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Learn big data and analytics from top industry players: Birla Institute of Management Technology

Posted on by Imarticus Learning

Dealing with big data has become an inseparable part of this digital world. It is very common for companies to deal with a large volume of data daily nowadays. With big data as the latest technology, it is very easy for companies to analyse and process large amounts of data. It also proves to be more effective than the previous less effective methods. Besides, big data offers more conventional business intelligence tools.

Read along to know how you can learn big data and analytics concepts with industry experts so that you are prepared when dealing with big data. Learning big data concepts will also give you an upper hand in the industry when compared to your fellow professionals.

What is Big Data Analytics?

Big data analytics allows to process massive amounts of data and analyse the same to find information that might assist businesses in making wise decisions about their operations, such as market trends, hidden patterns, customer preferences and correlations.

With the help of big data analytics, various company professionals can analyse data sets and draw conclusions and insights that turn out to be meaningful and prompt. Notably, business intelligence (BI) queries address all the questions and inquiries that come their way concerning the operations and performance of a particular company.

Importance of Big Data Analytics

Companies can gather existing data and use big data analytics to grab new chances. You can become a successful big data analyst by learning big data from executives with an MBA course. Such a learning experience will help you to understand the practical aspect of the work as well.

The most important benefit that data analytics provides to businesses is when they incorporate big data with highly developed analytics. The importance of the combination can be explained as follows:

Reducing cost

Considering the matter of cost reduction for keeping a vast amount of data in a collective place, big data analytics is the key to it. Notably, it integrates analytic functions that are based on cloud, for instance, a data lake. Also, big data analytics acts as a helping hand for companies in finding more effective ways of operations. 

Making quick and prompt decisions

Companies can quickly go through the evaluation and make speedy and prompt educated decisions. The speed and capacity provided by automated and prompt analytics to inspect new sources of data are also important as they can be very helpful for pouring specifics from IoT.

Developing new products and services

Organisations may fulfil the needs and wants of customers when they want. It is easily possible with the help of data analytics that assists in determining their demands and level of content. Various organisations get the chance of creating new products and services and rendering those to customers with the help of big data analytics. 

What the Big Data Analytics Course Will Teach You?

A big data analytics programme will teach you how to effectively use big data in businesses of all sizes. This course will help you to understand the practical importance of the advantages and features that big data analytics has to offer. Let’s examine the key takeaways of this course in detail which are enumerated as follows:

Acquisition and retention of customers

The digital footprints of customers provide all the information about their taste and preferences, wants, purchasing power, patterns, etc. Big data is useful in tracking customer trends so that organisations can customise their goods and services to meet the needs and wants of customers, on an individual basis. This is immensely advantageous for building the name of your brand and increasing the customer base.

Concentrated and specific promotions

Big data analytics emphasises companies to produce tailored products for the target market without spending a single penny on infective marketing campaigns. By tracking POS transactions and internet purchases, businesses can use big data to study purchaser patterns. Focused and target marketing strategies are then created using information derived from big data. With the help of big data analytics, organisations can achieve customer satisfaction and increase the goodwill of the brand as well. 

Identification of potential risk

Efficient risk management is an integral part of any business operations as it tends to operate in high-risk settings. Efficient risk management helps to derive solutions to deal with the problems that come the way. Organisations heavily depend on big data analytics to produce risk management strategies.

Innovate

Innovation is another key feature that big data analytics offers to organisations. With the help of big data, a company can authorise new upshots while updating existing ones. The huge amount of data and its analysis help companies to determine what works for the company and attract the target market. A company can aim at product development when it knows exactly what the needs and wants of its consumers are. It will help it to develop and launch products accordingly. 

Boost Performance

Performance analysis has a direct effect on the position of a business. The information that is provided by big data increases the potential of a company and enhances its operational efficiency.

Big data tools have the potential to increase the effectiveness of a company. It can also enable you to enhance your customer interaction. 

If you are looking forward to becoming a big data analyst, then there are various courses available for you to choose from. Register yourself for the PG diploma in management, covering fintech and data science  by Imarticus and Birla Institute of Management Technology and start your journey towards a successful career.

Which languages should you learn for data analytics?

Posted on by Imarticus Learning

Data science is a fascinating topic to work in since it combines high statistical and mathematical abilities with practical programming experience. There are a variety of programming languages in which a prospective data scientist might specialize.

In this article, we will tell you how by learning machine learning and taking a python course you can obtain a Data analytics Certification. 

big data analytics courseWhile there is no one-size-fits-all solution, there are various factors to consider. Many factors will determine your performance as a data scientist, including:

  • Specificity: When it comes to sophisticated data science, re-inventing the wheel each time can only get you so far. Master the numerous packages and modules available in the language of your choice. The extent to which this is feasible is determined by the domain-specific packages that are initially accessible to you! 
  • Generality: A smart data scientist will be able to program in a variety of languages and will be able to crunch statistics. Much of data science’s day-to-day job is locating and processing raw data, sometimes known as ‘data cleaning.’ No amount of clever machine learning software can assist with this. 
  • Productivity: In the fast-paced world of commercial data science, getting the work done quickly has a lot of appeal. This, however, is what allows technical debt to accumulate, and only rational procedures may help to reduce it.
  • Performance: In some circumstances, especially when working with enormous amounts of mission-critical data, it’s crucial to maximize the performance of your code. Compile-time languages are often substantially quicker than interpreted languages and statically typed languages are far more reliable than dynamically typed languages. The clear trade-off is between efficiency and productivity.

These can be viewed as a pair of axes to some extent (Generality-Specificity, Performance-Productivity). Each of the languages listed below can be found on one of these spectra. 

Let’s look at some of the more popular data science languages with these key ideas in mind. What follows is based on research as well as personal experience from myself, friends, and coworkers – but it is by no means exhaustive! Here they are, roughly in order of popularity:

    • R: R is a sophisticated language that excels in a wide range of statistical and data visualization applications, and it’s open-source, which means it has a vibrant community of contributors. Its current popularity is a reflection of how effective it is at what it accomplishes. 
    • Python: Python is a fantastic language for data research, and not only for beginners. The ETL process is at the heart of most of the data science processes (extraction-transformation-loading). Python’s generality is appropriate for this task. Python is a tremendously interesting language to work with for machine learning, thanks to libraries like Google’s Tensorflow.
    • SQL: SQL is best used as a data processing language rather than as a sophisticated analytical tool. Yet ETL is critical to so much of the data science process, and SQL’s endurance and efficiency demonstrate that it is a valuable language for the current data scientist to grasp. 
    • Java: There are several advantages to studying Java as a primary data science language. Many businesses will value the ability to easily incorporate data science production code into their existing codebase, and Java’s performance and type safety will be significant benefits. However, you won’t have access to the stats-specific packages that other languages provide. That said, it’s worth thinking about, especially if you’re already familiar with R and/or Python.

 

  • Scala: When it comes to working with Big Data using cluster computing, Scala + Spark are wonderful options. Scala’s characteristics will appeal to anybody who has worked with Java or other statically typed languages. However, if your application doesn’t deal with large amounts of data, you’ll likely discover that adopting alternative languages like R or Python will increase your productivity significantly.

 

Conclusion

At Imarticus we commit to giving the best quality education, so if you are interested in getting a data analytics certification, taking a python course, and learning machine learning come and visit us! 

Related Article:

https://imarticus.org/what-are-top-15-data-analyst-interview-questions-and-answers/

How Data Science is Making Personalization of Customers Feasible?

Posted on by Imarticus Learning

How Data Science is Making Personalization of Customers Feasible?

Data science opens the door to an enormous number of possibilities in customer experience management. It plays an increasingly important role in all areas of the customer relationship management lifecycle, but countless companies have yet to make this advanced technology part of their marketing tools.

One of the main reasons is the lack of full visibility of what can help them engage better with customers and the inability to quantify potential improvements. Nowadays, with the amount of information available to both consumers and businesses, the key to success is knowing how to offer personalized offers that appeal to each consumer. 

Data Science for the Hypersonalization of Customers

To better understand how data science can make sales and marketing actions more effective, it helps to think about one of the main responsibilities of these groups: acquiring new customers. To optimize commercial strategies in a highly competitive market, working around qualified leads is the basis for success. In that sense, data science can greatly improve projections and help a company increase sales by effectively identifying those who represent real business opportunities. 

Intelligent data analysis allows the segmentation of leads based on their specific criteria, such as needs, purchasing power, geographic location, and other exclusionary criteria. In this way, it is possible to optimize prospecting efforts, allowing companies to increase their closing rates and, ultimately, business profitability. 

Role of Data Science

Data science extracts value from data through the combination of multiple fields, such as statistics, artificial intelligence, and data analytics. Data science involves the preparation of data for analysis, including steps such as data gathering, scrubbing, presentation, and manipulation. Data scientists can pursuit analytical operations and are able to review results to reveal patterns and enable businesses from different fields to gain informed insights.

To optimize commercial strategies in a highly competitive market, working around qualified leads is the basis for success. In that sense, data science can greatly improve projections and help you increase sales by effectively identifying those who represent real business opportunities. Today, more and more people are opting for a Data Scientist Career, as it is in increasing demand in many industries.

Why Imarticus for data science online course?

Not only is data science being key for market forecasting and finding good investment opportunities but also for smart marketing. As competition in the market increases, it is becoming more and more necessary to shape the business according to the demands of end-users. Data science makes it possible to offer products/services that address the needs of each user. 

Here at Imarticus, we offer an industry-designed curriculum on DSP Data Science Prodegree. In partnership with many industry leaders, we will introduce you to real business projects and case studies, throughout high-quality tech-enabled education. With one of our courses at Imarticus, not only will you learn data science, but also, we will provide you full placement upon completion of the program.

Conclusion

Data science opens a door to an enormous number of possibilities in customer experience management. It gives sales and marketing professionals a new way to make key data-driven decisions on how to deploy resources and engage prospects and leads more effectively, eliminating the reliance on guessing answers and relying on gut instincts in making critical decisions. You can subscribe to a data analytics course in India offered by Imarticus and become a well-profiled professional in this field! 

Top Big Data Analytics Challenges in Health Insurance!

Posted on by Imarticus Learning

Have you ever wondered that by the end of 2025 there will be more than 200 Zettabytes of data available in global cloud storage?

This ever-increasing data is either available in an unstructured or semi-structured form. The health insurance sector is one of the major contributors to this global data.

The rapid digital transformation of the insurance sector is powered by artificial intelligence, machine learning and predictive analysis. Big data in the field of health insurance has started playing a crucial role.

In order to transform the unstructured data into a structured one, organizations need detailed algorithms. Trained professionals from the field of data analytics can build and apply these algorithms in a strategic way to make the best use of the data.

Big Data Analytics Courses in India

There are no two ways that data analytics is transforming the insurance sector at a much faster pace, yet the unique nature of the health insurance market poses many challenges to meeting the requirements. If you are looking to make your career as a data analyst in the health insurance sector, you should first understand some major data-related challenges existing in the health insurance sector.

In order to facilitate flawless services, two major challenges faced by the health insurance sector are Regulatory compliance and data integrity.

Regulatory Compliance

Most of the challenges in any process which is governed by rules and regulations majorly set by the state are the matter of regulatory compliance. Even the slight shift in the set of the state and the federal regulators may result in a major shift in terms of execution and thus always having a close eye on the latest developments has become the need of the hour.

One such regulatory Act in the health insurance sector is the Health Insurance Probability and Accountability Act (HIPAA). Despite the understanding of HIPPA’s privacy policies, very few insurers are aware of its data security and protection.

For example, e-PHI contains the electronic records of personal health information as guided by HIPAA’s security rule book. These guidelines ensure the insurer will maintain the confidentiality of the data they receive through e-PHI.

In order to safeguard crucial and confidential data, insurers need to identify and protect the data from potential threats and need to ensure that the entire workforce during execution follows all the compliance.

Data Integrity

Data integrity is not a very new challenge, many solutions to it exist, but the lower standards in terms of quality can cause major issues.

The main challenges related to data integrity lie in the health reports of patients. To deal with these challenges, special data understanding is required. In addition to this, the nature and scope of the patient-provider relationship lie in precisely capturing the events such as illness, diagnosis, prescription, claims, etc.

The problem lies in identifying the policyholders who are not in active engagement with the insurers. Another related problem lies in identifying the policyholders who stop filing prescription-related claims.

What would a Data Analyst do to overcome these challenges?

Big Data Analytics Course in IndiaIf you are looking for some data analytics courses in India, to build your career as a data analyst in the health insurance sector, you can contribute at every stage, right from data mining to data architecting to statistics.

Data analysts design the required infrastructure that suits the organizational requirement of data integrity and compliance dynamics. Data analysts play a crucial role in designing independent systems which help them analyze the data, engineer the data and eventually get the best out of the data.

To get a clear sense of what data analysts do, we should see data analysts as data architects, data scientists, data engineers, and statisticians at different phases of the project.

If all this information regarding big data in health insurance has piqued your interest, you must research more about the data analytics courses in India which would provide you with the next steps to get that much closer to becoming a full-fledged data analyst yourself.

Join the latest webinar on this for FREE

https://imarticus.org/Learn-Data-Analytics/

How Businesses Are Building Futures With Data Analytics?

Posted on by Imarticus Learning

Data analytics handles the raw data from the resources using technology, algorithms, and mechanics into a simpler and human-friendly version to help data businesses and organizations. It is now an important factor that drives the business as well as the decision-making process in everyday lives.

In one way or the other, business and organizations depend on data analysis to improve their trade. It wouldn’t be wrong to say that business analytics and Artificial Intelligence have both a major role in building the future everywhere.

  • General analysis: A business needs everyday updates to keep up with the market and trends. It is necessary to identify the low and high points to work on these areas to make the necessary changes. Such random analysis can find new opportunities and can predict the capability of the new strategies.

    This is highly important for all types of businesses in any field. Whether it is the retail, healthcare, medical, technology, food industry, online industries, etc. They all rely on big data to help improve their services.

  • Business improvement: The pandemic has brought the world to a standstill for a while. It is now slowly improving its pace but there are several companies and businesses that suffered a huge loss. In order to get their business back on track, they can rely on data analysis. Finding what is trending or what the public is expecting is the key to a restart. Identifying the trend allows planning and strategy to make necessary changes in the projects to make it plausible for the future and improve the business.
  • Automation: Automation is something the general public does not think about too much but most of them are using it every single day. The use of AI and Machine Learning has integrated to come up with ideas such as Alexa, Siri, Google voice, etc.

    Apart from these, voice-controlled automobiles, electric devices, etc are all part of this. These are definitely in the initial stages right now and have better prospects for the future where they can be applied in many more domains and areas. It is even possible to attract more customers and urge them to use the same.

  • Managing data: The biggest huddle in data analysis is managing the raw data. With the accumulation of data growing in every department of life, it is significant to have proper usage of the stored data. Data analytics Certification could be used to find ideal solutions for the problems in each department.

    Data is useful for every business and department and it is up to the data scientists to find the use of these pieces of information. This is exactly why data scientists are the hottest job profiles in the current scenario. They are needed by every business to build their future by creating strategies for success and predicting their prospects. Since people provide their data in one form or the other through various mediums, managing this data holds a key role in the future of businesses and public lives.

Conclusion
Data analytics was started as a part of technology is now an integral part of lives that has an impact on almost all levels and departments of life. Such reliance on data increases the competition between businesses and it can be healthier when they can identify their key to success from the data available.

But ultimately, it all comes down to how each of them identifies and interprets the same pool of data with their insights and implements them along with the same pulse of the public. After all, going with the trend is more reliable than finding something new and making it successful.

How Can You Learn About Healthcare Data Analytics and Get Training and Certification Online?

Posted on by Imarticus Learning

The healthcare field has seen many improvements with the application of data analytics. From record-keeping, medical device calibrations, research on disease management, predictions of epidemic outbreaks, and suggestions of personalized health and treatment measures, data-analytics, ML, AI, and big data all play crucial and ever-increasing roles. Online courses are excellent as they address the pressing personnel shortage for certified data analysts and scientists. They do not make specialists of you. However, they do equip you with a generalist’s overview of the healthcare sector, update and refurbish the required skills, and offer certifications in a short period.
A paid Data Analytics courses, on the other hand, will help you hone your skills by practical learning application, effective mentoring and makes you a job-ready contributor to healthcare data analytics projects. It also serves to boost the first-timer’s confidence. During the interview rounds for your dream career and job, you will, of course, be tested on how you propose to use your skills to tackle problems that will arise and a good grasp of modeling and your industry-relevant measurable certification will go a long way.
Requisite Educational Qualifications:
Being an introductory and fundamental course, there is no necessary qualification specified. Data analysts can learn Data Analytics online and sometimes might need a basic degree with an understanding of subjects like mathematics, computer science, statistics, engineering, economics, etc. Most of these courses improve foundations and strengthen your skills. Hence, many pursue online courses at reputed institutes to give themselves the knowledge of how to apply their learning across various verticals. And truth be told, today it is all about data and no field including the healthcare sector, is free from using the same for furthering growth, efficiency, and technology.
Classroom learning during your Data Analytics Training will be needed to acquire crucial role skills including the comprehensive capture, cleaning, and organization of databases, the applications of data to business strategy, and effective communication of the analysis reports. Familiarity with excel techniques and statistics will be a plus point.
What the course teaches:
Let’s explore what most courses cover or do not cover and are moot requirements for a data analytics job-role.

A. Technical Skills:

Computer programming and CS Fundamentals including

  • Dealing with unstructured non-clinical and clinical data including blog posts, videos, reviews, social media posts, audio clips, medical images and videos that don’t fit into tables and are complex to handle.
  • SQL Coding and Databases score in operations like delete, add, query or extract functions used for transforming structures and in analytical functions when working with relational databases like patient records and insurance claims.
  • The platform of NoSQL/Hadoop is preferred with knowledge of Pig, Hive, cloud tools and so on for situations involving the transfer of data, storage, sampling, summarization, filtration, and exploration of data. Apache Spark and Scala frameworks are similar to Hadoop but much faster in handling very big-data volumes.
  • AI, MLand Neural Network knowledge and techniques are essential if you wish to score in the emerging uses of data-analytics to healthcare.
  • Data Visualization techniques that include formatting, editing, graphs, charts, etc. are easy with tools like ggplot, Matplottlib, and d3.js Tableau to make effective data forecasts, presentations and case studies.

·   Language proficiency in 

  1. R Programming.
  2. Coding in Python is recommended for versatility in its applications. Python can be used for all medical and healthcare processes and comes with a variety of libraries for nearly all verticals, browsers, etc.

B. Non-transferable Skills:
These are essentially not taught and depend on practice –

  • Quantitative and problem-solving aptitude skills
  • Grasp of inferential logic, an innovative approach, and great communicative skills
  • Above average skills in attention to detail, reporting and programming skills
  • Business acumen, team-skills, dedication, flexibility, and continued learning form a confident learner

Conclusion:
In parting, do acquire Data Analytics Training certifications online or in a paid course. Attend boot-camps, hackathons, MOOCs, etc. all of which give you support, exposure and mentorship in ML, ConvNet, and data analytics practical techniques. The demand-supply gap for data analysts ensures great payouts and undying scope over the next decade, according to the 2011 reports from Mckinsey and the survey by Accenture.

Attend and learn data analytics from a reputed institute like Imarticus Learning to emerge job-ready and with certification from day one. They stress on the non-transferable skills and personality development as well. Hurry and be an early bird!

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

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.