How Imarticus Helps For A Data Science Career in Pandemic Times?

Data-driven strategies have shot up in popularity after the coronavirus pandemic wreaked havoc on business plans. Data science is a key player in sustaining businesses not just now, but in the future when similar turbulent circumstances threaten to bring down shutters on previously stead organisations.

As a result, hundreds of companies across India and overseas are looking to add more data scientists to their repertoire. This comes from a need to drive more data-driven decisions and make businesses more resilient to change.

Here are some specific reasons that make a case for how choosing a data science career can be beneficial in times like these:

  • A need for general expertise

While previously companies favored data science specialists, today they prefer generalists and Jacks of all trades. While specialists come with in-depth knowledge and specific skill sets, they often cannot think beyond their domain. Companies today need someone who has skills to use across the board so that they can both learn on the job and be useful where they’re needed.

  • A need for understanding project flows

Many companies who are delving into data science now are probably unsure of their footing and their way forward.

Data Science CourseA data scientist is critical in companies like these, as they bring expertise to the table and understand the flow of projects much better than anyone else. With a data scientist at the helm, all other players in the process can fall into place. This reduces the pressure on upper management to figure out project flows; they can now leave it to the experts.

  • Higher chances for growth

Data scientist generalists are more likely to grow with the company– something many organisations prefer. Unlike a specialist, who already has a defined skill set, rookie data scientists can be shaped and molded into an ideal employee for the company. In the process, the data scientist becomes an intrinsic part of the organisation, learns business tactics and applications and develops skills through experience rather than through specializations. As a result, they become both experts and creative problem-solvers.

  • Immediate requirements

Businesses are struggling to stay afloat during the aftermath of the pandemic and are realizing their urgent need for data-driven business plans. As a result, many of them have put out feelers and immediate job offers for data scientists. This is in complete contrast to other fields that are seeing scores of job cuts, furloughs and pink slips, and goes to show that data science is only increasing in popularity.

It is worth keeping in mind that, despite recruitment into data science roles, many companies have slashed budgets and can’t afford to pay more experienced scientists at this stage. Rookie data scientists form the perfect compromise–  they’re eager to learn, have the necessary skills and can be accommodated within tighter budgets without reduced salaries.

  • Opportunities for upskilling

As rookie data scientists settle into their roles, many companies consider upskilling them for higher positions or specific technical projects.

Data Science CareerThis is an invaluable opportunity for fresh data scientists as the company takes care of all the costs and only asks for your attention and application in exchange. Adding a data science course to your CV will also help you get a leg up on the competition when you’re ready to switch roles or companies.

The final word

The data science landscape has shifted significantly in response to the coronavirus pandemic. As a result, rookie data scientists who are only just entering the field have a once-in-a-lifetime chance to make their mark and cement their place for when things stabilize.

What is Data Science

What is Data Science

Data science is a field with a plethora of possibilities and is evolving very quickly in our day and age. Hence since it does not have any clear cut boundaries, coming up with an exact definition for it becomes a tough task. In simple terms, data science is the process of collecting information as well as creating actionable insights from unorganised raw data. It involves taking raw data and making sense of it.

Data is something that can not be easily understood by a common individual. It depends on machines to understand and interpret it, process it and then change it into something meaningful.

Data has become completely intertwined with everything we do today and the modern world can not function without it. Various companies, countries and individuals are looking to digitise their information as fast as possible to increase efficiency. Taking up a data science course is highly recommended for people to gain more knowledge and information on the following topics.

How does Data Science work exactly?

When a person chooses to go into the field of data science they would need to meet a large number of requirements in order to be good at their job. This can be done by choosing to go through a data science course. These disciplines include being able to products a complete, thorough and clean output of the raw data that has been provided.

Other requirements include engineering, mathematics, statistical knowledge, advanced computing and creativity. This would allow the individual to search through the data in an efficient manner and organise the messy raw information in front of them. They will then need to convey only the important parts that will assist in driving innovation and efficiency. As mentioned earlier, a data science course will be of an advantage for those interested to enter the field.

Data science is heavily dependent on artificial intelligence and machine learning. AI helps in creating models and using algorithms to predict outcomes. There are five stages to data science. They are:

  • Capture: This involves the acquisition, entering and extraction of data
  • Maintain: This step involves warehousing, cleaning, staging, processing and structuring of data.
  • Process: Here data is mined, classified, modelled and summarised
  • Communicate: The data is reported and visualized. Then various decisions are made regarding the data.
  • Analyze: A qualitative and predictive analysis of the data is done.

A data science course would harbour more information in further detail, thus improving your understanding of this career path.

Where is Data Science Used?

Data science is helping us move forward in our ever-expanding world. It has helped us reach various goals and helped improve the efficiency of work. It is being used in various fields today. Some of these fields have been listed below.

  1. Self-driving cars: Using AI and machine learning, transport today has reached a whole new level. Companies like Tesla, Volkswagon and Ford have begun incorporating complex AI features into their vehicles, thus leading a range of autonomous cars. Using small cameras and tiny sensors, these cars have the ability to send information back and forth in real-time.
  2. Healthcare: Data science is being used in healthcare to a large extent today. This ranges from storing patient information in a compact and efficient manner through a database to making new breakthroughs in the fields of disease study.
  3. Cybersecurity: Data science makes it possible to source through large data sets and thus is perfect for detecting any kind of malware present. This thus makes it ideal for use in cybersecurity.

Data science is hence a very important part of our lives today. For anyone looking to work in such a feeling, it is ideal that they go through a data science course. A data science course would equip the individual with all the necessary information and tools to succeed in this particular field.

Also Read: Resources to Learn Data Science Online

Top Python Projects You Should Consider Learning?

Understanding Python

Python is a high-level programming language that is used by programmers for general purpose. There’s a whole lot you can do after learning python, it can be used to develop web applications, websites, etc. You can also develop desktop GUI applications using python. Python is more advanced than other traditional programming languages and provides more flexibility by allowing you to focus on core functionalities and taking care of other common programming tasks.

One of the major benefits of python as a programming language is that the syntax rules of Python is very transparent and allows you to express models without writing any additional codes. Python also focuses on readability of codes. Building custom applications without writing additional code is also an advantage that Python offers. Being an interpreted programming language Python allows you to run the same code on multiple platforms without recompilation.

Why learn Python?

One of the major advantages of python programming language is that it is relatively simple to learn and has a smooth learning curve. It is a beginner-friendly programming language. The simple syntax used by Python makes it easy to learn programming language as compared to programming languages like Java or C++.

Python has a standard library and the external libraries are also available for users. This can help you to develop concrete applications quickly. You can easily learn python by enrolling in python programming course online. Let’s take a look at some of the top python projects that you can easily learn.

Guess the Number

This project will use the random module in Python. Some of the concepts that will be used while doing these projects are random functions, while loops, if/else statements, variables, integers, etc. Here, the program will begin by generating a random number that is unknown to the user. The user will have to provide and input by guessing this number.

If the user’s input doesn’t match the actual random number generated using the program, the output should be provided to indicate how close or far was the guess from the actual number. A correct guess by the user will correspond to a positive indication by the program.

You will need to apply functions to check three parts of this program; the first is the actual input by the user, secondly the difference between the input and the number generated and lastly to make comparisons between the numbers.

Password Generator

This is a very practical project given the use of password generators for everyday applications. You simply need to write a programme that helps to generate a random password for the user. Inputs required from the user are the length of the password to be generated, the frequency of letter and numbers in the password, etc. A mix of upper and lower case letters and symbols is recommended. The minimum length of the password should be 6 characters long.

Hangman

You are already familiar with “Guess the Number” game, this is more of a “Guess the Word” game. The user has to input letters as guess inputs. A limit is required to be set on the total number of guesses a user can make. It is advisable to give the user 6 attempts at most to guess.  You will need to apply functions to check if the user has made a letter input or not.

You will also need to check if the input shared is there in the hidden word or not. You will have to find a solution to grab a word that will be used for guessing. The main concepts applicable in the Hangman project are variables, Boolean, char, string, length, integer, etc. It is comparatively more complex than the projects mentioned above.

All You Need to Know About Hadoop!

Hadoop is an open-source software framework to store data and running applications on clusters of commodity hardware. It provides massive storage for different data types, enormous processing power, and the ability to handle virtually limitless concurrent tasks or jobs.

Hadoop programming is a vital skill in today’s world for people looking to build a career in Data Science. Hadoop processes large data sets across clusters of computers using simple programming models called MapReduce jobs.

Importance of Hadoop for Organizations?

  • The ability to store & process enormous data quickly makes Hadoop development a much-needed thing for organizations.
  • Hadoop’s distributed computing model processes big data in no time. With more computing nodes, you have better processing power.
  • Hadoop is equipped with fault tolerance and guard against hardware failure. If a node goes down, tasks are automatically redirected to other nodes to ensure that distributed computing doesn’t fail.
  • You can quickly scale your system and handle more data simply by adding nodes.

How is Hadoop Used?

Hadoop development is used in a variety of ways. It can be deployed for batch processing, real-time analysis, and machine learning algorithms. The framework has become the go-to technology to store data when there’s an exponential growth in its volume or velocity. Some common uses of Hadoop include:

Low-cost storage and data archive

Hadoop stores and combines data such as transactional, sensor, social media, machine, scientific, clickstreams, and the modest cost of commodity hardware makes it more likable. The low-cost storage lets you keep data and use it as & when needed!

Secure for analysis & discovery

Since Hadoop was designed to deal with massive data, it is efficient in running analytical algorithms. Big data analytics on Hadoop can help organizations operate efficiently, uncover opportunities and derive next-level competitive advantage. This approach provides opportunities to innovate with minimal investment.

Data lake

Data lakes back up data stored in original form. The objective is to offer a raw view of data-to-data scientists and analysts for discovery and analytics. It helps them ask new questions without constraints. Data lakes are a huge topic for IT and may rely on data federation techniques to create logical data structures.

IoT and Hadoop

Hadoop is commonly used as a data store for millions of transactions. Massive storage and processing allow Hadoop to be used as a sandbox to discover and define patterns monitored for instruction.

Build a Career in Data Science:

Data analytics is a lucrative career and is high in demand and low in supply. It’s a field requiring plenty of expertise to master. But what if you have the ambition but lack the know-how? What do you do?

Data science courses or Data Analytics courses can help you gain better insights into the field. For a person to be technically sound, education, training, and development are the foremost steps.

Data Science Course

Imarticus Learning offers some best data science courses in India, ideal for fresh graduates and professionals. If you plan to advance your Data Science career with guaranteed job interview opportunities, Imarticus Learning is the place to head for today!

The certification programs in data science are designed by industry experts and help students learn practical applications to build robust models and generate valuable insights.

The rigorous exercises, live projects, boot camps, hackathons, and customized capstone projects will prepare students to start a career in Data Analytics at A-list firms and start-ups throughout the program curriculum.

The industry connections, networking opportunities, and data science course with placement are other salient features that draw attention from learners.

For more details on the transformative journey in data science, contact Team Imarticus through the Live Chat Support system and request virtual assistance!

10 Most Popular Analytics Tools In Business

The increasing importance and demand for data analytics have opened up new potential in the market. Each year, new tools and programming languages are being launched aimed at easing up the process of analyzing and visualizing the data.

While many such advanced business intelligence tools come up in paid versions, there are great free and open-source data analytics courses and tools available in the market too. Read on to find out about the 10 best and most popular data analytics tool for business right now.

1. R Programming
R is the most popular programming language cum tool widely used by experts for the purpose of data analytics and visualization. The tool is free and open-source in nature and allows the users to alter its code set for clearing bugs and updating the software on their own.
2. PYTHON
Python is an open-source and free OOP based scripting language popular in the data analytics market since the start of the 90s. Python supports both structured and functional programming methods and is very easy to learn and operate upon. Python is expert in handling text-based data.
3. Tableau Public
Tableau Public is another free software and business intelligence tool which is capable of connecting all kinds of data source be it Excel-based data, Data Warehouse or web-based data. Tableau creates maps, graphs and dashboards with real-time updates presenting on the web. The data can be shared over social networks too.
4. SAS
Sas is a leading analytics tool and programming language specifically developed for the purpose of interacting with and manipulating data by the SAS institute in 1966 with updates presented during the 80s and 90s. Data present in SAS can be accessed, analyzed and managed easily from any sources and is capable of predicting behaviors of customers and prospects along with recommending optimized communication models.
5. Excel
One of the most popular and underrated data analytics and visualization tool in the market, Excel was developed by Microsoft as part of their MS Office and is one of the most widely used tools in the industry. All kinds of data analytics tools still require Excel to work in some kind of way and it is very easy to be learnt and operated.
6. KNIME
KNIME is a leading open source and integrated analytics tool developed by a team of software engineers from the University of Konstanz in January 2004. KNIME allows the users to analyze and model the data through visual programming integrating components of data mining and machine learning via its modular data pipelining concept.
7. Apache Spark
Developed in 2006 by the Berkeley’s AMP Lab of University of California, Apache is a fast large-scale data processing, analysis, and visualization tool capable of executing applications around 100 times faster in memory and 10 times faster on disk. It is popular for data pipelining and machine learning models development allowing it to double up as business intelligence tool.
8. RapidMiner
RapidMiner is another powerful data analytics tool which can double up as business intelligence tool owing to its capability to perform predictive analysis, behavioral analysis, data mining, etc. The tool can incorporate with any other data source types such as Excel, Microsoft SQL, ACCESS, Oracle, Ingres, IBM SPSS, Dbase, etc.
9. Google Analytics
A freemium and widely recommended product for data analytics, Google Analytics is a perfect offering from Google for the Small and Medium-scale enterprises who don’t possess the technical knowledge or the means to gather that knowledge in the present course.
10. Splunk
Splunk is an analytics tool mostly directed to searching and analyzing machine-generated data. The tool pulls up all text-based log data and provides the means to search through it for gathering any relevant or required data.

Data Science Resume: A Complete Guide!

‘Information Scientist’ is at the first spot on the list of the best positions in 2019. It compensates fairly and furthermore offers an exceptionally difficult and remunerating vocation way. In that capacity, the quantity of information science positions have expanded thus have the quantity of candidates.

Regardless of whether you overlook the opposition, you actually need to demonstrate that you have what it takes to be a piece of the organization. Anyway, what is the initial step to sacking the information science position you had always wanted? A heavenly and all around created continue with Certifications.

Indeed, even before you meet the employing administrator, they will have shaped an assessment on you through your resume. Thus, it should be eye catching and lead them to call you for a meeting.

Procure Data Science Certifications – Click Here

The Basics

Most applicants commit the huge error of setting one up resume and sending it off every single possible manager (and generally erroneously cc-ing them all). This is an extremely unfruitful practice; it will not get you the outcomes you need.

Along these lines, if an organization puts out a promotion for an information researcher whose essential ability is Python and you send them a resume clarifying how you are King of R, then, at that point sorry; it won’t work.

Every one of your resumes ought to be custom-made to the position and opportunity you are applying for. A similar resume can be conveyed to a couple of various managers, yet and still, at the end of the day minor changes should be made.

Likewise, remember the accompanying pointers as you start making your information science continue:

Keep the resume one page long. Until and except if you have 15+ pertinent involvement with the field, don’t go more than one page.

Use whitespace liberally.

Use headings and subheadings where suitable. It makes the resume more discernible. Highlighting does as well.

Utilize clear text styles. Most up-and-comers trying to be extravagant, utilize cursive textual styles (like Lobster). Or on the other hand they take it to the next outrageous and utilize relaxed ones (like Caveat). Keep away from these limits. Keep it utilitarian and expert. Use text styles like Arial, Times New Roman, and Proxima Nova.

Try not to exaggerate the tones.

Edit and language structure check your resume consistently. Run it through Grammarly or have a companion take a gander at it. Indeed, even one spelling misstep can destroy your impression.

Get Certifications – Click Here

Segments to remember for your information science continue

Here are the essential areas to be incorporated. You can add and preclude as you wish, however these exemplify the fundamental subtleties that an employing supervisor would have to know. The request can likewise be as you wish.

Resume objective/outline

Work insight

Key/center abilities

Instruction and certificates (assuming any)

Any activities or distributions

Essential data about you

Interests area (or one that shows your character like ‘generally pleased with’)

Become familiar with the IT Skills Here

What to remember for each segment

Resume objective/rundown

This is the principal area that the selection representative’s eyes will fall upon. It is an extremely vital segment since it will assist you with securing your opportunity and propel the spotter to peruse the remainder of your resume where you elucidate upon your accomplishments.

All in all, which one do you compose? Level headed or outline?

Assuming you are a new alumni or a fresher in this field, you compose a resume objective. In the event that you have applicable experience and results in the field, you compose a synopsis.

Here’s the way to compose a resume objective

Ongoing alumni from XYZ University with a Bachelors’ in Computer Science. Applied my insightful and vital abilities in building projects that won me the Global Data Science Challenge in 2018. Anxious to apply my abilities to tackle genuine issues now.

Intriguing. You’d need to peruse further, no?

Here’s the point at which you would not have any desire to peruse further

Ongoing alumni from XYZ University with a Bachelors’ in Computing and IT. Hoping to learn information science innovations and become gifted at them.

Oopsies. That one gets thrown in the receptacle. Notice your abilities, any accomplishments on the off chance that you have them, and how you can help the business rather than the opposite way around. Then, here’s the means by which to compose a resume outline:

Aspiring information science engineer with 5+ long periods of involvement. Spend significant time in utilizing Tableau to make clearness creating information models that distil a lot of information into effectively got perceptions. Victor of the Annual Tableau Challenge.

Here’s the way to not compose it

Information science engineer with broad experience can do factual examination, information cleaning, information perception and furthermore lead groups.

End: stay away from obscure cases. Incorporate hard realities and numbers to make your ability more unmistakable.

Work insight

Notice your work insight in switch sequential request. This will permit you in the first place the most great focuses since your obligations and results would have increased since your vocation started. Then, pick your best undertakings to incorporate. No compelling reason to make reference to each project you’ve chipped away at under the sun.

At long last and above all, focus on sway. Each datum science resume will specify measurable examination, information perception, and information mining. In any case, the effect that you would’ve made would be novel to you. So incorporate hard realities and numbers about how your endeavors and abilities assisted the organization with developing.

Here’s a potential arrangement :

Position and friends name

Worked from ____-____

Area

Key accomplishments

<Here you talk about the effect you have made through your obligations and any critical honors that you may have won>

Here’s a guide to make it more clear:

Information researcher at Goldman Sachs

Jan 2015-October 2019

Bangalore, India

Key accomplishments

Made and carried out models for anticipating advance benefit. Accomplished a 20% improvement rate in the nature of credits supported.

Driven an information representation group of 20 to work on the nature of factual revealing.

Won the Global GS Data Science Competition 3 quarters in succession.

Once more, keep away from unclearness. Backing your cases with statistical data points.

Key/center abilities : If the construction of your resume permits it, partition your abilities into hard abilities and delicate abilities.

Hard abilities in information science incorporate : Python, R, SQL, APIs, Data Cleaning, Data Manipulation, Command Line, and so forth

Delicate abilities incorporate : administration, logical reasoning, key reasoning, innovativeness, collaboration, and so forth

Training and confirmations

The vast majority incorporate this segment before the work experience area. Be that as it may, the last is more pertinent to the recruiting cycle, particularly in the event that you have been in the business for somewhere around 2 years. Thus, place it in like manner.

In the event that you’ve passed college, there’s no compelling reason to incorporate your tutoring. Likewise, follow a converse sequential request wherein you notice your latest degree first. Notice any intriguing ventures or grants you won during your program or any numerical/figuring clubs/social orders you were a piece of.

In the event that you have any certificates, incorporate those also. For instance, when you are going after an information science related position, an affirmation of information science from a rumored foundation would assist you with getting the meeting call.

IIT Bhilai Data Science Program

Fundamental data

This incorporates your name, city, state (and nation on the off chance that you are going after an abroad position). Additionally, incorporate your dynamic email address, phone, connection to your LinkedIn profile, and blog interface in the event that you have one. Since you are going after an information science job, enrollment specialists will need to see which projects you have worked on or are at present chipping away at. In this way, incorporate a GitHub interface also.

Wrapping Up

These will assist with directing you in making your information science continue. It is pretty much as significant as some other part of the recruiting interaction. In this way, make a point to give it your best by following the above tips and rules. We’ll see you on the opposite side of being recruited!

‘Information Scientist’ is at the first spot on the list of the best positions in 2019. It compensates fairly and furthermore offers an exceptionally difficult and remunerating vocation way. In that capacity, the quantity of information science positions have expanded thus have the quantity of candidates.

Regardless of whether you overlook the opposition, you actually need to demonstrate that you have what it takes to be a piece of the organization. Anyway, what is the initial step to sacking the information science position you had always wanted? A heavenly and all around created continue with Certifications.

Indeed, even before you meet the employing administrator, they will have shaped an assessment on you through your resume. Thus, it should be eye catching and lead them to call you for a meeting.

Procure Data Science Certifications – Click Here

The Basics

Most applicants commit the huge error of setting one up resume and sending it off every single possible manager (and generally erroneously cc-ing them all). This is an extremely unfruitful practice; it will not get you the outcomes you need.

Along these lines, if an organization puts out a promotion for an information researcher whose essential ability is Python and you send them a resume clarifying how you are King of R, then, at that point sorry; it won’t work.

Every one of your resumes ought to be custom-made to the position and opportunity you are applying for. A similar resume can be conveyed to a couple of various managers, yet and still, at the end of the day minor changes should be made.

Likewise, remember the accompanying pointers as you start making your information science continue:

Keep the resume one page long. Until and except if you have 15+ pertinent involvement with the field, don’t go more than one page.

Use whitespace liberally.

Use headings and subheadings where suitable. It makes the resume more discernible. Highlighting does as well.

Utilize clear text styles. Most up-and-comers trying to be extravagant, utilize cursive textual styles (like Lobster). Or on the other hand they take it to the next outrageous and utilize relaxed ones (like Caveat). Keep away from these limits. Keep it utilitarian and expert. Use text styles like Arial, Times New Roman, and Proxima Nova.

Try not to exaggerate the tones.

Edit and language structure check your resume consistently. Run it through Grammarly or have a companion take a gander at it. Indeed, even one spelling misstep can destroy your impression.

Get Certifications – Click Here

Segments to remember for your information science continue

Here are the essential areas to be incorporated. You can add and preclude as you wish, however these exemplify the fundamental subtleties that an employing supervisor would have to know. The request can likewise be as you wish.

Resume objective/outline

Work insight

Key/center abilities

Instruction and certificates (assuming any)

Any activities or distributions

Essential data about you

Interests area (or one that shows your character like ‘generally pleased with’)

Become familiar with the IT Skills Here

What to remember for each segment

Resume objective/rundown

This is the principal area that the selection representative’s eyes will fall upon. It is an extremely vital segment since it will assist you with securing your opportunity and propel the spotter to peruse the remainder of your resume where you elucidate upon your accomplishments.

All in all, which one do you compose? Level headed or outline?

Assuming you are a new alumni or a fresher in this field, you compose a resume objective. In the event that you have applicable experience and results in the field, you compose a synopsis.

Here’s the way to compose a resume objective

Ongoing alumni from XYZ University with a Bachelors’ in Computer Science. Applied my insightful and vital abilities in building projects that won me the Global Data Science Challenge in 2018. Anxious to apply my abilities to tackle genuine issues now.

Intriguing. You’d need to peruse further, no?

Here’s the point at which you would not have any desire to peruse further

Ongoing alumni from XYZ University with a Bachelors’ in Computing and IT. Hoping to learn information science innovations and become gifted at them.

Oopsies. That one gets thrown in the receptacle. Notice your abilities, any accomplishments on the off chance that you have them, and how you can help the business rather than the opposite way around. Then, here’s the means by which to compose a resume outline:

Aspiring information science engineer with 5+ long periods of involvement. Spend significant time in utilizing Tableau to make clearness creating information models that distil a lot of information into effectively got perceptions. Victor of the Annual Tableau Challenge.

Here’s the way to not compose it

Information science engineer with broad experience can do factual examination, information cleaning, information perception and furthermore lead groups.

End: stay away from obscure cases. Incorporate hard realities and numbers to make your ability more unmistakable.

Work insight

Notice your work insight in switch sequential request. This will permit you in the first place the most great focuses since your obligations and results would have increased since your vocation started. Then, pick your best undertakings to incorporate. No compelling reason to make reference to each project you’ve chipped away at under the sun.

At long last and above all, focus on sway. Each datum science resume will specify measurable examination, information perception, and information mining. In any case, the effect that you would’ve made would be novel to you. So incorporate hard realities and numbers about how your endeavors and abilities assisted the organization with developing.

Here’s a potential arrangement :

Position and friends name

Worked from ____-____

Area

Key accomplishments

<Here you talk about the effect you have made through your obligations and any critical honors that you may have won>

Here’s a guide to make it more clear:

Information researcher at Goldman Sachs

Jan 2015-October 2019

Bangalore, India

Key accomplishments

Made and carried out models for anticipating advance benefit. Accomplished a 20% improvement rate in the nature of credits supported.

Driven an information representation group of 20 to work on the nature of factual revealing.

Won the Global GS Data Science Competition 3 quarters in succession.

Once more, keep away from unclearness. Backing your cases with statistical data points.

Key/center abilities : If the construction of your resume permits it, partition your abilities into hard abilities and delicate abilities.

Hard abilities in information science incorporate : Python, R, SQL, APIs, Data Cleaning, Data Manipulation, Command Line, and so forth

Delicate abilities incorporate : administration, logical reasoning, key reasoning, innovativeness, collaboration, and so forth

Training and confirmations

The vast majority incorporate this segment before the work experience area. Be that as it may, the last is more pertinent to the recruiting cycle, particularly in the event that you have been in the business for somewhere around 2 years. Thus, place it in like manner.

In the event that you’ve passed college, there’s no compelling reason to incorporate your tutoring. Likewise, follow a converse sequential request wherein you notice your latest degree first. Notice any intriguing ventures or grants you won during your program or any numerical/figuring clubs/social orders you were a piece of.

In the event that you have any certificates, incorporate those also. For instance, when you are going after an information science related position, an affirmation of information science from a rumored foundation would assist you with getting the meeting call.

IIT Bhilai Data Science Program

Fundamental data

This incorporates your name, city, state (and nation on the off chance that you are going after an abroad position). Additionally, incorporate your dynamic email address, phone, connection to your LinkedIn profile, and blog interface in the event that you have one. Since you are going after an information science job, enrollment specialists will need to see which projects you have worked on or are at present chipping away at. In this way, incorporate a GitHub interface also.

Wrapping Up

These will assist with directing you in making your information science continue. It is pretty much as significant as some other part of the recruiting interaction. In this way, make a point to give it your best by following the above tips and rules. We’ll see you on the opposite side of being recruited!

Why Indian Businesses are adopting Fast Oracles Self-driving Database?

The era of automation and cloud is here. In order to drive success and delight customers, Indian companies are looking to Fast Oracle Self driving databases. 

Data drives the world. Be it consumer insights such as shopping behavior, music preference or payment preferences. Today companies are leveraging the power of technology to crunch large amounts of data. Digital transformation has become an integral factor behind a company’s growth. When we take a closer look at how large amounts of data is processed and stored, we know that the possibilities are endless thanks to automation and cloud.

Companies such as Oracle have been leaders in the space for decades are now combining technology pillars such as machine learning, automation and cloud to provide solutions such as ‘self-drive database’. In simple terms, what it means with minimal human intervention, the power of data, businesses are able to achieve high performance at a lower cost.

Take for example, a clothing brand that wants to improve their point of sale interactions to enhance customer experience. How can they do it? By using the ‘Autonomous Cloud Service’ by Oracle, the brand is able to extract and manage relevant data which can support the end customer experience. All this can be done in an Agile business courses enterprise by unlocking deep insights from large amounts of data.

Furthermore, services such as Autonomous Database not only automate the whole process but also take into consideration data privacy, protection against cyber-attacks, data thefts and storage. Organisations that undergo Agile business training can unlock the true potential of autonomous services.

Some of the key attributes which can help companies invest in such services are:

Automation of Management Processes

Oracle Cloud Infrastructure and Autonomous Database provide companies with integrated solutions such as data management, repair, tuning and upgrade to ensure business continuity and growth. 

Reduces Cost of Operations

Due to minimal or zero human intervention, businesses can focus on leveraging key customer insights derived from data thereby reducing operational costs. 

Data Privacy

Data Privacy has become a top priority for most organisations today and implementing a software database storage solution provides them with the opportunity to safeguard data on cloud against cyber-crimes.

Take Strategic Decisions Fast

In an agile world, a key factor that comes into play is when businesses are provided with an opportunity to take quick, decisions. From a strategy perspective, Autonomous Data Warehouses offer this as a part of the solution.
Conclusion
Using a traditional database is time-consuming, and Indian businesses are catching on. Due to the exponential value that self drive databases provide Indian companies are adopting this in order to accelerate growth.

Spark Vs MapReduce

Spark and Hadoop MapReduce are both open-source frameworks from the Apache stable of Software. Since 2013 when Spark was released it has literally overtaken and acquired more than twice the number of Hadoop’s customers. And this lead is growing.

However, big-data frameworks are directly linked to the customer’s need for a particular framework and its uses. Therefore a literal comparison is difficult and we need to discuss what Spark and MapReduce are used for and their differences to evaluate their performance.

The performance differences between Spark and MapReduce:

The main differences between the two are that is that MapReduce processing involves, reading from data and then writing it to the disk, whereas Spark process data within its memory. This feature makes Spark very fast at processing data.

However, MapReduce has a far greater potential for processing data compared to Spark. Spark is faster by a 100-fold increase in speed and its ability to process data within the memory has scored with its customers preferring it over MapReduce.

Where MapReduce is useful:

As pointed out above the potential for data processing is high in MapReduce. It  is useful in applications using:

  • Large data sets linear-processing:

Hadoop-MapReduce enables very large data sets to be processed in a parallel fashion. It uses the simple technique of dividing the data into smaller sets processed on different nodes while gathering the results from these multi-nodes to produce a single set of results. When the resultant data set produced is bigger than the RAM capacity Spark will falter whereas MapReduce performance is better.

  • The solution is not for speedy processing: 

Where processing speed is not critically important Hadoop MapReduce is a viable and economical answer. Ex: If data can be processed at nights.

Where Spark is useful:

  • Rapid processing of data: 

Spark’s processing speeds are within the memory and about 10 fold better in terms of storage data and a 100 fold in terms of RAM data.

  • Repetitive data processing:

Spark’s RDDs allow it to map all operations with the memory. MapReduce will read and write the resultant set to the disk.

  • Instantaneous processing:

Spark enables such processing if instantaneous decision-making is required.

  • Processing of Graphs:

Spark scores in repetitive iterative tasks as in graphs because of its inbuilt API GraphX.

  • Machine learning:

Unlike MapReduce, Spark has an inbuilt ML library. MapReduce needs an ML library to be provided by an outside source to execute the same task. The library has many innovative algorithms that both Spark and MapReduce use while computing.

  • Combining datasets:

Spark is speedier and can combine data sets at high speeds. In comparison, MapReduce is better at combining very big data sets albeit slower than Spark.

Conclusion:

Spark outperforms Hadoop with real-time iterative data processing in memory in

  • Segmentation of customers demonstrating similar patterns of behavior thus providing better customer experiences.
  • Management of risks in decision-making processes.
  • Detection of fraud in real-time is possible due to its ML library of algorithms being trained on data that is historical and inbuilt. 
  • Analysis of industrial big-data analysis in machinery breakdown is a plus feature of Spark.
  • It is compatible with Hive, RDDs and other Hadoop features.  

AI and Machine Learning in Robotics!

Artificial intelligence (AI) has to transform almost every industry we can imagine, and industrial robotics is no different. The powerful combination of robotics and artificial intelligence or machine learning opens the door to completely new automation possibilities.

Artificial intelligence courses and machine learning are currently only used to a limited extent and are increasing the capabilities of industrial robot systems. We have not yet reached the full potential of robotics and machine learning, but the current applications are very promising.

4 Basics of Artificial Intelligence and Machine Learning in Robotics
There are four areas of robotic processes that influence AI and machine learning to make today’s applications more efficient and profitable. The scope of AI in robotics includes:

Vision – AI helps robots see elements they’ve never seen before and see objects in greater detail.
Reaching – Robots also capture objects they’ve never seen before using AI and machine learning to help them determine the best position and orientation to pick up objects.
Motion Control – Machine Learning helps robots interact dynamically and avoid obstacles to maintain productivity.
Data – AI and machine learning help robots understand physical and logistical data models in order to be active and act accordingly.
AI and machine learning are still in their early stages for robotic applications, but they are already having an important impact.
Two types of applications for industrial robots using artificial intelligence and machine learning
Supply chain and logistics applications see some of the first implementations of AI and machine learning in robotics.

In one example, a robotic arm is responsible for handling frozen food crates that are closed cold. Frost causes the shape of objects to change – the robot not only displays different parts from time to time, but also constantly displays parts of different shapes. AI helps robots to recognize and capture these objects even though they are different shapes.

Another great example of machine learning is selecting and storing over 90,000 types of parts. This number of types of parts would be useless without machine learning, but now engineers can regularly send images of new parts to the robot, and the robot can then successfully capture those parts.

AI and machine learning will have a transformative impact on industrial robots. While these technologies are still in their infancy, they will continue to push the boundaries of what is possible with industrial robotic automation for decades to come.

The State of Analytics Firms in India

Big Data and analytics is here to stay and provides tangible value to thousands of organizations around the world. India also seems to play an important role in propagating analytics and data science as disciplines around the world. What we are seeing right now is faster adoption of analytics and consequently, a huge demand for companies that provide these services.

Here is a quick snapshot of the analytics industry in India.

Growth Rate of Analytics Companies in India: 27% in 2016

While that sounds great, it is only the established players who contributed significantly to the India growth story. The percentage increase in the number of analytics companies seems abysmally small compared to 2011-2014, when number of analytics companies more than doubled each year.

Average Employee Strength: 162 employees

On average, Indian Analytics companies have 160 employees on their payroll as of 2016, according to Analytics magazine. While this isn’t a huge number, it is a healthy jump from the average of 115 employees since last year. However, compared to global standards for Analytics firms, India is above average – almost 73% of analytics companies in India have less than 50 employees, compared to 86% globally.

Company Type

  • 37% – of analytics providers in India are essentially full-service outsourcing firms in IT or processes that have separate offerings for analytics or big data.
  • 47% – analytics providers are boutique analytics firms, with majority focus on providing analytics as a service.
  • 47% – analytics providers are boutique analytics firms, with majority focus on providing analytics as a service.
  • 4% – training & education firms and 1% staffing firms in analytics space

 

The Big Players

The 10 largest IT/ BPO service providers with Analytics capabilities (Based on employee strength in analytics are:

  • TCS
  • Accenture
  • Cognizant
  • IBM
  • Genpact
  • Infosys
  • Wipro
  • Capgemini
  • Deloitte
  • HCL

 

The 10 largest Boutique analytics firms in India

  • Mu Sigma
  • Fractal Analytics
  • Manthan
  • Axtria
  • Latentview analytics
  • Absolutdata
  • Blueocean Market Intelligence
  • Analytics Quotient
  • Ugam Solutions
  • Bridgei2i

 

Analytics Hubs in India

Bangalore continues to lead the most number of analytics firms in India, i.e. almost 30%, with Delhi-NCR catching up fast at 26% of all Analytics firms being based there, and Mumbai with 17%.
Analytics firms in Mumbai have the highest employee strength on average (311 employees per company), followed by Bangalore (192) & Chennai (191).

Zenobia Sethna has over 12 years of content and writing experience at leading Indian eLearning companies. She currently works as AVP – Learning Design and Effectiveness at Imarticus Learning, where she spends her free time writing blog articles on Soft Skills, Finance and Analytics.