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Data Analytics vs Data Science what are the differences?

Data Analytics vs Data Science what are the differences

If you are considering careers in Data Analytics or perhaps Data Science and like to know little more about each. You may be interested in this guide which provides more insight into the differences between Data Analytics vs Data Science. Data science is a broad field that includes a variety of tasks and skills. It primarily involves identifying patterns in large datasets, training machine learning models, and deploying AI applications. The process usually begins with defining a problem or question, which guides the subsequent stages of data analysis and interpretation.

After defining the problem or question, the next step is data mining, which involves extracting relevant data from large datasets. However, raw data often contains redundancies and errors. This is where data cleaning comes in, correcting these errors to ensure the data is accurate and reliable, providing a solid base for further data analysis.

After cleaning the data, the next step is data exploration analysis. This involves understanding the data’s structure and identifying any patterns or trends. Feature engineering, a related process, involves extracting specific details from the data using domain knowledge. This can highlight important information and make the data easier to understand, facilitating more effective analysis.

Data Analytics vs Data Science

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Here is a bullet-pointed summary highlighting the key differences between data analytics and data science for quick reference:

  • Scope of Work:
    • Data Analytics: Focuses on processing and performing statistical analysis on existing datasets.
    • Data Science: Encompasses a broader scope that includes constructing algorithms and predictive models, and working on new ways of capturing and analyzing data.
  • Objective:
    • Data Analytics: Aims to answer specific questions by interpreting large datasets.
    • Data Science: Seeks to create and refine algorithms for data analysis and predictive modeling.
  • Tools and Techniques:
    • Data Analytics: Utilizes tools like SQL and BI tools; techniques include descriptive analytics, and diagnostic analytics.
    • Data Science: Uses advanced computing technologies like machine learning, AI, and deep learning; requires knowledge of Python, R, and big data platforms.
  • Complexity of Tasks:
    • Data Analytics: Typically deals with less complex tasks, more focused on visualization and insights from existing data.
    • Data Science: Deals with complex algorithm development and advanced statistical methods that can predict future events from data.
  • Outcome:
    • Data Analytics: Produces actionable insights for immediate business decisions.
    • Data Science: Develops deeper insights and predictive models that can be used to forecast future trends.
  • Required Skill Set:
    • Data Analytics: Strong statistical analysis and the ability to query and process data; more focused on data manipulation and visualization.
    • Data Science: Requires skills in coding, machine learning, and often a deeper understanding of mathematics and statistics.

Machine learning

Once the data has been explored and the features engineered, the next stage is predictive modeling. This involves using the data to predict future outcomes and behaviors. The results are often displayed visually through data visualization, using graphical tools to make the information easier to understand, enhancing overall data comprehension.

Machine learning and AI are crucial components of data science. Machine learning involves developing algorithms to learn from and make predictions based on data. AI involves creating systems that can perform tasks that usually require human intelligence, such as recognizing patterns in data and making complex decisions based on that data, improving the overall effectiveness of data analysis.

Programming skills

Coding is a fundamental skill for data scientists, who need to write instructions for computers to execute tasks. Python and R are two of the most commonly used languages in data science. Alongside coding, data scientists also need to be familiar with big data platforms like Hadoop and Apache Spark, which are used for storing, processing, and analyzing large datasets, facilitating a more efficient and effective data analysis process.

Database knowledge and SQL are also important skills for data scientists. They need to be able to store, retrieve, and manipulate data in a database. SQL, or Structured Query Language, is a programming language used for managing and manipulating databases, forming a crucial part of the data analysis process.

While data science is a broad field, data analytics is a more focused area. It involves querying, interpreting, and visualizing datasets. Data analysts use techniques like predictive analytics, prescriptive analytics, diagnostic analytics, and descriptive analytics to understand a dataset, identify trends, correlations, and causation, predict likely outcomes, make decision recommendations, and identify why an event occurred.

Data analysts also need strong programming skills and familiarity with databases. They need to write, test, and maintain the source code of computer programs. They also need a strong understanding of statistical analysis, which involves collecting, analyzing, interpreting, presenting, and organizing data.

While Data Analytics vs Data Sciences are distinct fields, they are closely related and often overlap. Both involve working with large datasets and require a strong understanding of coding, databases, and statistical analysis. However, data science has a broader scope and can involve complex machine learning algorithms, while data analysis is more focused on answering specific questions with data. Regardless of the specific field, both data scientists and data analysts play a crucial role in helping organizations make data-driven decisions, improving the overall effectiveness and efficiency of these organizations.

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Machine Learning vs Deep Learning what are the differences?

Machine Learning vs Deep Learning what are the differences 2023

With artificial intelligence (AI) exploding into our lives this year more than ever before you might be interested to know a little more about the technologies that have been used to create many of the AI tools and services that are currently being developed and released in early development. The world of AI is a fascinating place and features lots of new technologies in terms that we are trying to get to grips with. This guide will provide more information on the differences between Machine Learning vs Deep Learning.

At its core, machine learning is a subset of AI that enables software applications to predict outcomes more accurately without being explicitly programmed to do so. It’s the art of giving computers the ability to learn from data, identify patterns, and make decisions with minimal human intervention. Machine learning algorithms can handle historical data as input to predict new output values. This encompasses various types, including supervised, unsupervised, and reinforcement learning.

Machine Learning vs Deep Learning

Simplifying the differences

  • Definition:
    • Machine Learning is a subset of AI that enables machines to improve at tasks with experience.
    • Deep Learning is a subset of Machine Learning that uses layered neural networks to simulate human decision-making.
  • Approach:
    • Machine Learning algorithms often require structured data to learn and make predictions.
    • Deep Learning algorithms learn from data that is often unstructured and high-dimensional, like images and audio.
  • Complexity:
    • Machine Learning models are generally simpler and can work on traditional CPUs.
    • Deep Learning models are more complex, involving many layers in neural networks, and usually require GPUs for computation.
  • Data Requirements:
    • Machine Learning can work with smaller datasets and still perform well.
    • Deep Learning requires large amounts of data to understand and learn effectively.
  • Performance:
    • Machine Learning models may plateau on performance as more data is fed in.
    • Deep Learning models tend to improve their performance with more data and complexity.
  • Usage Scenarios:
    • Machine Learning is suitable for tasks like spam detection, simple recommendation systems, and predictive analytics.
    • Deep Learning excels at more complex tasks like image recognition, speech recognition, and natural language processing.
  • Interpretability:
    • Machine Learning models are often easier to interpret and understand.
    • Deep Learning models, due to their complexity, are typically considered “black boxes” with lower interpretability.

Deep Learning, a subset of machine learning, takes inspiration from the human brain. Here, artificial neural networks, which mimic the way neurons signal each other, are used to process data in complex ways. These neural networks have multiple layers that can learn increasingly abstract concepts, allowing DL algorithms to handle unstructured data such as images and text more effectively than traditional Machine Learning algorithms.

The difference between Machine Learning vs Deep Learning can be intriguing. Deep learning algorithms are generally more complex, requiring a deeper architecture compared to their machine learning counterparts. While machine learning can work with smaller datasets, deep learning requires a large volume of data to perform optimally. In terms of hardware, DL often relies on high-end GPUs due to its higher computational power demands. As for application scope, machine learning is suitable for problems with limited data and computational resources, whereas deep learning excels at tasks that involve massive amounts of data.

Machine learning in action

Machine learning is a transformative technology, an innovation that fundamentally changes existing processes, habits, or industries in a significant and often disruptive way. Making a significant impact on our everyday digital experience, often in ways we might not immediately recognize. Let’s delve into two of the most ubiquitous applications of machine learning: email filtering and recommendation systems.

Email Filtering Systems

Email filtering is a critical function that most of us benefit from every time we open our inbox. Here’s how machine learning contributes to this process:

  • Spam Detection: Machine learning models are trained to distinguish between spam and non-spam by learning from vast quantities of labeled data. These models look for specific patterns that are commonly found in spam emails, such as certain keywords, sender’s email addresses, or even the formatting of the email.
  • User Behavior: Over time, these algorithms adapt to the individual user’s behavior. If a user frequently marks messages from a particular sender as spam, the ML system learns to automatically filter similar messages in the future.
  • Continuous Learning: The beauty of machine learning in email filtering is its ability to continuously learn and adapt. As spammers evolve their tactics, the machine learning models keep up by learning from the new patterns that emerge.

Recommendation Systems

Recommendation systems are another area where machine learning shines, particularly in streaming platforms like Netflix. Here’s how they work:

  • Personalized Suggestions: Machine learning algorithms analyze your viewing history to make personalized movie or show recommendations. They use complex algorithms to find patterns in your choices and compare them with other users who have similar tastes.
  • Content Attributes: These systems also examine the attributes of the films and shows you watch, including genres, actors, and even the directors, to find and suggest content with similar characteristics.
  • Improving Engagement: The goal is to keep you engaged with the platform by effectively predicting what you might enjoy watching next. A well-tuned recommendation system can be a key differentiator for a service like Netflix in retaining its user base.

Both these applications are clear examples of machine learning’s capacity to enhance user experience in very practical and impactful ways. By harnessing the power of ML, services can provide a level of personalization and efficiency that simply wasn’t possible before.

Deep learning driving innovation

Deep learning, with its advanced capabilities in handling intricate tasks, is indeed revolutionizing sectors where traditional machine learning techniques may fall short. Let’s delve deeper into how deep learning propels innovations in autonomous vehicles and voice assistants.

Autonomous Vehicles

In the realm of autonomous vehicles, deep learning plays a pivotal role, especially in the following aspects:

  • Computer Vision: Deep learning models, through convolutional neural networks (CNNs), enable vehicles to interpret visual information from cameras. These networks are adept at processing and analyzing images to recognize traffic signs, pedestrians, other vehicles, and road markings.
  • Sensor Fusion: Deep learning algorithms can integrate data from various sensors such as LIDAR, radar, and cameras to create a comprehensive understanding of the vehicle’s surroundings, a process known as sensor fusion. This is critical for safe navigation and real-time decision-making.
  • Predictive Analytics: Deep learning also helps in predictive analytics, where the vehicle can anticipate potential hazards or the behavior of other road users. This predictive capacity is vital for the proactive safety measures required in autonomous driving.

Voice Assistants

For voice assistants like Siri and Alexa, deep learning has brought about significant improvements:

  • Natural Language Processing (NLP): Deep learning models, particularly recurrent neural networks (RNNs) and transformers, have greatly advanced the field of NLP. They enable voice assistants to understand and generate human language with a level of fluency that is increasingly natural and responsive.
  • Speech Recognition: Voice assistants are becoming more adept at accurately transcribing spoken words into text, thanks to deep neural networks that can capture the nuances of human speech, including accents and intonation.
  • Contextual Understanding: Beyond recognizing words, deep learning allows these assistants to grasp the context of a conversation. This capability means they can handle follow-up questions, remember user preferences, and even detect subtleties like sarcasm or implied meaning.

Enhancing Reliability and Interactivity

The advanced capabilities of deep learning are not just making these technologies possible but are also enhancing their reliability and interactivity. Autonomous vehicles are becoming safer and closer to widespread adoption. At the same time, voice assistants are transitioning from being simple command-based interfaces to more interactive and engaging companions capable of carrying out complex tasks.

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The future of AI

Deep learning serves as the backbone of some of the most cutting-edge technologies today. Its ability to process and learn from enormous datasets is what enables machines to perform tasks that require a level of understanding and decision-making that was once thought to be exclusively human.

The technical depth of Machine Learning vs Deep Learning can be overwhelming, but at their core, these technologies are built on a few fundamental principles. Both use algorithms, which are sets of rules and statistical techniques to analyze and interpret data. Training a model on a dataset to perform a specific task, such as recognizing speech or classifying images, is a cornerstone of both Machine Learning and Deep Learning.

With the continuous evolution of these technologies, one can’t help but be excited about the potential advancements they promise. Companies like Google invest heavily in both Machine Learning vs Deep Learning to enhance their products and services. Whatever technology used the goal is to create systems that can learn and adapt—just like we do.

Machine learning is an exceptional tool for data analysis and prediction, well-suited for less complex tasks. Deep learning, on the other hand, elevates this capability, allowing machines to perform highly complex tasks by emulating the intricate workings of the human brain. Both Machine Learning and Deep Learning are driving us towards a future where technology seamlessly integrates into our daily lives, simplifying tasks, and unlocking new possibilities. As you delve deeper into these domains, remember the balance between data, computational requirements, and the task’s complexity is key to finding the right technological solution for your needs.

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Bing DallE 3 vs ChatGPT DallE 3 the differences compared

Bing DallE 3 vs ChatGPT DallE 3

You might have thought that the DallE 3 AI model in the Microsoft Bing Image Creator and the DallE 3 AI model integrated into the OpenAI ChatGPT service would provide identical results. But unfortunately this is just not the case and there are some big differences between the two.

If you would like to learn more about the differences and which suits your needs the best you’ll be pleased to know that Christian Heidorn and Igor from the AI advantage YouTube channel has created a fantastic Bing DallE 3 vs ChatGPT DallE 3 comparison video. Providing an overview of what you can expect from each.

While these tools fundamentally the same in name and you would thought AI models, do they differ significantly in their capabilities, strengths, and limitations? The first point of comparison lies in the differences in image generation. This difference is largely due to the unique algorithms and training data used by each tool, which influence the style, detail, and overall aesthetic of the generated images.

Bing Image Creator DallE 3 vs ChatGPT DallE 3

In terms of use cases, both tools have been tested in a variety of scenarios to determine their efficacy. For instance, when tasked with creating a video thumbnail, Bing Image Creator emerged as the superior tool. Its ability to generate detailed and polished images made it the preferred choice for this particular task.

However, the tables turned when the task was to create a book cover. In this scenario, Bing Image Creator was again the clear winner, but for a different reason. ChatGPT DallE 3 has content restrictions that limit its ability to create darker, grittier images, making Bing Image Creator the more suitable tool for this task.

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Textures

When it came to generating textures, Bing Image Creator was again preferred due to its ability to create more detailed and polished images. This is a testament to the tool’s versatility and its ability to adapt to different use cases.

Film posters creation

The results were mixed when the task was to create a film poster. Bing Image Creator produced images that looked more like movie posters, but DallE 3 in Chat GPT Plus produced higher quality images. This highlights the fact that the best tool for a given task depends on the specific requirements of that task.

Accuracy

In terms of quality and accuracy, both tools have their strengths. Bing Image Creator excels in creating detailed and polished images, while DallE 3 in Chat GPT Plus shines in producing high-quality images. However, the quality and accuracy of the generated images can vary depending on the specific use case.

Limitations

As for limitations, each tool has its own set of constraints. For instance, ChatGPT DallE 3’s content restrictions can limit its ability to create certain types of images. On the other hand, Bing Image Creator, while versatile, may not always produce the highest quality images.

Despite these limitations, both tools have significant potential for future improvements. With advancements in AI and machine learning, these tools can be further refined to improve their image generation capabilities. Additionally, they can be used in conjunction with each other to achieve the desired results, demonstrating the potential for synergy between different AI tools.

There isn’t a clear winner between DallE 3 Bing Image Creator and ChatGPT DallE 3. The best tool depends on the specific use case, highlighting the importance of understanding the strengths and limitations of each tool. As AI continues to evolve, these tools will undoubtedly continue to improve, offering even more possibilities for image generation.

OpenAI DallE 3 AI image creator

DallE 3 represents a significant advancement over its predecessor, DallE 2, in the domain of text-to-image generation. One of the most notable improvements is in its ability to capture nuance and detail, allowing for a higher degree of accuracy when translating textual prompts into images. This precision makes it easier for users to see their ideas visually represented in a way that closely aligns with their intentions.

Another innovative feature is its integration with ChatGPT. Users can utilize ChatGPT as a brainstorming tool to refine their prompts, enhancing the creative process. The synergy between DallE 3 and ChatGPT extends to the capability for iterative design; users can request modifications to generated images with simple textual inputs. This makes the whole experience more interactive and tailored to individual needs.

In terms of ethical and safety considerations, DallE 3 incorporates several important features. It is programmed to decline requests that ask for images in the style of a living artist, mitigating concerns about artistic plagiarism. Additionally, OpenAI has put in measures to curtail the generation of content that is violent, adult, or hateful. It also declines requests for generating images of public figures by name, and has improved safety performance in areas like harmful biases and misinformation, thanks in part to collaboration with red teamers—domain experts who stress-test the model.

DallE 3 also tackles a common issue in text-to-image systems: the tendency to ignore certain words or details in prompts, which has led users to master the art of “prompt engineering.” With DallE 3, the images generated adhere more closely to the text, reducing the need for such engineering. Finally, OpenAI is exploring ways to trace the provenance of generated images, with ongoing research into a provenance classifier tool.

Availability-wise, DallE 3 will be accessible to ChatGPT Plus and Enterprise customers, initially via an API and later in Labs. Users retain the rights to the images they create, offering freedom in how they choose to use or commercialize them.

Quick summary of DallE 3 features

  • Improved Nuance and Detail: Offers a higher level of accuracy in translating text prompts into images, capturing more nuance and detail compared to previous versions.
  • Integration with ChatGPT: Built natively on ChatGPT, allowing users to refine their prompts and brainstorm ideas through a conversational interface.
  • Iterative Design: Users can request modifications to generated images by providing additional input through ChatGPT.
  • Ethical Considerations:
    • Declines requests for images styled after living artists.
    • Limits the ability to generate violent, adult, or hateful content.
    • Mitigates risks related to visual over/under-representation and harmful biases.
  • Public Figure Limitations: Programmed to decline generating images of public figures by name.
  • Safety Improvements: Collaborates with red teamers to stress-test the model and improve its risk assessment and mitigation efforts.
  • Reduced Prompt Engineering: Designed to adhere closely to text prompts, minimizing the need for users to master “prompt engineering.”
  • User Rights: Users retain the rights to the images they generate, allowing for a range of uses including commercialization.
  • Availability: Accessible to ChatGPT Plus and Enterprise customers via an API initially, and later in Labs.
  • Provenance Classifier: OpenAI is researching ways to trace the origin of generated images, including the development of a provenance classifier tool.

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Several Important Differences Between Counter-Strike 2 And CS:GO

Counter-Strike 2

At the end of March 2023, Valve launched closed beta testing of Counter-Strike 2, a new version of the competitive shooter CS:GO. The game is not very different from the original, but still created a huge stir in the gaming community. It’s worth noting that visually CS2 looks almost the same as the previous version, but there are important updates. Moreover, the changes relate not only to the interface but also to the gameplay. If you are ready to plunge into the world of Counter-Strike 2 matches as much as you love CS:GO matches, then this article written by eZstah is for you.

The Most Important Differences Between Counter-Strike 2 And CS:GO

Since the rumors about the release of Counter-Strike 2, the number of CS:GO players has increased significantly. If in December 2022 the average number of players was 629,325, then in the summer of 2023 the figure increased to 900,000-1,000,000 players. Perhaps every player is eager to get access to Counter-Strike 2 to see the improvements. Very soon, more players will have this opportunity, but for now, let’s look at them.

Smoke Grenades

This is the most noticeable change that will definitely change the behavior of players in certain game situations. Previously, the smoke lasted 18 seconds and was constant, completely limiting visibility: it was possible to safely plant a bomb or change position.

The smoke effect has increased to 20 seconds, players can interact with them. For example, a shot will create a hole, and a fragmentation grenade will help you disperse the smoke for a while. Apparently, smoke grenades in CS2 will become less versatile. There are no longer guarantees that the enemy will lose control of the territory. However, the importance of the review remains the same. Gamers will have to find more interesting ways to provide it.

Removing Skyboxes

There are other important mechanics with grenades. Global Offensive used a system of skyboxes – invisible textures on top of the map from which grenades bounced. Players have learned to use this when throwing, but there will be no skyboxes in Counter-Strike 2. Because of this, many grenades will stop working at the beginning of the rounds, but others will appear since there are no more obstacles in the sky. Grenades can now be thrown across the entire map, which will allow players to realize a huge number of new rounds on each of the maps.

Refusal of Tick Rate

In CS:GO, the server updates data at a certain frequency. It’s called tickrate. The higher it is, the more accurately the system processes the data. For example, you may not get credit for a hit because the computer did not have time to quickly send information about it to the server. Tick rate greatly influences the way grenades move when deployed. Valve’s CS:GO servers use a tick rate of 64. That’s why some people prefer to play on FACEIT and other platforms, where it is 128. By the way, the same indicator is in Valorant from Riot Games, which is presented as an important advantage of the game.

However, in Counter-Strike 2, Valve didn’t just increase the stats to 128, as many expected. Instead, the server will instantly respond to all player actions thanks to the new system, which will make the game much more accurate and fair.

Character Movement

Those who have tried Counter-Strike 2 know that the models in the shooter began to move noticeably smoother than before. This is not very convenient yet, but you can get used to it, especially if a person plays not only CS. In CS:GO, strafe is of great importance – the ability to suddenly jump out from around a corner, shoot at an enemy, and return to a safe place without taking damage. This is the basis of close and mid-range duels in Valve’s shooter. Now it’s not so easy to jerk an opponent: it’s much easier for him to get into timing and respond with a shot.

Graphics Updates

It seems that the improved picture is aimed only at visual perception, but in the case of a competitive shooter at the level of CS:GO, this is not entirely true. In Counter-Strike, any information matters. Bomb explosions now look brighter, with blood stains exactly matching the direction of the shot. With this, players will, in theory, be able to determine where the opponent is. To a lesser extent, this concerns lighting and new textures: many players who are accustomed to setting graphics to a minimum for the sake of performance will simply not see these textures. However, for some, due to better visuals, it will be easier to see the enemy.

Sound Updates

The sound in Counter-Strike 2 has become clearer. It conveys the surroundings better. This means that with a good headset and gaming experience, a gamer will be able to recognize the exact direction from which the shot came.

Mini-fixes For AWP

The sniper rifle has received small but important changes. Firstly, you can no longer simply hold down the right mouse button to enable double zoom. Secondly, now when fired there is a clear trajectory, like from a tracer. It will be easier to find the sniper. Perhaps, in this way, Valve wants to slightly reduce the importance of this role and, accordingly, increase the importance of all others.

New Anti-cheat

The last thing worth mentioning is Valve’s fight against cheaters, which has been going on ever since the launch of the first version of Counter-Strike. Lately, VAC has been working better and better, but it is not perfect, which can ruin the enjoyment of the game for many players. The capabilities of the new engine will allow developers to add improved anti-cheat to CS2. It is claimed that VAC Live 2 will identify violators in real-time. If the system detects a dishonest player, it will immediately cancel the match.

Wrapping It Up

The release of Counter-Strike 2 opens up new possibilities for players to develop more sophisticated strategies. You can use new advantages to achieve the same results as Oleksandr “⁠s1mple” Kostyliev or Valerii “b1t” Vakhovskyi from Navi. We also recommend using Profilerr services to learn more about pro players, as well as make adjustments to the settings for a more effective game. The service is open to users from all over the world, including players from US, Canada, or Ukraine.

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Non-Owned vs. Hired Auto Coverage: What Are the Differences?

Did you know that, on average, six million motor vehicle crashes occur yearly in the United States? These incidents result in at least three million people sustaining injuries. Nearly three in four collisions also cause property damage.

All that makes having auto insurance necessary if you use vehicles for your business. Without it, you can face high costs, even litigation, if you or your employees cause a car crash.

The types of car insurance you need depend on who owns the vehicles you use for your business. You may need non-owned or hired auto coverage if your company borrows or rents cars or trucks.

This guide discusses both types of coverage, what sets them apart, and which one you should get, so read on.

What Is Non-Owned Coverage?

Non-owned auto coverage is for vehicles your company doesn’t own, lease, or hire. An example is a personal car one of your employees owns and uses for your business.

Non-owned auto insurance provides liability coverage. It kicks in if the vehicle you don’t own but use for business gets involved in an accident.

For example, an employee uses their car to drive and meet your business’s clients. Along the way, they accidentally rear-ended another vehicle.

Non-owned insurance can help cover the damages sustained by the other vehicle. It can also help pay for the other driver’s medical costs.

However, your employee must exhaust the limits of their auto coverage first. Only after this will the benefits of a non-owned insurance policy kick in.

Non-owned insurance also often provides coverage for litigation costs. For example, the owner of the vehicle your employee crashed into decides to sue your business. In this case, your coverage can help pay for your company’s legal costs.

What About Hired Auto Coverage?

Does your business hire, lease, or rent cars or trucks? If so, you need a hired auto insurance policy. It can cover you or your employees who may get involved in an accident while driving such vehicles.

Suppose you’re driving a rental and crash into another person’s vehicle. This led to the other car sustaining damage or the other driver getting injured.

If you have hired auto coverage, it can pay for the other person’s medical expenses. Likewise, it can help pay for the needed repairs to the other party’s vehicle. It can also cover your company’s legal costs if the other driver sues your business.

Please note that you can only get this auto insurance for vehicles you hire, lease, or rent from a third party. You can’t do the same for those owned by employees, business partners, or family members.

What Sets the Two Apart?

The primary difference between the two has something to do with who owns the vehicles they cover.

Non-owned auto coverage is for vehicles your business uses but doesn’t own, rent, or lease. These include cars your employees own and use to perform jobs for you. The same applies to cars or trucks you borrow from family members or friends to conduct work.

Hired auto coverage is for cars or trucks your company rents or leases and uses for business. In this case, their owner is a third party, such as a vehicle rental company.

When Should You Get Which?

Most insurers bundle hired and non-owned auto (HNOA) insurance policies together. This is your best bet if your company uses rental cars and your employees also use their vehicles for work. Typically, these bundles come with discounts or lower insurance rates.

However, you can buy hired auto coverage separately from non-owned auto insurance. You can opt for this route if your business has no employees who use a car to conduct work. You can also do the same if your company only uses rental or leased vehicles.

What Do HNOA Policies Exclude?

Please be careful not to confuse non-owner car insurance with non-owned coverage. Non-owner policies are for those who don’t own a car but often drive borrowed or rented vehicles. It covers the policyholder and the car’s owner in case of an accident.

Neither non-owned nor hired auto insurance provides coverage for the policyholder. They don’t cover you, your employees, or the non-owned, rented, or leased vehicles you or your workers use.

Let’s say one of your employees crashes their car while on the way to perform a work errand. In this case, your non-owned auto coverage won’t pay for the following:

  • The property damage sustained by your employee’s car
  • Your employees’ medical bills if they got hurt in the crash

Also, remember that hired or non-owned auto policies only apply to work-related incidents. So, if you get into a crash while using a rental for personal reasons, your hired auto coverage won’t kick in. A non-owned policy won’t cover an employee who uses their car for personal reasons, either.

Hired and non-owned auto policies also have strict rules regarding negligence. They typically don’t provide coverage for crashes due to:

  • Driving while under the influence of drugs or alcohol
  • Vehicle defects caused by a lack of maintenance
  • Substandard driver record

So, if you or your employees meet any of those, expect your policy not to provide coverage.

What Policies Cover What HNOA Insurance Doesn’t?

Collision and comprehensive coverage can help cover damage to non-owned vehicles. They can help with the costs of fixing or replacing the insured car.

Worker’s compensation covers injuries sustained by employees at work. FYI, over 2 million non-fatal job injuries occurred in the U.S. in 2021 alone.

Many of those job-related injuries result from motor vehicle collisions.

Fortunately, worker’s comp applies to such incidents. As long as the injured employee was on the job while driving, it should kick in.

Never Go Without Proper, Adequate Insurance

Remember: You need non-owned insurance if your business uses a vehicle it doesn’t own, rent, or lease. Conversely, you need hired auto coverage if your company rents or leases cars or trucks.

Regardless of which you need, please purchase them ASAP. Otherwise, you’re at risk of liabilities if you or your employees get into a crash in a non-owned or hired car.

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