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Tag: Data Science

The Increase in Data Science Education in India, Explained!

Posted on by Imarticus Learning

Data science jobs and related roles are increasingly becoming some of the most coveted jobs across industries. This is partly due to how the data science field can cut across industries to be of value, but also thanks to its resilience in tough times and the needs it has responded to.

Data ScienceOver the past few months, colleges and academic institutions have seen a significant rise in enrollment in data science courses in India. The choice is wide– potential students can choose from full-time, part-time or short and snappy online courses to either fill a gap in their skillset or experiment outside their comfort zones.

Although the potential for online learning had been realised by many even a few years ago, certain situations contributed to its exponential rise in recent times.

WFH and Remote Learning During the Coronavirus Pandemic

As lockdowns and shelter-in-place restrictions were imposed on countries all over the world, schools and colleges also had to pull down the shutters. Learning was taken online; in many institutions, exams and lessons were replaced by the opportunity to take online courses that otherwise wouldn’t have been accessible. Whether as a result of this or to fuel this trend, online education providers also reduced or waived off subscription fees and made certain courses available to all regardless of budget or geographies.

As a result, there was a surge in remote and online learning, not just from universities that students were enrolled in but also from coveted universities on the other side of the world. With the demand for data scientists expected to increase, professionals see new opportunities for growth. This, in turn, fueled the desire for upskilling and even pivoting careers as the economy slowed down.

Exposure to Global Universities and Opportunities

Online learning has made courses available in virtually any country from international universities and institutions. By making education accessible globally, online learning significantly increases the scope of the curricula as well as the teaching standards. Another benefit of this exposure is also the ability of graduates and professionals to connect with industry experts in other countries.

Data Science

Enrolling for data science courses in India that are offered by global universities is also a fantastic learning opportunity.

It exposes students to data science landscapes in other countries as well as lays bare the scope and possibilities they have well within their reach.

Once countries open up and travel restarts, students might also consider physically enrolling in these universities to explore topics further. Having a certificate or two in your portfolio indicates to the interviewer or the recruiter that you are interested and have done preliminary research which has only served to whet your appetite further.

Completely Online Courses

Until very recently, full-fledged online courses weren’t popular or even encouraged by governmental departments in India. Indian universities and colleges have not been permitted to deliver over 20 per cent of a degree online for several years. However, in the first move of its kind, the government gave the green signal for fully online courses in order to democratize education and erase barriers to learning caused by transport, accommodation and overall access.

The approach to fully online degrees is still cautious and restricted to particular subject areas. That said, it is still a welcome shift, especially for those looking to find data science jobs but lacking the access to opportunities that a lot of metropolitan cities and countries enjoy.

Conclusion

Online learning has significantly cut down barriers to entry that involve finance and access. It is a welcome step towards democratizing knowledge and making certain domains of the job market accessible to virtually anyone with a smartphone and a stable internet connection.

Seeing as data science jobs are set to increase in number, now is the ideal time for this surge in data science education, so that students are well-prepared for roles of the future.

5 Tips To Successfully Start a Data Science Job Remotely!

Posted on by Imarticus Learning

While the news of mass layoffs has inundated the market, certain industries continue to hire with one eye on the future. The data science realm is one such job market. Quite a lot about the recruitment and onboarding processes have changed; this makes transitioning into a new role a lot more complicated.

Keeping all this in mind, it is imperative that, as a candidate, you take things into their own hands. You can prepare an action plan to approach the first day of your remote data science career with enthusiasm– and this post will help you along the way.

Tip #1: Ask for A Preview of the Process

Proactively arm yourself with a blueprint of the onboarding process– this is especially relevant in current remote working scenarios. Depending on the job role you’ve been hired for, your onboarding process may be elaborate or short and snappy. Understanding what it will look like for you is a great way to avoid spreading yourself out too thin in the first few days or virtually walking in without a clue. It will also highlight any gaps you may need to fill in your skillset, in which case you might need to enrol in a data science course.

Tip #2: Reach Out to Your Teammates

It’s much harder than usual to connect with first-time teammates and colleagues in a virtual environment; however, since someone has to do it, it can be you. Not only will this allow you to establish your presence and role in the team, but it will also paint a favorable picture of you in times when first impressions are rather restricted to screens and voice calls. Try to gauge how best your team works, what communication tools they use and what they do outside of work. This personal rapport will go a long way.

Tip #3: Ensure You Have Continuous Access to Technology

Technology is the backbone of the remote working process– especially so for data science roles. Before your first day, it is a good idea to take stock of all the tools you have and how you can add to them if required. You can first start with hardware– laptops or desktops, sufficient working space, additional accessories– before moving on to software. If you find that you need something to perform your role, it is always advisable that you reach out to the onboarding team and see if they can help.

Tip #4: Be Forthcoming in Your Questions and Help

A virtual environment makes it significantly more difficult to read and react to facial or virtual clues. If you’re curious about something or don’t understand a task, it is best to be forthcoming about it. This tactic leaves no grey areas or causes for misunderstanding. Similarly, don’t hesitate to offer help where you feel like you have more to offer. This tip will make you a more valued member of your team as well as cement the skills and talents you bring to the table.

Tip #5: Weigh in Your Emotional Responses, Too

When starting a data science career remotely, it is easy to feel lonely and disconnected with your teammates despite working on the same projects. However, it is always recommended that you check in with yourself periodically and understand if you are adjusting. Reach out to colleagues to build a friendly rapport with them. Take time away from the computer and stick to strict work hours as much as possible so you don’t burn out.

Industries across the board have shifted operations to a work-from-home basis in order to cease the spread of COVID19. If you’ve been lucky enough to land a remote data science job, it’s best to head into it with a determined mind and an action plan in hand!

Why An Unstable Job Market Has Increased Demand For Online Learning?

Posted on by Imarticus Learning

There has been an unprecedented rise in the number of lay-offs, furloughs and companies shutting down over the last few months. Job uncertainty, a bitter pill to swallow, has affected lakhs across the board, regardless of where they stand on the corporate ladder.

In the wake of this phenomenon, working professionals have turned to online courses to upskill themselves and stay ahead of the curve. So have recent and fresh graduates, who find themselves facing an upturned job market with all doors effectively barred for all but the best of the lot.

Analyses have found that the fields of artificial intelligence, data science and machine learning have been most sought out when it comes to online courses of varying lengths.

Data science Online CourseIf you’re looking to get trained online in the same fields, you’re in the right place. If you’re still on the fence about online courses in general, here is a comprehensive list of benefits that you can expect to reap:

Flexibility in the time and place of learning

You get more flexibility in studying online. You can study more easily around your existing work schedule (and your hobbies). This benefit is much better felt when you’re taking a class that allows you to learn and do assignments at your own pace. You can also choose your preferred work environment as well as use your own technology to furnish your learning as you see fit. The only prerequisites to online learning are a stable internet connection and laptop, computer or smartphone that you can attend your courses on.

Lower costs

For fresh graduates and new professionals, it can be difficult to justify dropping thousands on yet another college course or diploma. However, choosing online courses reduces costs by a significant level. You might need to pay fixed costs– such as tuition fees and book purchases– but you’ll find that the costs of travel, transport and additional day-to-day expenses reduce significantly. This is especially beneficial if you’ve got your eye on a course from an international university but haven’t been able to justify the costs of accommodation, stay and visas.

A wider range of courses

Opting for online courses gives you a more comprehensive range of topics and industries to choose from. Additionally, online learning has made courses from international universities and institutions available to those in virtually any country. By making education globally accessible, online learning significantly raises the scope of curriculums as well as the standards of teaching.

An overarching benefit of this wide range is also that graduates and professionals are able to connect with industry experts in other countries, especially if the country they’re from does not currently have the resources or the demand for such niche courses.

Building specializations

Traditional education systems often work to teach the basics of a domain or topic; however, not many offer specializations that are both useful and affordable. This gap is one that online learning can fill. Professionals and graduates can fill gaps in their skill set or pursue an education in a niche topic through online courses. This is especially useful for professionals who have sound core competencies and are ready for a higher level of analyses or research. This is also beneficial for anyone who wasn’t able to pursue a topic out of pure passion; it allows hobbies and personal interests to be catered to without the risk of jeopardising your existing career.

Conclusion

Whether its working professionals looking to invest their time in bettering their skillset, fresh graduates hoping to add a little extra to their resume or individuals finally getting around to learning what they’re passionate about, more and more people are turning to online courses to upskill and supplement their existing knowledge.

The Hike In Demand In Data Science Can Place The Way For Greater Youth Employment ..!!

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The Hike In Demand In Data Science Can Place The Way For Greater Youth Employment ..!!

These days the gold rush is around the oil of the digital era – data. According to LinkedIn, the career in the field of data science has seen exponential growth, becoming the Harvard University has labeled the position of a data scientist as “the sexiest job of the 21st century”.
With the AI hype in recent years, more and more companies are becoming more data-driven. This has, in turn, created a huge gap in the demand-supply curve for skilled professionals in the world of data. The demand for Analytics skills is going up steadily, but there is a huge deficit on the supply side.
There is a profoundly visible rise in data-related job profiles, like data analysts, data engineer data scientists, database developers, DevOps professionals; new profiles are being invented, like decision scientists. 2019 saw a 2.9 million open jobs requesting for analytical skills.  According to recent data from job sites like Indeed and Dice, it is a great time to be a data scientist entering the job market. The average salaries for data scientists and analysts have grown suggestively. The current demand for qualified data professionals is just the beginning.
India currently has the highest global concentration of analysts. Even with this, the scarcity of data analytics talent is particularly high and the demand for talent is expected to rise as more global organizations are outsourcing their work. In the next few years, the size of the analytics market will evolve to at least one-third of the global IT market from the current one-tenth.”, says Srikanth Velamakanni, the co-founder and CEO of Fractal Analytics.
The Google Analytics data suggests that in recent years, there has been a significant rise in people who get curious about data, and data science training. With this large scale demand for skilled professionals, there are two scenarios that are developing. It is seen that industry professionals are upgrading their skill sets in the field of data science and machine learning. Corporates are promoting this kind of skill up-gradation by means of internal training as well. Technology professionals who are experienced in Analytics are in high demand as organizations are looking for ways to exploit the power of Big Data.
Another, more interesting scenario is that more and more youngsters, from college students to fresh job seekers, are flocking to the world of data. This is a positive note, as it will increase the creative pool in the field and also provide more innovative solutions to current scenarios. New and innovative ideas and approaches are already being tried and tested in different real-time scenarios that have powered the AI hype even more.
From a career point of view, there are so many options available, in terms of the domain as well as the nature of the job. Since Analytics is utilized in varied fields, there are numerous job titles for one to choose from. According to the Indeed report, data science job searches follow a somewhat seasonal pattern. In 2017 and 2018, searches peaked in April or March, reflecting the influx of students searching for internships, or soon-to-be graduates looking for their first jobs. Organizations are using various hackathons and hiring competitions to find a suitable talent pool out of the masses.
There are more data scientists entering the job market — either from graduate programs or after getting “nano degrees” from massive open online courses. Along with the rise in demand, there was also a similar rise in various data science training institutes, certifications, and courses.
An important factor to consider in these is the authenticity of such institutions and the value of such training in the market. With hands-on projects and good exposure, courses of institutions like Imarticus Learning stand out among all courses in the job market.

Data Scientist Profile In 2019 Education And Skills Set

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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!

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

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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.

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

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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?

 

Why Is Statistics Important For Data Science?

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Why is statistics important for Data Science?

Data Science is a scientific discipline, one that’s highly informed and dictated by computer science, mathematics, research, and applied sciences. Data is an integral part of today’s world– everyday individuals and corporations generate tonnes of data that can only be visualized and understood by experts.

Big Data Analytics Courses

Statistics provides the means and tools to find structure in big data as well as give individuals and organizations a deeper insight into what truths their data is showing. Statistics is one of the most fundamental steps of an insightful data science course– it’s also the linchpin that ties the whole process together from start to fruitful finish.

Finding structure in data, however large or small, and making predictions are crucial stages in data science that can make or break research. Statistical methods are the tool of choice here as using their methods, one can handle a plethora of analytical tasks to good results.

Enables classification and organization

This is a statistical method that’s used by the same name in the data science and mining fields. Classification is used to categorize available data into accurate, observable analyses. Such an organization is key for companies who plan to use these insights to make predictions and form business plans. It’s also the first step to making a massive dump of data usable.

Helps to calculate probability distribution and estimation 

These statistical methods are key to learning the basics of machine learning and algorithms like logistic regressions. Cross-validation and LOOCV techniques are also inherently statistical tools that have been brought into the Machine Learning and Data Analytics world for inference-based research, A/B, and hypothesis testing.

Finds structure in data

Companies often find themselves having to deal with massive dumps of data from a panoply of sources, each more complicated than the last. Statistics can help to spot anomalies and trends in this data, further allowing researchers to discard irrelevant data at a very early stage instead of sifting through data and wasting time, effort, and resources.

Facilitates statistical modeling

Data is made up of series upon series of complex interactions between factors and variables. To model these or display them in a coherent manner, statistical modeling using graphs and networks is key. This also helps to identify and account for the influence of hierarchies in global structures and escalate local models to a global scene.

Aids data visualization

Visualization in data is the representation and interpretation of found structures, models, and insights in interactive, understandable, and effective formats. It’s also crucial that these formats be easy to update– this way, nothing needs to undergo a huge overhaul each time there’s a fluctuation in data.

Beyond this, data analytics representations also use the same display formats as statistics– graphs, pie charts, histograms, and the like. Not only does this make data more readable and interesting, but it also makes it much easier to spot trends or flaws and offset or enhance them as required.

Facilitates understanding of distributions in model-based data analytics

Statistics can help to identify clusters in data or even additional structures that are dependent on space, time, and other variable factors. Reporting on values and networks without statistical distribution methods can lead to estimates that don’t account for variability, which can make or break your results. Small wonder, then, that the method of distribution is a key contributor to statistics and to data analytics and visualization as a whole.

Aids in mathematical analysis and reduces assumptions

The basics of mathematical analysis– differentiability and continuity– also form the base of many major ML/ AI/ data analytics algorithms. Neural networks in deep learning are effectively guided by the shift in perspective that is differential programming.

Predictive power is key in how effective a data analytics algorithm or model is. The rule of thumb is that the lesser the assumptions made, the higher the model’s predictive power. Statistics help to bring down the rate of assumptions, thereby making models a lot more accurate and usable.

In just 2018, 16,000 freshers got enviable jobs in the analytics workforce, so the demand is high and unceasing. However, a mistake quite a few undergraduates make is majoring in Computer Science if there isn’t a course fully dedicated to data analytics, machine learning, or AI.

The fact of the matter is that ‘deep learning is applied statistics in disguise’! For more details, you can also visit – Imarticus Learning and can drop your query by filling up a simple form through the site or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.

How Is Data Science Changing The Process Of Web Design?

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A data science course will cover a lot of ground in learning about data sciences and its interlinkages with the creation and management of data and involves research and studies into how data is used to accomplish tasks that can change processes in web management and design.
Let us now explore how AI and data sciences have actually impacted web design.
AI and data analysis:
Today’s AI devices no longer depend on humans for the input of data or limit their insights to only the data inputs provided. Rather, self-learning AI devices are scoring and ML algorithms can not only clean and format large volumes of data they have turned self-learning and making predictions from data across the board in different formats and from different sources a breeze. This impacts web design too, as like AI it depends on the human interpretation of data and making gainful insights.
The web design process:
The normal process of web design will start with seeking information from various surveys and focus groups and this data is banded together, organized, cleaned, formatted, assimilated and organized by a team of human beings. Next, the coding process begins. A model or prototype for coding is drawn up and this is tested through beta-testing.
Once it emerges successfully from testing the software is declared ready for use. However, many inadvertent flaws end up in the code and land in the final design for want of effective prevention methods to vulnerabilities in coding. These are set right while in use through newer versions and updates. The modern Data Science Course uses AI and data science to create and reuse code to make it secure against such vulnerabilities.
Focus on AI and Data Analysis:
A change from the current methods of web design allows AI and data analysis to function differently from the current focus groups. Data that is collected is taken up by AI for in-depth data analysis using the vast swathes of Internet resources. This provides for the ability to overcome human errors, improve coding, streamline the creation process and create more security. This, in turn, increases the traffic of users, figures in the search engines with SEO optimization and enhances the web designs.
Use of Code:
The use of coding is an integral part of web design. What will, however, change with AI handling the coding from scratch is that one would no longer look for solutions to fix vulnerabilities. It would just mean perfect coding without vulnerabilities and most users, governments, businesses, and enterprises using such data science course solutions will no longer have to worry about Internet threats and hackers or data breaches and unauthorized tampering of data. Web designing stands to gain in many ways like keeping the app developers and owners of the websites free from the problems of the past, ensuring the protection of proprietary data, and encouraging the online conduct of business.
Apps and new versions:
In the near future, it will be AI that decides in conjunction with ML, DL and more how new software gets designed, why a website menu needs to be a certain way, which apps are best suited and whether updates or re-versioning is needed for any particular app. With the increased sophistication brought in more repetitive tasks will be handled by bots and apps based on AI and thus leave more quality time for business decisions and doing business online. At the end of the day, manual human intervention will also be more sophisticated and need more in-depth skills to handle such changes. Web designing is surely set to become possible on much smaller budgets.
Opportunities for web designers:
A very potent question doing the rounds today is that AI will eventually cost the jobs of current web designers and coders. Just remember that technological advances do mean some jobs will be displaced but at the same time newer requirements and jobs are being created for those willing to tweak and make good use of their skills. AI is never about replacing human intervention. Rather it is about aiding human intervention in data sciences, ML, DL, and other emerging technologies. Experienced web designers should do a re-skilling course to stay abreast of the changes and be exposed to newer emerging jobs where the payouts and demand are bound to be much higher.
The Bottom Line
On the never static Internet subjects like AI, DL, ML and such emerging technologies hold great potential and should be embraced by doing a data science course. The reputed Imarticus Learning Institute is where you should head to for comprehensive learning and skill assimilation in emerging technologies.
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Why Do Data Scientists Need To Learn Java?

Posted on by Imarticus Learning

Java has today regained its prominence as the most popular language suite for developers and has outrun both R and Python. This is not surprising since Java boasts of the largest community of developers and also has applicability, compatibility, and ease of learning to aid it. AI, ML, and data sciences are all relying on the JavaScript suite and its applications and these are the areas seeing rapid evolution and need for personnel.

Further, when demand rises the payouts get better. Career aspirants and career-changers both are ready to learn data science and are flocking to these fields and this only adds to the popularity of Java as the ultimate weapon in the developer’s kit.

Here are the top reasons to learn data science and Java.

  1. The old-gold class: Being the oldest language in enterprise development it is frequently found that legacy systems have their infrastructure already running on Java. This means you have probably used R or Python for modeling and have to rewrite the models to suit the system running in Java.
  2. Wide frameworks: The Big-Data tools and frameworks like Flink, Spark, Hive, Hadoop and Spark are Java-based. Familiarity in the Java-stack is thus easier for analysts working with large data volumes and big data with Hive and Hadoop.
  3. Libraries aplenty: Java has toolsets and a great variety of libraries for ML applications and data science applications. Take a look at Deeplearning4j, Java-ML, Weka, or MLlib to quickly resolve and issues in data science.
  4. REPL and Lambdas: While Lambdas that came with Java 8 altered the verbosity in Java, the recent REPL of Java 9 adds iterative development to the developer kit. It is now easy to learn and work in Java than it initially was.
  5. Virtual Machine in Java:  JVM helps write multi-platform identical codes facilitating rapid customization of the tools required. With the IDEs variety on offer, developers can be more productive.
  6. Strongly Typed: This does not refer to classic static typing. Rather it deals with Java being able to specify the types of variables and data the developer needs to work with. The strong typing feature is especially useful in large data applications and is a feature that is well-worth the developer’s time in avoiding trivial unit test writing and in maintaining the code base of applications.
  7. Scala in JVM: Heavy data applications make learning Scala easier when you code in Java. The Scala framework is awesome since it provides data science support and other frameworks of the likes of Spark can be built atop it.
  8. Provides jobs: Other than the SQL requirements, it is Java that is most popular in the job-space as per the chart indexed below. All the more reason to learn data science and Java for developers!
  9. Scalability: Application scaling in Java is rapid and excellent making it the developer’s choice for writing complex and larger AI ML applications. Especially so if you are writing the program ground-up since then you only need the one language of Java coding.
  10. Speed: Java is fast and provides for fast integration in heavy large-scale applications. The likes of LinkedIn, Facebook and Twitter rely on Java for heavy data engineering.

A data scientist/ developer is the one who is the single point of contact for the data itself. They take the data both structured or unstructured and use a wide variety of engineering, statistical, mathematical, and programming skills to spot trends and arrange the data organizing and managing the data to resolve the targeted outcomes. In essence, they are the people the analysts look up to for the data they need to analyze.

Practical skills required:

Let the truth be told, even if you do your master’s or a Ph.D., to be a good and effective data scientist you will need to also garner training for technical skills in:

  • Proficiency in social sciences
  • Programming in R and Python
  • Coding and writing with the Java suite
  • BigData querying  on Hadoop framework
  • Coding and SQL-Databases
  • Apache Spark
  • AI, ML, and Neural networks
  • Visualization of data
  • Working with unstructured data

You could also bolster your knowledge in managing data through online MOOCs, tutorials, and courses. Ensure your training partner for paid courses is a reputed institute like Imarticus Learning as they offer to train you for professional certifications and also award certifications that are valued in the industry.

Conclusion:

If you’re an analyst, Data Scientist, Deep Learning or ML Engineer the Java skill quotient is worth improving when you are eyeing lucrative and in-demand development jobs. You should learn data science and Java at Imarticus Learning if you want to stay ahead of the job-curve.

For more detailed information regarding this 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, Gurgaon, and Ahmedabad.