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Több »Élménybeszámolók

Kenya 2013

Beküldte: Lekvar

nem lakóautós, de érdekes...

Luxemburg

Beküldte: Wobi

Jó 30 évet kellet várnunk, hogy újból eljussunk ide.

Biztosítás

Létrehozás dátuma: 2012.11.12.

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	IT Education Centre Data Science Course: Understanding Trainer Quality Before You Enroll Pune is a hub for many industries and our data is highly dependent on it. Currently, there is a huge demand for data science training in Pune as the demand for skilled professionals is also increasing at a very high rate. http://iteducationcentre.com/data-science-course-in-pune Data Science Classes in Pune is the highest paying profession in India. Both freshers and experienced professionals use data science to sell themselves in the competitive world. The demand is increasing at a very high rate and the placements are also very available, which is why data science is becoming very popular. In today's world, the internet is utilized on a massive scale. Whether an object or entity exists physically in a specific location, or is confined within a digital container of generated data, its unepresence and volume are constantly expanding. 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Why Trainer Quality Matters in Data Science Training Unlike many short-term certification programs, Data Science combines multiple technical disciplines, including: Python Programming SQL Statistics Machine Learning Data Visualization Artificial Intelligence Fundamentals Real-world Project Development Because the syllabus covers diverse technical concepts, trainers play an essential role in simplifying difficult topics and helping students apply them practically. Students joining a Data Science Course in Pune generally expect: Practical coding sessions Real-world projects Industry examples Interview preparation Portfolio guidance Placement assistance When these expectations are met, students usually have a satisfying learning experience. Why Some Students Feel Trainer Quality Differs Between Batches One of the most common observations shared by learners is that the teaching experience can vary depending on the trainer. This doesn't necessarily indicate a lack of expertise. Instead, it often reflects differences in teaching style, classroom interaction, and individual communication methods. For example: Some trainers explain concepts using real-world case studies. Others focus more on coding demonstrations. Some spend extra time with beginners. Others move at a faster pace for experienced learners. Because every student has a unique learning preference, opinions naturally vary. Different Learning Styles Create Different Experiences Students enrolling in IT Education Centre in Pune Data Science Course come from various educational backgrounds. A typical batch may include: Engineering graduates Working IT professionals Freshers Commerce graduates Career switchers Non-programming learners Teaching such a diverse classroom is never easy. A pace that feels perfect for an experienced programmer may seem fast to a complete beginner. Likewise, slowing down too much may leave experienced learners wanting more advanced content. This difference in expectations often explains why reviews about trainers can differ. Why Practical Learning Is More Important Than Teaching Style One of the biggest misconceptions among new students is believing that success depends entirely on finding the "perfect trainer." In reality, Data Science is a skill-based profession. Even excellent trainers cannot replace consistent hands-on practice. Students generally achieve better results when they actively work on: Python programming exercises SQL queries Machine Learning models Data visualization projects Kaggle datasets Portfolio development The classroom provides direction, but personal practice builds confidence. How IT Education Centre in Pune Addresses Student Learning The http://iteducationcentre.com/data-science-course-in-puneIT Education Centre in Pune Data Science Course is designed to provide students with a structured learning path covering essential Data Science concepts. The curriculum typically includes: Python Programming SQL Database Management Statistics Data Analytics Machine Learning Data Visualization Capstone Projects Interview Preparation Instead of depending solely on lectures, students are encouraged to strengthen their practical understanding through assignments, projects, and coding practice. This structured approach helps reduce the impact of differences in teaching styles across batches. Continuous Improvement Matters Every growing training institute receives feedback from students. Constructive feedback allows institutes to improve: Teaching methodologies Trainer development Practical assignments Student engagement Course content Project quality IT Education Centre in Pune has continued expanding its technical training programs, and maintaining trainer consistency remains an important area of continuous improvement—something that is common across many multi-batch technical institutes. Why Self-Learning Is Essential in Data Science Whether you choose IT Education Centre in Pune, another Data Science Institute in Pune, or an online platform, self-learning remains an important part of becoming job-ready. Successful students usually dedicate additional time to: Practicing Python Solving SQL problems Exploring real datasets Building machine learning models Reading documentation Participating in coding challenges No classroom alone can replace consistent practice. Tips Before Joining Any Data Science Institute Before enrolling in any Data Science Classes in Pune, ask these questions: Is the curriculum updated? Ensure the syllabus includes modern Data Science tools and Machine Learning concepts. Are practical projects included? Projects help students gain confidence and prepare for interviews. Is doubt-solving available? Regular support sessions improve learning significantly. Does the institute provide interview guidance? 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It is a combination of multiple technical areas such as: Python programming Statistics and probability Data analysis Machine learning Data visualization SQL and database concepts Business problem-solving Real-time project implementation Because of this, the role of a trainer becomes much more than just “teaching chapters.” A good Data Science trainer helps students: Data Science Classes in Pune Understand difficult concepts in a simple way Connect theory with real-world examples Solve coding and project-related doubts Build confidence in tools and technologies Guide students on practical implementation Prepare for interviews and job roles in the industry What Does “Inconsistent Trainer Quality” Really Mean? One batch may have a trainer who explains every topic with detailed real-time examples. Another batch may have a trainer who focuses more on theory and less on practical implementation. Some trainers may be excellent at teaching beginners. Others may be technically strong but may not always match every student’s learning pace. Why Students May Feel the Teaching Quality Varies There are several practical reasons why some students may feel that trainer quality is not the same in every batch. Let’s look at them one by one. 1. Different Trainers Have Different Teaching Styles Every trainer has a unique way of teaching. Some are highly interactive and energetic, while others are more structured and technical. Some focus heavily on coding practice, while others spend more time explaining the theory behind machine learning models. For example: A student from a programming background may enjoy a trainer who moves quickly into coding and projects. A complete beginner may prefer a trainer who spends more time on fundamentals and slower explanations. So, the same trainer can be seen as “excellent” by one student and “too fast” by another. This difference in expectations often leads to mixed feedback. IT Education Centre 2. Student Backgrounds Are Different A Data Science classroom usually includes a wide variety of learners, such as: Fresh graduates Engineering students Working IT professionals Non-technical career switchers 3. Batch Size Can Influence the Experience Trainer quality is not only about knowledge—it is also about how much attention each student receives. In some cases, if a batch has many students, personal doubt-solving time may reduce. This can make students feel that the learning is less interactive or less personalized. On the other hand, smaller batches often feel more engaging because students can ask more questions, interact more freely, and get more direct support from the trainer. This is why some students may compare their experience with another batch and feel that the teaching quality was different, when in reality the difference may have come from batch dynamics rather than trainer capability alone. 4. 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Growing Institutes Often Work with Multiple Trainers Popular institutes that run multiple batches across locations or online platforms often need a team of trainers instead of a single faculty member. This is common in large-scale skill training organizations. The advantage of this model is that students get more batch options, flexibility, and accessibility. However, one challenge is maintaining complete uniformity in delivery style across all trainers. Even if the syllabus is the same, trainers may differ in: Speed of coverage Depth of examples Assignment style Tool preferences Industry storytelling Student engagement methods This is why institutes must invest in standardized content, internal quality checks, feedback systems, and trainer alignment processes to maintain consistency. How Reputed Institutes Like IT Education Centre Can Make a Difference While trainer consistency is a common challenge in the training industry, the right institute works continuously to improve student outcomes. This is where the role of an established training brand like IT Education Centre becomes important. IT Education Centre has built a strong presence in the professional training space by offering courses in Data Science, Software Testing, Full Stack Development, Cloud, Digital Marketing, and many other in-demand domains. For students choosing a Data Science program, the value of an institute lies not only in one trainer, but in the complete learning ecosystem it provides. Here are some ways a structured institute like IT Education Centre can help address concerns around trainer consistency. 1. Structured Course Curriculum A strong institute ensures that the Data Science course follows a well-planned syllabus rather than depending entirely on individual trainer preference. This helps students receive a more uniform learning experience across batches. A structured Data Science curriculum generally includes: Python basics and advanced concepts Statistics for Data Science SQL and data handling Exploratory Data Analysis Machine Learning algorithms Data visualization tools Model evaluation techniques Real-time projects Interview preparation and resume building When the curriculum is properly structured, students are less dependent on the trainer’s personal teaching flow and more confident that important topics will be covered. 2. Focus on Practical and Industry-Oriented Learning One of the best ways to reduce the impact of trainer variation is to ensure that the course itself is designed around practical implementation. Institutes like IT Education Centre emphasize hands-on learning because Data Science is a skill-based domain. This can include: Coding practice sessions Project work using real datasets Assignments after every module Interview-based question solving Business case study discussions Resume and portfolio support Even if teaching styles differ slightly, a practical framework helps students stay aligned with real job requirements. 3. Student Feedback and Continuous Improvement The most reliable institutes understand that feedback is essential for long-term quality improvement. If students feel that certain sessions are too fast, too theoretical, or less interactive, this feedback can help management identify areas for improvement. IT Education Centre An institute that takes feedback seriously can improve consistency by: reviewing trainer performance regularly collecting student feedback after modules upgrading teaching methods arranging backup doubt sessions revising content delivery strategies assigning additional support for weaker areas 4. Doubt Support Beyond the Classroom At institutes like IT Education Centre , students often value the broader support system that can include: doubt-clearing sessions recorded lectures for revision trainer support for assignments project guidance interview mentoring peer learning opportunities This additional support can significantly reduce the impact of any temporary confusion students may face during regular classroom sessions. 5. Real Learning Depends on Student Effort Too While trainer quality is very important, Data Science is one of those fields where student effort matters equally. Even the best trainer cannot replace: regular coding practice revision of concepts independent project building solving interview questions experimenting with datasets reading documentation and exploring tools Students who actively practice alongside classroom learning usually gain much more confidence and clarity. So, the ideal learning model is not just “good trainer = guaranteed success,” but rather: Good trainer + structured institute + consistent student practice = strong learning outcome This is why institutes like IT Education Centre can be valuable—they provide the structure, guidance, and support that students can combine with their own effort to build a successful Data Science career. How Students Can Get the Best Learning Experience in a Data Science Course If you are planning to join ahttp://iteducationcentre.com/data-science-course-in-pune Data Science course, here are a few practical tips to make the most of your learning experience, regardless of trainer style: 1. Check the course roadmap before joining Understand what modules are covered and whether the course matches your career goals. 2. Ask about practical projects A good Data Science course should include project work, coding assignments, and real-world applications. 3. Use doubt sessions actively If you don’t understand a topic in class, ask questions early rather than waiting too long. 4. Revise every week Data Science concepts build on one another, so regular revision is important. 5. Practice outside class Even if the trainer is good, personal coding practice is essential. 6. Build small projects from the start Projects help you connect theory to real-world use and improve job readiness. 7. Use placement and interview support properly Resume reviews, mock interviews, and aptitude guidance can make a big difference. Why IT Education Centre Remains a Considerable Choice for Data Science Learning However, what matters most is whether the institute provides: a structured Data Science curriculum practical learning opportunities project-based training access to trainer and doubt support interview and placement preparation a student-focused learning environment This is where IT Education Centre continues to remain a relevant option for many learners. 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	#14173 2026.07.09. 11:00

komalb4 0 mániapont
	SevenMentor Data Science Course: Understanding Trainer Quality Before You Enroll Mumbai is a hub for many industries and our data is highly dependent on it. Currently, there is a huge demand for data science training in Mumbai as the demand for skilled professionals is also increasing at a very high rate. https://www.sevenmentor.com/data-science-courses-in-mumbaiData Science (with Generative AI & Agentic AI) classes in Mumbai is the highest paying profession in India. Both freshers and experienced professionals use data science to sell themselves in the competitive world. The demand is increasing at a very high rate and the placements are also very available, which is why data science is becoming very popular. In today's world, the internet is utilized on a massive scale. Whether an object or entity exists physically in a specific location, or is confined within a digital container of generated data, its unepresence and volume are constantly expanding. The impact of this expanding internet usage is evident across the spectrum—from the common person to business professionals and even scientists. The internet is utilized at every level; consequently—whether involving financial transactions, the exchange of money, or the large-scale transfer of personal data—all such information is stored within an internet database. To counter these risks, extensive preventive measures are implemented. Furthermore, in the modern world, various technological tools are utilized to mitigate such potential damages. This is why modern training institutes such as SevenMentor are increasingly focusing on practical learning methods, student interaction, and hands-on training to improve the overall learning experience. Why Trainer Quality Matters in Data Science Training Unlike many short-term certification programs, Data Science combines multiple technical disciplines, including: Python Programming SQL Statistics Machine Learning Data Visualization Artificial Intelligence Fundamentals Real-world Project Development Because the syllabus covers diverse technical concepts, trainers play an essential role in simplifying difficult topics and helping students apply them practically. Students joining a Data Science courses (with Generative AI & Agentic AI)in Mumbai generally expect: Practical coding sessions Real-world projects Industry examples Interview preparation Portfolio guidance Placement assistance When these expectations are met, students usually have a satisfying learning experience. Why Some Students Feel Trainer Quality Differs Between Batches One of the most common observations shared by learners is that the teaching experience can vary depending on the trainer. This doesn't necessarily indicate a lack of expertise. Instead, it often reflects differences in teaching style, classroom interaction, and individual communication methods. For example: Some trainers explain concepts using real-world case studies. Others focus more on coding demonstrations. Some spend extra time with beginners. Others move at a faster pace for experienced learners. Because every student has a unique learning preference, opinions naturally vary. Different Learning Styles Create Different Experiences Students enrolling in SevenMentor Data Science Course come from various educational backgrounds. A typical batch may include: Engineering graduates Working IT professionals Freshers Commerce graduates Career switchers Non-programming learners Teaching such a diverse classroom is never easy. A pace that feels perfect for an experienced programmer may seem fast to a complete beginner. Likewise, slowing down too much may leave experienced learners wanting more advanced content. This difference in expectations often explains why reviews about trainers can differ. Why Practical Learning Is More Important Than Teaching Style One of the biggest misconceptions among new students is believing that success depends entirely on finding the "perfect trainer." In reality, Data Science is a skill-based profession. Even excellent trainers cannot replace consistent hands-on practice. Students generally achieve better results when they actively work on: Python programming exercises SQL queries Machine Learning models Data visualization projects Kaggle datasets Portfolio development The classroom provides direction, but personal practice builds confidence. How SevenMentor Addresses Student Learning The SevenMentor Data Science Course is designed to provide students with a structured learning path covering essential Data Science concepts. The curriculum typically includes: Python Programming SQL Database Management Statistics Data Analytics Machine Learning Data Visualization Capstone Projects Interview Preparation Instead of depending solely on lectures, students are encouraged to strengthen their practical understanding through assignments, projects, and coding practice. This structured approach helps reduce the impact of differences in teaching styles across batches. Continuous Improvement Matters Every growing training institute receives feedback from students. Constructive feedback allows institutes to improve: Teaching methodologies Trainer development Practical assignments Student engagement Course content Project quality SevenMentor has continued expanding its technical training programs, and maintaining trainer consistency remains an important area of continuous improvement—something that is common across many multi-batch technical institutes. Why Self-Learning Is Essential in Data Science Whether you choose SevenMentor, another https://www.sevenmentor.com/data-science-courses-in-mumbaiData Science Institute in Mumbai, or an online platform, self-learning remains an important part of becoming job-ready.Best academic degree Successful students usually dedicate additional time to: Practicing Python Solving SQL problems Exploring real datasets Building machine learning models Reading documentation Participating in coding challenges No classroom alone can replace consistent practice. Tips Before Joining Any Data Science Institute Before enrolling in any Data Science (with Generative AI & Agentic AI)in Mumbai, ask these questions: Is the curriculum updated? Ensure the syllabus includes modern Data Science tools and Machine Learning concepts. Are practical projects included? Projects help students gain confidence and prepare for interviews. Is doubt-solving available? Regular support sessions improve learning significantly. Does the institute provide interview guidance? Resume preparation, mock interviews, and career support are valuable additions. Are students encouraged to practice independently? The best institutes create an environment that motivates continuous learning. Why Many Students Still Choose SevenMentor Despite mixed opinions about trainer experiences, many students continue enrolling in the SevenMentor Data Science Course because of its: Industry-oriented curriculum Practical project exposure Flexible batch options Technical learning environment Career guidance Placement assistance Focus on skill development As with any educational institute, individual experiences may differ depending on personal expectations, learning style, and effort invested. Why Trainer Quality Matters So Much in Data Science Data Science is not a simple theoretical subject. It is a combination of multiple technical areas such as: Python programming Statistics and probability Data analysis Machine learning Data visualization SQL and database concepts Business problem-solving Real-time project implementation Because of this, the role of a trainer becomes much more than just “teaching chapters.” A good Data Science trainer helps students:Data Science classes (with Generative AI & Agentic AI) in Mumbai Understand difficult concepts in a simple way Connect theory with real-world examples Solve coding and project-related doubts Build confidence in tools and technologies Guide students on practical implementation Prepare for interviews and job roles in the industry What Does “Inconsistent Trainer Quality” Really Mean? One batch may have a trainer who explains every topic with detailed real-time examples. Another batch may have a trainer who focuses more on theory and less on practical implementation. Some trainers may be excellent at teaching beginners. Others may be technically strong but may not always match every student’s learning pace. Why Students May Feel the Teaching Quality Varies There are several practical reasons why some students may feel that trainer quality is not the same in every batch. Let’s look at them one by oneData Science courses (with Generative AI & Agentic AI) in Mumbai 1. Different Trainers Have Different Teaching Styles Every trainer has a unique way of teaching. Some are highly interactive and energetic, while others are more structured and technical. Some focus heavily on coding practice, while others spend more time explaining the theory behind machine learning models. For example: A student from a programming background may enjoy a trainer who moves quickly into coding and projects. A complete beginner may prefer a trainer who spends more time on fundamentals and slower explanations. So, the same trainer can be seen as “excellent” by one student and “too fast” by another. This difference in expectations often leads to mixed feedback. 2. Student Backgrounds Are Different A Data Science classroom usually includes a wide variety of learners, such as: Fresh graduates Engineering students Working IT professionals Non-technical career switchers 3. Batch Size Can Influence the Experience Trainer quality is not only about knowledge—it is also about how much attention each student receives. In some cases, if a batch has many students, personal doubt-solving time may reduce. This can make students feel that the learning is less interactive or less personalized. On the other hand, smaller batches often feel more engaging because students can ask more questions, interact more freely, and get more direct support from the trainer. This is why some students may compare their experience with another batch and feel that the teaching quality was different, when in reality the difference may have come from batch dynamics rather than trainer capability alone. 4. Practical Learning Expectations Are Very High in Data Science Students usually join a Data Science course with the hope of learning not just concepts, but also practical job-ready skills. They want: Hands-on coding sessions Real-world datasets Project-based assignments Case studies Resume guidance Interview preparation Industry use cases If a trainer is more focused on concept delivery but less on project demonstration, students may feel the sessions are not practical enough. Similarly, if students expect deep AI or machine learning implementation from day one but the trainer spends more time building fundamentals, they may assume the training is not strong enough. In many cases, the issue is not poor teaching, but a mismatch between student expectations and the trainer’s approach to course progression. 5. Growing Institutes Often Work with Multiple Trainers Popular institutes that run multiple batches across locations or online platforms often need a team of trainers instead of a single faculty member. This is common in large-scale skill training organizations. The advantage of this model is that students get more batch options, flexibility, and accessibility. However, one challenge is maintaining complete uniformity in delivery style across all trainers. Even if the syllabus is the same, trainers may differ in: Speed of coverage Depth of examples Assignment style Tool preferences Industry storytelling Student engagement methods This is why institutes must invest in standardized content, internal quality checks, feedback systems, and trainer alignment processes to maintain consistency. https://www.sevenmentor.com/data-science-courses-in-mumbai
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	#13915 2026.01.27. 03:37

Veronia24 0 mániapont
	Reading this made me think about unexpected life events, like when you think you have everything under control. It's kinda like when I tried that one-tap game the other day. So simple, I thought I'd be a pro. Then, total chaos! Kind of reminds me of crazy cattle 3d actually, just utter pandemonium in the best way possible! Had me laughing so hard, my stomach hurt. Sometimes you just need that little bit of absurdity, huh?"
	#13914 2026.01.27. 03:18

Mollieelson 0 mániapont
	Wow, this reminds me of playing idle games! Remember Monkey Mart? It's oddly addictive, slowly expanding your little supermarket. My kids are obsessed! I think they found a version at Monkey Mart. The simple gameplay is surprisingly engaging, and seeing your monkey employees hustle is just funny. Such a time-waster in the best way!
	#13913 2026.01.27. 03:08

Thomas46 0 mániapont
	I swear, sometimes life feels like trying to navigate a precarious situation with a very fragile egg. You think you've got it, and then BAM! Disaster strikes. It's like playing Eggy Car sometimes – you're so focused, inching forward, then one tiny bump and splat! Egg everywhere. But hey, gotta laugh it off and try again, right?
	#13909 2026.01.26. 10:01

Alpaca 0 mániapont
	Discover The Ultimate Slope Descent With Slope Game-The Best Right Now If you’re hunting for a quick, pulse-pounding gaming experience that fits into a break between tasks or a late-night browser session, Slope Game is worth a try. This unblocked 3D runner puts you in control of a rolling ball as it careens down an ever-shifting, gravity-defying track. The goal is simple: survive as long as you can while navigating sudden drops, sharp turns, and neon-lit obstacles. The result is a focused, addictive race against the clock and your own reflexes. Play now free at: Slope Game
	#13902 2026.01.21. 02:24

Donaldvarris 0 mániapont
	Has anyone else found a surprising hobby during stressful times? I started playing Geoguessr to de-stress from dissertation writing, and it's strangely calming. I used to know nothing about geography, but now I can (sort of) identify countries based on telephone poles! Anyone else feel that? By the way, is there a reliable way to play geoguessr free ?
	#13887 2025.12.26. 05:07

minakylie 0 mániapont
	What makes io games so appealing is their easy-to-learn, hard-to-master design. Most games start with basic mechanics, but as you grow stronger or larger, the competition becomes more challenging.
	#13882 2025.12.24. 08:53

ameliajohn 0 mániapont
	I've been looking into different insurance viewpoints lately, and this post really emphasizes some essential ideas about risk management and planning ahead. It's interesting to observe how selecting the right policy may make a significant difference, not only financially but also in terms of peace of mind. Students juggling many duties might benefit from tools like Dissertation Help Uk, which can help them stay focused while dealing with hard academic assignments. Combining practical life planning with appropriate support systems appears to be the best strategy.
	#13856 2025.11.19. 14:29

GaborApa Turkish Valentine 3252 mániapont
	írta Erkac 2013.01.29. 18:14 Bocs, de csak most értem ide... Szóval Neked az volt mármint ez a + a kármentes szerződők jelölésére volt volt persze biztosítótól függően. Na most már nem lesz. Ez évtől már többször is módosíthatják díjaikat a biztosítók.Persze ez csak az új kötésekre vonatkozik. Ne aggódjon senki, nem azért hogy esetleg olcsóbb legyen.... A hsz-od eleje megnyugtatott a vége meg, nem de köszi
	Mindenhol ... otthon #2386 2013.01.29. 20:16

Erkac Szentendre 45 mániapont
	"Te Erkac! Miért nem volt nekem bonus 10+++++++ biztosításom?" Bocs, de csak most értem ide... Szóval Neked az volt mármint ez a + a kármentes szerződők jelölésére volt volt persze biztosítótól függően. Na most már nem lesz. Ez évtől már többször is módosíthatják díjaikat a biztosítók.Persze ez csak az új kötésekre vonatkozik. Ne aggódjon senki, nem azért hogy esetleg olcsóbb legyen....
	#2382 2013.01.29. 18:14

Miska Szob 516 mániapont
	írta GaborApa 2013.01.21. 21:53 Ezt nem is értem... A vége meg teljesen felfoghatatlan Aki autósújságot olvas az biztonságosabban vezet? Szerintük újságból lehet megtanulni a rutint? Vagy, aki vért ad az ezzel előre megmenti áldozatait? Olyan mintha a hírcsárda lenne de nem az Akkor minden oké! Mi adtunk vért!
	Miska #2313 2013.01.21. 23:01

GaborApa Turkish Valentine 3252 mániapont
	írta Erkac 2012.11.24. 22:33 http://www.facebook.com/photo.php?fbid=378287878920235&set=pb.142736389142053.-2207520000.1353791691&type=3&theater Egy kis reklám! nagyon tetszik....:) Te Erkac! Miért nem volt nekem bonus 10+++++++ biztosításom?
	Mindenhol ... otthon #2312 2013.01.21. 21:56

GaborApa Turkish Valentine 3252 mániapont
	írta takizse 2013.01.21. 18:12 Üdv ! Újabb okos döntés !!!!!!!!!!!!!!!!!!!!!!!! http://www.hir24.hu/gazdasag/2013/01/21/kibabralnak-a-jo-soforokkel/#comments Remélem csak kacsa. Ezt nem is értem... A vége meg teljesen felfoghatatlan Aki autósújságot olvas az biztonságosabban vezet? Szerintük újságból lehet megtanulni a rutint? Vagy, aki vért ad az ezzel előre megmenti áldozatait? Olyan mintha a hírcsárda lenne de nem az
	Mindenhol ... otthon #2311 2013.01.21. 21:53

takizse Úri 23 mániapont
	Üdv ! Újabb okos döntés !!!!!!!!!!!!!!!!!!!!!!!! http://www.hir24.hu/gazdasag/2013/01/21/kibabralnak-a-jo-soforokkel/#comments Remélem csak kacsa.
	"Az ember nem az, amit gondol magáról, hanem az, amit róla gondolnak." #2304 2013.01.21. 18:12

Erkac Szentendre 45 mániapont
	http://www.facebook.com/photo.php?fbid=378287878920235&set=pb.142736389142053.-2207520000.1353791691&type=3&theater Egy kis reklám! nagyon tetszik....:)
	#1557 2012.11.24. 22:33

Erkac Szentendre 45 mániapont
	"Értem én, hogy Te most mindent átkötsz, és lesz itt egy jó nagy gubanc de mikor jelentkeztek már be a Mikulástalira? " Sajnos a Mikulás talira most nem megyünk.Mondjuk máskor sem voltunk... Majd jövőre. Ma Mátéval voltunk az IKEA-ban és egy répának öltözött lágy halálra ijesztette. Ezek után a mikulás látványától gyalog indulna haza....
	#1556 2012.11.24. 21:11 1 embernek tetszik

GaborApa Turkish Valentine 3252 mániapont
	írta Erkac 2012.11.19. 18:14 Az 2013-as átkötésben tapasztaltak szerint a következő biztosítóknál általában jó díj jön ki átkötés nélkül is: -K&H,UNIQA,Wabard, -az ASTRA ügyfeleinek 20-40%-os emelést ad ha nem kötöd át,ezért érdemes odafigyelni, -a többi társaságnál területfüggően jönnek ki a díjak, ezeket is fontos átnézni. E-mail:fullrisk@fullrisk.hu Értem én, hogy Te most mindent átkötsz, és lesz itt egy jó nagy gubanc de mikor jelentkeztek már be a Mikulástalira? Amúgy, tök jó, hogy nem házasságkötő vagy. Minden novemberben kiszámolhatnánk, hogy most újrakössük, megkössük valaki mással, vagy hagyjuk a francba, hisz a másik se sokkal jobb.
	Mindenhol ... otthon #1480 2012.11.19. 18:55 1 embernek tetszik

Erkac Szentendre 45 mániapont
	Az 2013-as átkötésben tapasztaltak szerint a következő biztosítóknál általában jó díj jön ki átkötés nélkül is: -K&H,UNIQA,Wabard, -az ASTRA ügyfeleinek 20-40%-os emelést ad ha nem kötöd át,ezért érdemes odafigyelni, -a többi társaságnál területfüggően jönnek ki a díjak, ezeket is fontos átnézni. E-mail:fullrisk@fullrisk.hu
	#1477 2012.11.19. 18:14

GaborApa Turkish Valentine 3252 mániapont
	írta Erkac 2012.11.13. 21:27 Ma megszületett az év CASCO díj rekordja: Renault Twingo teljeskörű casco éves díj 15800ft!!! És a Twingo remek választás bicikli helyett A biciklim lehet, hogy már nem is ér ennyit, de ha ilyet tudsz a lakóautóra is akkor szólj
	Mindenhol ... otthon #1421 2012.11.14. 08:06

Erkac Szentendre 45 mániapont
	Ma megszületett az év CASCO díj rekordja: Renault Twingo teljeskörű casco éves díj 15800ft!!! És a Twingo remek választás bicikli helyett
	#1418 2012.11.13. 21:27

GaborApa Turkish Valentine 3252 mániapont
	írta Erkac 2012.11.12. 22:06 Ez így van! Sőt a tó kerülő biciklikre külön biztosítás kell. Egyébként minden utánfutó-lakókocsi egy kategória (forgalmiban O1) 750KG-ig éves díja 1500ft-tól egy évre területtől függően.Az ennél nagyobbak (ki gondolná O2) külön kategória tök mindegy hogy futó vagy lakókocsi vagy csónakszállító ez is van vagy 3000ft egy évre. Közben megnyugodtam. Van egy utánfutóm, ami évek óta egy havernál van és Ő intézi. De azért köszi. Az nem lakó, max. ha sátrat verünk rá :) A tókerülő bicikli az érthetően külön kategória Veszélyes üzem :)
	Mindenhol ... otthon #1412 2012.11.13. 17:40

Erkac Szentendre 45 mániapont
	Ez így van! Sőt a tó kerülő biciklikre külön biztosítás kell. Egyébként minden utánfutó-lakókocsi egy kategória (forgalmiban O1) 750KG-ig éves díja 1500ft-tól egy évre területtől függően.Az ennél nagyobbak (ki gondolná O2) külön kategória tök mindegy hogy futó vagy lakókocsi vagy csónakszállító ez is van vagy 3000ft egy évre.
	#1393 2012.11.12. 22:06

csiburento Budapest 312 mániapont
	írta GaborApa 2012.11.12. 16:45 Erkac! Minden utánfutóra kell, vagy csak a lakókocsikra? Minden járműre, ami a táplálék láncban a bicikli fölött van, kell kgfb...
	Alá vagyon íródva. #1392 2012.11.12. 20:44

GaborApa Turkish Valentine 3252 mániapont
	Erkac! Minden utánfutóra kell, vagy csak a lakókocsikra?
	Mindenhol ... otthon #1385 2012.11.12. 16:45

GaborApa Turkish Valentine 3252 mániapont
	Aktuális a téma, és van köztünk olyan is aki ért hozzá.
	Mindenhol ... otthon #1382 2012.11.12. 16:05

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