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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!

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Future of Big Data Hadoop Developer in India

Posted on by Imarticus Learning

In this era of electronic and digital devices, most people are using Big Data, ML, AI and such without really understanding what goes on to provide those services. Data is at the very center of any application and the sheer volumes of data generated, the variety of sources and formats, the need to manage, clean, prepare and draw inferences for business purposes and making decisions is being used extremely widely. And this spawning of data, means the projects involve Big Data and that technology has to evolve and changes to manage it. This also indirectly implies the need for Hadoop developers. The relationships are symbiotic and spur growth in each other’s needs.

Why Choose Big Data Hadoop As a Career

• Since data is an asset people trained on handling the large amounts of data performing analytics on it and providing the right gainful assets for business decisions are also fast being considered invaluable assets.
• Those employees who do not re-skill to include managing Big Data face the risks of getting laid off. For example, TCS, Infosys, and many other data giants laid off nearly 56,000 people in just one year.
• 77% of the companies and verticals across industries are adapting to use Big Data. Thus many are recruiting data analysts and scientists. Even the non-IT sector!
• The payouts are second to none in the category and a large number of aspirants are taking up formal Hadoop careers, both newbies and those changing careers mid-way.
• Data is growing and will continue to be used even in the smallest of devices and applications creating a demand of personnel to handle Big Data.

The Hadoop Career Choice

Pros:
• Big data applications and demand for trained personnel shows tremendous growth.
• Job scope is unending since data continues to grow exponentially and is used by most devices today.
• Among the best technology for managing Big Data sets Hadoop scores as the most popular suite.
• The salaries and payouts globally are better than for other jobs.
• Most verticals and industries, a whopping 77%, are switching tracks to use Big Data.
• Hadoop is excellent at handling petabytes of Big Data.
Cons:
• Your skills need to be of practical nature and constantly updated to keep pace with evolving technology.
• You need a combination of skills that may require formal training and is hard to assimilate on your own before you land the job.

How to Land that Dream job

Today it would be exceptional if a company does not use Hadoop and data analytics in one form or the other. Among the ones that you can easily recollect are New York Times, Amazon, Facebook, eBay, Google, IBM, LinkedIn, Spotify, Yahoo!, Twitter and many more. Big Data, Data Analytics, and Deep Learning are widely applied to build neural networks in almost all data-intensive industries. However, not all are blessed with being able to learn, update knowledge and be practically adept with the Hadoop platform which requires a comprehensive ML knowledge, AI deep learning, data handling, statistical modeling and visualization techniques among other skills.
One can do separate modules or certificate Big-Data Hadoop training courses with Imarticus Learning who provide such learning as short-term courses, MOOCs, online classrooms, regular classrooms, and even one-on-one courses. Choices are aplenty with materials, tutorials and options for training being readily available thanks to high-speed data and visualization made possible by the internet.
Doing a formal Hadoop training course with certification from a reputed institute like Imarticus Learning helps because: 
• Their certifications are widely recognized and accepted by employers.
• They provide comprehensive learning experiences including the latest best practices, an updated curriculum, and the latest training platforms.
• Employers use the credential to measure your practical skills attained and assess you are job-prepared.
• It adds to your resume and opens the doors to the new career.
• Knowledge in Big Data is best imbibed through hands-on practice in real-world situations and rote knowledge gained of concepts may not be entirely useful.
The best courses for Big data Hadoop and Advanced Analytics are available at the IIMs at Lucknow, Calcutta, and Bangalore at the IITs of Delhi and Bombay. This is an apt course for people with lower experience levels since their curriculum covers a gamut of relevant topics in-depth with sufficient time to enable you to assimilate the concepts.
The Big data training courses run by software training institutes like Imarticus are also excellent programs which cost more but focus on training you, with the latest software and inculcating practical expertise. Face-to-face lab sessions, mandatory project work, use of role-plays, interactive tutoring and access to the best resources are also very advantageous to you when making the switch.
Job Scope and Salary Offered:
Persons with up to 4 years experience can expect salaries in the range of 10-12 lakhs pa at the MNCs according to the Analytics India Magazine. Yes, the demand for jobs in this sector will never die down and is presently facing an acute shortage.
Hadoop Course Learning:
You can use online resources and do it yourself using top10online courses.com. However, formal training has many advantages and is recommended. Join the Hadoop course at a reputed institute like Imarticus Learning.
Hadoop has a vast array of subsystems which are hard to learn for the beginner without formal training. The course helps you assimilate the ecosystem and apply these systems to solving real-world industry-related problems in real-time through assignments, quizzes, practical classes and of course do some small projects to show off your newly acquired skills. The best part is that you have certified trainers leading convenient modes and batches to help you along even if you are already working.
The steps that follow are the Hadoop progressive tutorial in brief.
• Hadoop for desktop installation using the Ambari UI and HortonWorks.
• Choose a cluster to manage with MapReduce and HDFS.
• Use Spark, Pig etc to write simple data analysis programs.
• Work on querying your database with programs like Hive, Sqoop, Presto, MySQL, Cassandra, HBase, MongoDB, and Phoenix.
• Work the ecosystem of Hadoop for designing applications that are industry-relevant.
• Use Hue, Mesos, Oozie, YARN, Zookeeper, and Zeppelin to manage your cluster.
• Practice data streaming with real-time applications in Storm, Kafka, Spark, Flume, and Flink.
• Start building your project portfolio and get on GitHub.
Conclusion:
In parting, India and the bigger cities like Bangalore, Hyderabad, and Mumbai are seeing massive growth in the need for Hadoop developers. You will also benefit from a Hadoop training course in Data Analytics and it is worth it when your certification helps you land the dream career you want. So don’t wait. Take that leap into Hadoop today!

7 Skills That Data Scientists Need To Know Via Big Data Analytics Courses

Posted on by Imarticus Learning

Data analytics is one of the most sought-after careers of today. Being a good data scientist involves developing a lot of skills essential to the job.
Here are a few skills you need to have on your resume if you want to become a good data scientist:

1.  Being capable of handling unstructured data

Unstructured data refers to any data that cannot be made to fit into any database tables. This data can include customer reviews, audio clips, blogs, posts, or even videos. Arranging such data into specific categories can be quite the daunting task. As a data scientist, you must be able to work with a lot of unstructured data. Some software that you need to know how to use for this purpose are NoSQL, Microsoft HDI insight, Polybase, Apache Hadoop, Presto etc.

2.  Good knowledge of Mathematics and Statistics

A good understanding of statistics is essential for anyone looking to become a data scientist. You must be familiar with all kinds of statistical concepts such as distributions and tests. Also, making predictions requires that you are familiar with the basic operation of calculus and linear algebra.

3.  Using data to tell a story

It is always easier for clients to understand data analytics if it is presented in a visual format using graphs, charts etc. Therefore you must have the capability to visualize raw data in a form that the layman can understand.

4.  Programming Skills

As a data scientist, you will be working with a lot of software that will require you to enter the code manually. As such, you must have a good knowledge of programming languages such as R and Python, which are normally used in data analytics. You must be able to write, understand and correct any code no matter the circumstances.

5.  A Competitive Spirit

As a data scientist, you will have to work on your toes more often than not. Therefore, it is essential for you to have a competitive spirit that will help you thrive. Hackerearth and NMIMS are two of the platforms that conduct hackathons, seminars and other competitions where you can gain more knowledge and understand all the latest trends in data analytics.

6.  Working on Projects

You must take up some live projects so that you get some hands-on experience in the field. This is important since most companies are looking for data scientists who are experienced in the field.

7.  Academic Qualifications

Most companies prefer their data scientists to have done their master’s degrees in the fields of computer sciences, mathematics, statistics and physical science. If you’re interested in working with research companies, then it will be advantageous to have a PhD in the same subjects.

How to Up skill Your Career in Big Data Analysis?

Posted on by Imarticus Learning

We are in the midst of a digital revolution; it only means that Big Data analytics is, and will continue to be one of the most important fields, working as a catalyst, to assist businesses to unveil the insights from data and aid business growth. It is predicted that in the coming years, big data analytics will continue to grow by leaps and bounds, and will impact our lives in ways beyond our current understanding. Therefore, initiating a Career in data analytics or big data could be the wisest move that one makes. However, breaking into this field is not as easy as it sounds, as there is a combination of skills and technology available, a person planning a career in data analytics might not know which skill to pick up, and where to start from.

You need to be academically relevant to the field, like a Bachelor’s or a Master’s degree in the relevant filed like, statistics, math, or a PhD will further improve your chances of not only professional success but great salary packages as well. So if you have a chance to acquire an advanced degree please do so. If not, and you are in the mid of your career than picking up a course or certification would be the next best option. Certified Analytics Professional, Data Science Associate, PG Data Analytics by Imarticus Learning, Big Data and Hadoop by Imarticus Learning, and other similar certifications are available to choose from.

A career in data science will need to be of two types 

Technical Skills

You will need to upscale or build on the already existing technical skills to succeed in this ever-evolving field. Working knowledge of Statistics, understanding the concepts of Data Mining, Data Science with R, Python or SAS, Application of Machine Learning, Deep Learning and Artificial Intelligence, Software Programming Languages and Database Management, and lastly Data Visualisation, are a few skills, of these you could choose to build deep knowledge in a few or all skills, depending upon the scope of work interest and business requirement.

Business Skills

A person planning to work in the analytics field, irrespective of their level and designation, should know how to correlate their tasks with the larger picture, hence Business Acumen, Inquisitiveness in Problem Identification, Approach and Solution, Creativity and most importantly Communication are a few non-negotiable skills to be developed, so that in this ever competitive data world you can get a hold on the desired position.
This field of big data and analytics is not static, in the coming years, there will be a technology explosion, demanding change and flexibility from professionals. If your intent is to enter the field or sustain your position in the field, then upgrading your skill-set becomes essential.
No matter how analytically sound you are, and how great your knowledge is in advanced analytics, nothing can teach or replace the ability to think through a situation. The way big data functions now, is changing, and new technologies will replace the ones mentioned above. But if you are naturally inquisitive and motivated to dig deep, with rock-solid determination to find answers or solutions, then you will always be a desired candidate for corporations.

What are The Different Fields in Data Analytics?

Posted on by Imarticus Learning

One of the most popular technology-empowered jobs out there, data analytics consists of various disciplines in the field of data science. There are plenty of different areas in which data analytics is applied, with the banking sector being the foremost. As the world starts adopting data analytics techniques, there are different jobs that are present in the field of data analytics.

Here are four of the main fields in the data analytics sector:
1.Data analyst:
Some companies use the terms “data scientist” and “data analyst” interchangeably. Data analysts generally work with SQL databases and pull data out of the same. The job also entails becoming a master of Tableau and Excel and occasionally analyze results of A/B testing and leading the Google Analytics account. Other roles can also include reporting dashboard data and producing data visualizations.
2.Data Engineer:
Data engineers are generally bought in when companies start getting a lot of traffic and need someone to set up the infrastructure to move forward. There’s also a need for somebody to provide constant analysis and this job can generally be posted under “Data Scientists” or “Data Engineers” as well.
Data engineers require a decent knowledge of machine learning, and heavy statistics as these are one of the main assets companies look for when they’re starting out themselves. Software engineering skills are seen as more of a secondary requirement during the initial phase. Data engineers generally get to own all their work but won’t have much guidance and could reach a point of stagnation.
3.Machine Learning Engineer:
There are many companies where data ends up being their main product. Data analysts or machine learning will be a huge part of their internal processes here. A machine learning engineer who has an education in statistics, physics or mathematics will have a bigger role in these situations. If they’re looking at continuing in an academic path even afterward, then this is a great role to fulfill.
Most companies which look out for machine learning engineers are consumer-facing and have huge data which they offer out to other companies.
4.Data science generalist:
Companies look for data science generalists to join other data scientists internally. Companies that take interview care about data but aren’t necessarily a data company themselves. They will be on the lookout for individuals who can work on a wide variety of hats, including touch production code, analysis, data visualization and more.
Data science generalists are sought after to fulfill any specific niche which a company feels their team lacks. This can include areas such as machine learning or data visualization for example.
Thus, it’s important that you’re always on the lookout for a job that satisfied your skill set the best. There are so many options available for those interested, and with data analytics shaping the world we live in, it will serve you well if you can find your own niche.
Join Imarticus to get the best in big data analytics courses and fast forward your career graph in the field of data science. We offer data analytics at our centers in Thane, Pune, Bangalore, Chennai, Hyderabad, Coimbatore, Delhi.

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.

What is the difference between data science and data analytics?

Posted on by Imarticus Learning

 

One of the biggest jobs in the technological center is working with big data. There are plenty of roles within this sector and two of the most popular ones include data science and analytics. While a lot of companies tend to hire similar candidates for these roles, there is still a difference between the two.

It is important that you understand the two roles before you choose a career path in either. If you’re looking to kickstart a career in the field of big data, then knowing the difference between data science and analytics is a good pointer to keep in mind.

What is data science?

Data science is a broader term for different methods and models used to get information. Under data science are the statistics, scientific methods, and math along with other tools which can be used to manipulate and analyze data. If there is a process or a tool that can be used on data to analyze it and extract information from the same, then it falls under the umbrella of data science.

As a practitioner of data science, you’ll have to connect data points and information to figure out connections which can be more useful for a business. It requires you to explore the unknown and find newer patterns or insights which can then be turned into actionable decisions from a business perspective. Data science attempts to delve into connections and figure out methodologies which work for the betterment of a business.

What is data analytics?

Data analytics is more concentrated and specific than data science. It is focused on achieving a specific goal by sorting through large data sets and looking for ways to support the same. Analytics are more automated as they can help in providing better insights in certain areas. Data analysis also involves analyzing large data sets to find smaller, more useful pieces of information to fulfill an organization’s goals.

Analytics basically sorts data into things that an organization knows or doesn’t know and can be used to measure any event in the present, past or even future. It moves from insights to impact and connects patterns and trends along with the true goals of a company, keeping the business aspect in mind.

Knowing the difference:

Data analysts and scientists perform different roles and companies must know exactly what they’re looking for. Data analytics usage in industries such as travel, gaming or healthcare, where analysts can extract specific data to improve business, data science is used in more broader categories such as digital advertising or internet searches.

Data science also plays a role in developing machine learning and Artificial Intelligence. Companies are looking at systems which allow computers to go through large amounts of data. They then formulate algorithms, developed by analysts which can sift through the same and find connections which can help them reach their objectives and thus, bring in more revenue.

Imarticus provides the best data analytics course to make it easier for anybody looking to enter the world of big data science and kickstart their career. 

For more details regarding this, you can directly visit – Imarticus Learning and can drop your query by filling up a simple form through the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon, and Ahmedabad. 

What is Data Wrangling and Why is it Important?

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Data has changed the digital landscape drastically in the past few decades. From analyzing and providing insights real-time to enhance one’s life, data is integral to everything we do. 

It is impossible today to live in a world where we do not encounter data. Whether it is watching recipes on YouTube to adding friends on social networking sites, data is everywhere. Due to the abundance of data, there is also an abundance of knowledge and insights which we never had before.

However, if the data is outdated or irrelevant, it serves no purpose.  This means that there is a real need today for data wrangling. Data wrangling is the art of providing the right information to business analysts to make the right decision on time. It aids organisations by sorting through data and access them for further processing and analytics.

Apart from this data wrangling also involves removing unnecessary data, organising them in a consumable fashion.
Data wrangling also provides organisations with the right information in a short span of time to access the right information thereby helping make strategic decisions for the business. It also helps business perform all these tasks at a reduced cost and more efficiently with minimal human intervention.

Here are the top reasons why data wrangling should be everyone’s priority

Credibility of data
When large amounts of data are processed for interpretation chances are all of it is not relevant or outdated. Although data wrangling is a tedious process, conducting it will ensure that the data secured is not outdated or irrelevant.  Therefore, data wrangling provides credibility to data analytics courses. It picks the right data required in order to provide the necessary solutions to a problem. 

Build trust amongst stakeholders
When valuable information is extracted and presented to the stakeholders involved it build trust. Data should not only be presented in a simple format, but it also must add value to the circumstances. This means that any data that is extracted must be able to benefit the organisation or individual one way or another. This can be achieved through data wrangling, making it an important activity to carry out in an organisation.

Aid Machine Learning
Machines of today have the ability to create, process and understand data to arrive at plausible solutions thereby aiding a company’s overall growth and success. In order to optimise the vast volumes of data obtained from various sources, data wrangling becomes an important task.

It is not possible for a machine to scale and learn from new information if the data itself is corrupt or unnecessary.  Data which is historic in nature which allows the machine to learn and adapt can only be procured through data wrangling. If the quality of data that is fed into an AI is useless, the results which it will produce will also be irrelevant.

Conclusion
Data wrangling is extremely relevant today due to the large amounts of data that gets proceeded every day.  We will not be able to do thorough analytics if we do not have a strong infrastructure of data storage and hence companies are investing heavily in data wrangling tools.

Importance of Data Analysis and why you should learn it?

Posted on by Imarticus Learning

Inspection,cleansing, transformation and modelling of data in order to achieve information that further suggests conclusions and assists with decision making is what data analysis is all about. It’s a rapidly booming field of study for the youth, and companies are always on the hunt to find people who are masters at this procedure so as to increase their growth.

Analytical and logical tools are used to determine and accurately learn data analysis. These skills need to be learnt and honed over time in order to land yourself a good position in this field.              

Analyzing data is important for any business, old or new. It provides a clear understanding of customer behavior and much more essential business intelligence to promote growth and rectify mistakes if any. The first step in this huge process is defining an objective, without which the purpose of the study is lost.

Posing questions is the next step, after which comes data collection through various online and offline tools and techniques. This is the most crucial part of the process, as you need to define your objectives to learn data analysis as accurately as possible.

Learn data analysis by learning the essential tools and the most basic ones used in this line of work. One of the most widely used programs for data analysis is Excel. The other ones are Python, SQL & R. It is easy to get defocused with so many programming languages available and not knowing which one to learn first.A road map always helps while learning something new. R is a good place to start in terms of programming language. R Studio is an essential program to have to learn data analysis.   

If you want to learn data analysis, do not get intimidated by the courses available. You can look up educational websites and just by investing a few bucks, and you can know all there is to it. The most important part to remember before starting is having a fair idea of which software or program does what. It is always better to practice till you’re perfect, rather than spend time only on reading about it. There are also a lot of offline courses available to keen learners in order to learn data analysis.

If you’re sure about pursuing this field, then investing in a good college, institute, or course can help bring out the best in you. While there are many crash courses for the same, not many degree courses are available to learn data analysis. Interning under data analysts in your city of choice and your company of choice will also contribute largely towards technical and practical knowledge. Companies generally welcome promising interns and are willing to work towards their progress as a professional while seeking a fresh approach to business from them in return.        

The best way to excel in this line of work is choosing a specific skill you want to take forward professionally. It is the best bet for making the most of there sources available to you to learn data analysis.             

We offer data analytics courses at our centers in Mumbai, Thane, Pune, Ahmedabad, Jaipur, Delhi, Gurgaon, Bangalore, Chennai, Hyderabad, Coimbatore.                                     

Top tips on how to apply data analytics in your project

Posted on by Imarticus Learning

Data science and analytics are two extremely useful tools that can give accuracy to your project and help automate repetitive tasks. With the demand and scope of data analytics growing with each passing day, companies are trying to integrate everything and get as much information on it as they can.

Data science techniques and analysis are quite helpful because they can be used to enhance the decision-making capacity of your manager, predict future revenues, understand market segments, and produce better content. In the healthcare sector, this technology can be used to diagnose patients correctly.

But how do you integrate data analytics into your professional projects? For that, a sound knowledge of the same is required. Even if you learn the basics of data analytics, it will give a major boost to your career. The entire world is moving towards digitization, and so data analytics is required to gather, analyse, and make sense of the data in front of you.

In order to become an expert in data analytics, and incorporate it seamlessly into your project, you need to have a data analytics training.There are many data analytics courses that you can take for a better understanding of data science and analysis. Here is a list of some of the best data analytics courses available online.

  • Introduction to Data Science

This data analytics training course requires a basic understanding of R programming language and provides an in-depth insight into the necessary tools and concepts used in the data science industry. They also work with powerful techniques for analyzing data and use real-world examples to help you gain clarity over the concepts.

  • Applied Data Science with Python

It is being offered by the University of Michigan. It aims to introduce learners to the specialized version of data science through Python. It is for learners with an understanding of Python, and want to expand their knowledge by incorporating the essentials of statistical,machine learning, information visualization, text analysis, and social network analysis techniques into their projects.

  • The Python Mega Course: Build 10 Real World Applications

This data analytics training is aimed at people with no background of Python, but are interested in learning basic as well as advanced skills of Python and data analysis. It is for people with no previous or little programming experience.

It does not rely on a lot of theoretical teaching but focuses instead on giving problems to the students that they can solve by doing. This course uses video, quizzes, real-world examples to familiarize learners with Python in the beginning and then enhance their skills later.

  • Social Media Data Analytics

This is one of the best data analytics courses available online that especially caters to social media. It is for people who want to use their data analysis skills to get the best out of social media.This course involves giving assignments and mini-projects, which would require you to use your data analytics skills to leverage your social media presence.