Contact Us
×

Tag: big data analytics courses

How is Big Data Analytics Used For Stock Market Trading?

Posted on by Imarticus Learning

How is big data analytics used for stock market trading?

Big Data Analytics is the winning ticket to compete against the giants in the stock market. Data Analytics as a career is highly rewarding monetarily with most industries in the market adopting big data to redefine their strategies. Online stock market trading is certainly one area in the finance domain that uses analytical strategies for competitive advantage. 

Capital market data analysts are important members of a corporate finance team. They rely on a combination of technical skills, analytical skills and transferable skills to compile and communicate data and collaborate with their organizations to implement strategies that build profitability. If you’re interested in a career in financial analysis, there are several subfields to explore, including capital market analysis.

Organizations and corporates are using analytics and data to get insights into the market trends to make decisions that will have a better impact on their business. The organization involved in healthcare, financial services, technology, and marketing are now increasingly using big data for a lot of their key projects.

The financial services industry has adopted big data analytics in a wide manner and it has helped online traders to make great investment decisions that would generate consistent returns. With rapid changes in the stock market, investors have access to a lot of data.

Big data also lets investors use the data with complex mathematical formulas along with algorithmic trading. In the past, decisions were made on the basis of information on market trends and calculated risks. Computers are now used to feed in a large amount of data which plays a significant role in making online trading decisions.

The online trading landscape is making changes and seeing the use of increased use of algorithms and machine learning to compute big data to make decisions and speculation about the stock market.

Big Data influences online trading in 3 primary ways:

  1. Levels the playing field to stabilize online trade

Algorithmic trading is the current trend in the financial world and machine learning helps computers to analyze at a rapid speed. The real-time picture that big data analytics provides gives the potential to improve investment opportunities for individuals and trading firms.

  1. Estimation of outcomes and returns

Access to big data helps to mitigate probable risks in online trading and make precise predictions. Financial analytics helps to tie up principles that affect trends, pricing and price behaviour.

  1. Improves machine learning and delivers accurate predictions

Big data can be used in combination with machine learning and this helps in making a decision based on logic than estimates and guesses.  The data can be reviewed and applications can be developed to update information regularly for making accurate predictions.

In a nutshell, large financial firms to small-time investors can leverage big data to make positive changes to their investment decisions. Information is bought to the fingertips in an accessible format to execute trading decisions.

If you are a trader, you will benefit from a Big Data Analytics course to help you increase your chances of making decisions. It is highly beneficial for those involved in quant trading as it can be used extensively to identify patterns, and trends and predict the outcome of events. Volume, Velocity, and Variety are the pillars of Big Data that aid financial organizations and traders in deriving information for trading decisions.

How Analytics And Data Science is helping OYO To Enhance Customer Experience?

Posted on by Imarticus Learning

How Analytics And Data Science is helping OYO To Enhance Customer Experience?

According to the CEO and Founder of OYO Rooms Ritesh Aggarwal, the use of analytics and data science helps identify not only the right demand but also the right action for each customer to enhance their experience. Its pan-India 223 city presence boasts of over 2 million check-ins and a total worth of 260 million dollars currently. OYO has used data science technology and analytics successfully in the hotel booking and servicing of accommodation renting segment tapping the mobile users who use the internet and advancements in technological apps to get the best deals and prices.
The OYO story:
In May 2013 OYO started with one hotel booking and had grown to over 8500 hotels and 75K rooms spread over well-targeted metros, commercial hubs, small cities, pilgrimage towns and foreign leisure destinations like Nepal, Malaysia, etc. Their analytics and data science efforts helped provide unmatched prices for well-stacked and standard hotel services while setting the bar for in-room customer experience and budget-accommodation availability in India. OYO’s inspirational story is the result of its CEO’s entrepreneurial debut, and his success is truly inspirational.
Offering standardized stay experiences OYO is spread across 223 cities in all We have revolutionized the legacy-driven hospitality space in India by standardizing the in-room experience and delivering predictable, affordable and available budget-room accommodation to millions of travelers in India,” says Ritesh Agarwal, founder, and CEO, OYO Rooms.
Ritesh hails from Orissa and travelled from the young age of 17 to many hundreds of B and Bs, hotels, resorts, guest houses, etc. to make a curated list of them and help discover such locations that were obscure till date. The introduction of price affordability, standardization of services and customer behavior predictability were the contributive factors to overhauling the way and use of booking with OYO and its analytics and data science program. The importance of training and experience in predictive analysis, data analytics, handling of big data of several petabytes, creating smart self-learning algorithms, and using the latest techniques of neural networking of the ML with AI cannot be undermined according to Aggarwal.
OYO and technology:
The services provided with OYO bookings are standardized with customers getting ac rooms, flat-screen TV, 24×7 customer support, WiFi, complimentary breakfast, quick availability searches, and app-based booking. Of course, the comfortable customer experience brought loyalty and increased its app reach and revenues by leaps and bounds. The app saw 5 million downloads in the first few weeks and OYO cashed in on data of room searches, availability, fair pricing, standardized services, etc. through its analytics-supported app.
Additionally, cab bookings, room-service requests for beverages, laundry, food, etc. were linked in through smart neural networking to provide a seamless 5 second 3-tap experience. Thus sales, technology, intelligent data analytics, satisfied, loyal customers and owner engagement driven by the analytical ability of the app helped OYO emerge as the 2018 unicorn amid the disrupted industries and stiff competition from CoHo, NestAway, ZiffyHomes, Homigo, WudStay, and SquarePlums.

The analytics and statistics:
According to an HVS report cited by Ritesh Aggarwal, unbranded hotels numbering 2 million are available as against the 112k branded ones. That is a huge, potentially untapped customer market that OYO plans to utilize in its growth to make OYO services a household name and brand to reckon with. Even the funding of OYO was strategically planned to raise 260 million dollars from Sequoia Capital, SoftBank Group, Lightspeed, and GreenOaks Capital. It hopes to raise its capital to over 500 million dollars with SoftBank’s help putting it in the unicorn league.
Parting notes:
Whether it be a bus booking, a train reservation, a connecting flight, the last-mile cab availability, intra and intercity travel, long or short stay vacations, quick food, and laundry services, or undiscovered destinations, OYO has plans to keep its customers numbers growing by catering to their needs reflected in the smart analytics app and media. Their inclusion of shared vacation stays, resort accommodation, and service apartments like Chennai-based Novascotia Boutique Homes to their hotel bookings was strategic inclusion planned for the internet savvy mobile user and a trend reflected in the search use of customers in its analytics-based strategic market expansion plans.
Data science analytics is best learned in classrooms with plenty of hands-on and industry-relevant experience. Certification, able mentorship of certified trainers and an assured placement program gives such training courses the leading edge in launching your career. If the OYO story inspires you, then do a Big Data Analytics Course at the reputed Imarticus Learning. Perhaps you will also take to utilizing the opportunity provided to get entrepreneurial ideas and mentorship assistance to start a successful venture. All the best!

Related Articles:

Analytics & Data Science Jobs in India 2022 — By AIM & Imarticus Learning

The Rise Of Data Science In India: Jobs, Salary & Career Paths In 2022

 

Is Data Analytics in The Demand?

Posted on by Imarticus Learning

If you are looking to enter the field of data science, then here are a few tools that you must consider learning. They will not only give you the required skills but also will help build your case. If we imagine these tools in the form of a pie chart then the percent occupied by these tools would be as follows;
R Programming- 26%
Python-23%
SAS- 20%
Tableau- 17%
Spark- 14%
For the first time in data science, the open sourced tools have taken away the crown as opposed to the licensed tools. These tools are commonly used by various professionals in data science like analysts and developers. They mainly are a part of the machine learning operations, data visualizations, and big data operations.
Today, data has taken over quite the ubiquitous nature and is being treated as an asset by many top firms in the industry. Many experts believe that these tools are soon going to be the next big thing to change the way data works. Which is why it is important to learn how to work with them and more importantly, find out which tools fit you the best.
Apart from those mentioned above, there is also a great demand for many open sourced tools. These tools are basically those that can be downloaded free of cost. They are Tableau Public, Refine, KNIME, Rapid Miner, Google Fusion Tables, NODEXL, WolframAlpha, Google Operators, FrontlineSolvers, Dataiku and so on. There are also others like SQL, Big Data Hadoop, and Pig which have great demand in the data analytics market.
Many of these tools help you out greatly in the process of data analytics. When it comes to a data analyst, there are a few end goals that have to be achieved. These professionals have to analyze data, extract valuable information from it so as to boost the performance of an organization and so on.
For instance, let’s talk about Tableau Public. This is a very simple tool, extremely easy to use and it democratizes visualization. It forms the base for data visualization in order to communicate similar such insights to the users. With the help of this tool, one can investigate a hypothesis quickly, explore the data as well as confirm whatever your intuitions about the data are.
Open Refine is another tool which was earlier known as Google Refine. It is essentially a data cleaning software, which ensures that the data is good enough to go in for analysis. There are many uses of this tool. These include cleaning of messy data, data transformation, parsing of data from websites, the addition of data to data sets by fetching it from web services.
Thus, there are many tools available in the industry today to choose from if you are interested in the big data analytics courses. In order to learn most of them, you can definitely take up professional training courses like the ones that are offered by Imarticus Learning, which will help you become industry endorsed.

Data Lake And Big Data Analytics

Posted on by Imarticus Learning

 
If you have been in the IT space and data analytics space for some time now, you might have come across the term Data Lake at least once. But since the technology is in its early days, not a lot of people known what it is all about and thus in this article we will discuss all about data lakes, their benefits and how they are helping in data analytics.
What is a Data Lake?
In the most simplest of terms, a data lake is a centralized storage or repository that allows you to store all your structured and unstructured data, be it of any scale. The main significant difference between a data lake and other centralized repository options available in the market is the fact that a data lake will allow you to store your data without the need of any restructuring and also allows you to run various kinds of data analytics right on the repository.
The various data analytics option present in a data lake starts from dashboards and goes all the way up to visualisations and big data processing, and even real-time analytics and machine learning to help the user for making better decisions.
The Need For A Data Lake
As you might have already guessed, the need for access to a data lake is more important in this day and age than ever before, since the number of companies dealing with big data is constantly on the rise. A recent survey, conducted by Aberdeen found that companies which used data lake facilities were able to perform 9 per cent better to those who didn’t; this fact alone can contribute to the need of using a data lake.
The Benefits of a Data Lake
Similar to any other technology in the market, Data Lake too comes with a host of advantages which helps it stand apart from the rest. Some of the most significant ones are as mentioned below.

  1. Capability to store and run analytics, thus deriving results from unlimited data sources
  2. Capability to store all types of data, both structured and unstructured, thus covering everything from social media posts to CRM data
  3. Increased flexibility from other systems in the market
  4. Option to eliminate data silos
  5. Ability to run unlimited queries at any point in time

Data Lake and Data Analytics
As mentioned in the earlier paragraphs, data lakes in today’s world have multitude applications, one of the most significant being the ability to run data analytics on a host of different data types.
Companies which deal with a massive amount of big data, often face with the difficulty of storing different formats at different locations, thus making data analytics a virtually impossible option. But with data lakes, all forms of data, both structured and unstructured can be stored in one place, thus allowing the user to run analytics and visualization from one dashboard and derive results. On top of that, having a single data lake, companies save up on huge amounts of money and make higher profits in the long run.

How Data Sciences Principles Play an Important Role in Search Engines

Posted on by Imarticus Learning

Organisations today have started using data at an unprecedented rate for any and everything. Hence, it is mandatory that any organisation that has adopted data will need to analyse the data. Here is the real job of a search engine which can search and get results back in milliseconds.
The notion where people believe search engine is only used for text search is completely wrong as search engines can find structured content in an enhanced way than relational databases. Users can also check on portions of fields, such as names, addresses at a much quicker pace and enhanced manner. Another advantage of search engines is that they are scalable and can handle tons of data in the most easier and faster manner.
Few of the benefits of using search engine tools for data science tasks which are taught in big data analytics courses include:
Exploring Data in Minutes: Datasets need to be loaded to search engines, and the first cut of analysis are ready within minutes sans codes. This is the blessing of modern search engines that can deal with all content types including XML, PDF, Office Docs to name a few. Although data can be dense or scarce, the ingestion is faster and flexible. Once loaded the search engines through their flexible query language can support querying and the ability to present larger result sets.
Data splits are Easier to Produce: Some firms use search engines as a more flexible way to store data sets to be ingested by deep learning systems. This is because most drivers have built-in support for complex joins across multiple datasets as well as a natural selection of particular rows and columns.
Reduction of Data: Modern search engines come with an array of tools for mapping a plethora of content which includes text, numeric, spatial, categorical, custom into a vector space and consist of a large set of tools for constructing weights, capturing metadata, handling null, imputing values and individually shaping data according to the users will.
However, there is always room to grow there is an instance where modern search engines are not ready for data science and still evolving. These areas include analysing graphs, iterative computation tasks, few deep learning systems and lagging behind search support for images and audio files. There is still room for improvement and data scientists are working towards closing in on this gap.

Big Data for Big Banks – You Should Know

Posted on by Imarticus Learning

The growth of Big Data
Data is not just the new oil, but the new land too. In short, data is perhaps the most important resource to have in this century. With billions of data points and information being collected across the world every second through the internet and other avenues, the data size is increasing manifold. The upcoming technology is focusing on how to organize and sort this huge amount of data to derive insights and read patterns.
This, in effect, is referred to as Big Data Analytics Courses. Every major or minor firm, big or small player, in the consumer retail sector to healthcare and financial series, is using insights generated out of this big data to shape and grow their businesses. The lending business is no exception and can benefit immensely from the use of data. Fin-tech is changing the way the banking industry operates and making banking operations smoother, automated and more cost-effective. From fraud mitigation to payment solutions, Fintech is changing the way we think about banks.     
Data in lending business  
From the origination of the role to its continuation and life cycle management data can drive decision making in lending business. The patterns that can be read out of consumer data can predict the loans requirement, the capability of repayment of loans, the frequency of late payments or defaults and even the need for the consumers to refinance their loans. The fin-tech start-ups have already begun using the data in such a way, and hence the alternative lending businesses have bloomed over the last few years. Many banks are either merging with such alternative business lenders or taking the help of third party service providers to help boost their capabilities and skills to use big data analytics in business.
The areas of thrust
The major areas where lending business can be aided through the use of big data analytics are the portfolio risk assessment, stress tests, default probabilities and predicting the loan patterns of consumers. Credit card business already uses such technology extensively in assessing and evaluating their consumers.
For example, the credit card issuers tracked the repayments data of the users and based on the profession or the region; they may at times predict if the balances are going to be resolved or if they are going to be paid up front. They then design their marketing strategies keeping the results of analytics in mind in those areas or regions or regarding those specific consumers.
In the bygone years, the only way banks used to evaluate the creditability of a prospective borrower was to assess his or her records of past loans and repayment history. However, with new real-time data points, banks can study behavioural patterns and take appropriate decisions. Refinancing loans is another important area where technology and finance have come together to make life easier for consumers and banks alike. 
The algorithms can predict when a borrower may need to refinance his loans and can credit the amount in his account within seconds without all the paperwork and unnecessary delays. Another area that has transformed with the advent of big data and technology is the internal auditing of banks. With a digital record of every transaction or decision-making process, compliance rules and regulations are now easier to adhere to and track. 
Lastly, and perhaps most importantly, customer feedbacks have become important in this industry like never before. The algorithms can sift through loads and loads of data in the form of feedbacks and can implement solutions to enhance customer experiences on a real-time basis. Technology has changed almost everything around us and the lending operations to are no exception to the rule. In the years to come, banking may undergo a drastic transformation with elements that at this time, we may even be unable to imagine.
 

Sports Analytics: How Data Analytics Is Changing The Game Strategy?

Posted on by Imarticus Learning

Big Data Analytics Training Courses

We have witnessed how Big Data and its analysis have reshaped the operation of many businesses. Recognizing the facts of big data analytics courses, the true scope of data analysis, the sports world making use of analytics. As we speak, the world of sports is improving its capabilities using sports analytics.

So, What Is Sports Analytics?
Sports analytics can be roughly translated to the use of data related to sports such as statistics of players, Weather conditions, pitch information, etc. to create predictive models to make informed decisions. The primary objective of sports analysis is to improve team performance. Sports analytics is also used to understand and maintain the fan-base of big teams.

The Sports Analytics was brought to the public eyes in 2011 by a movie called “Money ball” featuring attempts of the Oakland Athletics Baseball team’s 2002 season. The coach, Billy Beane restored the team using empirical data and statistical analyses on players’ performance.

He used trials with sabermetrics to improve his team and ended up finishing at the first place American League West on that season. Today with the advancement in Big Data technology every sports team is crunching data to gain a competitive advantage.

Changing the Strategy
Sports analyzers nowadays use wearable devices to collect data from players. The miCoach is such a wearable device developed by Adidas. This device attached to players’ jersey records data like heart rate, speed, and acceleration of the player. Analyzing this data, the team management is able to select the suiting players for the game. It also enables them to track the condition of players and allow them to rest before they get injured.

Video analytics is also being increasingly used across various sports for collecting data. In the NBA games, a company named SportsVU installed 6 cameras around the arena. Using advanced metrics, they were able to produce information about which move and which shots are best suited for each player. Such analytical results help teams to derive game strategies matching the strength of their players.

Big Data Analytics Training CoursesThe same is used to learn about the players of the opposite team’s players to find their weaknesses. Arsenal is one of the major football clubs to make huge investments in big data analytics courses.

They use a system that tracks 1.4 million data points per game and analyses all the data using an automated algorithm.

The Future of Sports Analytics
Without any doubt, sports analytics will continue to evolve, and the game strategies will heavily rely on the insights from the analysis than instinct. The next breakthrough sports world expecting from analytics is in the area of predicting a player’s mental ability to adjust with the rigors of the professional sports world.

There are already researches about finding the correlation between emotional regards of responsibility and on-field performance.

The current analysis is not capable of measuring an athlete’s desire to be the top performer. Lack of such features brings a slight chance for drafting busts. Looking at the rate at which the sports analytics have grown to today’s state, It is sure that more of these data-driven advancements in sports can be expected in the upcoming years.
 

5 Simple Facts About Big Data Analytics Courses – Explained

Posted on by Imarticus Learning

Data Science, Machine Learning or the Big Data Analytics Courses whatever one might refer it as, the subject matter has witnessed colossal growth over the last two decades due to the increase in collection of data, improvement in data collection techniques and methods, and a substantial enhancement in the power of computing data. Various data analyst jobs are pooling talent from multiple branches of engineering, computer scientist, statisticians and mathematicians and is increasingly demanding an all-around solution for numerous problems faced by the businesses in managing their data.
As a matter of fact, not a single stream of business, engineering, science etc. has remained far from the reach of data analytics and are employing various data analysis tools on an on-going basis within their respective industries. Perhaps it can be one of the best times for students to enroll in the big data analytics courses and be future ready as the future is in data analytics.
But, as data analytic jobs are deemed to be in an upward trend shortly, here are some simple facts one needs to know about data analytics before embarking a big data analytics course or a career in data analytics. 

  1. No Data is Ever Clean

Theoretically, as taught during a  data analytics course,  analytics in the absence of data is just a group of theories and hypothesis, whereas data aids to test these theories and hypothesis towards finding a suitable context. But, when it comes to the real world, data is never clean and is always in a pile of mess. Organisations with established data science centres to say that their data is not clean. One of the major issues organisations face apart from missing data entries, or incorrect entries is combining multiple datasets into a single logical unit.
The various datasets might face many problems which prevent its integration. Most data storage businesses are designed to be well integrated with the front-end software and the user who generates the data. However, many-a-times, data is created independently, and the data scientist arrives at the scene at a later stage and often ends up being merely a “taker” of data which is not a part of the data design.

  1. Data Science is not entirely The user will need to clean some data manually

A vast majority of people do not wholly understand what data analytics is? One of the most common misconceptions about data analytics is that the various data analysis tools thoroughly clean the data. Whereas, in reality, as the data is not always clean, it requires a certain degree of manual processing to make it usable, which requires intense amount of data processing, which can be very labour intensive and time-consuming, and the fact remains that no data analysis tools can completely clean the data at the push of a button.
Each type of data poses its own unique problem, and data analyst jobs involve getting their hands dirty and manually processing data to test models, validate it against domain experts and business sense etc.

  1. Big Data is merely a tool

There is quite a lot of hype around the Big Data, but many people do not realize that it is only a collection of data analysis tools which aids working with a massive volume of data promptly. Even while using Big Data, one requires the utilise best data modelling practices and requires a trained eye of an expert analyst.

  1. Nobody cares how you did something

Executives and decision making are often the consumers of various models of data science and continuously require a useful and a workable model. While a person performing one of many data analyst jobs might be tempted to provide an explanation to how data was derived, in reality, these executives and decision makers care less how the data was acquired, and are more interested in its authenticity and how can it be used to improve any of their business functions.

  1. Presentation is Everything

As most of the consumers of analytic solutions are not mathematicians and are experts in their respective fields, presentation plays a vital role in explaining your findings, in a non-technical manner, which is understandable to the end user. A PowerPoint presentation loaded with infographics can aid a data scientist in conveying the end-user their message in a language and mode of communication with is easy of them to understand.

10 High Value Use Cases for Predictive Analytics in Healthcare

Posted on by Imarticus Learning

Healthcare organisations are having their moment when it comes to Big Data and the potential it offers through its analytical capability. From basic descriptive analytics, organisations in this sector are leaping towards the possibilities and its consequent perks of predictive insights. How is this predictive analysis going to help organisations and patients? What are the top roles a Data Analyst look for?
Let’s break this down into ten easy pointers:

  • Predicting Patient Deterioration

Many cases of infection and sepsis that are being reported among the patients, can be easily predicted via predictive insights that Big Data offers. Organisations can use big data analytics to predict upcoming deteriorations by monitoring the changes in the patient’s vitals. This helps in the recognition and treatment of the problem even before there are visible symptoms.

  • Risk Scoring for Chronic Diseases

Based on lab testing, claims data, patient-generated health data and other relevant determinants of health, a risk score is created for every individual. What this does, is that it leads to early detection of diseases and a significant reduction in treatment costs.

  •  Avoiding Hospital Re-admission Scenarios

Using predictive analysis, one can deduce risk factor(s) indicating the possibility for re-admission of the patient to the hospital within a certain period. This helps the hospitals design a discharge protocol which prevents recurring hospital visits, making it convenient for the patients.

  • Prevention of Suicide

The Electronic Health Records (EHR) provides enough data for predictive algorithms to find the likelihood of a person to commit suicide. Some of the factors influencing this score are substance abuse diagnose, use of psychiatric medications and previous suicide attempts. The early identification helps in providing the mental health care potential risk-patients will need at right time.

  • Forestalling Appointment Skips

Predictive analysis successfully anticipates ‘no-shows’ when it comes to patients and this helps prioritise giving appointments to other patients. The EHR provides enough data to reveal individuals who are most likely to skip their appointments.

  • Predicting Patient Utilization Patterns

Emergency departments of regular clinics have varying staff strength according to the fluctuations in patient flow. In this case, predictive analysis helps to forecast the utilization pattern and the requirements of each department. It improves patient wait-time and utilisation of facilities.

  • The Supply Chain Management

Predictive analysis can be used to make efficient purchasing which in turn has scope for massive cost-reduction. Such data-driven decisions can also help in optimizing the ordering process, negotiate the price and reduce the variations in supplies.

  • Development of New Therapies and Precision Medicine

With the aid of predictive analysis, providers and researchers can reduce the need of recruiting patients for complex clinical trials. The Clinical Decision Support (CDS) systems have started to predict the patient response to treatments by analysing genetic information and results of previous patient cohorts. It enables the clinicians to select treatments with more chances of success.

  • Assuring Data Security

By using analytic tools to monitor the data access and utilization pattern, it is possible to predict the chances of a potential cyber threat. The system detects the presence of intruders by analysing changes in these patterns.

  • Strengthen Patient Engagement and Satisfaction

Insurance companies encourage healthy habits to avoid long-term high-cost diseases. Here, predictive analyses help in anticipating which communication programmes would be the most effective in each patient by analysing past behavioural patterns.
These are possible perks of using tools like predictive analyses in healthcare that optimise processes, increase patient satisfaction, enable better care mechanisms and reduce costs. The role of Big Data is clearly essential as demonstrated and a targeted use can show high-value results!

Who Can Learn Big Data and Hadoop?

Posted on by Imarticus Learning

Who Can Learn Big Data and Hadoop?

Did you know that top tech firms like IBM, Microsoft, and Oracle have all successfully incorporated Hadoop, as their software programming environments last year? While this may be definitely enlightening, these aren’t the only firms vying for professionals with expertise in Hadoop.

Some of the other big names looking for Hadoop professionals are Amazon, eBay, Yahoo, Hortonworks, Facebook, and so on. No wonder professional training institutes like Imarticus Learning, which provides excellent, comprehensive training courses in Hadoop are becoming well sought after lately.

This may be because Hadoop happens to be among the top ten job trends in the current time period.

While anyone who is an intelligent technologist, can very easily pick up the skills for Hadoop, there happen to be certain pre-requisites that a candidate must fulfill. While there happens to be no hard and fast rule about knowing certain tricks very well, but it is kind of mandatory for a candidate to at least know the workings of Java and Linux.

This does not mean an end of the career, for those who aren’t well versed in this programming software. A candidate can very well be a multitasker and learn Big Data and Hadoop, at the same time spending a few hours in learning the ropes of both Java and Linux. While knowing Java is not strictly necessary, but helps you gain a profitable edge over your contemporaries and colleagues.

There happen to a few tools like Hive or Pig, which are built on top of Hadoop and they happen to offer their very own language in order to work with data clusters. For instance, if a candidate wishes to write their own MapReduce code, then they can do so in any programming language like Python, Perl, Ruby or C. The only requisite here is that the language has to support reading from standard input and writing to standard output, both with Hadoop Streaming.

There also happen to be high-level abstractions, which are provided by the Apache frameworks association, like the aforementioned Pig and Hive. These programs can be automatically converted to MapReduce programs in Java.

On the other hand, there are a number of advantages to learning Java. On one hand, while you can reduce the functions in your language, but there are some advanced features that are only available via the Java API. Often a professional would be required to go in deep into the Hadoop code, to find out why a particular application is behaving a certain way or to find out more about a certain module. In both of these scenarios, knowledge of Java comes in really handy.

There are a number of career opportunities in the field of Hadoop, from roles like Architecture, Developers, Testers, Linux/Network/Hardware Administrator, and so on. Some of these roles do require explicit knowledge about Java, while others don’t. To be considered an expert in Hadoop, and be recognized as a good data analytics professional, you must learn the inner workings of Java.

Loved this blog? Read something similar here:
Reasons Why You Should be Studying Hadoop and Python
The Beginner’s Guide To Hadoop
Why Hadoop?