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Tag: Data Science Training

How Can You Choose The Right Programming Language For Data Science?

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How Can You Choose The Right Programming Language For Data Science?

Data Science has made its mark among the most popular programming languages of this era. In a rapidly growing tech-heavy industry, the demand for data science professionals is only increasing. If you are looking for a data science career, programming expertise is a necessity apart from analytical and mathematical skills.

However, before you zero in on your choices for the programming language required for a job, you need to know about the various types of the programming language you can pursue a course in to become an expert in data science.

Python

A highly popular and dynamic programming language, Python is extremely prevalent among data science enthusiasts. It is also among the easiest languages to master, and its capacity to sync with Fortran or C algorithms only increases its demand among your professionals. Additionally, as data science, machine learning, predictive analysis, and artificial intelligence make ints foray into regular jobs, demand for professionals skilled in Pyhton is constantly increasing. If your interests lie in data mining, scientific computing or w development, Python is what you need to learn.

R

If you have completed a basic course in data science and now want to excel in a particular language that helps you with statistically oriented jobs, R is your best option. This might be slightly difficult to master as compared to Python, however, if the statistical analysis is your calling, R is your key. However, R is less of a general-purpose language used for programming, hence, you should pursue R only if you are interested in statistics and data analysis. The additional advantage though is, R can help you deal with linear algebra, even complex ones.

SQL

A mandate for any skilled data scientist, SQL or more commonly known as Structured Query Language, retrieves data from organized data sources and is the most used database language. SQL manipulates, updates and researches into existing databases. Any expert data science would require to pull out and analyze data from the database; this is exactly where your knowledge of SQL will fit the bill. Also, owing to its simple syntax, SQL is among the most readable languages in data science.

Javacou

If your interest lies more in learning a general-purpose language, Java is your answer. Supported by Oracle, Java is a unique computing system that makes migrating between platforms easier. Also, Java is widely used among organizations to create and launch mobile or web applications. If you are a skilled software engineer, developing engineer or software architect, Java will help you make the most of learning programming stack.

Scala

Next on the list is Scala, highly popular as a programming language with an immense user database. If you are interested or have to eventually wok with data sets that are really heavy and high on volume, Scala will help you nail the functional bit along with the strong static type bit as well. Scala is an open-source and general programming language, that can be operated within Java or JVM itself. Scala is your best option when it comes to working with processor clusters and Java codes.

SAS

Very similar to R in terms of usage, SAS is also used for statistical analysis, though unlike R it is not an open-source programming language. Noted to be among the oldest language used for statistics, SAS is highly reliable and often finds its use in predictive modeling, business intelligence, and complex analytics. Organizations keen on using a secure and stable platform for their analytical needs mostly use SAS since it offers a variety of packages that help in statistical analysis and machine learning.

Conclusion

While learning any of the above-mentioned programming languages will help you make the most of your data science career, if you are more enthusiastic and want to climb the career ladder faster, it is always advisable to go for more than one language. This not only gives you flexibility while changing jobs but also makes you a skilled professional.

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.

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.
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.

Evolution Of Data Science In India

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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.

What Are The Best Online Courses in Data Science Using Python?

Posted on by Imarticus Learning

 

Truth be told, today it is all about data and using it to further business growth and productivity using cutting-edge technology to sift through huge volumes of data. The data and its analysis provide gainful insights that are used in strategic decision making, various business-related predictions, and for gaining foresight into market conditions.

Online Vs Classroom courses:

Online courses are trending and are the latest in a series of measures to equip yourself with new skill sets. You may be either looking to make a career or change professions. Such courses fill the gap in addressing the chronic shortage of trained personnel in the data science field. While the online courses do not make experts of you, they serve the purpose of giving you an overview of the subjects involved and making a generalist of you. You may also find a few free online data science certificate courses.

In contrast to the online data science certificate, the paid classroom certification courses help acquire hands-on practical-learning applications,  an improved skill set, an effective framework for learning and mentorship,  and boosts your confidence. Many also help you acquire certifications that come in handy as measurable proof of your being able to practically apply your skill sets to work issues and industry-relevant applications. However, in terms of being useful to get your dream career and job, remember that many aspects of learning about data sciences and programming languages are best learned through the hands-on approach.

Why Python?

Python is a free open-source general-purpose programming language that is very useful in data sciences. It allows you to create CSV files that help read data in spreadsheets. It also permits other complicated outputs using computational ML clusters. It has a wide range of available libraries like Pandas for tasks like data import from Excel sheets, processing time-series analysis and everything in between. Its ML libraries like PyBrain and Scilkit-Learn provide ready-made components and modules for data processing and neural networks. It is a bit slow but makes up for its excellent compatibility with other languages, simple syntax, and applicability to varied verticals.

Requisite Educational Qualifications:

Most online data science courses are introductory and fundamental in nature and do not require any formal qualification or degree. Data analysts can definitely use the resources online to learn data analytics when they have a good understanding of subjects like Computer Science, Mathematics, Statistics, Economics, Engineering, etc. Allied subjects like Machine Learning, Neural Networks, Deep Learning, and such courses are also available in the mode of online courses.

Online courses do improve your foundation in theory. Many career aspirants pursue such online data science certificate courses at reputed training institutes to equip themselves on how to apply their learning to different situations and verticals. Classroom learning, however, becomes essential to pick up crucial skills like comprehensive data capture, organization of databases, cleaning of data, applying insights to business decisions and strategy and the effective presentation of the insights gained. Proficiency in Microsoft Excel techniques, having good mathematical abilities and the knowledge of statistics are huge advantages.

  • The criteria for selecting these courses are that the online data science certificate
  • Cover all relevant data science process topics.
  • They use free open-source libraries and tools.
  • Basic machine learning algorithms are covered.
  • It covers basic applications and the theory behind them.
  • Projects, assignments and hands-on supervised practical sessions are provided.
  • Lead instructors are certified, engaging and presentable.
  • Courses are rated at least 4.5 on a scale of 5.
  • Frequency of courses can be on-request or monthly.

Conclusion:

Learning reinforcement and hands-on practice scores! With so many resources and a learning data science in Python for free and on one’s own is never easy. It emerges that the paid-courses are better than the online courses in Data Science for their widely accepted certification, mentorship by certified trainers, personalized personality-development modules, a skill-oriented approach with tons of practice and assured placements.

The Imarticus Learning courses deliver skilled well-rounded personnel in a variety of latest technology courses which are based on industry demand. If you want to be job-ready with data science certification from day one, then don’t wait.

For more details in brief and further career counseling, you can also search for – Imarticus Learning and can drop your query by filling up a simple form from the site or 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. Hurry and enroll.