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Data Scientist Profile In 2019 Education And Skills Set

Posted on by Imarticus Learning

Data Scientist Profile In 2019 Education And Skills Set

A data science career is one of the most sought after in modern times. The harnessing of data has been made possible by advancements in AI, ML, Deep Learning and Neural Networks over the past three decades. And, the very volume of data being generated is so humungous that the term big has become Peta volumes of data and Peta times as big.
The job is not only highly paid, in high demand, but is also very satisfying. Let us then take a look at the education, skills, and attributes required to make a data science career.
The successful data scientists of 2019 look a little like this to those aspiring to be one. According to bigdata-madesimple.com, the typical data scientist is 69% a bilingual male, has about 8 years of work experience of which 2.3 years are as a data scientist. 74% of them have a Masters or doctoral degree and 73% of them are fluent in Python or R. But that is not the whole truth. What if you are fluent in Java and are a female?
There are almost as many PhDs-28 percents to be exact, as there are graduates and lower aspirants who are almost 24.2 percent of the aspirants. This would lead one to infer that a Ph.D. is not essential and it is the skill and abilities that count for more than just the degrees. You could land a job with an IT background if you are in that 9 percent of the cases or land an internship in 8 percent of the cases too.
The main contributors serving as the magic doorways were experienced in the field of consultancy services in 6 percent of the cases, from the field of data analysis in 13 percent cases, or from the IT field in 9 percent of the cases. A sizeable 50 per cent also came in with experience as data scientists meaning the offers were more than acceptable in monetary terms to shift jobs. 15 percent formed the other category in terms of their fields of specialization.
The popular educational background subjects were 22 percent from Computer Science, 21 percent from Economics and a mere 12 percent from data sciences. This is probably due to lack of data science degrees or that there is ample scope for academic circles to include this as a subject at the college levels.
Which university you study in may not improve your employability as a data scientist. While 31 percent had studied at the top 50 as per the 2019 Times Higher Education Ranking, 24 percent came from universities ranked at 1001 and more. More than half of the participants had taken online courses with 43 percent having completed at least 1 such course of an average of 3 courses. The popularity of these courses would indicate that aspirants took courses to increase their skills and competitive edge in the job market. Fortunately, the university ranking does not appear to matter when being employed as a data scientist.
Python is the leading preferred data science course among the programming skills globally which is closely followed by R. In India and the USA the skills in R and Python are both valuable unlike in the UK and other areas where Python led the charts. About 70 percent of the data scientists in 2018 had previously worked in the tech industry. In 2019 just 43 percent were from the tech industries and 57 percent from other industries and the financial sector.
Country-wise statistics show that the industrial sector in the UK recruited more data scientists than the tech segment which is not the trend in other areas. The normal pattern is broken by India in terms of it having fewer PhDs and larger numbers of graduate data scientists. The USA has the least number of new hires in the data scientist role compared to other countries and the preference for Python as the choice of a programming language is slightly higher in the non-Fortune 500-list of firms.
Conclusions:
The survey of the data definitely indicates that the data science career is one of the best for career aspirants. It also indicates that your interest in acquiring the skills is very crucial to your achieving the task. Training institutes like Imarticus Learning are at the forefront in turning out wholesome data scientists with the skills to fit any employer’s bill of employability. The icing on the cake is that this data-based career is for all aspirants immaterial of educational background, degrees, sex or location. Reach out to Imarticus today. Hurry!

AI and Food: Safer and More Tasty Food?

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In February 2019, Tristan Greene wrote an article in The Next Web and quoted an IBM research study that suggested that artificial intelligence could improve the taste of food by creating new hybrid flavors. It took a part of the Internet by storm, less for its clickbait headline and more for its actuality. Greene was writing facts when he began his article with this: “AI will soon decide what we eat”.

Let’s explore the what, the why, and the how. We are sure you already know the why so we’ll mostly skip it.

Artificial Intelligence + Food. Really?

That seems to be a sensible question but not a surprising one. AI and machine learning have already taken over the world with them influencing everything from blockchain to computer vision to chemistry. So why not food production?

Now IBM, other tech giants, and new startups are changing that by feeding AI systems millions of different types of data in the areas of sensory science, consumer preference and flavor palettes to help generate new or advanced flavours that can literally put your mouth on fire. Or make it drool all day. Or make even the most tasteless food taste like heaven. Kale and quinoa, anyone?

The food industry has already scrambled to use artificial intelligence and machine learning for its sake. Take, for example, the world’s first automatic flatbread-making robot called Rotimatic which limits user control to just putting the ingredients into the appliance. It does all the dirty work by itself and claims to bake hot flatbread in under a minute.

Not just kitchen appliances, the food that we eat and its ingredients are also being influenced by AI and other techniques even as we debate whether genetically modified food products are safe for human consumption. Researches involving changes in the cooking style, omission or replacement of certain ingredients, and others have all been suggested by AI-driven tools. While none of them have hit the shelves yet, this new tool by IBM looks like it’s just around the corner.

According to the study, IBM and a company pioneering in flavors and food innovation named McCormick & Company created a novel AI system whose aim is to create new flavours. Published in February 2019, the blog post promised that some of its findings will be available on the shelf by the end of the year. While it is September and we still wait, let’s have a look at the scope of AI in the food industry.

How Does AI Help Food Become Better?

To answer this question, Greene uses the analogy of Google Analytics tools. Publicly available data like recipes, menus, and social media content about these recipes along with trends in the food industry are fed to AI systems. These then generate fresh, actionable insights.

An example is a tool that can show restaurants what the most popular food will be every month for the next 12 months. If this is a possible scenario, the restaurant can prepare itself and maybe even surprise its customers into submission, eventually becoming popular and running a successful service.

The same goes for farming models where new techniques are needed to plant and grow more produce as the population gets out of the window due to lack of space. Everyone involved in researches dealing with AI and the food industry is positive about what can be done.

Existing data is of prime importance if such tools are to bear any results. In the above example involving IBM, the tool is able to create new flavors because of the existence of data on different flavours that we currently have. In a way, AI is only helping us discover flavors sooner.

AI Everywhere in the Food Industry

Till now, we spoke about the use of AI in farming, food recipes, and restaurants. But what about food processing? Media suggests that AI is everywhere – from its help in sorting foods to making supermarkets more super.

According to a Food Industry Executive, there are a lot of examples that highlight the significance of AI in the food industry. Some of them are listed below, thanks to Krista Garver:

  • Food sorting – AI helps understand which potatoes (by their size and quality and age) should be made into French fries and which ones are suitable for hash browns or potato chips or some other food. This involves the usage of cameras and near-infrared sensors to study the geometry and quality of fruits and vegetables
  • Supply chain management – This is obvious: food monitoring, pricing and inventory management, and product tracking (from farms to supermarkets)
  • Hygiene – AI can detect if workers are wearing all the necessary equipment. Since AI tools are fed data about what constitutes 100% hygiene, they can constantly check the attire of workers and rate them on the basis of their current clothing. Is a worker not wearing a plastic hat? An alert goes to his manager
  • New products – This is similar to the IBM example seen above. Predictive algorithms can be used to understand what flavors are most popular in people of certain age groups. Why do kids love Kinder Joy? What is or are the ingredients that make them go bonkers?
  • Cleaning – This is the most promising one where ultrasonic sensing and optical fluorescence imaging can be used to detect bacteria in a utensil; this information can then be used to create a customized cleaning process for a batch of similar utensils.

Conclusion

It is mind-numbing (mouth-watering, too?) to visualize these products actually coming into form in a few years. Which is why there is no doubt that AI will revolutionize the food market. The only question that then remains: has the revolution already begun now that you can’t say no to a bunch of addictive products?

A Look at Ideal Agile Implementation in an Organization!

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A Look at Ideal Agile Implementation in an Organization!

They say Agile project management is the new normal. Despite its demerits, it has stood up as the most effective project management concept in the corporate world, managing to deliver work even when there’s no desired outcome specified. In today’s competitive professional environment, the mantra is to start working and worry about the output later.

In simple words, an Agile system involves producing and delivering work in short bursts and then refining it until it matches the client’s often ambiguous requirements.

Based on findings furnished by multiple reports in 2018, Agile is inching ahead as the most reliable project management tool, just behind predictive approaches like Waterfall. However, a majority of those who implement Agile into their workplace do not have much idea about what to expect, how much time it takes for the streamlining, and what an ideal system looks like.

This is why it is important to visualize an ideal Agile implementation framework. Let’s study the four major characteristics or outcomes of the Business Analyst Course in India that have been implemented in the right way. Starting with how success looks like…

Top Characteristics of Ideal Agile Implementation

It is better to understand what went wrong with the implementation in your organization rather than sitting with folded hands and waiting for a rescue team. Let’s have a look.

Agile Creates a Controlled Work Environment

Agile implementation in its ideal form gives more benefits to its users than other traditional project management tools. The biggest advantage is that even though employees might have to push themselves to complete a short burst of tasks for (say) two days rather than stressing over the same tasks over a few weeks, they will feel better after the tasks are done. This prevents burnouts and allows them to have better conditioning during their time off.

For example, in an Agile environment, a team of six employees will aim to complete a set of tasks between Monday and Friday. An ideal implementation will allow them to sign off on Friday and enjoy a weekend without the anxiety that may have arisen had they paused the work on Friday in an attempt to resume it on Monday.

Apart from this, a successful Agile implementation yields many more benefits from a human resources point of view:

  • Workplaces become less resistance to change
  • Employees have clearer career objectives
  • Simplified role transitions as tasks are designed in a way that allows anyone with little experience to assume new duties
  • Problem-solving environment

Allows Hybrid Systems

Any organization that deals with a large number of clients is bound to have some type of project management in place. Based on statistics, the most popular is the Waterfall project management system where it follows a hierarchical flow of duties.

In such scenarios, Agile does not act as a disruptive mechanism. Instead, it allows for collaboration which can only improve the productivity and overall morale of the workforce.

According to Agile coach Johan Karlsson, many aspects of hardware development benefit from Waterfall processes, whereas software development has much to gain from an Agile approach. This is where the hybrid mechanism bears fruit as companies can look at fully utilizing their resources.

In an ideal hybrid case, a Waterfall process is used on the top level and Agile is used for operations-level work. This ensures that there is less to no friction between teams as they embrace both the management techniques.

A Perfect Environment for Employees

According to a recent report by McKinsey and Co., the Agile environment is creating a war for talent. This has led to competitive hiring and an increase in the retention rate of talent that organizations feel are indispensable or “too great to lose.” This is a cause of the very characteristic of an Agile environment.

As is known, employees are given tasks based on their skillset. This means that an employee can choose to excel in a skill that she already has sufficient exposure in, thereby traversing to advanced levels. For instance, a Scrum Master can look at managing his team not just in an Agile environment but also in other project management that his organization may be using. This meta example shows how employees can climb the corporate ladder while focusing on and expanding their gained skillset.

Another major advantage that often gets overlooked is Agile’s ability to break complacency. In every work sector in the world, employees feel that they hit a state of complacency after some time in a specific role. Since Agile expects team members to extend their skillset based on the feedback loop with the client, employees stand to gain better work experiences.

Changes in the Organization Culture

In a mature Agile environment, the employees are not the only ones who receive the benefits. Everyone from the team members to the management to the human resources experiences a shift in how they work, which is often very sudden and different than what they had been doing in the past.

It not only engages every person involved in a company but also brings out the best in them. As a result, it transforms from being an average company to one that is full of energy and productivity.

Just as Agile uses a feedback system to improve project deliverability, its environment is based on the same system. Agile identifies great talent and that in turn helps the environment flow like a stream of water.

All said and done, it is pragmatic to note that Agile is a talent-based system. You cannot expect a company to move mountains with Agile implementation if the members do not possess the skills required to even initiate a project. Therefore, to implement an ideal Agile environment, talent acquisition is an essential requisite.

What have been your experiences when it comes to Agile implementation? Share them in the comments section below to start a discussion.

Artificial Intelligence as an Anti-Corruption Tool

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Artificial Intelligence as an Anti-Corruption Tool

It is obvious why anyone would want to put a cork on corruption and then throw it into space to disintegrate itself. So, when a group of scientists from the University of Valladolid in Spain put together a computer model that can predict instances of graft in government agencies, the whole world took notice.

Here are some of the top takeaways from the study that was published in FECYT – Spanish Foundation for Science and Technology. It was first published online in Springer on 22 November 2017.

What was the study about?

According to the research paper published in Springer, the study created a computer model based on neural networks that would send out warnings for possible instances of graft occurring in a government office. These warnings can then be used for corrective and preventive measures, which in other words, means changing the way a government functions or weeding out certain bad apples, for lack of a better term.

The model in the study uses corruption data extracted from several provinces in Spain where graft occurred between 2000 and 2012. A lot of different factors were at play that defined how a routine incident of corruption would occur in any given government agency.

The researchers’ aim was to understand what factors play a key role and then administering changes to those factors in an attempt to eradicate corruption. Of course, this process will be iterative, as no “bad habit” can be weeded out in a single go.

What were the findings?

According to the paper published by the researchers, the following are the key takeaways. Since Spain follows a customized form of parliamentary monarchy, it can be easily translated and adapted for other similar governments. Of course, the data will need to be updated.

  • Public corruption is a cause of multiple factors such as:
    • Taxation of real estate and a steady increase in property prices
    • Nature of economic growth, GDP rate, and inflation
    • Increase in the total number of non-financial institutions
    • Sustenance of any single political party for a “long time”
  • Since data on actual cases of corruption was used to create the model, it provides for a better look at the factors compared to how the model would have been had it depended solely on the perception of corruption. Such studies have not yielded much either have they garnered any interest from the public
  • Corruption can be predicted three years before they are bound to happen

Where does AI come in the picture?

Since all of this sounds too good to be true, it is wise to ask what the role of artificial intelligence is in this study. In order to do that, let us go back to other studies that have tried to predict corruption.

All of the previous studies on the subject have depended on data that were more or less subjective indexes of perception of corruption, reports Science Daily. What this means is that the only type of data being used is something that is available on the public domain. Since the government can sometimes come in between the sourcing of this data by private agencies like Transparency International, the database stops becoming useful. All it will give the model is data that does not reflect the true gravity of the situation.

On the other hand, when actual data is used, there is much for artificial intelligence to feed itself and then bring out a model that can be used to predict the very nature of corruption. This is where this new study excels when compared with historical reports.

The biggest role of artificial intelligence in this exercise is to find a correlation in a set of data through a process that attempts to mimic the human brain functionality. If we feed human being scores of detailed court cases about corruption charges on government employees, he will take decades to dig through them and still end up without an actionable conclusion.

A neural network, on the other hand, analyses the data, studies its various factors, and creates a relationship between them to see what connects with what. Let’s take a rough example to make this clear.

If out of 10 cases of corruption, 8 of them involves a particular modus operandi and a similar cause for the exchange of money to take place, then such a connectionist system will flag that as a recurring factor. This information, along with several others, is then used to reach a conclusion. Of course, this example is an imaginary one, and the amount of data fed in the study were a lot higher, which makes the result even more constructive. Over 12 years of data of actual corruption cases are bound to give such an AI tool enough ground datum to work with. But, it should still be noted that no amount of data is sufficient when one is trying to execute the predictive analysis.

Finally, according to the Chr. Michelsen Institute, this type of predictive tool that depends on patterns can well be a smart anti-corruption tool. Its ability to handle big data and detect anomalies in it is what makes it a promising new system that could be adopted by late the 2020s. However, it also points out the single biggest concern over its use: it will force for more surveillance in the world as data about even the smallest corruption cases will be added into the system. Data that will include personal details of individuals.

Conclusion

It is a great relief that AI in theory at least does not mimic the dangers portrayed by science-fiction films and is being seen as a technology that can help humanity lead a better life. Its usage in anti-corruption neutral networks is a step in the right direction, and with added research, it will pave way for better governance. Where those being governed have one less accusation to make against the government.

Tips to Write a Great Resume

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Tips to Write a Great Resume

Getting ready for a job change soon? Are you dreading creating a resume? Its alarming but true
that a vast majority of job seekers are rejected at the initial resume screening due to a poorly
written, unprofessional resume.

Are you in search of that stand-out resume that will catch the recruiter’s attention? Resume
writing has most likely changed since the last time you looked for a job. So, improve your odds
of creating an effective resume by following the tips mentioned below:

Determine appropriate length and format
Use an easy to read format. Include bullet points instead of long sentences. Recruiters scan a
vast number of resumes everyday so ensure your resume is easily readable. Steer clear of
quirky fonts. The sections should be aligned neatly with columns, bullet points, appropriate font
and blank/breathing space (to avoid clutter and chaos).
Equipped with these design elements your resume will be an easy read. Your expertise and
skills will be better highlighted, and the recruiter need not dig through the information for it.

List Skills
Expertise is what recruiters are looking for among job applicants. A short, organized list of
attributes placed below the summary statement will easily draw their attention and also
communicate your value. These attributes should be easily demonstrated and in-demand in
your field. Specificity is extremely important too.

Be wary not to overload this section. Cramming it with general, outdated and irrelevant skills
could clearly backfire and not achieve the desired result. Also, do not list skills that resemble
personality traits.
This section has become extremely important as you can encapsulate a lot of the keywords (if
relevant) from an employer’s job posting. And that increases the probability of an employer
noticing your resume.

Don’t just mention your skills or expertise, highlight with examples
Begin with a summary statement, a brief paragraph of your expertise and how you would be
best suited to fulfill this particular employer’s requirements.
Pay particular attention to highlighting your achievements with examples. This will provide the
recruiters a better understanding of why you will be a perfect fit for this role that you’re
applying for. It is also best to highlight your career growth trajectory within a company.
However, this section should not be labelled as an objective statement. An objective statement
describes your professional goals and what you’re looking for. Remember that your resume is a
marketing document. So, say goodbye to your resume objective and replace it with a career
statement that showcases your value to an intended employer.

Customize your resume to suit the audience
Sending out a generic resume for all job postings is a thing of the past. You need to tailor your
resume according to the job description and lingo used by the employer. Include words in your
resume that match the keywords they chose to use thereby assuring the employer that you
understand their needs and what they do.
You do not need to overhaul the entire resume for every job posting. Be smart about it. Look
for ways to subtly copy the employer’s language. For instance, include a brief mention of your
ability to execute the ‘specific skill’ in your summary statement. Rearrange the job headings in
your resume to mirror the important qualifications listed by an employer in the job ad.
The current job market expects such a high degree of specialization that using a generic resume
will make you obsolete.
The more closely your resume mimics the job posting, the greater the probability that you will
not be eliminated in the initial screening process.

Details to Omit

Old jobs
Provide a snapshot of your recent experience. A modern resume has no place for jobs that you
did right out of graduation or 15+ years ago. Stick to your most recent and solid professional
experience in your resume.
Mailing Address
There is no need to include a mailing address. An employer will either email or call you if
interested in your candidacy. Further, employers may be slightly hesitant if you are applying
from out of state.
Personal Information
Your marital status, age and religious preference is irrelevant. Education, experience, skills and
contact information is what the recruiter or hiring manager is interested in.
References
Showcase references only when requested. Do not clutter your resume with an overload of
information. A resume needs to be simple and clean- it’s a valuable piece of real estate and it
needs to market your skills and experience.
Social Media Accounts
Unless your Facebook, Twitter and Instagram accounts are pertinent to the position being
applied for, it is best to leave them out too.
Do not include pronouns. It’s weird. Always write your resume in silent first person.

Proofread and edit your resume
Finally, ensure that your resume undergoes several rounds of proofreading to eliminate any
spelling or grammatical errors.
While there are proofreading tools to help you with this, it is also advisable to get a trusted
third party (friends or colleagues) to review your resume. A fresh set of eyes might help you
find ways to correct or improve it.

Top 10 Tech Tips And Tools That Data Scientist Should Know?

Posted on by Imarticus Learning

Top 10 Tech Tips And Tools That Data Scientists Should Know?

The future will see the unlocking of nearly 3.1tn USD of data harnessed and held proprietary by governments and businesses. The present number of people who clean and handle such data from multiple sources and in multiple formats is grossly insufficient to handle the present and future volumes of data.

The technology, skills, and training of people on emerging skills are racing ahead and require an eclectic blend of technological knowledge, tools, techniques, skills and best practices learned from day-to-day slip-ups and lessons learned from them. The infrastructure and machines are seeing rapid changes to unleash computing power, processing power, hardware and software storage power.

One of the most popular careers of modern times is a data scientist. Data science continues to grow because there are far fewer people than the huge volumes of data we are constantly being generated globally every nanosecond. And just as this data continues to grow the demand for data science careers grows. And this lot of aspirants will never fail to find a highly paid job for the next couple of decades if they do a Data Science course to re-skill themselves and stay abreast of emerging techniques in the field.

We now explore the topmost tech tips, apps, and useful tools for data scientists that have the potential to make their work a bit easier.

Analytics Platform- KNIME:

This tool used for raw data analysis tool is good for extracting useful information from it. Being a free open-source application it makes it easy to build analysis and extraction apps around raw data sets.

Lambada- AWS (Amazon Web Service): 

The Lambada platform is an event-driven server-less platform helping put models into production in an Amazon Web Service environment. A 3USD fee is charged for access and data scientists with a creative theory can test it on raw or live data. Besides eliminating storage and infrastructure needs one uses a cloud-based environment and has no waiting for implementation or developer intervention.

Python suite:

This suite is taught in a data science course and forms part of the toolkit. While you do not need mastery in it Python knowledge is essential to handle your work better.

Flask micro-services: 

Part of the Python suite, the micro web framework Flask tool is useful for writing programs in Python and transforming them into web calls. It is very useful in microservices building and creates large datasets shortcuts.

PySpark:

PySpark from the Python suite can scale humungous volumes of data. It is used with ML and Data platform ETLs for creating the pipelines.

Feature-tools and engineering:

Deep Learning allows data scientists to use datasets that are semi-structured while turning their features into useful insights and applications of this kind of data. Feature tools use such data to define associations between data tables, to produce and generate a coherent model. It can effectively take the grunt out of the data scientist’s job.

RapidMiner:

Any data science course will teach you that data cleaning and preparation is the most time-consuming part of working with data. RapidMiner automates and makes this chore more manageable and easier. Most times the delays in cleaning raw data in big data projects cause time delays that prove fatal to the project.

Athena from Amazon: 

Athena is an AWS tool very useful for storing large tranches of data and datasets. Google BigQuery and Microsoft Azure are competing platforms very similar in nature but with a suite of different capabilities and tools.

Fusion Tables in Google:

Google’s Fusion Tables launched in 2009 scores in data visualization and is useful to gather, share data tables and visualize data.

Microsoft Power BI:

The 2014 version of Power BI is a business analytics solution using raw data to create models, intelligence and visualizations on their own company dashboards adding to the value and applications of raw data.

Parting notes:

Data science is a well-paying career choice that is exciting, satisfying and challenging. Making raw data useable, involves cleaning, parsing, and making the data transferable and useful. Without tools, this work can be beyond human capacity and it is the technology that steps in to automate, quicken and make the job easier. Doing the data science course at Imarticus Learning can unleash the innovator in you by skilling you with comprehensive knowledge and the appropriate technology and tools to make a career in data analysis.

For more details regarding this in brief and for further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.

How Artificial Intelligence Help To Transform Employee Productivity?

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How does Artificial Intelligence Help To Transform Employee Productivity?

Every company moves towards becoming a tech company, and AI-enabled computers and bots will enter the field of how recruitment, on-boarding, training, and working happens at our workplaces. Here are just some of the areas where successful artificial intelligence courses used by tech companies to train smart machines could be used in the foreseeable future.

Onboarding and recruitments:

Did you know that many companies use AI-enabled systems to scan and identify the right person for the job in most smart tech companies? They can effectively and accurately wade through millions of applications and gain foresight from their profile sampling techniques to invite the deserving candidate.

Pymetrics uses neuroscience-inspired “games” to assess Artificial Intelligence Courses with emotional and cognitive features of the profile avoiding any human bias on gender, status, race or socioeconomic factors. They compare the new profiles to their inhouse data of profiles of persons who were successful at the job being recruited for. It can also make lateral options a choice for candidates who are not just right for the particular job but fit other open vacancies.

Similarly, Montage, with the top 100 amid Fortune 500 companies as clients use an AI-driven interviewing tool which can undertake automated scheduling, on-demand text interviewing and such to reduce unconscious biasing in recruitments.

Chatbots are not just for customer service and help the new recruits settle in better. Unabot, used by Unilever is a good example of using NLP (natural language processing) to answer queries on payroll and HR with advice to employees in plain and simple human language.

On-the-job training:

The entire learning process and training is full of examples of AI-interventions. They help garner from older experiences and transfer to new recruits the wealth of information required for being successful on the job. Honeywell uses AR/VR to capture the work experience and learn “lessons” from it to be passed on to new hires. Such tools keep records, use image recognition technology, play these back, provide real-time feedback, issue reminders, and help in a VR experience of the role.

Augmented workforce

Fears that AI will replace workers and take over their jobs, is baseless. The very aim of AI is to aid the workers and one should exploit the help in increasing productivity, efficiency and augmentation of the workforce since AI brings many benefits to its applications. Humans can better use their faculties for creative and human-interaction based areas of work in artificial intelligence courses since machines do need human interaction and maintenance too.

Machines have proven skills in repetitive tasks, providing insights into large volumes of data and the potential for predictive trend analysis. PeopleDoc, Betterworks and such can go a long way in bettering the day-to-day workplace experience with monitored processes and workflows and processes and RPA-robotic process automation.

Surveillance in the workplace

Are you aware that according to a Gartner survey, half the companies with 750million USD make gainful use digital data-gathering tools to monitor employee performance and activities? This includes employee engagement and satisfaction levels. Some companies use tracking devices to monitor bathroom breaks and audio analytics to determine voice stress levels. Others use the carrot of fitness and exercise programs through traceable Fitbits. Workplace Analytics is used by Humanyze on staff email and IM data, and microphone-equipped name badges. Not all AI is bad as bullying, stalking and security are good goals. Right?

Workplace Robots

Physical autonomous movement robots are fast becoming the means of access for warehousing and manufacturing installations. Robots like Segway have a delivery robot while, security robots like Gamma 2 keep the trespassers away, and ParkPlus helps you find parking slots. Include the automatic shuttles and driverless cars at workplaces and wonder why we humans are still complaining.

Conclusion: 

Though the concepts have been around for ages the past two decades have seen a phenomenal and sustained increase in ML/AI applications. Artificial intelligence is the ability of machines to simulate neural networks and human intelligence without the use of any human intervention or explicit programming. Machine learning is a subset of AI technology that develops complex algorithms based on mathematical models and data training to make predictions whenever new data is supplied to it for comparison.

Do you want to succeed in artificial intelligence courses? Then learn with Imarticus Learning for becoming career-ready and skilled. Why wait?

For more details in brief and for further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi and Gurgaon.

Why is Business Analysis an Important Part of Business?

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Why is Business Analysis an Important Part of Business?

Business analysis is the process of identifying business needs, lending and determining solutions to business problems. It involves devising techniques to make an appropriate plan and act on it. Business analysis is one of the most important pillars of a business configuration since it is how business analysts break down the problems into multiple smaller ones and determine the allocation of both funds and labour to solve the problem.

In a scenario that is as rapidly changing as the one today, companies that are able to identify problems and adhere to solutions are at a better chance of succeeding as compared to others who can’t.

become a Data Analyst

For every company, it is equally important to gain a strong foothold in the financial market. This is where the role of business analysts play out, and what a business analysis course may not essentially teach you is how to directly, or indirectly make an impactful contribution to your company’s finances.

Here is a breakdown of the true importance of business analysis in a business, or for an organization.

● Increase in ROI– To know where a business stands, it is essential to evaluate its ROI (Return on Investment); the ROI measures its business valuation and whether there is an increase or decrease in revenue brought in. Business analysis can tell you whether you need to make further adjustments to bring in more ROI, if costs need to be reduced and benefits need to be increased and vice-versa.

● Decrease in cost– Using business analysis to create various strategies to reduce project costs, briefing the team how to work accordingly and what strategies can help reduce company costs is an important factor. It also opens up avenues with various ways to solve recurring problems with affordable methods.

● Decision making- The most important aspect of business analysis is decision making on behalf of the company to the stakeholders or investors. Logical discussions about the company have been functioning, what the problem areas are or what factors are governing the increase of overall company expenditure and how to reach a higher margin of profit, are factors that influence decision making and offer clarity on the way ahead for business.

● Business requirements- The whole idea of business analysis is to understand the business in detail, and what requirements are needed for its growth while also identifying the hindrances that may prevent the same. Business analysis helps make various decisions that influence the growth of the organization, including analysis of processes and data that will pave the way for a profitable situation.

● Benefits everyone in the business- While business analysis helps owners and founders to change the management is the company performances are not upto the mark, it also helps them identify spheres, where work-force needs to be more concentrated. It also helps managers to explore alternatives while assigning teams to projects or understand what implemented methods would increase sales and productivity.

● Identify market status of the business- It is very important to know where your business stands at par with the total market scenario, its value and who are your closest competitors. This is exactly where business analysis fits in.

● Crisis anticipation- Business analysis has you prepared for both good and bad times. Its predictability model often helps you understand and anticipate a crisis that might affect the entire market or just your organization, well in advance.

● Assessment of change- Post every business review, problems are identified, suggested methods are implemented and outcomes are discussed. Teams are intimidated and asked to bring about certain changes in their work processes, However, to note if all of that has actually brought about a positive impact and removed or at least reduced the problem to an extent, is only possible through a business analysis.

To become an effective business analyst, formal training in a business analysis course is highly recommended. At the end of it, you are not only a successful professional who knows all the nitty-gritty of business analysis, but also have a sound knowledge of techniques used to gain fluency and fluidity in devising business analysis strategies.

A comprehensive course has you well prepared for any future endeavours that you may have as a business analyst while making sure you possess all the right skills to make a meaningful impact during your stint in an organization.

How To Pay Taxes Without Leaking Your Personal Data Using Machine Learning?

Posted on by Imarticus Learning

How To Pay Taxes Without Leaking Your Personal Data Using Machine Learning?

The cutoff date to file taxes in the US (April 15th) teaches all taxpayers many a lesson. Did you know that 1 in 3 Americans wait until April each year and get stressed when filing taxes? The major fallout of this behavior is that besides the stress and worry, one is tempted to cut corners and hurry through tax filing thereby exposing themselves to potential personal data loss or stealing.
Think about this fact. Tax scams saw a 60 percent increase in 2018 in comparison to the previous three years where the rates had actually shown a decline. That is why we are going to explore what we need to do when we file taxes to ensure safety, privacy, and efficiency of the machine learning course working behind the filing of our taxes.

Password strength:

Strong passwords can protect your online tax account. Use the IRS recommendation of a long password with at least one uppercase letter, one symbol, one lowercase letter, and one number. Avoid using your name, child’s name, pets name, DOB, school name and such easily discoverable data. Once they guess your password your account can be comprised at their will and social media sites like Facebook, Instagram, Twitter and such are always used by these scam generators to study your profile.

Watch for phishing:

Online phishing by overseas cyber criminals use websites which look very like the original IRS site to scam you. Once you enter your details on the link sent to you your data has been compromised and security breached. Here’s what to look for. An email that claims you will face legal action and prompts you to use the link to provide your personal details. Don’t panic.
Also look for giveaways like letters starting without your name or the ‘Dear Customer’ from the so-called tax department officials, poor sentence construction, grammatical errors, or threats of penal action. Periodically check your account for activity and never click on the links!

Do not use your phone for transfers:

If ever you receive phone calls from the IRS claiming you are a defaulter or have to pay a court-fine and need you to pay through phone transfers then call the IRS directly at 800-829-1040 and speak to a legitimate IRS staff member. Go ahead and block such calls as the banks, IRS or any other financial institutions have long since stopped using phone calls to call you. Even when you panic, remember smart frauds can phish over the phone with misusing the Machine Learning Course and reflecting the 911 number on your display.

VPN authentication:

Always use a VPN. The service uses remote server encryption and can go a long way when you use WiFi networks both untrusted and public networks to send your tax returns to the IRS. Hackers will need to work very hard to get past the encryption and this ensures the security of your personal data.

Stay clear of great offers:

In the USA 2 in 3 taxpayers find tax-filing complicated and use a tax returns professional to file their returns. Especially so, when in a hurry! The major issue here is to distinguish between the ethical and legal professional and the scam-master posing as one just to steal and hijack your personal data. That’s why it’s always better to plan in advance and know your tax- professional’s credentials before you employ them.
The American saying that there is no free lunch anywhere is true here too. Beware of tax professionals offering to get you huge refunds especially when you know that your financial position is not so good. Does that offer sound like a bonus from heaven? Then stop! It is a scam. If your tax professional is refusing to issue a receipt for payments, then beware. No references, only cash payments or lacking testimonials are also good cause for concern.
To watch your step, ask the tax professional for his Preparer Tax Identification Number or PTIN which needs to be mandatorily mentioned with their signature when they file returns on behalf of you. Stay abreast of the latest scams and fraud methods where the machine learning course behind your personal data could be exploited for unlawful gain.
Conclusion:
Tax season or otherwise the above-mentioned measures are effective and not time-consuming to enforce all year round while preventing data loss and personal identity thefts. To learn more about cybersecurity and the machine learning course behind the tax scams do a course at the reputed Imarticus Learning Academy. All the best with your tax returns!
For more details regarding this in brief and for further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

5 Ways Data Science Can Help You Work Smarter, Not Harder!

Posted on by Imarticus Learning

5 Ways Data Science Can Help You Work Smarter, Not Harder!

The world of decisions today runs on data. From every time we do a Google search, or use our smartphones to each of our everyday activities, we leave a trail of data on our choices, lifestyles, and habits.  The internet and total volumes of our data are being efficiently managed by the ever-adaptive data science training applications of AI, ML and Deep Learning. Data is the basis of enhancing our lifestyles and entertainment, enabling our banking and communications and empowering our financial productivity and economic growth.

What is data science?

Data science uses the large volumes of data we produce to make logical conclusions, develop models and generate forecasts and predictions through an intricate process of cleaning raw data, parsing and processing it to finally using algorithms to resolve issues and problems. Businesses thrive by using these in-depth insights from Data science training to make decisions related to their productivity, efficiency, growth, and management. It is no wonder then that many of them are heavily invested in the benefits of data science.

The five-pronged strategies for businesses:

Here are five ways in which data sciences make your operations smarter, less expensive and more efficient.

Sentiment analysis:

Sentiment analysis is fast becoming essential before taking decisions on branding, product launches, marketing areas, and even posting information on social media like Facebook, Instagram, and Twitter. Social perception analysis is easily achieved by data science that wades through very huge volumes of relevant data to provide you specific sentiment analysis to base your decisions upon. Advanced techniques and tools like RapidMiner can help you not have to rely on gut-feeling instead. With effective sentiment analysis, one can correct their test market efforts without it being an expensive waste of resources, time and efforts.

Relationship value attribution:

ROI is directly related to customer satisfaction. However, all customers, clients, products, and partners are not of equal value. ROI is determined by the resources spent and time and effort spent in acquiring the business.  Hence relationship value attribution becomes crucial in determining the allocations and budgets spent. Using logic and weights data sciences makes a distributional array of your calendar of events in professional relationships, which helps target the right customer at the right time, improve your productivity and the effectiveness of your UX experiences.

Future demand forecasts:

Demand and supply gauging is the crux of business decisions. The entire process of planning, sourcing, resource allocation and budgeting is dependent on these choices. It is improbable that you will treat such an important decision lightly. Data analysis and data science training when done right and on sufficient relevant data can be very accurate in predicting demands, making forecasts, improving your stock and inventory, tweaking the logistics, providing the metrics for efficient performances and enabling all decisions that lie in between. Of particular use in e-commerce platforms and stock market-based products stocking, the price differences and rates are constantly changing and too little or too much can have a tail-spinning effect.

Fault finding analysis:

No organization is perfect and has tremendous scope to discover ways to encash its strengths and counter its weaknesses. The larger the growth of an enterprise the more difficult it is to spot weaknesses much less rectify them. Data analysis can fill this gap and provide a complete weakness analysis reports to help with rectifying the fault-finding analysis insights. It provides you with the overall view and how each of the departments dovetail together to spot the weaknesses early on.

There have been many instances of these inter-relationships not being corrected in time resulting in over-production, product starved markets, errors in logistics leading to rejections and losses and so on. Underperformance is quickly spotted by data science techniques and applications.

Gauging trends:

Data science can monitor large volumes of data effectively to spot even distant emerging trends.  Since the process goes on continuously and behind the scenes due to automation and AI the algorithms can find and highlight them with little or no manual investigation. Trend analysis is one of the biggest benefits that can help you revise business strategy and models while staying ahead of the curve of competitors.

Conclusions:

All businesses can benefit from data science used effectively. It is the platform on which you can base your new products, build brands, strengthen the lacunae, and make effective allocations of finance and resources. The timely decision of data science training is enabled by putting forecasts and predictions which are data-based in the hands of decision-makers. If you are interested in learning more about data science do a training course at Imarticus Learning the pioneers in data science education. Why wait?