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Tag: Data Science certification

Introduction To Python Set and Frozen-set Methods

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Introduction To Python Set and Frozen-set Methods

Python is a widely-used programming language. No doubt that learning Python will bring you better job prospects. The advantage of Python is that it is generally smaller than other languages like Java. As a programmer, you will need to type only fewer indentations, which makes them highly readable highly. Many global market leaders like Google, Facebook, Amazon, Instagram, etc. are using Python. Learning Python will help you find a career in machine learning as well. A program widely used in GUI applications, image processing, multimedia, text processing and many more, Learning Python is highly advisable if you are looking at a career in IT.

Sets in Python

Sets in Python represent a collection of data. This unordered collection is mutable and iterable. It doesn’t have duplicate elements. The Python set is similar to the mathematical set. Using a set is recommended over a list because the set has several advantages. Based on the hash table, it is very easy to check if a particular element is included in the set. This is not possible if you use a list.

Properties of a Python set()

There are no parameters to create an empty set. If you create a dictionary using a set, the values will not remain after conversion. However, the keys will remain.

Sets have many methods:

  1. Method of addition (x): This method is used to add one or more items to a set if currently it is not included in the set.
  2. Method of union (s): This method is used to unite two sets. The operator use is ‘|’, which is similar to set1.union(set2)
  3. Method of intersection (s): This method is used for the intersection between two sets. You can also use the ‘&’ sign as the operator here.
  4. Method of difference (s): This method is used to return a set that contains replicates the elements present in the invoking set, but not present in the second set. The operator used here is ‘-‘.
  5. Method of the clear set (): This method is used to empty the whole set.

Operators for sets and frozen sets

Operator Function
key in s containment check
key not in s non-containment check
s1 == s2 s1 is equal to s2
s1 != s2  S1 is not equal to s2
s1 <= s2 s1is subset of s2
s1 < s2 s1 is a proper subset of s2
s1 >= s2 s1is superset of s2
s1 > s2 s1 is a proper superset of s2
s1 | s2 the union of s1 and s2
s1 & s2 the intersection of s1 and s2
s1 – s2 the set of elements in s1 but not s2
s1 ˆ s2 the set of elements in precisely one of s1 or s2

Frozen sets

Frozen sets, unlike the set, are immutable. Once created, the frozen set remains the same. It cannot be changed. This is the reason we use a frozen set as a key in Dictionary. It accepts an iterable object as its input and makes them unchangeable. However, it is not guaranteed that the order of the element will be preserved. In a frozenset, if no parameters are passed to the () function, an empty frozen set is returned. If you change the frozenset object by mistake, you will get an error saying that the item assignment is not supported by frozenset object.

Conclusion

Equivalent to the data set in mathematics, Python set is a data structure similar to the mathematical sets. It consists of several elements, but the order in which the elements should be deployed in the set is undefined.  You can perform addition, deletion, iterate, union, intersection, and differences on the sets.

How To Use Data Science For Predictive Maintenance?

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How To Use Data Science For Predictive Maintenance?

Most businesses constantly face an issue while analyzing whether their critical manufacturing systems are performing to their full capacity while ensuring a consistent reduction in the cost of maintenance. Causes of potential concerns need to be identified early to help organizations come up with more cost-effective plans.

This is where predictive analysis fits the bill. Predictive analysis is used to predict if an in-house machine will malfunction or work correctly. Predictive analysis also helps to plan maintenance in advance, predict failures, classify failure types and recommend necessary actions to be taken after a system fails. The scope of data science is vast and predictive analysis only helps in proving that further.

Factors influencing the success of predictive maintenance

There are three factors that influence if a predictive model is going to be successful or not:

Having the right data

One of the most crucial factors influencing predictive maintenance is having enough data that helps analyze factors that may lead to failure. Additional system features like operating conditions, technical properties also need to be taken into consideration. Additionally, it is also important to make an inventory that will help note the kinds of failure that can occur, and which are the ones that can be predicted.

If at all there is a failure, what the failure process might look like. Having the right data for predictive maintenance also helps understand which parts of the system may have failed and how improvement in terms of performance can be brought about. A system has a vast life span of over a couple of years, which means data collection needs to be done over a couple of years to ensure correct statistics are taken into consideration. A basic data science course will teach you everything about data collection methods.

Framing a predictive maintenance model

The next step is to decide the best modeling strategy for the collected data and how it can lead to the desired output. While there are always multiple modeling strategies to choose from, a predictive maintenance framing strategy should keep a couple of things in mind:

Desired output for the model

Quantity of data collected

Measurements required to predict is a system will succeed or fail

Advance time to predict before a system fails

Setting performance targets for the model such as accuracy, precision and more

Evaluating predictions in predictive maintenance model

A predictive maintenance model predicts whether a system will succeed or fail, what are the conditions under which it might fail and how to ensure that it runs smoothly amongst others. After a predictive model is built, it gets highly essential to analyze how accurate the predictions have been, under what circumstances has the model been able to predict a certain failure or success conditions, and what can be done to combat the same.

Usage of data science in predictive maintenance

Using data science in building predictive maintenance models goes a long way and has its own set of advantages. Here is a lowdown of the ways in which data science has proved to be beneficial for the same:

Minimizing the cost of maintenance

Data science helps understand when to repair a system or machine and prevents unnecessary expenditures by predicting how frequently maintenance should be done.

Root cause analysis

Data science digs deeper into the causes of high failures and understands why systems malfunction occasionally. It also helps suppliers deal with the potential supply of materials accordingly.

Reduce unnecessary downtime

Predictive maintenance is required to predict if an ad when systems might malfunction. A prior data science analysis only helps in lessening the risk of unforeseen disasters.

Efficient planning for smooth operations

Data science ensures that there is no time wasted in fixing systems that are not vital or replacing equipment that has no usage. This way it plans labor efficiently and also ensures that the operations of the business run smooth.

A course in predictive maintenance and building models is an interesting choice for professionals enthusiastic about pursuing a data science career.

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.

The Common Data Science Interview Questions To Remember..!!

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Data science interviews are often considered to be difficult and it might be difficult for you to anticipate what questions you will be asked. The interviewer can ask technical questions or throw you off guard with questions you hadn’t prepared for. To pursue a full-fledged data science career, it is important for you to be up to date on an array of questions that might be asked during the interview, ranging from programming skills to statistical knowledge, or even field expertise and plain communication skills.
Here is a segmentation of the various categories along with the list down of the possible questions you can expect in each category as an interviewee during a data science interview.
Statistics
As an interviewee, it is essential for you to be prepared on statistical questions since statistics is considered to be the backbone of data science.

  • What are the various sampling methods that you know of?
  • Explain the importance of the Central Limit Theorem.
  • Explain the term linear regression.
  • How is the term P-value different from R-Squared value?
  • What are the various assumptions you need to come up with for linear regression?
  • Define the term- statistical interaction.
  • Explain the Binomial Probability Formula.
  • If you were to work on a non-Gaussian distribution, what is the dataset you would use?
  • How does selection bias work?

Programming
Interviewers may ask completely general questions on programming to test your overall skills or may try and test your knowledge on big data, SQL, Python or R. Listed are a couple of questions that may turn out to be relevant for you to crack that interview like a pro.

  • List the pros and cons of working with statistical software.
  • How do you create an original algorithm?
  • If you were to contribute to an open-source project, how would you do it?
  • Name your favorite programming languages and explain why do you feel comfortable working in them.
  • What is the process of cleaning a dataset?
  • What is the method you would take for sorting a large list of numbers?
  • How does MapReduce work?
  • What is Hadoop Framework?
  • If you are given a big dataset, explain how would you deal with missing values, outliners and transformations.
  • List the various data types in Python.
  • How would you use a file to store R objects?
  • If you were to conduct an analysis, would you use Hadoop or R, and why?
  •  Explain the process using R to splitting a continuous variable into various groups in R.
  • What is the function of the UNION?
  • Explain the most important difference between SQL, SQL Server, and MSQL?
  • If you are programming in SQL, how would you use the group functions?

Modeling
While a Data Science Course will teach you the basics of modeling, at an interview you may be asked technical questions like building a model, your experiences, success stories and more.

  • What is a 5-dimensional data representation?
  • Describe the various techniques of data visualisation.
  • Have you designed a model on your own? If yes, explain how.
  • What is a logic regression model?
  • What is the process of validating a model?
  •  Explain the difference between root cause analysis and hash table collisions.
  • What is the importance of model accuracy and model performance while working on a machine learning model.
  • Define the term- exact test.
  • What would you rather have; more false negatives than false positives and vice versa?
  • Would you prefer to invest more time in designing a 100% accurate model, or design a 90% accurate model in less time?
  • Under what circumstances would a liner model fail?
  •  What is a decision tree and why is it important?

Problem Solving
Most interviewers will try and test your problem-solving ability during a data science interview. You may be asked trick questions or be subjected to topics that evoke your critical thinking abilities. Listed are some questions that will help you prepare for an upcoming interview.

  • How would you expedite the delivery of a hundred thousand emails? How would you track the response for the same?
  • How would you detect plagiarism issues?
  • If you had to identify spam social media accounts, how would you do so?
  • Can you control responses, positive or negative to a social media review?
  • Explain how would you perform the function of clustering and what are the challenges you might face while doing so.
  • What is the method to achieve cleaner databases and analyze data better?
    For more such articles, feel free to click on the below link:
    How To Build A Career in Data Science?

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.

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.
For more details, you can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi, Gurgaon, and Ahmedabad.

Why Do Data Scientists Need To Learn Java?

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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.

Evolution Of Data Science In India

Posted on by Imarticus Learning
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Evolution of data science in India
In today’s bustling world an unprecedented amount of data is being generated by businesses and firms. The pinnacle of data proliferation pressurized on the businesses to employ specialized professionals apt for the tasks. The amount of digital data that exists today is at a staggering rate and is bound to increase by a large extent in the future. Data science is considered a young profession or a relatively new concept which became popular after a decade in 2010.
Initially, statistics and statistical tools were implanted in data science which later clubbed with the growing technology and newer and sophisticated concepts like Artificial Intelligence, Internet of Things and Machine learning for better results. Data science is basically the study of data which is in structured or unstructured form. It is a process of collecting, storing, analyzing and processing the data using various statistical tools and machine learning for producing meaning insights.
Revolutionary data science in India
Before the arrival of data analytics, the blue-chip companies and consulting businesses ruled the analytics market. By removing the barriers in the industry cloud technology has made it possible for start-ups to emerge in this field fearlessly. Big businesses to start-ups all rely largely on data science for solving their simple to complex problems.
Hence, it is appropriate to say data is the new oil for businesses. Many developed countries like USA, UK, Singapore, and Australia are a favorite destination for Indian analytic experts next to software professionals. A study by Analytics India Magazine reveals that the US pays $11b to India for data analytics annually.
The evolution of data science in India is tremendous and hence India is amidst top 10 countries for analytics in the world with over 600 analytic firms out of which half of them are start-ups and the number is expected to increase with the increased efficiency in its solutions and products.
Banking and finance are the prominent industries that use data analytics and revenue generated from these industries is more than 30% in India. Apart from finance and banking, marketing, pharma, advertising, and healthcare are other sectors which rely on data analytics by a large extent. Thus, data analytics, data science, and big data industry are set to double in India by 2020.
Digitalization banking data science to a great extent
In the present business, data is considered the most valuable thing even more vital than money, since data analysts use them for figuring more opportunities. Data science is a multi-disciplinary subject which effectively produces smarter business moves convincing the customers generating more revenue. Digital business is able to build a larger customer base by utilizing smarter data analytic techniques to identify the tastes and preferences of individual customers in India and brings about a satisfying solution.
Once online retailing has established a positive outcome from using big data to understand its customers, the same big data technique is applied in different sectors like engineering, medical, academic research, and social science to name a few. The availability of the huge amount of data has led to its widespread application for arriving at effective and efficient solutions or products.
Cloud and its impact on millennials favorite data science 
Cloud computing has created a revolution in data sciences which has made data centers more accessible at moderate prices thus creating a boom in the Indian market. The trending fact is that earlier only Bangalore and Delhi were major contributors in this sector in India but Pune, Chennai, and Hyderabad are not far from joining the racing market.
Data science is a vast sector with the widespread application of its techniques business identify opportunities, frame better goals and create productive solutions. This has created more demand for professionals who can analyze and scrutinize the data by understanding better insights within the data.
To flourish in this exciting field full of challenges one needs Data Science Training for gaining a comprehensive knowledge of the subject. The millennials like data science as it does not pose and restrict their functionality. Data science and millennials are interconnected as highly responsive and engaging marketing based on their preferences using data analytics drive today’s marketing with the use of advanced cloud technology.
To sum up
As you can see the evolution of data science over the last ten years has been tremendous and will continue to do so with the splurging demand for data analytics across many sectors. Data science is a promising sector which got prominent attention with the advancement in technology. As consumers are embracing digitalization, more scope of data science in India is inevitable.