterprise Miner
    Szkoleniu uczy jak budować kredytowe karty scoringowe przy użyciu SAS Enterprise Miner 14.1 i metodyki zalecanej przez wiodących ekspertów kredytowych i finansowych.

    3 zaawansowany Classroom Live Web Classroom e-Learning
    Zaawansowane modelowanie predykcyjne w SAS Enterprise Miner
    Kurs ten obejmuje zaawansowane tematy z wykorzystania SAS Enterprise Miner, w tym jak zoptymalizować wydajność modeli predykcyjnych. Kurs kontynuuje temat rozwoju modeli predykcyjnych, który rozpoczyna się w ramach kursu Analityka praktyczna w SAS Enterprise Miner, m.in. poprzez wykorzystanie dwustopniowego węzła modelowania. Ponadto omówiono niektóre z najnowszych węzłów modelowania oraz najnowsze metody doboru zmiennych. Wskazówki dotyczące efektywnej pracy z SAS Enterprise Miner uzupełniają kurs.

    4 ekspert Classroom Live Web Classroom
    Modelowanie drzew decyzyjnych
    Kurs ten obejmuje modele predykcyjne o strukturze drzewa oraz metodologię tworzenia, przycinania i oceny drzew decyzyjnych. Ponadto, kurs ten omawia wiele pomocniczych zastosowań drzew, takich jak eksploracja danych, redukcja wymiarów i imputacja braków danych.

    4 ekspert Classroom Live Web Classroom
    Modelowanie sieci neuronowych
    Ten kurs pomaga zrozumieć i zastosować dwa popularne algorytmy sztucznej sieci neuronowej: perceptrony wielowarstwowe i podstawowe funkcje radialne. Omówiono zarówno teoretyczne, jak i praktyczne zagadnienia dopasowania sieci neuronowych. W szczególności szkolenie uczy, jak wybrać odpowiednią architekturę sieci neuronowej, jak określić odpowiednią metodę uczenia, jak wdrożyć modele sieci neuronowej w rozproszonym środowisku komputerowym oraz jak budować niestandardowe sieci neuronowe przy użyciu procedury NEURAL.

    4 ekspert Classroom Live Web Classroom e-Learning