#Predictive and Prescriptive Analytics

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Aretove Technologies specializes in data science consulting and predictive analytics, particularly in healthcare. We harness advanced data analytics to optimize patient care, operational efficiency, and strategic decision-making. Our tailored solutions empower healthcare providers to leverage data for improved outcomes and cost-effectiveness. Trust Aretove Technologies for cutting-edge predictive analytics and data-driven insights that transform healthcare delivery.

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Predictive vs Prescriptive vs Descriptive Analytics Explained 

Business analytics leveraging data patterns for strategic moves comes in three key approaches – descriptive identifying “what has occurred", predictive forecasting “what could occur” and prescriptive recommending “what should occur” to optimize decisions. We decode the science behind each for aspiring analytics professionals.

Descriptive analytics convert volumes of historical data into insightful summaries around metrics revealing business health, customer trends, operational efficiencies etc. using direct analysis, aggregation and mining techniques producing current reports. 

Predictive analytics forecast unknown future probabilities applying statistical, econometric and machine learning models over existing data to minimize uncertainties and capture emerging behaviors early for mitigation actions. Risk models simulate scenarios balancing upside/downside tradeoffs. 

Prescriptive analytics take guidance one step further by dynamically recommending best decision options factoring in key performance indicators for business objective improvements after predicting multiple futures using bell curve simulations. Optimization algorithms deliver preferred actions.

While foundational data comprehension and wrangling abilities fuel all models – pursuing analytics specializations focused on statistical, computational or operational excellence boosts career-readiness filling different priorities global employers seek!

Posted By:

Aditi Borade, 4th year Barch,

Ls Raheja School of architecture 

Disclaimer: The perspectives shared in this blog are not intended to be prescriptive. They should act merely as viewpoints to aid overseas aspirants with helpful guidance. Readers are encouraged to conduct their own research before availing the services of a consultant.

[techtinkerer]

What are the most popular Predictive Analytics techniques?

Data mining: Data mining is the process of sifting through massive amounts of raw data to find anomalies, patterns, and correlations that can help surface insights to inform decision-making. 

Decision trees: A tree-like structure that uses questions and decisions to predict based on past data. Decision trees can help identify critical features and understand relationships between input variables and their outcomes.

Neural networks: A machine learning algorithm that mimics the human brain to identify patterns in data. Neural networks can use multiple models, including regression, classification, clustering, and time series, to handle large amounts of data and complex relationships.

Linear regression: A simple and interpretable technique

Time series analysis: A method that uses previously observed data to predict future values. Time series models help predict metrics or behavior over time or when making decisions that involve uncertainty over time.

Clustering: A technique that groups similar data points together based on criteria. However, choosing the optimal number of clusters can be difficult, and clustering models may trade off simplicity for accuracy.

Although these methods vary in approach and complexity, they all serve one common purpose: to predict future outcomes that can help businesses capitalize on what lies ahead. 

Predictive analytics are mostly industry-agnostic. You can employ them to run your business smoothly, regardless of industry or market. 

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Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making best data analytics training course in Moradabad.

[smartinfologiks]

Predictive Analytics vs Descriptive Analytics vs Prescriptive Analytics

Here’s a three-minute guide to understanding and choosing the right descriptive, predictive, and prescriptive analytics for use across your business chain.

With enormous data available to businesses concerning the supply chain, companies are now adopting analytics solutions to extract meaningful and insightful volumes of data to help enhance decision-making.

Deeming all the analytics solutions can be a challenging task. Fortunately, these analytics alternatives are categorised extensively into three different types. None of them is better than another they co-exist and complement each other. For businesses to have a holistic view of the market and how a firm competes effectively within the market need a dynamic analytic environment which encompasses the:

Predictive Analytics: Predicting & Understanding the Future

Predictive analytics follows its roots to “predict” what might occur. These analytics are all about understanding the future. Predictive analytics is useful in providing firms with actionable insights based on real-time data. It also offers estimates concerning the likelihood of a future outcome. Note: no statistical algorithm can “predict” the future with 100 percent assurance. Businesses use these stats to forecast what might occur in the future. Because predictive analytics is based on probabilities. Predictive analytics combines historical data discovered in ERP, CRM, HR, and POS systems to identify data patterns and apply statistical models and algorithms to seize the relationship between multiple data sets. It can be used by companies to forecast customer behaviour and buying patterns to identify emerging trends in sales activities.

Descriptive Analytics: An Insight into the Past

Descriptive analytics does precisely what its name implies: they “describe,” or summarize, raw data and create it into something interpretable by humans. They’re merely the analytics that demonstrates the past. Descriptive analytics is helpful as they enable us to learn from past behaviours, and understand how they can influence future outcomes. This underlying data is a count or aggregate of a filtered data column to which typical math is applied. Leverage descriptive analytics to understand at an aggregate level what is happening in your firm, and when you are willing to summarize and describe multiple business facets.

Prescriptive Analytics: Aforethought on Possible Outcomes

This relatively novel stream of prescriptive analytics enables users to “prescribe” different possible actions and instruct them towards a solution. In short, these analytics are all about advice and suggestions. This analytics attempt to quantify the impact of future decisions to suggest possible outcomes before the decisions are made. At its best, prescriptive analytics not only predicts what will happen but also suggest why it will happen, offering suggestions concerning actions that will benefit from predictions. If implemented appropriately, they can greatly impact business decision-making as well as the company’s bottom line. Biggies in the market have already adopted prescriptive analytics to optimize production, scheduling, and inventory to ensure they’re providing the right products at the right time as well as optimizing the customer experience.

Still, in business, the most compelling areas sakes, marketing, operation, and finance departments are pedestals on descriptive and predictive analytics. Reach out to Smartinfologiks to optimize your business with the analytics solution, today!

[ckmstudies]

June 24th:

Almost three and a half hours of studying done today! I need to hit just under three hours tomorrow to make my 20 hour weekly study goal! This will be the first time in four weeks that I've made it. I'll probably go for 25 hours next week considering I'm still behind. I'm supposed to have finished areas three and four including the second mini exam by tomorrow but I've just finished area three tonight. I also started my review of area three for my area three practice exam that I'm hoping to take tomorrow. Tomorrow's goals are to finish reviewing area three and take the practice exam as well as finish modules one and two of area four. I really need to grind it this next week to be anywhere near ready for this exam in just 25 days. My study course recommends over two weeks of revision before the exam and there's no way I can make that but I can try for a week and a half at least.

Today's accounting topic: Auditors use descriptive, prescriptive, predictive, and diagnostic analytics to analyze accounting information to help with understanding the client's course of business as well as identify material misstatements.

Other activity: I went to the craft store with my mom and looked at all the yarn which was super calming but I didn't buy any because I already have too much and I couldn't think of anything to make.

[rise2research]

The Automation Revolution: How Embedded Analytics is Leading the Way

Embedded analytics tools have emerged as game-changers, seamlessly integrating data-driven insights into business applications and enabling automation across various industries. By providing real-time analytics within existing workflows, these tools empower organizations to make informed decisions without switching between multiple platforms.

The Role of Embedded Analytics in Automation

Embedded analytics refers to the integration of analytical capabilities directly into business applications, eliminating the need for separate business intelligence (BI) tools. This integration enhances automation by:

Reducing Manual Data Analysis: Automated dashboards and real-time reporting eliminate the need for manual data extraction and processing.

Improving Decision-Making: AI-powered analytics provide predictive insights, helping businesses anticipate trends and make proactive decisions.

Enhancing Operational Efficiency: Automated alerts and anomaly detection streamline workflow management, reducing bottlenecks and inefficiencies.

Increasing User Accessibility: Non-technical users can easily access and interpret data within familiar applications, enabling data-driven culture across organizations.

Industry-Wide Impact of Embedded Analytics

1. Manufacturing: Predictive Maintenance & Process Optimization

By analyzing real-time sensor data, predictive maintenance reduces downtime, enhances production efficiency, and minimizes repair costs.

2. Healthcare: Enhancing Patient Outcomes & Resource Management

Healthcare providers use embedded analytics to track patient records, optimize treatment plans, and manage hospital resources effectively.

3. Retail: Personalized Customer Experiences & Inventory Optimization

Retailers integrate embedded analytics into e-commerce platforms to analyze customer preferences, optimize pricing, and manage inventory.

4. Finance: Fraud Detection & Risk Management

Financial institutions use embedded analytics to detect fraudulent activities, assess credit risks, and automate compliance monitoring.

5. Logistics: Supply Chain Optimization & Route Planning

Supply chain managers use embedded analytics to track shipments, optimize delivery routes, and manage inventory levels.

6. Education: Student Performance Analysis & Learning Personalization

Educational institutions utilize embedded analytics to track student performance, identify learning gaps, and personalize educational experiences.

The Future of Embedded Analytics in Automation

As AI and machine learning continue to evolve, embedded analytics will play an even greater role in automation. Future advancements may include:

Self-Service BI: Empowering users with more intuitive, AI-driven analytics tools that require minimal technical expertise.

Hyperautomation: Combining embedded analytics with robotic process automation (RPA) for end-to-end business process automation.

Advanced Predictive & Prescriptive Analytics: Leveraging AI for more accurate forecasting and decision-making support.

Greater Integration with IoT & Edge Computing: Enhancing real-time analytics capabilities for industries reliant on IoT sensors and connected devices.

Conclusion

By integrating analytics within existing workflows, businesses can improve efficiency, reduce operational costs, and enhance customer experiences. As technology continues to advance, the synergy between embedded analytics and automation will drive innovation and reshape the future of various industries.

To know more: data collection and insights

data analytics services

[xaltius]

Business Analytics vs. Data Science: Understanding the Key Differences

In today's data-driven world, terms like "business analytics" and "data science" are often used interchangeably. However, while they share a common goal of extracting insights from data, they are distinct fields with different focuses and methodologies. Let's break down the key differences to help you understand which path might be right for you.

Business Analytics: Focusing on the Present and Past

Business analytics primarily focuses on analyzing historical data to understand past performance and inform current business decisions. It aims to answer questions like:

What happened?

Why did it happen?

What is happening now?

Key characteristics of business analytics:

Descriptive and Diagnostic: It uses techniques like reporting, dashboards, and data visualization to summarize and explain past trends.

Structured Data: It often works with structured data from databases and spreadsheets.

Business Domain Expertise: A strong understanding of the specific business domain is crucial.

Tools: Business analysts typically use tools like Excel, SQL, Tableau, and Power BI.

Focus: Optimizing current business operations and improving efficiency.

Data Science: Predicting the Future and Building Models

Data science, on the other hand, focuses on building predictive models and developing algorithms to forecast future outcomes. It aims to answer questions like:

What will happen?

How can we make it happen?

Key characteristics of data science:

Predictive and Prescriptive: It uses machine learning, statistical modeling, and AI to predict future trends and prescribe optimal actions.

Unstructured and Structured Data: It can handle both structured and unstructured data from various sources.

Technical Proficiency: Strong programming skills (Python, R) and a deep understanding of machine learning algorithms are essential.

Tools: Data scientists use programming languages, machine learning libraries, and big data technologies.

Focus: Developing innovative solutions, building AI-powered products, and driving long-term strategic initiatives.

Key Differences Summarized:

Which Path is Right for You?

Choose Business Analytics if:

You are interested in analyzing past data to improve current business operations.

You have a strong understanding of a specific business domain.

You prefer working with structured data and using visualization tools.

Choose Data Science if:

You are passionate about building predictive models and developing AI-powered solutions.

You have a strong interest in programming and machine learning.

You enjoy working with both structured and unstructured data.

Xaltius Academy's Data Science & AI Course:

If you're leaning towards data science and want to delve into machine learning and AI, Xaltius Academy's Data Science & AI course is an excellent choice. This program equips you with the necessary skills and knowledge to become a proficient data scientist, covering essential topics like:

Python programming

Machine learning algorithms

Data visualization

And much more!

By understanding the distinct roles of business analytics and data science, you can make an informed decision about your career path and leverage the power of data to drive success.

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Types of Data Science | IABAC

Types of Data Science include descriptive analytics (analyzing past data), diagnostic analytics (identifying reasons for trends), predictive analysis (forecasting outcomes), and prescriptive analyses (recommending actions). It also covers machine learning, big data, NLP, computer vision, AI, and data engineering. https://iabac.org/

[drdemonprince]

kontextmaschine was never afraid to reblog a post, no matter how low note or niche, and type directly into the reblog text some totally unhinged idiosyncratic shit. he wasnt afraid to post about the kind of porn he was into (girls peeing themselves, guys dominating him). he would share nearly any persona anecdote from his past or present. he talked about the finest gradients of his motor control disability and slight changes in how he jerked off with equal clinical fascination and absolutely no shame. he resolutely did not care if one of his takes on history or on the current political climate didnt sound good, so long as he had support for it. in historical and political matters he was descriptive and analytic, not prescriptive -- which meant his interpretation on what had happened and why could madden just about anyone, but it was never without its reasoning. he correctly predicted the vicissitudes of the portland real estate market. he spoke about being super rich and super intelligent without any of the usual performative hedging that people who are not so secretly proud of such things normally do, so you could actually engage with him directly as That Type of Guy and he wouldnt be threatened by it. if you disagreed with him or even if you insulted him outright, he did not care, he would engage with you earnestly if you said anything that was interesting to him. he just rolled through life motivated by his own strange constellation of beliefs and took things at face value and was never ashamed of what he felt or what fascinated him, and i think there's a lot that many of us can learn from that even if a lot of us have really different value systems from him

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Various services provided by Aretove includes Data Science, Predictive Analytics, Applied AI, Business Intelligence, Data Engineering, Big Data & Analytics

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Business Potential with Data Management and Analytics Services

In today's data-driven world, businesses are increasingly relying on data to make informed decisions and drive strategic growth. As organizations collect and generate vast amounts of data, the need for effective data management and analytics services becomes paramount. This is where data analytics consulting services play a crucial role. By harnessing the power of data, businesses can gain valuable insights, enhance operational efficiency, and achieve a competitive edge.

The Importance of Data Management

Data management is the foundation upon which successful data analytics is built. It involves the process of collecting, storing, and organizing data in a manner that ensures its accuracy, accessibility, and security. Effective data management allows organizations to maintain high-quality data that is essential for accurate analysis and decision-making.

Data Collection and Integration

The first step in data management is collecting data from various sources. This can include transactional data, customer interactions, social media, and more. Integrating these diverse data sources into a unified system is crucial for creating a comprehensive view of the business landscape. Data analytics consulting services assist organizations in designing and implementing robust data integration strategies, ensuring that all relevant data is captured and consolidated efficiently.

Data Storage and Organization

Once data is collected, it needs to be stored and organized in a structured manner. This involves setting up databases and data warehouses that can handle large volumes of data while maintaining data integrity. Data management services ensure that data is stored securely and is easily retrievable when needed. Effective data organization also involves categorizing data into meaningful segments, making it easier to analyze and derive insights.

Data Quality and Governance

Maintaining high data quality is essential for accurate analysis. Data management services focus on implementing data quality frameworks that ensure data is accurate, complete, and consistent. Data governance practices are also put in place to manage data policies, standards, and compliance. This helps organizations avoid data-related issues and ensures that data analytics efforts are based on reliable information.

Leveraging Data Analytics

Once data is properly managed, the next step is to leverage it through advanced analytics. Data analytics services help businesses transform raw data into actionable insights that can drive decision-making and strategic planning.

Descriptive Analytics

Descriptive analytics involves analyzing historical data to understand past performance and identify trends. This type of analysis provides a clear picture of what has happened in the past and helps organizations gain insights into their operations. Data analytics consulting services use various statistical and analytical techniques to uncover patterns and trends in historical data, enabling businesses to make data-driven decisions.

Predictive Analytics

Predictive analytics goes a step further by using historical data to forecast future outcomes. By applying statistical models and machine learning algorithms, businesses can predict trends, customer behavior, and potential risks. This proactive approach allows organizations to anticipate challenges and opportunities, making it possible to take preemptive actions. Data analytics consulting services provide expertise in building and deploying predictive models that can enhance business forecasting and planning.

Prescriptive Analytics

Prescriptive analytics offers recommendations for actions based on data insights. It involves using optimization techniques and simulation models to suggest the best course of action for achieving desired outcomes. This type of analytics helps businesses make informed decisions by providing actionable recommendations that align with their strategic goals. Data analytics consulting services assist organizations in developing prescriptive models that guide decision-making and improve overall performance.

The Role of Data Analytics Consulting Services

Data analytics consulting services are essential for helping businesses navigate the complexities of data management and analytics. These services provide expertise and support in various aspects of data analytics, including strategy development, implementation, and optimization.

Strategy Development

Data analytics consultants work closely with organizations to develop data strategies that align with their business objectives. This involves identifying key performance indicators (KPIs), setting data goals, and defining the scope of analytics initiatives. By creating a comprehensive data strategy, consultants ensure that data analytics efforts are focused and effective.

Implementation and Optimization

Once the strategy is in place, data analytics consultants assist with the implementation of data management systems and analytics tools. This includes setting up data infrastructure, integrating data sources, and deploying analytics solutions. Consultants also provide ongoing support to optimize data processes and ensure that analytics tools are used effectively.

Insights and Recommendations

One of the primary benefits of data analytics consulting services is the ability to generate actionable insights and recommendations. Consultants analyze data to uncover trends, identify opportunities, and provide recommendations for improving business performance. These insights help organizations make informed decisions and drive strategic growth.

Conclusion

In an era where data is a valuable asset, effective data management and analytics services are crucial for unlocking business potential. By leveraging data analytics consulting services, organizations can ensure that their data is well-managed, their analytics efforts are targeted, and their decision-making is driven by actionable insights. As businesses continue to navigate the complexities of the data landscape, investing in data management and analytics services will remain a key factor in achieving long-term success.

[umarblog1]

Java vs. Other Programming Languages: Which Course Should You Take?

In conclusion, selecting the right programming language course is a significant decision. Java, with its versatility and wide range of applications, is a strong contender, but your choice should align with your career goals and interests. There's no one-size-fits-all answer to the question of which programming language course to take. Your decision should be a thoughtful one, taking into account your unique circumstances and aspirations. For top-quality training, you can refer to any institution that provides the Best Java Training Course in Moradabad, Rampur, Bareilly, Noida, and various other cities in India to kickstart your programming career. Your journey towards becoming a proficient programmer starts here.

[anishmary]

No One Will Ever Teach You About Data Analytics in a Very Simple Way with Real-Time Examples

In today's modern digital world, data is all around us. Every time you use your smartphone, buy something online, or share something on social media, you're creating data. But why is data important, and how can we use it to make better decisions? That's where data analytics comes into play, and in this blog, we'll explain it in simple terms.

What is data analytics?

At its core, data analytics is like a detective for the digital world. It's the process of looking closely at data, cleaning it up, changing it into useful information, and figuring out what it tells us. This information helps people and organisations make smart choices, solve problems, and even guess what might happen in the future.

How does data analytics work?

Imagine you run a small ice cream shop in your neighborhood. You sell different flavours, from classic chocolate to unique mango. To run your shop well, you need to make good choices about which flavours to have and when to offer them. That's where data analytics can help, and I'll show you how it works using a simple example.

Data Collection

Every day, you keep a record of which ice cream flavours you sell and keep an eye on the weather. You also note whether it's a regular weekday or a fun weekend.

Data Analysis

After a few months, you have a pile of notebooks filled with sales data. You use a special computer programme to look at all this information.

Descriptive Analytics: You make graphs and charts to see which flavours are liked the most. For instance, you notice that chocolate sells well all year.

Diagnostic Analytics: Digging deeper, you discover patterns, like mango ice cream being a hit on hot weekends and vanilla being popular on weekdays.

Predictive Analytics

Here's where it gets exciting. Using past data, your analytics tool can make guesses. It tells you that the next weekend will be very hot. So, you decide to order more mango ice cream to be ready for the extra demand.

Prescriptive Analytics

Now, it's not just about guessing. You also get advice on what to do. Your analytics tool suggests that during the weekdays, you could offer a discount on vanilla to bring in more customers.

Implementing Changes

You follow the advice and give discounts on vanilla during the weekdays. You also make sure to have plenty of mango ice cream for the upcoming hot weekend.

Seeing Results

When the hot weekend comes, customers are happy to see lots of mango ice cream, and sales go up. On weekdays, the discount on vanilla brings in more customers and boosts sales.

This process keeps going, and you continue using data analytics to make good choices about flavours, discounts, and how much ice cream to have in stock. Your ice cream shop becomes more successful because you're using data to understand what customers like and how the weather affects sales.

In short, data analytics is like having a crystal ball for your business. It helps you understand what happened in the past (descriptive), why it happened (diagnostic), guess what might happen in the future (predictive), and suggest what you should do about it (prescriptive). Whether you run an ice cream shop or a big company, data analytics helps you make smarter decisions in real-time.

If you want to learn more about data analytics or need help using it in your business, you can talk to experts or take specialised courses. For example, ACTE Institute offers comprehensive data analytics training programmes that can give you the knowledge and skills you need, including job placement and certification. So, get ready to learn, explore the tools, and start turning data into valuable insights!

[iknew2day]

‏1- التحليلات الوصفية Descriptive analytics:

‏هذا النوع من التحليلات يجاوب على سؤال ماذا ح��ث في الماضي؟ حيث يتم استخدم أدوات ذكاء الأعمال وتصوير البيانات بهدف وصف مشكلة أو حدث معين حصل في الماضي وتحديد ماهي أبعاده وأنماطه.

‏2- التحليلات التشخيصية Diagnostic analytics:

‏هذا النوع من التحليلات يجاوب على سؤال لماذا حدثت هذه المشكلة أو هذا الحدث وماهي أسبابه؟ حيث يتم استخدام أدوات التعرف على تحليل المشكلة وتتبع جذورها و استخدام أدوات متقدمة في استدعاء البيانات واكتشافها.

‏3- التحليلات التنبؤية Predictive analytics: حيث يجاوب هذا النوع من التحليلات على سؤال مالذي سيحدث في المستقبل؟ حيث يتم استخدم خوارزميات الذكاء الاصطناعي للاستشراف المستقبل والتنبؤ بأحداثه بعد تدريب النموذج على البيانات التي تم جمعها في الماضي.

‏4- التحليلات التوجيهية Prescriptive analytics: حيث يجاوب هذا النوع من التحليلات على سؤال مالذي يجب فعله لتفادي مشكلة تم التنبؤ بها؟ حيث يتم استخدام أدوات المحاكاة ونماذج التحسين والتوأمة الرقمية للتعامل مع المشاكل التي تم توقعها لتقليل مخاطرها وأثرها على الجهة.