Tag: <span>learn science</span>

14 Aug

Why It Is Important to Get Trained From a Data Science Institute
Posted in Education by anshbookstore on August 14, 2022 @ 12:58 am

Data science is one of the most sought after career choices these days, as thousands of freshers and also the experienced ones are seeking a job in this sector. The sudden upsurge in this industry is because most of the organizations are now digging into the data resources. A large amount of data that is created every day is treated as a profitable resource which, when tapped correctly, can help the businesses to grow and flourish is a positive manner.

Therefore, the jobs as data scientists and analysts are so much on the rise and for this, interested candidates are seeking good training and guidance from market experts. Some of the major advantages in joining a data science training module are:

Get Certified

If one wants to grab a good opportunity in this sector, then it is important to have certification in various domains related to data science. This will help one become accredited and also help one learn various tools and techniques in this field so that one can grab the job easily by impressing the recruiters. Getting certified is the first step that one needs to take in this competitive market where everyone is trying to hone their skills to the maximum.

Understand Different Roles

When one thinks of data science, then the role of data scientists is the one that comes to one’s mind. But there are many roles that one can grab in this field. While getting trained in data science, one can learn about the different roles like data engineer, data analyst, database architect, business intelligence manager, business analyst, etc. and how they function and what is the job description of each of them.

Learn from Experts

One of the most important things about the training courses is that one will get to learn from the best. Most of the trainers are experienced in the same field and are usually working at a big firm in the niche of data science. Their knowledge will help one learn and understand the various details of science and how the projects are conducted and what all things one should keep in mind to be successful at one’s job.

Get Promoted

If one is already working in a firm and if one is interested in getting promoted to a higher post then getting oneself certified in advanced niches and tools involved in data science can help one get promoted. Data science is a field which is still evolving and thus new tools and techniques are discovered and created every day. Thus, for keeping oneself ahead of others and be informed, it is important to get oneself enrolled in courses which cover these new topics.

Career Change

Lastly, if one is bored with one’s recent job, then getting a career job change can be one’s only option. And what can be a better choice than the data science field which is currently running on a boom with millions of job positions getting opened every month. If one is an expert in any field like statistics, programming, finance, marketing, etc., then one can also use one’s domain knowledge as the key to understanding data science to make a new career out of it.



Source by Shalini M

15 Jul

Intricacies of Machine Learning in Data Science
Posted in Education by anshbookstore on July 15, 2022 @ 12:40 am

Machine learning served as APIs

Machine learning is no longer just for geeks. Nowadays, any programmer can call some APIs and include it as part of their work. With Amazon cloud, with Google Cloud Platforms (GCP) and many more such platforms, in the coming days and years we can easily see that machine learning models will now be offered to you in API forms. So, all you have to do is work on your data, clean it and make it in a format that can finally be fed into a machine learning algorithm that is nothing more than an API. So, it becomes plug and play. You plug the data into an API call, the API goes back into the computing machines, it comes back with the predictive results, and then you take an action based on that.

Machine learning – some use cases

Things like face recognition, speech recognition, identifying a file being a virus, or to predict what is going to be the weather today and tomorrow, all of these uses are possible in this mechanism. But obviously, there is somebody who has done a lot of work to make sure these APIs are made available. If we, for instance, take face recognition, there has been a plenty of work in the area of image processing that wherein you take an image, train your model on the image, and then finally being able to come out with a very generalized model which can work on some new sort of data which is going to come in the future and which you have not used for training your model. And that typically is how machine learning models are built.

The case of antivirus software

All your antivirus software, typically the case of identifying a file to be malicious or good, benign or safe files out there and most of the anti viruses have now moved from a static signature based identification of viruses to a dynamic machine learning based detection to identify viruses. So, increasingly when you use antivirus software you know that most of the antivirus software gives you updates and these updates in the earlier days used to be on signature of the viruses. But nowadays these signatures are converted into machine learning models. And when there is an update for a new virus, you need to retrain completely the model which you had already had. You need to retrain your mode to learn that this is a new virus in the market and your machine. How machine learning is able to do that is that every single malware or virus file has certain traits associated with it. For instance, a trojan might come to your machine, the first thing it does is create a hidden folder. The second thing it does is copy some dlls. The moment a malicious program starts to take some action on your machine, it leaves its traces and this helps in getting to them.



Source by Shalini M

15 Jun

Random Facts Versus Whole Science Approach to Homeschool Teaching
Posted in Education by anshbookstore on June 15, 2022 @ 12:17 am

When it comes to learning science, most of us were taught in the public school system, which is a big proponent of the random fact teaching methodology. In other words, science was a single subject taught in a vacuum separate from other subjects. When it comes to teaching difficult or complex subjects such as science, it makes more sense to take a holistic approach. Here’s why.

The Science Random Fact Junk Drawer

There has been much news lately about the American education crisis in regards to a lack of interest in STEM (science, technology, engineering, math) disciplines. The United States is falling behind other developed countries when it comes to new technologies and discoveries, mainly because it is producing fewer graduates with related degrees.

One of the reasons for this lack of interest in STEM disciplines is due to the way kids are taught. Students often learn a bit of science here and a bit of science there without being provided any logical way to connect the dots. This collection of random facts can be likened to your junk drawer at home – you know there’s a screwdriver in the midst of all those rubber bands and paper clips and batteries and gadgets somewhere, you just can’t find it amongst all the clutter.

The same holds true for kids learning science. For instance, if a child learns a little something about the earth and the moon and how the shadow of our planet can cause a lunar eclipse, that’s an interesting, but random, fact. You might also have taught your child some astronomy concepts and explained how the moon affects the ocean’s tides. Perhaps your child has also learned something about gravity and the moon’s gravitational pull. But if you are using many mainstream homeschool science curricula, those facts were never pulled together to show the student how the moon is at the core of all these facts and they are interrelated. That’s why it’s so difficult for many kids (and adults alike!) to make the leap between one science fact and how it impacts so many other areas of the world around us. This also makes it very hard to extract a random fact later because the child must rely on rote learning.

The Whole Science Teaching Approach

A better, more effective way to teach homeschool science is through an exponential approach. By helping kids make their own connection between subjects, they are much better equipped to draw broader conclusions. This is also a great way to encourage their natural curiosity and develop hands-on experimentation that offers exciting new discoveries in the child’s mind.

The whole science homeschool teaching approach is all about extrapolation. Once your student has assimilated some core concepts they are prepared to expand that knowledge and apply it to different, everyday situations.

For instance, let’s go back to that random fact about the moon’s gravitational pull on earth. That’s a physic concepts and that explains much about a lunar eclipse, which is a topic generally brought up in astronomy. Those same gravitational forces are at work when it comes to oceanic tide cycles, a topic that may be part of biology learning. By painting the bigger picture, a student can connect the dots between physics and astronomy and biology herself and become excited about learning more.

This approach also compartmentalizes and organizes bits of information so they can easily be retrieved at will and on demand. And it aids the homeschool science teacher, who often doesn’t understand the information herself, present complex concepts and help the student come to a conclusion that need not be foregone.

When it comes to teaching a difficult subject such as science, the homeschool teacher would be wise to use a whole science approach rather than relying on a random fact methodology.



Source by Dr Rebecca Keller

15 May

Tips For Winning a Science Fair Project With a Rock Set
Posted in Education by anshbookstore on May 15, 2022 @ 11:27 pm

Collecting rocks is a popular hobby that kids and adults can enjoy together. More than just a fun activity, rock collecting is a great way to study rocks and geology. It can also make a great science fair project. This article provides tips on how to win a science fair project with an amazing rock set.

Rock collecting can be done for fun or for learning or both at the same time. Many children return from the beach or park with a pocketful of assorted rocks, drawn to shapes, colors, and textures. Taking a more systematic approach to rock collecting can help kids take their fun to another level while they also discover the underlying geology.

For a science fair project, it’s more impressive if the student has collected many samples in person. It makes for interesting stories to include in the presentation. Photos of the adventure mounted to a foam board or set in a photo album can help tell the story.

To collect your own rock set, you will need to choose a good location for the hunt. Check local geological maps and look for hills, cliffs, beaches, and quarries. Pick up interesting rocks on trips. When collecting in person, label each sample with a number and location to help later identification. If using a rock hammer to collect samples, wear goggles and gloves.

However, not everyone has time to collect their own rock set. The good news is you don’t have to collect your own because you can purchase a rock set containing just about any kind of rocks you could ever find on your own. For many busy families, a store-bought rock set provides a good place to start.

To win a science fair project, your rock set should include examples of all three rock types as listed below. There are three types of rock categorized by formation:

o Igneous

o Sedimentary

o Metamorphic

Igneous rocks form from cooling magma, or molten rock. Volcanic or extrusive rocks result from volcanic activity at the Earth’s surface and fast cooling of lava. The rapid cooling produces fine-grained rocks like obsidian and basalt. Plutonic or intrusive rocks form beneath the surface, from slowly cooled magma. These rocks, like pumice and granite are typically rougher and have larger crystals.

Sedimentary rocks form through deposition in water. Small rock particles are eroded and accumulate in lakes, oceans, and rivers. Over time, these particles settle in layers and compress into rock, such as sandstone, limestone, and chalk.

Metamorphic rocks are igneous or sedimentary rocks that have undergone extreme pressure and temperature conditions, resulting in new forms. Marble forms from limestone, while quartzite develops from quartz.

For a winning science fair project, consider using a rock tumbler to polish some of the samples. Rock tumblers smooth rocks by moving them around in grit and other polishing compounds. Rocks of a similar hardness should be polished together, so first identify and classify samples on the Mohs scale. The process takes about a month, starting with a rough grind to smooth edges and moving to finer grit and polish with each step. Follow all tumbler directions for the best results. Careful recording of the amounts and types of rock, polishing materials, and duration will create an informative science fair project. Note any changes in the tumbler contents or actions taken to improve the process.

All of the tips provided so far are essential for winning a science fair project. However, if you really want to take your project to a higher level, you’ll need to become fluent in speaking rock talk. This is what separates the true rock lovers from the more casual passers-by. You’ll need to dig a bit (no pun intended) into the science of how rocks are formed. Often rocks are made up of several minerals. Once a child knows how rocks and minerals form, finding different types becomes easier. Understanding chemistry is useful. Elements such as carbon, iron, and fluorine are the simplest building blocks of minerals. A specific combination of elements forms a mineral, such as quartz or mica. Minerals have characteristic crystalline structures made up of repeating elements. Kids enjoy identifying minerals with a rock set and tools to test hardness. The systematic approach involves looking at the streak color left by a rock, along with its ability to scratch glass or be scratched by a metal probe. All this extra knowledge will make your science fair project more impressive while building your own knowledge, understanding and appreciation for rocks.



Source by Joe Kanooga

15 Apr

Books For Teachers: Brian Clegg’s Getting Science
Posted in Education by anshbookstore on April 15, 2022 @ 10:36 pm

Getting Science by Brian Clegg targets an audience of elementary school teachers who feel less than confident about teaching science in their classrooms. While I am not in his target audience, I’m close to it. (I love science and teach in small groups of homeschooled students.) Clegg did some things authors should do. He caught my attention, told me stuff I needed to read or wanted to learn, and kept my attention throughout the book. I learned a bit and further solidified prior knowledge. It’s a good book, and after reading this it, I hope many primary school teachers do read it.

Clegg starts his writing with reasons why science can be a little scary. Journal articles and academic writing in general is stuffy and uses inflated words instead of simple-to-understand, everyday language. Science articles weren’t always written that way, and they certainly don’t need to be written that way, but it is custom and tradition now. It takes a bit of effort to sift through that language, but luckily, you don’t need to. You can be an effective and fun science teacher without the stuffy journals. Learn from reading popular books and science shows instead.

Clegg also talks about what science is and should be. Science is an adventure. It should be fun. It should fill you with wonder. Science tries to figure out how the universe works. That doesn’t sound so scary, right?

His first chapter talks about how to engage the kids in the lesson. People like people, so he suggests putting the science in context and finding it in real life. What was the scientist who made the discovery like? How did that scientist grow up? What in his or her life led him to think and experiment the way he did in order to make the discovery? In addition to involving the people and a little history, find the science in real life. If you’re talking about cell division, you could mention making bread and perhaps bring yeast into the classroom. He suggests sprinkling the discussion with amazing, and gross, facts. Kids like gross. He emphasizes that the kids should do stuff with their hands. Watching a demonstration is better than just hearing about it, but the best bet is to have the kids do the experiment or demonstration themselves. We learn by doing. And mostly, make it fun.

If nothing else, teachers should read the first chapter of the book.

The second chapter talks about why we have labs. People aren’t good observers. Many people don’t know the difference between causality and correlation. Anecdotes are not data. Disproving is much easier than proving. All of these people facts lead to why we have laboratories. Fortunately, labs are no longer just filled with middle-aged white men in lab coats, and personalities of all different types can be found in scientific laboratories.

Clegg talks about different scientific eras in his third chapter. 500BC to 1500AD is the classical period. During this time, the prevailing “theory” prevailed because it was argued successfully. There really wasn’t much science involved. Some of this classical thinking is still around today in the form of astrology and the four elements. The clockwork era of science was from 1500AD (the end of the middle ages) to around 1900AD. This era was filled with scientific discoveries and theories that make sense. Newton said force equals mass times acceleration. That makes sense. Spontaneous generation theories disappeared because people figured out flies deposited eggs on raw meat. Clegg calls the current era counter-intuitive. That is, this era of science doesn’t seem to make sense. Just think of the phrases quantum theory, relativity, and light is light but it can act like a wave or a particle.

Chapters 4, 5, and 6 talk about cool things in science and Clegg gives suggestions for learning and teaching the topics. What is life? Why don’t humans have fur? How does cloning work what are the five states of matter (Yes, five. It’s not just solid, liquid, and gas). How do mirrors work? What’s the difference between mass and weight? What are black holes? What are wormholes? His explanations are pretty easy to follow.

Chapter 7 makes a case for making science hands on. Chapter 8 talks about finding and seeing science in the real world and how to make experiments come alive, but not in a Weird Science like way. Chapter 9 talks about science on the web. Which web sites are trustworthy, and how can you tell if a site is trust worthy. He also gives hints on how to search the web. Chapter 10 gives ideas on how to keep up to date in science and Chapter 11 tells you to go inspire the world.

The book was easy to read and didn’t take a long time. Even so, it managed to pack a lot of good information in it. Are you a primary or elementary school teacher? If so, go to your library and check out this little treasure.



Source by Gwen Nicodemus

16 Mar

Become the Future Data Scientist by Pursuing the Data Science Training
Posted in Education by anshbookstore on March 16, 2022 @ 10:22 pm

THE DATA SCIENCE COURSE: THE BEST FOR THE ONES WHO LOVE NUMBERS

Data Science, the most booming careers in the field of technology, is playing a crucial role in the field of IT industry. The knowledge base and the skills acquired by pursuing data science training assist the organizations in achieving high profitability and productivity, thereby gaining a competitive edge over others.

Learning data science is highly challenging as it is a broad and fuzzy field. It even involves a lot of fun if you are fine with dealing with numbers and algorithms.

WHAT ALL HAS TO BE DONE TO PURSUE TRAINING IN DATA SCIENCE?

The data science is all about dealing with the data generated on a daily basis and flowing into the organizations’ databases. It is all concerned with studying the origin of the information, what does it represent and then transforming it into a valuable resource. This requires mathematical skills, statistical skills and as well as programming and communication skills.

The proper interpretation and analysis of data by the data scientists assist the organizations in reducing its costs and increasing the efficiency and effectiveness of the organization.

But always remember that before pursuing data science training, always keep into consideration the following points:

a) Learn to love data

First of all, the most essential step that one has to undergo is to develop an interest in numbers and algorithms. The more you learn, the more you will be motivated to pursue it because generally, the ones who pursue data science end up quitting midway.

Always love what you learn; this will definitely assist you in developing an interest in dealing with big data which is associated with numbers and algorithms.

b) Learn by doing

When you involve yourself while learning then you will definitely feel interested in learning. What it means is that always work on projects because that’s the best way through which you are actually applying your theoretical knowledge practically.

It will assist you in developing the required skills that are actually useful and are applicable while dealing with data.

c) Learn how to communicate the valuable information

The process of gathering, analysis, and interpretation of data will be fruitful if and only if you are able to communicate and present the results i.e. the insights extracted from the raw data to the top executives and associates of the company.

Hence it is highly necessary to learn the communication skills for becoming a data scientist.

d) Never maintain the same level of difficulty

Data Science is all about climbing a steep mountain. If you stop climbing and start feeling comfortable then you will never make it. The moment you feel comfortable, just work with an even larger dataset. Always face challenges in life, then only you will be able to reach greater heights in your life be it personal or professional.



Source by Shalini Madhav

14 Feb

A New Definition of Science – The Textual Foundation That Represents the Real World
Posted in Education by anshbookstore on February 14, 2022 @ 9:02 pm

The Wikipedia defines science as follows. Science is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe. Definitions from various sources has to do with knowledge, investigation, study, observation, experimentation, laws, structure, behavior, explanation and systematicity.

They describe science and scientific activities, instead of pointing out what the enterprise is. What science looks like? They also don’t point out what enables science, why and how humans obtain the capability to advance in science. They describe the appearances and many facets of science but don’t make known the nature of science. We are going to find out.

After writing some articles on relations between written language and science, it is time for us to provide a new, text-based definition of science, which is important as a basis for carrying out future discussions of related issues. We have already proposed in previous papers that written language is the foundation of science.

The idea to exclude non-texts

We consider written language as the core of science, while non-texts are the goals, materials and occurrences.

Certainly, scientific activities include both texts and non-texts. Both are indispensable, with non-texts seem to be the real things. Without non-texts, the world wouldn’t exist, not to mention science. However, judging by the properties, we now decided to exclude non-texts from science. Otherwise, science would include virtually all information we can experience. That might lead to uncertainty, vagueness, misunderstanding, chaos and confusion.

Furthermore, we learn science mainly from books and papers. The achievement of scientists is judged by their publications. Some great discoveries are incidental. But they must be fitted into the existing textual framework to become part of the science.

When science is defined based on texts, its nature and properties will be well presented. Science-related investigations will be provided a clear basis. In fact, this definition doesn’t contradict with the common definitions, since texts constitute the systematic enterprise which supports the functions science fulfills.

The non-scientific texts

Texts are omnipresent in our lives, recording everything. But only a portion of them is considered scientific texts. The scientific or non-scientific texts are not different in that they are symbolic and sequential. Although they possess the capability of being science, they do not necessarily fulfil the function.

Descriptive texts

Texts of literature, narrative, fictions, art, instruction, music, advertisement, daily conversation, chatting message, etc. are descriptive and conveying. The sake of them is to describe the non-textual reality, which are the goal, in the center and being emphasized. This kind of texts are important in documenting, communicating the events, understanding of which are not reliant on the texts. The texts are peripheral to the non-texts and not attempting to build their own foundation. On the contrary, scientific texts are needed to understand the phenomena because of the properties of texts and the difficulties in observing the phenomena.

Mentalistic texts

This kind of texts are foundational but don’t represent facts. Collectively, we call them mentalistic texts. They include texts of religion, ethical belief, moral concept, philosophy, and pseudoscience. They tend to center on texts, but are not based on facts, based on vague facts or only reflect biased facts. Representing reality is not their goal. Nor are they intended to be verified. Subjectivity is an element common to this kind of texts. It is some kind of description or insistence on one’s own thought, opinion and argument, refraining from changes, rejecting challenges or denying their failure to account for the facts.

Although these texts don’t aim to represent reality, most of them are derived from facts or imaginations. They serve as an emotional need, spontaneous mental behavior and alternatives to science in some cases. Although not being scientific, they are still able to establish.

—————————————————-

There is no absolute distinction between descriptive, mentalistic and scientific texts. Some portions in descriptive texts or mentalistic texts might be scientific. The same facts could be studied in different kind of texts. For example, texts about history could be descriptive if they focus on the events; or scientific if they derive some regular patterns; or mentalistic if they adhere to creationism.

Indeed, scientific texts might have evolved from descriptive texts and mentalistic texts. That is why modern science was formerly called “natural philosophy”, which emerged from the integration of description of nature and the representational aspect of philosophy.

The text-based definition of science

Then comes the third kind of texts – science, defined as:

Science is the textual foundation that represents the real world.

Criteria of this definition

For the key properties of written language and science, refer to the paper “Language – The Core of Science”[1]. The basic ones are sequentiality and clarity. Now we added a third property – representation of reality. Being representational implies being processed, foundational, established and centered on.

The three properties are used for judgment on whether a text is scientific or how scientific it is. In the paper “Scientific Strength of Writing Systems – The Aspects”, we had explained the sequentility and clarity aspects. The “representation of reality” aspect is discussed in the following subsection.

Establishment of the representation of reality by means of visual processing

The key difference between representation and description is the center is texts for the former, while non-texts being the center of the latter. The accumulation of science is based on existing representational texts, while descriptive texts conform to the facts as they are. Since non-texts are centered on, the properties of texts given in The Paper are not fully exploited in descriptive texts, although which might choose proper or beautiful language in their composition.

The visual characteristic of texts makes it suitable for visual processing, which is needed to build a representation of reality. Through mental processing of the representational texts, we are able to extract consistency, commonalities and regularity, to clarify, refine and simplify information, to find contradictions, to discover new theory by reasoning, to approve or disapprove a new theory, to incorporate new theories into existing knowledge, to establish relations between existing knowledge, to organize and categorize knowledge as it expands. All these are achieved by intensive textual thinking.

The sequential growth of symbolic representation is constantly checked with facts, observations and experiments for validation. The explanation of the facts in textual means is accurate and deterministic, unlikely to change and are relied upon, while the represented non-texts are themselves not sequentially related, not clearly observed or even invisible. Due to the infinite expansion of observations and experiments, the textual representations also expand accordingly in an orderly manner.

Conclusion

Given the new definition of science, our discussions of science-related matters will be on a clear, focused and targeted course. It becomes clear that the science-centered world is in essence founded on scientific texts and the textual mind. Technology, engineering and many life-changing practices are integrated with and reliant on the textual representations.

In the science-text unity, we had put more emphasis on the written language. Now, as we are shifting towards science, there is a new horizon ahead.

References

https://en.wikipedia.org/wiki/List_of_academic_fields

http://en.wikipedia.org/wiki/Science

——————————————–

[1] Referred to as “The Paper” hereafter.



Source by Charley Pein

15 Jan

Get Certified Data Science Training
Posted in Education by anshbookstore on January 15, 2022 @ 8:40 pm

With the global technological development, a lot of data is being processed each and every day. It has become ubiquitous and unsustainable for any Business Holder to keep it structured and track a resource. To overcome this major difficulty, Data Science – the fast expanding field, has been developed. Every field such as medicine, finance, media or manufacturing has huge sets of data. Therefore the need of data scientists’ skills is sought after everywhere, i.e. they are not bounded to one particular industry!

What do Data Scientists do?

Data Science is an amalgamation of mathematics, statistics, business understanding and programming skills. Therefore, Data Scientists are partly mathematicians, partly computer scientist and partly trend spotters. A Data Scientist helps companies interpret and manage data; deal with processes and systems and solve complex problems with a strong business sense. Their main roles include:

  • Collecting large sets of structured and unstructured data from various sources.
  • Determining the data sets and variables.
  • Ensuring validity, accuracy, uniformity of data.
  • Analyzing data to interpret trends and patterns.
  • Discovering solutions and opportunities.

Some of the prominent Data Scientist job titles are:

  • Analyst
  • Engineer/Mining
  • Administrator
  • The Machine Learning Engineer
  • Advanced Analytics Professional

Exploring Data Science:

The few courses, one needs to undergo to become a Data Scientist include Python, SQL, R, Blockchain, Statistical Analysis, Visualization, Machine Learning, Deep Learning, Artificial Intelligence, Hadoop, Spark, Internet of Things (IoT), Six Sigma, Mind Mapping, to name a few.

If you have natural curiosity, creative and critical thinking, desire to search out the answers to unasked questions and realize the full potential of data, provided that these concepts of data science excite you, it is the perfect time to consider data science as a career option. The stats suggest that these skills are in high demand and transitioning careers in as little as 6 months of commitment.

A computer programming background, innovative business strategies and ability to communicate complex logics to non-technicians in an easy way are prerequisites to becoming a Data Scientist.

The perks of becoming a Data Scientist:

Data Scientists are in high demand with an offer of handsome salaries. Around 80% of companies focus on investing a large proportion of professionals who can analyze data effectively to prepare better strategies for the future. Data Science training is the pathway to getting hired in the top fortune companies, the Giants, such as Amazon, Microsoft, Google, PayPal, Facebook, Uber, Apple who constantly look for Data Experts. The role is to link the business and technical sides, identify the trends and strategize plans to increase their sales and profits. This field also offers freedom to work on the projects that matters/interests you. Across the globe, both large and small organizations, irrespective of the field, require Data handlers to interpret and analyze the data they create every single day.



Source by Shalini M

16 Dec

Data Science and Its Rising Importance In Cybersecurity [Big Data Analytics]
Posted in Education by anshbookstore on December 16, 2021 @ 8:27 pm

Data Science & Cybersecurity – what is big data analytics? Why is machine learning applications so important? Why did InfoSec Professionals require to learn about DS? What to know about “data bots” as a data science professional? Differences in data science vs machine learning? How to crack cybersecurity jobs with data science advantage?

DS is a multi-sided field that uses scientific techniques, methods, algorithms, and security practices to extract information and insights.

With the help of DS tools such as Machine Learning and Big Data Analytics, businesses can now get access to meaningful insights hidden within massive data-sets.

This is where DS can help create a significant and lasting impact.

DS and cybersecurity, two of the most popular career paths, are on a collision course. Very intelligent, seasoned, senior managers do not fully understand the importance, or the complexities, of DS and cybersecurity. “There’s a mad rush in the cyber security solutions space to use the terms machine learning, analytics, and DS in conjunction with security products. The CERT Data Science and Cybersecurity Symposium highlighted advances in DS, reviewed government use cases, and demonstrated related tools. Applied DS for Cyber Security. In today’s world, we are assailed by ever-increasing amounts of data and increasingly sophisticated attacks. The programme is designed to build students’ knowledge and develop their expertise in network security, cryptography, DS, and big data analytics. The NACE Center and BHEF conducted research into two skills likely to be important in the future economy: data analytics and cybersecurity skills. A data scientist is a professional with a blend of skills in computer science, mathematics and cybersecurity domain expertise. Cyber Security is a fast-growing field in an ever-interconnected world. Learn why it matters and what data science has to do with it. Data science, along with technologies such as machine learning and artificial intelligence, has found its way into countless security products. Leading experts in the fields of data science and cybersecurity discussing a range of topics related to the role -DS has in addressing the issues.

The section of knowledge will illustrate the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in. With automation and AI able to pick up jobs that humans need them to, data analytics and cybersecurity might find it easier to hire skilled employees. Although machine learning tools are commonly used in numerous applications, the big boom of advanced analytics in cybersecurity is yet to come. And that will be interesting to see the future tools to cop up with. Fingers crossed.



Source by Vinayak SP

16 Nov

The Art of Teaching Homeschool Science
Posted in Education by anshbookstore on November 16, 2021 @ 8:11 pm

When it comes to homeschooling your kids in the subject of science, is there a cut-and-dried formula? While many parents have been led to believe there is, the reality is that it’s more beneficial to treat the process as an art than a science. It’s important for kids to use their natural curiosity to explore the world around them and truly enjoy science class. The following artful tips will help you teach homeschool science in a way that makes it fun and engaging.

Active Learning Through Open Inquiry

There are lots of scientific facts and data to be learned. But rather than have your child passively learn via memorization, science becomes interactive through the process of open inquiry. Encourage your kids to think about how things might work before telling them how scientists have discovered they actually do work; encourage them to question.

Kids are born with natural curiosity and will ask questions in the natural course of their learning. Rather than answer their queries with rote facts, answer them with questions of your own that make them think more deeply about a subject. If your child asks, “Why is the sky is blue?”, counter that with, “What do you think there is in the atmosphere that produces the color blue?” That’s open inquiry that encourages exploration and discovery.

Science Should Be Explored

There is a way of teaching that chokes off a child’s natural curiosity and then there is a way of teaching that encourages inquisitive minds to further explore scientific theories and facts.

If you think that science is a “hard” subject to learn, it’s probably because you were taught to memorize random facts or complicated equations. This is not the most effective approach to teaching science. Just knowing random facts about something doesn’t mean you understand it.

The same is true for your child. Don’t worry about having her memorize a set of facts, but encourage her to ask questions that come from her innate sense of curiosity. So rather than asking her to learn the Latin names for each species of North American bird, for instance, help her find the answers to things she wants to know, such as how birds are able to fly and how they communicate with each other and what types of food they eat.

Break the Rules of Experimentation

Performing hands-on experiments is a vital part of learning science. But rather than insisting that experiments follow a prescribed set of steps or rules, allow your child to dive right in and maybe even make mistakes. Guidelines are good but there’s no reason you can’t break the rules and, in fact, you should do so regularly. This encourages lots of questions and further experimentation to see what might happen if… if a variable is changed or if the experiment is moved or if the same results would occur with other variables. You never know, you and your child might just discover something new.

Teaching your kids science is about more than just following a prescribed curriculum. Every now and then you should step outside the box and see what happens. Have your kids think about broader possibilities and help them discover answers to their questions on their own. All science is really an art; it’s not black and white but shades of gray that color the results of those who dare to be curious and explore the world around them.



Source by Dr Rebecca Keller