In order to make decisions in business, analysts drive data from various sources. it will be put to test. later forecasts are made. Thus, business analysis is a process to analyze the business data so that decisions can be made
Introduction to Business Analytics Part 1 published by BeamSync.
BeamSync is providing business analytics training course in Bangalore. If you are looking for analytics training then visit BeamSync. Regular classes are running during the weekend.
For details visit: http://beamsync.com/business-analytics-training-bangalore/
Introduction to Business Analytics Part 1 published by BeamSync.
BeamSync is providing business analytics training course in Bangalore. If you are looking for analytics training then visit BeamSync. Regular classes are running during the weekend.
For details visit: http://beamsync.com/business-analytics-training-bangalore/
An introduction to analytics is a small presentation made for increasing awareness on analytics with some case studies of applying analytics in different functions.
These case studies are from informs.org which were openly available when the presentation was made. Due to confidentiality related obligations my personal experiences were shared - without naming clients - during the presentation. However, the case studies cannot be share on the PPT here. For more details or inputs on analytics you can reach me at twitter - @krdpravin or LinkedIn - https://in.linkedin.com/in/krdpravin
These are my insights on the article "Making Advanced Analytics Work for You" by Dominic Barton and David Court. This is an assignment, part of data analytics internship
Making advanced analytics work for you.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data....
Overview of Business Analytics and career lessons learnt / advice. Presentation delivered to Melbourne Business School - Masters of Business Analytics - July 2016.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
On Tuesday July 26, 2016 I presented at the Tableau 10 launch in front of approximately 1,000 people. The purpose was to explain how we'd been able to leverage Tableau for insights at GenesisCare. This is the presentation slide deck.
Introduction to Business Analytics and Simulation
http://nguyenngocbinhphuong.com/course/mo-phong-trong-kinh-doanh/
1) What is Business Analytics?
2) Types of Business Analytics: Descriptive, Predictive & Prescriptive
3) Data for Business Analytics: Structured & Unstructured or Semi-Structured
4) Models in Business Analytics: Logic-Driven Models & Data-Driven Models
5) Types of Business Simulation: Monte Carlo Simulation & System Simulation
An introduction to analytics is a small presentation made for increasing awareness on analytics with some case studies of applying analytics in different functions.
These case studies are from informs.org which were openly available when the presentation was made. Due to confidentiality related obligations my personal experiences were shared - without naming clients - during the presentation. However, the case studies cannot be share on the PPT here. For more details or inputs on analytics you can reach me at twitter - @krdpravin or LinkedIn - https://in.linkedin.com/in/krdpravin
These are my insights on the article "Making Advanced Analytics Work for You" by Dominic Barton and David Court. This is an assignment, part of data analytics internship
Making advanced analytics work for you.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data....
Overview of Business Analytics and career lessons learnt / advice. Presentation delivered to Melbourne Business School - Masters of Business Analytics - July 2016.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
On Tuesday July 26, 2016 I presented at the Tableau 10 launch in front of approximately 1,000 people. The purpose was to explain how we'd been able to leverage Tableau for insights at GenesisCare. This is the presentation slide deck.
Introduction to Business Analytics and Simulation
http://nguyenngocbinhphuong.com/course/mo-phong-trong-kinh-doanh/
1) What is Business Analytics?
2) Types of Business Analytics: Descriptive, Predictive & Prescriptive
3) Data for Business Analytics: Structured & Unstructured or Semi-Structured
4) Models in Business Analytics: Logic-Driven Models & Data-Driven Models
5) Types of Business Simulation: Monte Carlo Simulation & System Simulation
The New Self-Service Analytics - Going Beyond the ToolsKatherine Gabriel
In today’s business climate, using data to make quick decisions is a common ask across organizations. To fulfill such asks business users want more, faster, and better access to data and analytic tools. IT wants to balance this need for speed with the responsibility to protect the data assets from security, privacy, and quality risks. A common solution to this scenario is self-service BI or self-service analytics. Chances are you are already using self-service BI in some way, shape, or form or have heard a pitch from an analytic tool vendor!
Self-service BI has been around for several decades and yet business users keep asking for more and more. Has self-service BI failed to deliver on its promise? Is it time to revisit what self-service really means? How can business and IT work together to achieve better decision-making outcomes for their organization?
We cover:
• How to demystify what self-service analytics means
• New trends driving the self-service analytics evolution
• Best practices and lessons learned from real-life examples
• Recommendations for making progress within your organization
Advance your self-service journey.
Part 2 - 20 Years in Healthcare Analytics & Data Warehousing: What did we lea...Health Catalyst
Lessons learned over 20 years. This time we focus on technology lessons learned from experience at Intermountain Healthcare, Northwestern Medicine and Cayman Islands Health Authority
A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management
Data Analytics Course In Hyderabad-OctoberDataMites
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-hyderabad/
how to successfully implement a data analytics solution.pdfbasilmph
The adoption of data analytics in business has demonstrated a transformative power in modern entrepreneurship. By analyzing vast reservoirs of data, businesses can make informed decisions, optimize operations and predict trends, thus fueling growth.
It covers the basic of analytics, types of analytics, tools, and techniques of analytics, and a briefcase study to demonstrate the predictive analytics with decision tree algorithm of machine learning
20 Years in Healthcare Analytics & Data Warehousing: What did we learn? What'...Health Catalyst
The enterprise data warehouse (EDW) at Intermountain Healthcare went live in 1998. The EDW at Northwestern Medicine went live in 2006. Dale Sanders was the chief architect and strategist for both. The business inspiration behind Health Catalyst was, in essence, to create the commercial availability of the technology, analytics, and data utilization skills associated with these systems at Intermountain and Northwestern. Lee Pierce assumed leadership of the Intermountain EDW in 2008. Andrew Winter assumed leadership of the Northwestern EDW in 2009, and transitioned leadership of the EDW to Shakeeb Akhter in 2016. This webinar is a fireside chat among friends and colleagues as they look back across their healthcare IT decisions to answer these questions:
What did we do right and what did we do wrong?
What advice do we have for others in this emerging era of Big Data?
What does the future of analytics and Big Data look like in healthcare?
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. What is Business Analytics
• Using Tools and techniques to turn data into meaningful business
insights
Data
Data
Data
Tools &
Techniques Business
Insights
3. In theoretical sense
Business analytics is the practice of iterative, methodical
exploration of an organization’s data with emphasis on
statistical analysis. Business analytics is used by companies
committed to data-driven decision making.
4. Why do we need Business Analytics?
• Eliminate guesswork
• Get faster answer to your question
• Get insight into customer behaviour
• Identify cross selling and up selling opportunities
• Get key business metrics reports when and where you need them
5. Difficulties relating to business analytics
• huge volume of high-quality data.
• integrating and reconciling data across different systems, and then
deciding what subsets of data to make available.
• considered an after-the-fact method of forecasting consumer
behaviour
• requires a lot of storage space and must react extremely fast to
provide the necessary data in real-time
6. Types of Analytics
• Descriptive analytics – what happened?
• Predictive analytics- what would happen?
• Prescriptive analytics- what should we do?
• Decisive analytics – which technology (e.g. visual analytics)
to select for supporting the business decisions
7. Selecting the techniques
• Type of hardwares and softwares requirements
• Usage – Analysis, modeling, communication
• Audience-Shared or Private
• Documentation - Core or support
14. Developing the business cases
• Confirm the opportunity. Describe the situation and the business
opportunity that your proposal will impact. ...
• Analyse and develop shortlisted options. ...
• Evaluate the options. ...
• Implementation strategy. ...
• Recommendation.