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Skills required during Interviews for a Data Scientist @ Facebook, Intel, Ebay. Square etc.

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Skills required during Interviews for a Data Scientist @ Facebook, Intel, Ebay. Square etc.

Basic Programming Languages: You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries), and a database querying language like SQL

Statistics: You should be able to explain phrases like null hypothesis, P-value, maximum likelihood estimators and confidence intervals. Statistics is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision-making process and to design experiments.

Machine Learning: You should be able to explain K-nearest neighbors, random forests, and ensemble methods. These techniques typically are implemented in R or Python.  These algorithms show to employers that you have exposure to how data science can be used in more practical manners.

Data Wrangling: You should be able to clean up data. This basically means understanding that “California” and “CA” are the same thing – a negative number cannot exist in a dataset that describes population. It is all about identifying corrupt (or impure) data and and correcting/deleting them.

Data Visualization: Data scientist is useless on his or her own. They need to communicate their findings to Product Managers in order to make sure those data are manifesting into real applications. Thus, familiarity with data visualization tools like ggplot is very important (so you can SHOW data, not just talk about them)

Software Engineering: You should know algorithms and data structures, as they are often necessary in creating efficient algorithms for machine learning. Know the use cases and run time of these data structures: Queues, Arrays, Lists, Stacks, Trees, etc.

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What they look for? @ Mu-Sigma, Fractal Analytics

    • Most of the analytics and data science companies, including third party analytics companies such as Mu-sigma and Fractal hire fresher’s in big numbers (some time in hundreds every year).
    • You see one of the main reasons why they are able to survive in this industry is the “Cost Arbitrage” benefit between the US and other developed countries vs India.
    • Generally speaking, they normally pay significantly lower for India talent in India compared to the same talent in the USA. Furthermore, hiring fresh talent from the campuses is one of the key strategies for them to maintain the low cost structure.
    • If they are visiting your campuses for interview process, you should apply. In case if they are not visiting your campus, drop your resume to them using their corporate email id that you can find on their websites.
    • Better will be to find someone in your network (such as seniors) who are working for these companies and ask them to refer you. This is normally the most effective approach after the campus placements.

Key Skills that look for are-

  • Love for numbers and quantitative stuff
  • Grit to keep on learning
  • Some programming experience (preferred)
  • Structured thinking approach
  • Passion for solving problems
  • Willingness to learn statistical concepts

Technical Skills

  • Math (e.g. linear algebra, calculus and probability)
  • Statistics (e.g. hypothesis testing and summary statistics)
  • Machine learning tools and techniques (e.g. k-nearest neighbors, random forests, ensemble methods, etc.)
  • Software engineering skills (e.g. distributed computing, algorithms and data structures)
  • Data mining
  • Data cleaning and munging
  • Data visualization (e.g. ggplot and d3.js) and reporting techniques
  • Unstructured data techniques
  • Python / R and/or SAS languages
  • SQL databases and database querying languages
  • Python (most common), C/C++ Java, Perl
  • Big data platforms like Hadoop, Hive & Pig

Business Skills

  • Analytic Problem-Solving: Approaching high-level challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
  • Effective Communication: Detailing your techniques and discoveries to technical and non-technical audiences in a language they can understand.
  • Intellectual Curiosity: Exploring new territories and finding creative and unusual ways to solve problems.
  • Industry Knowledge: Understanding the way your chosen industryfunctions and how data are collected, analyzed and utilized.

 

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Curiosity is Vital: How Machine Inquisitiveness Improves the Ability to Perform Smartly

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Online Data Science Certification

What happens when a computer algorithm merges with a form of artificial curiosity – to solve precarious problems?

Meticulous researchers at the University of California, Berkeley framed an “intrinsic curiosity model” to make their learning algorithm function even when there is a lack of strong feedback signal. The pioneering model developed by this team visions the AI software controlling a virtual agent in video games in pursuit of maximising its understanding of its environment and related aspects affecting that environment. Previously, there have been numerous attempts to render AI agents’ curiosity, but this time the trick is simpler and rewarding.

The shortcomings of robust machine learning techniques can be solved with this mighty trick, and it could help us in making machines better at solving obscure real world problems.

Pulkit Agrawal, a PhD student at UC Berkeley, who pulled off the research with colleagues said, “Rewards in the real world are very sparse. Babies do all these random experiments, and you can think of that as a kind of curiosity. They are learning some sort of skills.”

Also read: Data Science – then and now!

Like several potent machine learning techniques rolled out in the past decade, Reinforcement Learning has brought in a phenomenal change in the way machine accomplish their things. It has been an intrinsic part of AlphaGo, a poster child of DeepMind; it helped playing and winning the complex board game GO with incredible skill and wit. As a result, the technique is now implemented to imbue machines with striking skills that might be impossible to code manually.

However, Reinforcement Learning comes with its own limitations. Agrawal pointed that sometimes it demands a huge amount of training in order to grasp a task, and the procedure can become troublesome, especially when the feedback is not immediately available. To simplify, the process doesn’t work for computer games where the advantages of specified behaviours is not just obvious. Hence, we call for curiosity!

Also read: After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

For quite some time now, a lot of research activity is going around on artificial curiosity. Pierre-Yves Oudeyer, a research director at the French Institute for Research in Computer Science and Automation, said, “What is very exciting right now is that these ideas, which were very much viewed as ‘exotic’ by both mainstream AI and neuroscience researchers, are now becoming a major topic in both AI and neuroscience,”. The best thing to watch now is how the UC Berkeley team is going to run it on robots that implement Reinforcement Learning to learn abstract stuffs. In context to above, Agrawal noted robots waste a nifty amount of time in fulfilling erratic gestures, but when properly equipped with innate curiosity, the same robot would quickly explore its environment and establish relationships with nearby objects.

Also read: CRACKING A WHIP ON BLACK MONEY HOARDERS WITH DATA ANALYTICS

In support of the UC Berkeley team, Brenden Lake, a research scientist at New York University who lives by framing computational models of human cognitive capabilities said the work seemed promising. Developing machines to think like humans is an impressive and important step in the machine-building world. He added, “It’s very impressive that by using only curiosity-driven learning, the agents in a game can now learn to navigate through levels.”

To learn more about the boons of artificial intelligence, and what new realms, it’s traversing across, follow us on DexLab Analytics. We are a leading Online Data Science Certification provider, excelling on online certificate course in credit analysis. Visit our site to enroll for high-end data analytics courses!

 

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Drawing a Bigger Picture: FAQs about Data Analytics

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Drawing a Bigger Picture: FAQs about Data Analytics

When the whole world is going crazy about business analytics, you might be sitting in a corner and wondering what does it all mean? With so many explanations, notions run a gamut of options.

It’s TIME to be acquainted with all the imperceptible jargons of data science; let’s get things moving with these elementary FAQs.

What is data analytics?

Data analytics is all about understanding the data and implementing the derived knowledge to direct actions. It is a technical way to transform raw data into meaningful information, which makes integral decision-making easier and effective. To perform data analytics, a handful number of statistical tools and software is used and et voila, you are right on your way to success!

How will analytics help businesses grow?

The rippling effects of data analytics are evident, from the moment you introduce it in your business network. And stop rattling! The effects are largely on the positive side, letting your business unravel opportunities, which it ignored before owing to lack of accurate analytical lens. By parsing latest trends, conventions and relationships within data, analytics help predict the future tendencies of the market.

Moreover, it throws light on these following questions:

  • What is going on and what will happen next?
  • Why is it happening?
  • What strategy would be the best to implement?

Also read: Tigers will be safe in the hands of Big Data Analytics

How do analytics projects look like?

A conventional analytics strategy is segregated into the following 4 steps:

Research – Analysts need to identify and get through the heart of the matter to help business address issues that it is facing now or will encounter in the future.

Plan – What type of data is used? What are the sources from where the data is to be secured? How the data is prepared for implementation? What are the methods used to analyse data? Professional analysts will assess the above-mentioned questions and find relevant solutions.

Execute – This is an important step, where analysts explores and analyses data from different perspectives.

Evaluate – In this stage, analysts evaluate the strategies and execute them.

How predictive modelling is implemented through business domains?

In business analytics, there are chiefly two models, descriptive and predictive. Descriptive models explain what has already happened and what is happening now, while Predictive models decipher what would happen along with stating the underlying reason.

Also read: Data Analytics for the Big Screen

One can now solve issues related to marketing, finance, human resource, operations and any other business operations without a hitch with predictive analytics modelling. By integrating past with present data, this strategy aims to anticipate the future before it arrives.

When should I deploy analytics in business?

An Intrinsic Revelation – Analytics is not a one-time event; it is a continuous process once undertaken. No one can say when will be the right time to introduce data analytics in your business. However, most of the businesses resort to analytics in their not-up-par days, when they face problems and lags behind in devising any possible solution.

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So, now that you understand the data analytics sphere and the significance attached, take up business analytics training in Delhi. From a career perspective, the field of data science is burgeoning. DexLab Analytics is a premier data science training institute, headquartered in Gurgaon. Check out our services and get one for yourself!

 

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Sherlock Holmes Has Always Been a Data Analyst. Here’s Why

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The job of a data analyst or scientist revolves around gathering a bunch of disorganized data, and then using them to build a case through deduction and logic. Finally, following that you will reach a conclusion after analysis.

Sherlock Holmes Has Always Been a Data Analyst. Here's Why

Below quote from Sherlock Holmes is relevant –

“When you have eliminated the impossible whatever remains, no matter how Improbable it is must be the truth.”​

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He always started each case by focusing on the problem.

The problem would sometimes arrive in the form of a letter, sometimes as an item in the newspaper, but most often, it would announce itself by a knock at the door. The client would then present the mystery to Holmes and he would probe the client for salient information. Holmes never relied on guesswork or on assumptions. For Holmes, each new case was unique, and what mattered were reliable and verifiable facts about the case. These gave the investigation an initial focus and direction.

Deduction, Reasoning & Analytics

“It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”

Similarly a data analyst is expected not to assume or formulate theories, which can make the reasoning biased. In his stories, Sherlock Holmes demonstrates his keen powers of observation and deduction from data in front of him. He can decipher how the light enters in Watson’s bathroom based on how his beard is shaved; he attests one person has lived in China from one of his tattoos; he discovers previous financial situation of a man who he had never seen before just looking to the hat the man had just used.

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A data scientist has powerful computational and statistics tools that help him finding patterns amid so much data.

 

In the end, a data analyst’s introduction can be similar to what Sherlock said:

“My name is Sherlock Holmes. It is my business to know what other people do not

know.”

–Team Cosmos

You can learn more about Data analysis by taking up Data analyst certification courses. DexLab Analytics also offers Business analyst training courses.

 

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Shadowing a Data Architect for a Day!

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Shadowing a Data Architect for a Day!

A data architect is a noteworthy role in the present analytics industry. One can naturally evolve from a data analyst or a database designer to a data architect after gathering sufficient experience in the field. The prominence of this role showcases the emergence of the online websites and other internet avenues which require the integration of data from several unrelated data sources.

These data sources can be anything from:

  • External sources, like market feeds (for e.g. Bloomberg) or other News Agencies (like, Reuters)
  • Or they could be internal sources like exiting systems that collect data, for instance HR operations that gather employee data

Here is a depiction of a day in the life of a successful data architect:

Data analyst certification from a reputable analytics-training institute can help to speed up your process of evolution from being a data analyst to becoming a successful data architect!

 

Shadowing a Data Architect for a Day! from Infographics


 

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Knock! Knock! It’s Time to Change Your Bad Data Habits

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Knock! Knock! It’s Time to Change Your Bad Data Habits

Do you follow your instincts instead of data and insights?

Do you prefer storing data in different databases, in separate formats with varying values?

Habits are subject to change. Though it may take some time, but eventually it evolves. Good and bad habits make a person. Good habits don’t demand attention, but bad habits often need to be looked into.

If you suffer from bad data habits, then you must make sure you deal with it. It has to be a thing from your past rather than a dominating present. After all, data is incredibly important for business organizations to proliferate and generate decent revenues.

 

As per Experian’s Data Quality Report, 83% of companies consider their revenue suffers from inaccurate and insufficient customer data. It happens because of time and money wastage on insubstantial resources, which leads to a humungous loss of productivity and profit.

Bad Data Habits: The Ugly Truth

Data is the essence of business. From email delivery to customer feedback to profit generation, the impact of data trickles from strata to strata.

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Sadly, many companies fail to fathom the significance of data and continue storing data on multiple systems, instead of a single location, in various formats without actually knowing ways to handle it. This eventually results into huge data pile-ups, where the entire data silo becomes difficult to manage.

However, if you have the right tools and a zeal to ensure data quality, you can confidently manage your data, eradicate duplications and fix errors before they inflict damage to your fundamentals. Besides, prudent strategies, time-to-time reviews and absolute determination are necessary; read this article to gain more insights about how to work on your bad data habits.

Let awareness do the work

Detailed information about customers is crucial for better assistance and quicker efficiency. So, you should always tell your customer support team to derive more information about their customers in order to serve better.

Understand your data needs

What data is important for your business? Once you know that, you will be able to apprehend your customer’s needs and expectations more effectively. Moreover, be sure that the data is accessible to all those who really needs it, otherwise it won’t be fruitful.

Introduce Standardised Data Quality Policies

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For high quality data, make sure you introduce standard data policies and procedures. Also, ensure that the people working in your organization are acquainted with the ways of recording and storing it.

Initiate Regular Reviews

Data degradation is common. Human beings commit mistakes. Hence, it is important to regularly review and cleanse data in order to avoid future discrepancies.

Integration and Installation of the Right Tools

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Integrate your network to ensure the data is stored on one server, but accessible from multiple locations. This will help you get an entire picture of your company’s business performance over varied mediums. Install any of the improved Data Cleaning Software to make sure your data is free of duplicates and perfectly formatted right from the start.

 

To brush up your analytics skills, get enrolled in a Data analyst course. Visit DexLab Analytics.

 

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CRACKING A WHIP ON BLACK MONEY HOARDERS WITH DATA ANAYTICS

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Tax officials are tightening up their ropes with improved Big Data analytics to crack a whip on hoarders of black money.

 

  • Under the bill for amending Section 115BBE of the Income Tax Act, transactions with unexplained deposits in banks will be taxed.
  • As per this amendment, tax officials can now tax people on such deposits at a rate of 60 percent (cess additional) as opposed to the previously determined 30 percent.
  • This new tax law is applicable from the 1st of April, starting this year!

 

Cracking a Whip on Black Money Hoarders With Data Anaytics

Cracking a Whip on Black Money Hoarders With Data Anaytics

How are the Income Tax officials leveraging Big Data Analytics to curb black money?

Here are the simple signals that showcase a rise of Big data analytics use and a more planned crack down on Black Money hoarding:

 

  1. The IT department is now increasingly becoming tech savvy, it is now making use of analytics tools to assess the personal bank deposits for an improved black money crack down action plan.
  2. The income tax officials are making use of Big Data analytics tools for the first time ever done in the history of the Indian economy, to further maintain a hawk’s eye affixed on the target of bringing down black money.
  3. This is a new venture and earlier such advanced tools were only employed on corporate tax assessments.

Continue reading “CRACKING A WHIP ON BLACK MONEY HOARDERS WITH DATA ANAYTICS”

What Does The Market Look Like for Hadoop in 2018 – 2022?

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What Does The Market Look Like for Hadoop in 2018 – 2022?

It will be a simple understatement to say that Hadoop took the Big Data market up by storm this past years from 2012-2016. This time-period in the history of data witnessed a wave of mergers, acquisitions and high valuation rounds of finances. It will not be a simple exaggeration to state that today Hadoop is the only cost sensible and scalable open-source alternative option against the other commercially available Big Data Management tools and packages.

Recently it has not only emerged as the de-facto for all industry standard business intelligence (BI), and has become an integral part of almost all commercially available Big Data solutions.

Until 2015, it had become quite clear that Hadoop did fail to deliver in terms of revenues. From 2012 to 2015, the growth and development of Hadoop systems have been financed by venture capitalists mostly. It also made some funds through acquisition money and R&D project budgets.

But it is no doubt that Hadoop talent is sparse and also does not come in cheap. Hadoop smarts a steep learning curve that most cannot manage to climb. Yet, still more and more enterprises are finding themselves be attracted towards the gravitational pull of this massive open-source system, of Hadoop. It is mostly due to the functionality that it offers. Several interesting trends have emerged in the Hadoop market within the last 2 years like:

  • The transformation from batch processing to online processing
  • The emergence of MapReduce alternatives like Spark, DataTorrent and Storm
  • Increasing dissatisfaction among the people with the gap between SQL-on-Hadoop and the present provisions
  • Hadoop’s case will further see a spur with the emergence of IoT
  • In-house development and deployment of Hadoop
  • Niche enterprises are focussing on enhancing Hadoop features and its functionality like visualization features, governance, ease of use, and its way to ease up to the market.

While still having a few obvious setbacks, it is of no doubt that, Hadoop is here to stay for the long haul. Moreover, there is rapid growth to be expected in the near future.

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Image Source: aws.amazon.com

As per market, forecasts the Hadoop market is expected to grow at CAGR (compounded annual growth rate) of 58% thereby surpassing USD 16 billion by 2020.

The major players in the Hadoop industry are as follows: Teradata Corporation, Rainstor, Cloudera, Inc. and Hortonworks Inc., Fujitsu Ltd., Hitachi Data Systems, Datameer, Inc., Cisco Systems, Inc., Hewlett-Packard, Zettaset, Inc., IBM, Dell, Inc., Amazon Web Services, Datastax, Inc., MapR Technologies, Inc., etc.

Several opportunities are emerging for Hadoop market with the changing global environment where Big Data is affecting the IT businesses in the following two ways:

  1. The need to accommodate this exponentially increasing amount of data (storage, analysis, processing)
  2. Increasingly cost-prohibitive models for pricing that are being imposed by the established IT vendors

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Image Source: tdwi.org

The forecast for Hadoop market for the years 2017-2022 can be summarised as follows:

  1. Hadoop market segment as per geographical factors: EMEA, America and Asia/Pacific
  2. As per software and hardware services: commercially supported software for Hadoop, Hadoop appliances and hardware, Hadoop services (integration, consulting, middleware, and support), outsourcing and training
  3. By verticals
  4. By tiers of data (quantity of data managed by organizations)
  5. As per application: advanced/predictive analysis, ETL/data integration, Data mining/visualization. Social media and click stream analysis. Data warehouse offloading; IoT (internet of things) and mobile devices. Active archives along with cyber security log analysis.

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Image Source: tdwi.org

This chain link graph shows that each component in an industry is closely linked to data analytics and management and plays an equally important role in generating business opportunities and better revenue streams.

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You Must Know These 7 Data Analytics Job Titles

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You Must Know These 7 Data Analytics Job Titles

These days leveraging data be it big or small has become a powerful tool for all enterprises. IT firms are successfully transitioning to digital businesses and opportunities within the companies themselves are increasing to fulfil the growing demands.

So, if you want to join this megatrend in the job market, read on to find out the most in-demand data analytics job titles for today’s professionals:

Data scientist:

This job title has been getting a lot of attention since the past few years now. So much so, that even Glassdoor named it as the best career choice for optimum work/life balance. Their salaries are also comparatively higher.

But the field is still cloudy in terms of the job functions. So, let us understand what it actually means to be a data scientist.

According to Burch Works data scientists are people who “apply sophisticated quantitative measures and computer skills to both structure and analyze the massive amount of unstructured data sets or stream data continuously with an intention to derive information and prescribe action.

The executive recruiting firm says that the coding skills of these professionals are the main distinguishing factor that separates them from other predictive analytics professionals and allows them to exploit data regardless of its size, source and format.

These data professionals often have a master’s degree or a PhD in quantitative disciplines, such as applied math or statistics. They have expert skills and knowledge in statistical and machine learning methods and know tools like SAS, R etc. they are also proficient in other Big Data software like Hadoop and Spark.

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Advanced analytics professional:

The professionals with this job role perform predictive analysis, prescriptive analysis, simulations, and all other forms of advanced analytics. Their role is however, significantly different from data scientists as they do not work with very large data sets and also not with unstructured data.

Data analyst:

A gamut of responsibilities fall under the job listings of a data analyst. They include ensuring data quality and governance, building different systems that enable businesses to gain user insights, performing actual data analysis and much more. However, the skill sets are similar and typically these professionals fit into the same category as advanced analytics professionals and data scientists, because they all can analyze data. But despite such similarities data analysts may be considered as more junior-level employees who are still in a way generalists and can fit into several different job roles within the organization.

Data engineers:

These are the wizards who work behind the scenes to make the jobs of data analysts and data scientists easier. They are technical professionals who have a deep understanding of Hadoop and other Big Data technologies like MapReduce, Hive, SQL and Pig, NoSQL technologies and other data warehousing systems.

Their primary job role is to construct the plumbing, build the data pipelines that clean, collect and aggregate data, organize it from different sources and then load them in data warehouses and databases.

Note that data engineers do not analyze data, but in other words keep the data flowing for processing so that other professionals can analyze them.

Business Analyst:

Business analysts can perform all the tasks that are almost the same for those who perform data analysis. However, business analysts generally have specialized knowledge of their specific business domain and then they apply that knowledge and analysis specifically for the business operations. For example, they may use their analytical skills to recommend improvement suggestions for the business.

Database Administrator:

These professionals are responsible for all things relevant to the operations, monitoring, and maintenance of the databases, often SQL or other relational database management systems also form their jurisdiction. Their tasks include installation, configuration, schemas definition, user training, and maintaining documents.

The database vendors like IBM, Oracle, Microsoft and others often offer certifications specific to their own proprietary technologies for such pros.

Business Intelligence professional:

BI professionals are responsible for adapting themselves with OLAP tools, reports and other data dashboards for looking at historical trends within data sets. Business Intelligence can have data visualization, and also include popular business intelligence platforms like Qlik, Tableau and Microsoft Power BI.

These were the most in-demand job titles in the data analysis industry, to help turn your career into the right direction take a look at our Big Data courses and have a job that you would thoroughly enjoy.

 

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