Academic community, future data scientists mainly study methods for data mining using open source software like R or Python. However, pharmaceutical, financial and other big companies are using commercial tools such as SAS, SPSS,.. Moving to commercial tools can sometimes be difficult and expensive. That is why companies often decide to hire a senior or a person with experience in working with commercial data mining tools. So, it legitimate is to ask ourselves questions: Is studying commercial data mining tools a privilege for those working in big companies? Is it possible to study commercial data mining tools for free? This presentation will show us the developing path of a data miner as well as which SAS services can be used for free while studying. Several characteristic examples will show different SAS OnDemand for Academics possibilities.
Relationship between Accounting Information and Decision Making in the Sri La...inventionjournals
Accounting information is used extensively by organizations in strategic decision making. This study analyses the relationship between accounting information and strategic decision making in the Sri Lankan manufacturing sector, specifically the relationship between accounting information and manufacturing and marketing related strategic decision making of companies operating in Sri Lanka’s manufacturing sector. Sample for the study consisted of 70 public quoted manufacturing companies operating in the country. The unit of analysis for this research was at company level. Primary data for the study were collected via a questionnaire survey which was conducted with the Chief Executives Officers of the selected manufacturing organizations. The relationship between accounting information and marketing and manufacturing related strategic decision making was analysed using Pearson’s Correlation. Findings from the study indicated that accounting information has a statistically significant strong positive correlation with both marketing related strategic decision making and manufacturing related strategic decision making of companies operating in Sri Lanka’s manufacturing sector
An Efficient Approach for Ultrasonic Characterization of Biomaterials using N...inventionjournals
The methodology for characterization of biomaterial is the source of signal, Biomaterial and the response detector. The high frequency ultrasonic signal generator generates signals of high frequency suitable for biomaterial characterization. In this system, Ultrasonic signals are made to fall on the biomaterial to be characterized. While passing through the biomaterial, the ultrasonic signals are absorbed, reflected and scattered along different directions. The transmitted and reflected received signals are sense and detect by receiving transducer. The sensor produces proportional current in microamperes. This current will be applied to sensing circuit, which converts current into proportional amplified voltage with the help of Op-Amp. An Analog to Digital Converter (ADC) converts analog signal into digital signal. This digital signal provides data to a computer. Data acquisition circuit interconnects the PC and driver software to which the data is input. Driver software makes NI LabVIEW to interact with hardware. The PC with LabVIEW platform is used to study characteristics of the biomaterials.
On English Vocabulary Teaching Methods in Chinese Senior High Schoolsinventionjournals
This paper discussed the problems of vocabulary teaching in Chinese senior high schools based on the present situation of English teaching in China. By surveying and analyzing the main factors that affect English vocabulary teaching in Chinese senior high schools, the author proposed possible vocabulary teaching methods, and finally brought up some solutions so as to improve the relevant English teaching.
Relationship between Accounting Information and Decision Making in the Sri La...inventionjournals
Accounting information is used extensively by organizations in strategic decision making. This study analyses the relationship between accounting information and strategic decision making in the Sri Lankan manufacturing sector, specifically the relationship between accounting information and manufacturing and marketing related strategic decision making of companies operating in Sri Lanka’s manufacturing sector. Sample for the study consisted of 70 public quoted manufacturing companies operating in the country. The unit of analysis for this research was at company level. Primary data for the study were collected via a questionnaire survey which was conducted with the Chief Executives Officers of the selected manufacturing organizations. The relationship between accounting information and marketing and manufacturing related strategic decision making was analysed using Pearson’s Correlation. Findings from the study indicated that accounting information has a statistically significant strong positive correlation with both marketing related strategic decision making and manufacturing related strategic decision making of companies operating in Sri Lanka’s manufacturing sector
An Efficient Approach for Ultrasonic Characterization of Biomaterials using N...inventionjournals
The methodology for characterization of biomaterial is the source of signal, Biomaterial and the response detector. The high frequency ultrasonic signal generator generates signals of high frequency suitable for biomaterial characterization. In this system, Ultrasonic signals are made to fall on the biomaterial to be characterized. While passing through the biomaterial, the ultrasonic signals are absorbed, reflected and scattered along different directions. The transmitted and reflected received signals are sense and detect by receiving transducer. The sensor produces proportional current in microamperes. This current will be applied to sensing circuit, which converts current into proportional amplified voltage with the help of Op-Amp. An Analog to Digital Converter (ADC) converts analog signal into digital signal. This digital signal provides data to a computer. Data acquisition circuit interconnects the PC and driver software to which the data is input. Driver software makes NI LabVIEW to interact with hardware. The PC with LabVIEW platform is used to study characteristics of the biomaterials.
On English Vocabulary Teaching Methods in Chinese Senior High Schoolsinventionjournals
This paper discussed the problems of vocabulary teaching in Chinese senior high schools based on the present situation of English teaching in China. By surveying and analyzing the main factors that affect English vocabulary teaching in Chinese senior high schools, the author proposed possible vocabulary teaching methods, and finally brought up some solutions so as to improve the relevant English teaching.
Oscillation results for second order nonlinear neutral delay dynamic equation...inventionjournals
In this paper, we establish sufficient conditions for the oscillation of solutions of second order neutral delay dynamic equations [r (t )( x (t ) p (t ) x ( (t ))) ] q (t ) f ( x ( (t ))) = 0 on an arbitrary time scale 핋.
Unapredjenje prodaje trening obuka menadzera poslovna znanjaMiodrag Kostic, CMC
Kako unaprediti prodaju! Kako poboljšati prodajne rezultate?
Želite li da više i bolje prodajete i kvalitetnije uslužujete vaše kupce?
http://www.prodajnaznanja.com/
Tehnicka dokumentacija i studija slučaja o vidu internet prevare: Phishing. Predmet testiranja su studenti informatike. 20 poslatih email zahteva za promenu podataka, pročitajte rezultat. Prica pored tehničke pripreme jednog prostog sistema za phishing ispituje svest i obazrivost mladih it stručnjaka, ali takodje objašnjava i koje su mere zaštite od ove sve češće pojave.
Kako se pravi prodajni tim? - How to build a sales team?C Automation
Prezentacija iz 2010-e godine prikazana na godišnjoj konferenciji najuspešnijih prodavaca i menadžera jedne multinacionalne kompanije. Razlog prikazivanja je moja uspešna izgradnja više prodajnih timova koji i dan danas funkcionišu širom Srbije.
Oscillation results for second order nonlinear neutral delay dynamic equation...inventionjournals
In this paper, we establish sufficient conditions for the oscillation of solutions of second order neutral delay dynamic equations [r (t )( x (t ) p (t ) x ( (t ))) ] q (t ) f ( x ( (t ))) = 0 on an arbitrary time scale 핋.
Unapredjenje prodaje trening obuka menadzera poslovna znanjaMiodrag Kostic, CMC
Kako unaprediti prodaju! Kako poboljšati prodajne rezultate?
Želite li da više i bolje prodajete i kvalitetnije uslužujete vaše kupce?
http://www.prodajnaznanja.com/
Tehnicka dokumentacija i studija slučaja o vidu internet prevare: Phishing. Predmet testiranja su studenti informatike. 20 poslatih email zahteva za promenu podataka, pročitajte rezultat. Prica pored tehničke pripreme jednog prostog sistema za phishing ispituje svest i obazrivost mladih it stručnjaka, ali takodje objašnjava i koje su mere zaštite od ove sve češće pojave.
Kako se pravi prodajni tim? - How to build a sales team?C Automation
Prezentacija iz 2010-e godine prikazana na godišnjoj konferenciji najuspešnijih prodavaca i menadžera jedne multinacionalne kompanije. Razlog prikazivanja je moja uspešna izgradnja više prodajnih timova koji i dan danas funkcionišu širom Srbije.
Filip Panjevic is a Co-Founder and CTO at ydrive.ai - startup dealing with self-driving cars, and one of the founders of Petnica Machine Learning School.
Filip's talk will focus on the story of Petnica School, how did it start, what has changed since the beginning, how the concept of school looks right now and why is that concept good for making new data scientists. This talk will be perfect for people who consider starting their careers in the data science field!
The talk will be a broad overview and thoughts about building one of the biggest data science communities in India. I will talk about how an ecosystem is created and value delivered to each stakeholder. I will be sharing my experience of building MachineHack and AIMinds and other platforms. One of the core agendas of the talk will be how these platforms have enabled a unique data science education and learning experience in India. The platforms built help students and engineers to imagine and work towards a career in data science.
In Drazen talk, you will get a chance to listen to how Data Science Master 4.0 on Belgrade University was created, and what are the benefits of the program.
PwC's recently released Responsible AI Diagnostic surveyed around 250 senior business executives from May to June 2019. The survey says that 84% of CEOs agree that AI-based decisions need to be explainable in order to be trusted. In the past few years, Deep learning has shown remarkable results in various applications, which makes it one of the first choices for many AI use cases. However, deep learning models are hard to explain, and since the majority of CEOs expect AI solutions to be explainable, deep learning has a serious challenge. Daniel Kahneman, in his book thinking fast and slow, presented two different systems the human brain uses to form thoughts and decisions: System 1: fast, intuitive and hard to explain System 2: slow, conscious and easy to explain In this talk I will present: A) PwC Responsible AI Survey B) A proposed deep learning framework that mimics the two systems of thinking C) The recent advances in the neural symbolic learning field.
Challenges in building a churn prediction model in different industries, presented by Jelena Pekez from Comtrade System Integration. Talk is focused on real-life use-case experience.
In my talk I am going to share with the audience a practical experience of using BI solutions for steering bank credit portfolios, make data actionable, communicate and collaborate on that data with relevant stakeholders. In our case, we have aimed for a solution that can use data-models based on Claud and on-premise, easily communicate and share information within the organization and keep track of that information flow. In addition, we want our solution to support various datasets and to have the flexibility of integrating the most popular DS languages – R and Python for the convenience and flexibility of our data science team. Our solution is based on Power BI plus the use of Azure Analytical Service and R.
The talk will have 3 parts. The overview of the practical applications of the AI and ML in the FinTech industry with a short explanation of the PSD2 directive and the disruption is caused. Application of the AI/ML from the perspective of the end-user, personal financial health, financial coach, etc. The overview of the architecture, technologies, and frameworks used with practical examples from the Zuper company.
We present a recommender system for personalized financial advice, which we designed for a large Swiss private bank. The final recommendations produced by the system were delivered to the end clients through a mobile banking platform. The recommender system is based on a collaborative filtering technique and can work with changing asset features, operate with implicit ratings and react to explicit feedback that clients can give using the mobile app. Moreover, we developed and implemented an approach to provide an explanation for each recommendation in the form “As you bought A, you might like B".
This talk shall focus on making real-time pipelines using cutting edge Big Data technologies and applying ML on gathered data. The first part of the presentation shall cover importance and necessity for streaming data processing. In addition, tools that could be used in order to build a streaming pipeline shall be proposed. The second part of this talk shall focus on making machine learning models in customer support. There shall be introduced success stories covering the need for more efficient customer support, problem resolution and gained benefits.
Presentation of the first complete AI investment platform. It is based on most innovative AI methods: most advanced neural networks (ResNet/DenseNet, LSTM, GAN autoencoders) and reinforcement learning for risk control and position sizing using Alpha Zero approach. It shows how the complex AI system which covers both supervised and reinforcement learning could be successfully used to investment portfolio optimization in real-time. The architecture of the platform and used algorithms will be presented together with the workflow of machine learning. Also, the real demo of the platform will be shown.
A lot of companies make the mistake of thinking that just hiring Data Scientists will lead to increased revenue or increased profit. For a company’s investment in Data Science to be successful the Data Scientists need to work on the right problems, with the right people, and with the right tools. In this presentation, I will talk about the lessons I have learned, and mistakes made in applying Data Science in commercial settings over the last 10 years. I will highlight what processes can increase the chances of Data Science investment being successful.
The talk would be focusing on reasons and method for creating models which maximize sales price Gross Margin but still has high confidentiality that quote would be accepted by the client. Price changes are dynamic things that are impacted by many different elements like cost of input material, labor cost, transportation cost, scrap material due to different ordered quantities, etc. Besides input cost segments, output price is also impacted by different marketing campaigns (own and others), seasonality, past and future customer behavior as well as the behavior of the product we are selling.
Andjela will share the best practices that Things Solver brings when it comes to data monetization. Things Solver clients sell more customize offerings and end up with happier customers. Andjela will share machine learning modules that do just that within Coeus. Things Solver platform.
In the past few years, many businesses started do understand the potential of real-time data analytics. And many of those invested time, energy and finances to make it happen, with weaker outcomes than expected. Reasons are few for this: too ambitious plans by leadership regarding leveraging data, not enough discipline defining goals and MVP for initial use cases, a plethora of tools and vendors available who claim that can solve all the problems, etc. So, how can we get the most value with reasonable costs out of fast (real-time) data? We will try to answer this question and give actionable advice.
University of Nottingham Ningbo China The advances of 5G, sensor, and information technologies have enabled the proliferation of smart pervasive sensor networks. Rapid progress in the design of biomedical sensors, advances in the management of medical knowledge, and improvement of algorithms for decision support, are fueling a technological disruption to health monitoring. Current technologies enable personalized A3 (anyplace, anytime, anywhere) health monitoring. Continuous health monitoring enables the extension of health care into home and workplace changing the modes of traditional health care delivery. Medical grade systems require innovative solutions for system dependability, medical decision support, data management, and interpretation, beyond current fitness and wellbeing applications. We will present innovative solutions for A3 health monitoring and discuss the use of blockchain technologies, and artificial intelligence addressing technical, medical, and ethical requirements for personalized health monitoring systems.
Data Quality is essential for e-commerce and automating it can reduce a business’s daily bottlenecks and promote its competitiveness. Product similarity can help reduce duplicate content leveraging all types of product information. But dealing with mixed-type data such as product data is a rather untypical but real business case and can be challenging.
Uroš Valant has almost 20 years of experience in planning, managing and delivering of various IT projects. He has the best and richest experience in the field of business analytics, project planning and implementation, database design and the management of development teams. In the last years, his focus is the field of predictive analytics, machine learning and applying the AI solution to a practical use in different field of work.
In his talk he will present to us interactive case study of the image recognition use and AI assisted design techniques in the textile industry.
The presentation will start as an engaging lecture where I will present the motivation behind the project based on my academic research (my Oxford PhD among others). I will tell the audience just how rampant corruption is in local governance and why is it so persistent. Then I will present our remedy: full budget transparency. I will show them our search engine and how it works, and will call the participants to download the APIs and play with the data themselves.
The talk will be divided into two parts. The first one is about geospatial open data and several Copernicus services where those data can be downloaded. The second one is about Forest and Climate project, as an example of geospatial analysis. The aim of the project was to identify the most suitable area for afforestation in Serbia by using satellite and Earth observation data. The results can be found at https://sumeiklima.org/.
Geospatial Analysis and Open Data - Forest and Climate
SAS OnDemand for Academics - Vladimir Marković
1. SAS OnDemand for Academics
Vladimir Marković, MSc CS & math
BI Team Leader, Banca Intesa Beograd
2. Agenda
• Uvod (5 minuta)
• Pre nego što počnete (15 minuta)
– Multidisciplinarnost, BI lanac i nivoi analitičnosti
– Metode, sredstva i vremenski okvir
– Definisanje ciljeva
– Planiranje usavršavanja
• SAS Ondemand for University - za početnike i studente (20 minuta)
– Registracija, instaliranje i podešavanja
– Prikaz GUI - osnovne funkcionalnosti
– Demo 1. Učitavanje podataka
– Demo 2. Korišćenje gotovih SAS taskova
– Demo 3. Učenje kroz „snippet“
• Zaključak (5 minuta)
3. Izazovi
• Kompanije
– Da li zaposliti formiranog analitičara ili ga napraviti unutar
kompanije?
– Kako napraviti analitičara?
– Kako zadržati analitičara?
• Studenti i zaposleni
– Kako naći posao kada kompanije traže iskusne i obučene
analitičare?
“Da bi bio uspešan u nečemu moraš se tome posvetiti i postati
jako dobar u tome. Nema magije, sve je vežba, vežba i vežba.“
4. Cilj prezentacije
• Dati smernice u planiranju usavršavanja za ulogu
analitičara
• Pokazati da softver poznatih vendora je dostupan
i akademskoj zajednici
• Pokazati kako učiti pomoću SAS Studio
5. Pre nego što počnete
• Multidisciplinarnost, BI lanac i nivoi analitičnosti
• Metode, sredstva i vremenski okvir
• Definisanje ciljeva
• Planiranje usavršavanja
6. Multidisciplinarnost
• DWH/BI znanja
– Integracija podataka (baze
podataka, fajlovi)
– SQL
– BI alati
– Big data alati
• Primenjana statistika
– Deskriptivna analiza
– Prediktivna analiza
– Preskriptivna analiza
• Specifične oblasti
– Bankarstvo, osiguranje,
marketing, maloprodaja...
8. Način, sredstva i vremenski okvir
- za i protiv -
• Metod učenja
– Trening i kursevi
– Mentoring - vežba kroz primere i uz rad
– Samostalno izučavanje
• Sredstva usvajanja znanja
– Literatura
– Vežba nad podacima i specifičnim alatima
– E-learning
• Vremenski okvir
– Kontinuirano
– Prema potrebi
9. Definisanje ciljeva
„Prvo, postavite određeni, jasni, praktični ideal; cilj. Drugo, osigurajte
potrebna sredstva da bi dosegli cilj; mudrost, novac, materijal i metode.
Treće, prilagodite sredstva svom cilju.“ - Aristotel
„Ako nešto ne možete postići, to ostavite i pređite na nešto što možete
ostvariti.“ - Al Kali
„Ciljevi određuju ono što ćete postati.“ - Julius Erving
„Kad dosegnete svoje ciljeve, postavite sebi nove. Tako rastete i
postajete moćnija osoba.“ - Les Brown
„Možda nećete postići sve ciljeve koje ste postavili - nitko ne postiže - ali
ono što je stvarno bitno je imati ciljeve i ići prema njima punim srcem.“ -
Les Brown
10. Planiranje usavršavanja
• Definišite jasan cilj usavršavanja
– Npr. ovladati logističkom regresijom ili SPSS alatom...
• Prikupljanje informacija o oblastima usavršavanja
– Poslovna potreba i primena,...
– Dostupna literatura
– Kursevi, treninzi, e-learning ,...
• Odredite budžet
– Vremenski okvir
– Novac
• Razmislite šta dobijate i čega se odričete
– Izlasci, porodica, putovanja,...
– Bolje plaćen posao, posao koji volite,...
11. Primer planiranja usavršavanja
Management
Overview: Exploring
the Platform for SAS
Business Analytics
Prsonalizing the SAS
Information
Delivery Portal
Using SAS Web
Report Studio
Accesing SAS from
Microsoft Office
Applications
Getting Started with the
Platform for SAS
Business Analysis
Creating BI
Dashboards Using
SAS
Creating
Information Maps
Using SAS
Creating Stored
Processes Using SAS
1: Essentials
Creating Stored
Processes Using
SAS: Additional
Topics
SAS Enterprise
Guide: Quering and
Reporting (EG 5.1)
SAS Enterprise
Guide:Advanced
Task and Quering
(EG 5.1)
SAS Programing 1:
Essentials
SAS Programing 2:
Data Manipulation
Techniques
SAS SQL 1:
Essentials
SAS Macro Language
1: Essentials
SAS Programing 3:
Advanced
Techniques and
Efficiencies
Creating Reports
and Graphs with
SAS Enterprise
Guide
Introduction to
Statistical Concepts
Rapid Predictive
Modeling for
Business Analysts
(EM 12.0)
Statistics 1:
Introduction to
ANOVA, Regression
and Logistic
Regression
Applied Analytics
Using SAS
Enterprise Miner
Predictive Modeling
Using Logistic
Regression
Power User – Content Manager
Business Analysis
Advance Business Analysis
Data Mining
Report Consumer
Advance Data Mining
• Nivo 1 – izrada izveštaja, jednostavnije ad hoc analize,
– Crosstable, list table, jednostavniji grafikoni, izrada prezentacija sa dinamičkim osvežavanje
podataka, excel pivot, …
• Nivo 2 – Napredne ad hoc analize
– query wizard, manipulacija sa fajlovima, parametrizacija analiza, osnovne statističke funkcije
• Nivo 3 – Istraživanje podataka
– PROC SQL, DATA STEP, pisanje SAS makroa i SAS koda, osnove ETL, izrada modela
Alati Nivo 1 Nivo 2 Nivo 3
SAS Information Delivery
Portal
Y
SAS Web Report Studio Y
SAS Add-in for MS Office Y Y
SAS Enterprise Guide Y Y
SAS Enterprise Miner Y
12. SAS Studio
• Registracija, instaliranje i podešavanje
• Početak rad i upoznavanje sa grafičkim interfejsom
• Demo 1. Učitavanje podataka
• Demo 2. Korišćenje SAS Studio Taskova
• Demo 3. Korišćenje SAS Studio Snippet-a
13. Registracija, instaliranje i podešavanja
1. http://www.sas.com/en_us/software/unive
rsity-edition.html
2. Popuniti registracioni upitnik
3. SAS Virtual Machine ili Amazon Web
Service
4. SAS VMware Virtual Machine
– Download SAS VMware Virtual Machine 1,7 GB
– Download VMWare Workstation Player
– Podešavanje prema uputstvu
14. Početak rada
1. Podizanje SAS VMware Virtual Machine
2. Iz Internet pretraživača pristupiti http://192.168.137.128
3. Start SAS Studio
15. Demo 1. Učitavanje podataka
• Podatke kopirati u folder koji smo predhodno dodelili SAS VMware VM
• Oni će biti vidljivi kroz SAS Studio
• Dvostruki klikom na fajl SAS studio generiše SAS code PROC IMPORT
16. Demo 2. Korišćenje SAS Studio taskova
• Data Exploration
• Summary Statistics
• Correlation Analysis
• Table Analysis
• Binary Logistict Regression
• Forecasting
18. Zaključak
Prvo, postavite određeni, jasni, praktični ideal; cilj. Drugo, osigurajte potrebna
sredstva da bi dosegli cilj; mudrost, novac, materijal i metode. Treće, prilagodite
sredstva svom cilju.
Da bi bio uspešan u nečemu moraš se tome posvetiti i postati jako dobar u
tome. Sve je to naporan rad. Ništa ne dolazi lako. Nema magije, sve je vežba,
vežba i vežba.“
SAS University Edition omogućava da za 1 sat besplatno uspostavite
okruženje i odmah počnete sa radom.
19. Reference
• SAS University Edition
http://support.sas.com/software/products/university-edition/
• „A Recipe for Success Using SAS University Edition“ – Sharon
Torrence Jones
• „An Introduction to SAS University Edition“ – Rony Cody
• „Essential Statistics Using SAS University Edition“ – Geoff
Der, Brian S. Everitt