The buzzwords in technology are no new to someone. Whether it be Artificial Intelligence, Machine Learning, Data Sciences or Analytics, each of these are invading in our lives promising us better future. However, it is believed that expertise interested in data sciences are not widely spread. Data Sciences is a field that can improve business, can help in other technological fields, can help in decision making and more. 

It is rightly said that prevention is better than cure. A wrong step in data sciences can affect the decisions and the results. One should avoid the following mistakes while dealing with data:

1. Assuming your data is ready to use and all you need
2. Not exploring your data set before starting work
3. Not using control group to test your new data model in action
4. Starting with targets rather than hypotheses
5. Automating without monitoring the final outcome

To study mistakes like these read https://www.cio.com/article/3271127/data-science/12-data-science-mistakes-to-avoid.html?nsdr=true

One of the most interesting thing about the field of technology is that it never stops growing. There are changes that helps in evolution. The rate at which technology is growing is unmatchable and the only way to match that pace is by polishing our skills and keep them up to date.  The following are the top 3 tech skills that are need for tomorrow: 

1. Blockchain Technology – Blockchain is the structure of data that records transactions. It is digitally signed and thus ensure its authenticity. It is a good way to manage cryptocurrency.
2. Artificial Intelligence – Artificial Intelligence is an ongoing technology which is helping humans by making machines intelligent and capable of working the way humans do. Though they are many applications based on AI which we are already using but still there are many unexplored technologies.
3. Augmented and Virtual Reality- Augmented Reality and Virtual Reality have already shown remarkable progress in the field of gaming and there are many more applications which can bring tremendous changes.

To know more visit: https://www.technotification.com/2018/04/top-3-tech-skills.html

Machine Learning is an ongoing trend in the field of technology. However, there are only few machine learning programmers available right now. For beginners who are eager to learn and work on machine learning must work on algorithms. With machine learning algorithms, there is no need of human intervention.  There are different algorithms which will work for you. 

There are basically three types of algorithms:

1. Supervised Algorithms: which uses labelled datasets for training algorithms
2. Unsupervised Algorithms: which uses unstructured datasets for results
3. Reinforcement Learning: it uses feedbacks in order to reinforce a behavior

There are top 10 algorithms of machine learning that are must known for machine learning programmers:

1. Linear regression
2. Logistic regression
3. Classification and regression tree
4. Naïve bayes
5. KNN
6. Apriori
7. K-means
8. Principle Component Analysis
9. Random Forest
10. AdaBoost

Know more about them at https://www.technotification.com/2018/05/top-10-ml-algorithms.html 

Artificial Intelligence is making our lives better each day. It has also spread its wing in the field of Academics and made it more convenient. With computers and other smart devices, technology is making education more accessible to students. Artificial Intelligence is not only helping students but also automating and speeding up administrative tasks helping organizations by saving time. It is believed that soon AI in education industry will grow by 50%. Below are the four ways in which AI is helping education industry to grow:

1. The automation of administrative work
2. The addition of smart content
3. Smart tutors and personalization
4. Virtual lecturers and learning environment

Read more about them at https://towardsdatascience.com/4-ways-ai-is-changing-the-education-industry-b473c5d2c706

A software project always consumes company resources. Whether it be the employees or days, working on a software project is a tough task and meeting its requirements becomes prime motive for a company. However, even after applying so much efforts, many software projects come to their end before they are released or leaves the costumer dissatisfied. This failure often leaves company and clients in disguise and employees begin to look for explanation why it went wrong.

There could be many reasons behind this. Following are the top 7 reasons:

1. Too few team members
2. Fundamental feature changes
3. Picking Wrong Technology for the job
4. Poor Prioritization
5. Bad Architectural Decisions
6. Unrealistic Deadlines
7. False Belief in The Power of Software

There can be many more reasons behind this. A company must cross check them to ensure success of a software.

To know more about the reasons visit https://www.cio.com/article/3282464/application-development/14-reasons-why-software-projects-fail.html#tk.cioendnote

What could be better than knowing what future lies ahead us? Predictive Analysis is one such branch of data analytics which can be used to make predictions of future unknown events and is growing with a rapid pace. On the other hand, innovation is an ongoing process which finds its application in almost every field. Without innovation, we would not have reached the platform at which we are now. A number of technological achievements have improved our lives.

These days, Innovation has found a guide in Predictive Analytics that helps to walk towards success.  Many innovations are made but majority of them never succeeds. Predictive Analytics is going to play an important role aiming towards new products ensuring greater economic stability and progress in coming years. 

To know more about how predictive analysis can help in innovation read https://www.smartdatacollective.com/predictive-analytics-methods-make-innovation-successful/

Artificial Intelligence is the trend and need of this hour. It has already found its applications in many fields. This technology is changing and improving the world at a tremendous speed and for our betterment. There is no doubt that AI is future. However not many of us knows its basic application in Business. Business needs time to time changes to meet the requirements. AI can help and change business in many ways.

Top five way in how Artificial Intelligence can help and upgrade your business are:

1. Cheaper Analytics
2. Hiring
3. Customization
4. Anticipation 
5. Security

Know more about it at:  https://www.techrepublic.com/article/top-5-ways-ai-will-change-business/

In this world of competition, companies need to take advantage of available data and take a look about what might happen in future. Predictive Analysis is one such branch of Data Analytics that aims to make predictions about future outcomes using various algorithms and other data analytics tools. Methods like data mining, big data, machine learning are back bone of Predictive Analysis and organizations are able to decode patterns and relations which helps them to detect risk and opportunity. Financial Services, Law Enforcements, Automotive, Healthcare are few fields which have already adapted this technology. 

To know more visit: https://www-cio-com.cdn.ampproject.org/c/s/www.cio.com/article/3273114/predictive-analytics/what-is-predictive-analytics-transforming-data-into-future-insights.amp.html

Whether you are a data analyst in a firm or a developer training its machine learning model, you deal with data. Rather you need data! Data is one of the essential things which is needed to create a foundation. The decisions and results are relied on the output you get from the data. Thus, data is important and like every other thing, it also works on the principle of Garbage In, Garbage Out.

Many people make mistake while feeding data to their data set with a hope to get better results.

However, they end up having an ugly dataset with a greater risk of damaging their product.

The 6 most common mistakes are: Not Enough Data, Low Quality Classes, Low Quality Data, Unbalanced Classes, Unbalanced Data, No Validation or Testing.

These mistakes can be fixed which could further help in fetching good results.

One just need to remember that their dataset is equally important to the model they are working on. Without a balanced dataset, getting a fine finish product is next to impossible.

To know how to fix those mistakes visit: https://hackernoon.com/stop-feeding-garbage-to-your-model-the-6-biggest-mistakes-with-datasets-and-how-to-avoid-them-3cb7532ad3b7

Destroying traditional jobs but creating new ones, technical innovations have changed the course of work over the years. The Industrial Revolution of the 18^th century marked the transition to new manufacturing processes, effectively increasing the output levels and discovering the modern industrial marvels. With AI improving the standard of living, the current and future generations are likely to witness taxing employment pattern changes.

AI would change the future of work by bringing about the following changes:

1)      Create new jobs: Tasks requiring the least of the human cognitive mind would be dealt with the application of modern AI powered robotics allowing individuals to devote their time to community services, volunteering etc.

2)      Bring Automation: A research carried out by Carl Benedikt Frey and Michael Osborne of Oxford University in 2013 reported that approximately 47% of jobs would be automated in the next few decades with non-routine jobs and tasks requiring  high cognitive and good social skills having the lowest probability of being automated compared to a greater probability involved in automation of manual jobs and routine jobs like data entry, production logistics etc.

3)      Increase the gap between the owner and the worker: AI is likely to widen the gap between high skilled and low skilled workers and also increase the persistent inequality between the owner and the workers by laying off workers that would inflate the profit margin of the owners as robots and chat-bots would not demand overtime allowances.

Gartner, the global research and advisory firm, reported that AI is creating more jobs than it is destroying by bringing about a net increase of nearly 2 million jobs by 2025. The core objective of AI should be to make human workers more efficient without laying them off. AI coupled with human intelligence is all set to revolutionize the economy we inhabit.

Read More at: https://www.analyticsinsight.net/is-artificial-intelligence-a-threat-to-your-job/

Artificial Intelligence, big data and machine learning have been ruling the industry in recent times. Starting from Amazon to Google, indulgence in predictive modelling is indispensible. When it comes to the human body, well, artificial intelligence plays a pivotal role in saving lives. Rampant use of AI is involved in CT scans in cases of stroke or brain injuries. Radiologists have a backlog of cases which might delay the detection of the criticality involved in a particular case. To the rescue comes AI, which by streamlining the CT scan interpretation workflow by triage process and automation of the initial screening process, radically reduces the time lapse in detection and diagnosis of time sensitive cases. To detect abnormalities demanding urgent attention such as intracranial haemorrhage, cranial fractures, midline shifts etc, Qure.ai has provided automated deep learning algorithms to assist physicians. The algorithms’ accuracy is equal to that of a physician and classification algorithms are used in radiology itself. TITAN X of NVIDIA, cuDNN and GeForce GTX 1080 GPUs were used that achieved almost 95% accuracy rate as compared to that of 97% by radiologists. Such AI algorithms tend to become a life saver in a world where there is an acute shortage of specialized radiologists.

Read More at: https://www.analyticsinsight.net/how-artificial-intelligence-predicts-life-threatening-brain-disorders/

Signs of exhilaration are heightened among the ‘Star Wars’ fans as China has invented a new weapon called ZKZM-500 similar to the laser blaster in the movie. Weighing more than 6.6 lbs., the 15 mm laser gun can fire more than 1,000 laser shots, each one lasting up to 2 seconds. Although the weapon is claimed to be non lethal by the Chinese government, the invisible energy beam produced by it can cause instant carbonization of human tissues and skin. Infact it has the ability to penetrate through windowhs thus igniting gas tanks and burning anything nearby. However burning hole through a body will take several zaps by the weapon. The gun can have wide usage such as it can be used by the Government to fire illegal banners in a protest or setting fire to the clothing of the protester. Although ready for mass production, its creator ZKZM Laser hasn’t yet found a licensed company ready to take up the $15,000/unit worth guns. The weapons will be in future handed over to the Chinese army and police.

Read more at: https://www.livescience.com/62973-china-laser-guns.html

The power of machine learning is growing exponentially. Almost no industry domain is remaining untouched with the wonders and powers of machine learning.  Machine learning is just an application of artificial intelligence whose algorithms helps to analyze the historic experience without being explicitly programmed to predict the future affairs. Before jumping into the world of machine learning, it’s important to know which languages are being used to analyze the data and predict the future. Here are those 5 languages which are being using for machine learning: 

1. Python

2. R Programming

3. LISP

4. Prolog

5. javaScript

why and how are these languages being used for machine learning? For detailed information,

https://www.analyticsinsight.net/top-5-machine-learning-programming-languages-you-should-master/

Although cloud storage has benefited the businesses in intelligence enterprises but still it shouldn’t be trusted blindly. Cloud storage techniques have helped digital data storage and the changes are remarkable.

The 2 challenges that are connected with cloud storage, cloud lock-in and management complexity are:

• Cloud lock-in: Failure to transfer data from one cloud storage facility to other.

• Management complexity: The inability to perform proper management of available storage environments.

Following are the solutions to these problems that may be seen as unsolvable.

1. Gateway device: It behaves as an agent between the on-premise storage and the cloud storage.

2. Hybrid cloud: With the help of hybrid cloud, cloud storage acts as an extension to on-premise data storage.

3. Multicolored controller: It permits the data to be seen at the same time.

For more information go to,

https://www.analyticsinsight.net/how-to-solve-the-challenges-of-data-management-on-cloud/

Nowadays businesses have opened new gates for certain innovations using cloud computing technology with a proper and selective approach. Most of the IT executives are now entirely focused on how to make cloud a way to achieve their business goals and their entire focus is on cost optimization instead of cost management. In terms of transformation, the cloud has been a central to many organizations. Big data technology has allowed storing and recapturing of the vast amount of information. Regardless of any sector, most of the organizations have transferred all their data to the cloud. But still, most of the executive’s are reluctant to adopt cloud due to security concerns.

Businesses can achieve maximum growth only by accessing, controlling and analyzing all flaws present in the cloud network. That is why multi cloud is more preferred.

Due to digital transformation most of the companies have enforced cloud services to become more profitable.

For more information, go to:

https://www.analyticsinsight.net/how-innovation-in-cloud-computing-drives-exceptional-business-growth/

Nowadays for every organization it has became essential to be associated with cloud as a platform, infrastructure and service. Cloud computing is a very helpful tool as it can be used to create new revenue opportunities for the organization. This era of cloud computing has expanded the efficiency of the computing by reinforcing memory, processing, bandwidth and storage. If you haven’t dispersed your organization to the cloud yet, these can be the right footsteps to follow:

Step 1: create an assessment

Step 2: choose a right cloud environment for your business

Step 3: decide your cloud architecture

Step 4: choose the right cloud computing provider

Step 5: make a strategy for risk mitigation

Step 6: make a plan for mitigation  

Step 7: execute your computing plans

Step 8: examine the implementation

Need a better understanding of these steps?  visit:

https://www.analyticsinsight.net/the-right-approach-to-integrating-cloud-computing-into-your-organization/

Of course we have achieved 3D printing, but somehow we are still not able to produce a metal object which is capable of replacing the real world articles. Now implementing machine learning with 3D printing we have the capability to have real world objects replaced by objects produced by 3D printers. In the world of manufacturing researchers are planning to produce self correcting and repairing machines. There can be multiple approaches to have self-correcting machines. What are they? 

For more information, visit:

https://www.analyticsinsight.net/the-confluence-of-machine-learning-and-3d-printing-will-revolutionize-manufacturing/

Whenever it comes to complications, we all know human brain is the most unpredictable and complicated organ of the human body. Any brain injury leads to damage of millions of cells due to lack of oxygen in the body. Such damages require immediate attention of the doctors. But somehow, making out and analyzing those reports results to the latency which more often comes out as life threatening news for the patient.   

So, how AI can contribute its role here? For increasing the efficiency of the workflow some AI algorithms has been applied to the machine which are now capable of detecting the abnormalities requiring urgent attention of the doctor.

Want to know more about how actually AI and deep learning is applied to radiology? 

Go to:

https://www.analyticsinsight.net/how-artificial-intelligence-predicts-life-threatening-brain-disorders/

Google has been dominating the search engine industry over the years, though it has been frequently criticized of not providing search results in non-English languages. To cater to the problem, it has resorted to semantic indexing thereby becoming proficient at providing multilingual search results. The spectrum of search contents have been widening with time thus hinting at an expanding and trending macro environment. The search engines use algorithms which are solely based on Artificial Intelligence which would be rather simpler with limited pre-defined inputs. In its quest to understand the true meaning of different search queries, the algorithms are required to understand the contextual meaning behind various pairs of words which is attributable to deep learning. Despite capturing 70% of the search engine market globally, certain discrepancies arise due to regulatory policies. However, according to Shout Agency, the core problem is not the structure of algorithms as Google can make educated assumptions indexing any language but discrepancies in search results persist. The crux of the matter entirely stems from the fact that Google has had limited opportunities to conduct deep learning in some language than others. A potential risk is involved due to smaller user base and fewer Google employees that can understand the language enough to determine the worth of the content which lowers the chance of Google to conduct manual penalties for content. This could lead to greater pervasiveness of spun content throwing away algorithms dependent on deep learning.

Read more at:  https://www.smartdatacollective.com/google-search-algorithms-use-big-data-multilingual-latent-semantic-indexing/

Of late, Agile methodologies have been taking root in data science boosting complex collaborations between data scientists and other developers. Agile can be easily ported over to Machine Learning and Artificial Intelligence domains due to its feedback-heavy, iterative nature and given that incessant improvement is an innate part of AI. Such methodologies are characterized by fast feedback loops and short development sprints. Agile projects, in distinction to old-school waterfall approaches, involve error correction and cyclical stakeholder input and primarily focuses on short term goals rather than the long-term view. AI researchers should think of research as an iterative, evolving process to remain receptive and adaptive as per Agile’s basic tenets. To ensure that projects do not grind to a halt, maintaining a buffer of solutions for implementation is a priority as data scientists work on multiple projects, each taking months to complete. The iterative nature of Agile well captures experimentation as a core part of AI and ML projects. Agile maximizes value throughout the development process.

Read More at: http://www.dataversity.net/case-agile-machine-learning/