Training is intended for business people who may well be doing what data scientists or technical developers would be doing - To Analyze Mounds of Data!
Data Analytics may sound frightening and technical, but this training is to have business-minds really understand data, AND identify some tools that make data analytics look like child's play!
The discovery workshop allows participants to take a strategic view of their project by working towards achieving the business objectives, using business models to visualise, to explore, challenge and settle on the requirements that bring the most value to a project. The requirements analysis activity can include process design, requirements analysis in a user story - acceptance criteria format, data modelling and prototypes. The Discovery workshop pack illustrates how the end product would look as a presentation.
businesses are challenged by the complexity and confusion that analytics can generate. Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees.
Design thinking into your business process can add huge business value, ultimately ensuring that the products you design are not only desirable for customers but also viable in terms of company budget and resources.
The discovery workshop allows participants to take a strategic view of their project by working towards achieving the business objectives, using business models to visualise, to explore, challenge and settle on the requirements that bring the most value to a project. The requirements analysis activity can include process design, requirements analysis in a user story - acceptance criteria format, data modelling and prototypes. The Discovery workshop pack illustrates how the end product would look as a presentation.
businesses are challenged by the complexity and confusion that analytics can generate. Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees.
Design thinking into your business process can add huge business value, ultimately ensuring that the products you design are not only desirable for customers but also viable in terms of company budget and resources.
Challenges Of A Junior Data Scientist_ Best Tips To Help You Along The Way.pdfvenkatakeerthi3
One of the most fascinating fields today that is enabling businesses to improve their operations is data science.
Databases, network servers and official social media pages.
How to be a Successful Data PM by Zillow Product LeadersProduct School
Main Takeaways:
-Data Product Managers treat data as a product
-Data & AI Fluency is an important core skills
-Be a great storyteller
-Understand Data Product Lifecycle
-Data Product Success Metrics
IT Business Analyst Job Description, Salary, and Roles (1).pdfeducationedge.ca
In the ever-evolving world of technology, there’s a crucial role that often goes unnoticed but plays a significant part in bridging the gap between IT and business: the IT Business Analyst. This article will demystify the role of an IT Business Analyst in simple language, and provide insights on how to become one for those looking to embark on this exciting career path.
Product Management in the Era of Data ScienceMandar Parikh
My slide-deck from a webinar on the same topic for the Institute of Product Leadership, April 4th, 2017
What does it take to build killer products in the “AI-first” era? What makes for a great Data Science-driven product and how do great Product Managers leverage Data Science to drive value for customers? Find out how to avoid the pitfalls of hype-chasing Data Science tactics. Learn how to work with Data Science and Engineering to build a compelling product and solve real problems.
Mandar takes a practitioner’s approach to present his recipe for success for building Data Science-driven products that drive enduring value for customers.
Data Science. Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms and technology. Uses mostly structured data. Uses both structured and unstructured data.
How to be a Successful Data PM by Zillow Product LeadersProduct School
Main Takeaways:
-Data Product Managers treat data as a product
-Data & AI Fluency is an important core skills
-Be a great storyteller
-Understand Data Product Lifecycle
-Data Product Success Metrics
Data scientists are the experts in analyzing and in delivering unique solutions for complex problems in business. They work on the wide unstructured information. They take an enormous range of messy data that make them structured and useful information.
Big data jobs are taking the highest rankings in the job market. Learn how you can excel in big data job roles as analysts, scientists, or engineers here.
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Data Product Management by Tinder Group PMProduct School
Main Takeaways:
- What is Data Product Management
- Who is a Data Product Manager and what do they do
- How and where to get started to get a role in Data Product Management
Delta Lake is an open format storage layer that delivers reliability, security and performance on your data lake — for both streaming and batch operations;
(please review the document)
Challenges Of A Junior Data Scientist_ Best Tips To Help You Along The Way.pdfvenkatakeerthi3
One of the most fascinating fields today that is enabling businesses to improve their operations is data science.
Databases, network servers and official social media pages.
How to be a Successful Data PM by Zillow Product LeadersProduct School
Main Takeaways:
-Data Product Managers treat data as a product
-Data & AI Fluency is an important core skills
-Be a great storyteller
-Understand Data Product Lifecycle
-Data Product Success Metrics
IT Business Analyst Job Description, Salary, and Roles (1).pdfeducationedge.ca
In the ever-evolving world of technology, there’s a crucial role that often goes unnoticed but plays a significant part in bridging the gap between IT and business: the IT Business Analyst. This article will demystify the role of an IT Business Analyst in simple language, and provide insights on how to become one for those looking to embark on this exciting career path.
Product Management in the Era of Data ScienceMandar Parikh
My slide-deck from a webinar on the same topic for the Institute of Product Leadership, April 4th, 2017
What does it take to build killer products in the “AI-first” era? What makes for a great Data Science-driven product and how do great Product Managers leverage Data Science to drive value for customers? Find out how to avoid the pitfalls of hype-chasing Data Science tactics. Learn how to work with Data Science and Engineering to build a compelling product and solve real problems.
Mandar takes a practitioner’s approach to present his recipe for success for building Data Science-driven products that drive enduring value for customers.
Data Science. Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms and technology. Uses mostly structured data. Uses both structured and unstructured data.
How to be a Successful Data PM by Zillow Product LeadersProduct School
Main Takeaways:
-Data Product Managers treat data as a product
-Data & AI Fluency is an important core skills
-Be a great storyteller
-Understand Data Product Lifecycle
-Data Product Success Metrics
Data scientists are the experts in analyzing and in delivering unique solutions for complex problems in business. They work on the wide unstructured information. They take an enormous range of messy data that make them structured and useful information.
Big data jobs are taking the highest rankings in the job market. Learn how you can excel in big data job roles as analysts, scientists, or engineers here.
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Data Product Management by Tinder Group PMProduct School
Main Takeaways:
- What is Data Product Management
- Who is a Data Product Manager and what do they do
- How and where to get started to get a role in Data Product Management
Delta Lake is an open format storage layer that delivers reliability, security and performance on your data lake — for both streaming and batch operations;
(please review the document)
As users spend more time on mobile devices, getting mobile sites right is crucial to success. If your client’s website is too slow to load, users will drop off. On the other hand, a fast-loading site with bad UX design makes it hard for users to complete their desired action. In a mobile-led world, consumer expectations are high.
Win customers with mobile sites - Improve conversions with small, but mighty, mobile site changes
Cut load times with Developer Tools - Identify inefficiencies in the critical rendering path
Speed up mobile site rendering
Key metrics for testing your site
Optimize mobile site transfer size
Optimize images and fonts
Focus on mobile user experience
Deliver user-centered mobile experiences - Craft a mobile site that delivers a great user experience
Make mobile sites drive conversions - Deliver a mobile site that lets users easily convert
Test and optimize mobile experiences - Discover what your users really want with testing
Create super fast sites with AMP - Get your content seen quickly with Accelerated Mobile Pages (AMP)
Create Progressive Web Apps - Learn to build engaging and reliable Progressive Web Apps
Engage users with APIs - Increase user engagement and conversions with new mobile web APIs
Text Analytics (unstructured - Twitter, Facebook posts) :
Information Extraction is the problem of distilling structured information from unstructured text, for example, finding entities such as persons and organizations, and the relationships between them. Using SystemT - a state-of-the-art Information Extraction System.
Text Analytics (unstructured - Twitter, Facebook posts):
Information Extraction is the problem of distilling structured information from unstructured text, for example, finding entities such as persons and organizations, and the relationships between them. Using SystemT - a state-of-the-art Information Extraction System.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
3. One principle of data science is to extract useful knowledge from clean
data - to solve a business-problem. May sound obvious, but this
involves processes within Data Analytics, namely:
✪ Identifying and Formulating the Problem;
✪ Choosing a method;
✪ Evaluating the solution;
✪ Formulating a Strategy;
Great Chefs become professionals by applying their skills (craft), not
just reading books about cooking.
Participants will be encouraged to play and build their skills using:
✪ Open source tools running on the Cloud;
✪ Managing data within My Data;
✪ Preparing data with OpenRefine;
Introduction
Training is intended for business people who may well be
doing what data scientists, or technical developers would be
doing - To Analyze Mounds of Data!
Data Analytics may sound frightening and technical, but this
training is to have business-minds really understand data, AND
identify some tools that make data analytics look like child's
play!
4. He loves to articulate business processes for business units and deliver
innovative solutions that make businesses flexible, resilient and leaders
in today’s global economy – leading them on a journey to productivity,
profitability and competitive advantage.
Dominic is intrigued and involved with Big Data and Data
Analytics. He has been acclaimed by IBM, and certified by
Big Data University ... click here ... to view his Badges.
Dominic Fernandez
is a project-oriented, business savvy, empathetic Principal at
Computants Inc.