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Tag: Big Data online Training

3 Ways Big Data Can Influence Decision-Making for Organizations!

Posted on by Imarticus Learning

An enterprise or any organization collects a massive amount of data daily while performing its operations. This data can be in the form of customer information while making purchases, vouchers,s, and bills by manufacturers, viewership on the online portals, etc.

For an upward movement in the market, it is significant that this big data does not lie untreated in the systems of the company instead it should be worked upon and put to good use to increase the efficiency of the company.

To screen and filter the big data, data analysts are hired to convert that data into a useful piece of information. It may be likely to occur to you that how big data influences an organization’s functioning. There are some decisions that are largely based on big data.

Influence of Big Data on Decision Making of an Organization

In the following three ways, big data creates an impact on the decision-making and the overall performance of a company.

  1. Promotional enhancement through real-time data

Whenever you shop from a branded store, you start receiving emails about their offers which sometimes claim that some deals are exclusively for you. Do you ever wonder how they send personalized emails to every customer based on their interests and their shopping histories?

Big Data CareerThese all are some promotional activities which the companies do by making use of big data. Big data influences the decision-making of the promotional activities of a company.

By doing this, customers may feel informed through the brand and it is sometimes the biggest and the most important step towards creating customer loyalty and long-term relationships.

  1. Expanding Operations Without Spending Too Much

To initiate a promotional activity or a campaign to attract customers, some companies spend a hefty amount of money which may or may not turn out to be 100 percent successful. However, by making effective use of big data these expenses can be avoided. If you already know which customer tends to buy within a specific price range, personalized promotions through the internet become easy.

Big Data Career

Moreover, wasteful expenditure can be avoided to a great extent. Real-time data can prove to be beneficial in determining some major issues in a particular product or service.

Companies can appoint data analysts who can screen the real-time data and make necessary changes as and when required.

  1. Speeding the Action

Whenever a company launches any product in the market, it is always hard for the company to anticipate the response it may get. Supposedly, the customers have questions or certain queries about the product, taking some time to answer them might affect the overall image of the company as well as the product.

Big data helps to tackle this problem in real-time. Queries can be handled in seconds than wasting several minutes and replacements can be made in fewer days as compared to the time it used to take earlier. Big data has brought about a paradigm shift in decision making which has made customer dealing and answering queries a much simpler task.

Conclusion

With edge-to-edge competition in the market, it is significant for any organization that it makes effective use of big data in its favor.

Big Data Career

With the proper use of big data, companies can foresee and predict the future market for their products and services as well. The points mentioned above have presented a lucid picture of how important big data has become lately.

Big Data CareerA big data career can prove to be beneficial and is considered among the most demanded career options.

For big data training, you must check out the courses and professional assistance being offered by Imarticus learning.

Big Data Engineer Salary: How Much Can You Earn as a Big Data Engineer?

Posted on by Imarticus Learning

Who is a Data Engineer?

As businesses across the globe are enthusiastically adapting the data-driven strategies to optimize their decisions, the demand of highly skilled Data Engineers has increased manifold. A skilled person who is able to convert the raw data into a self-explanatory form to analyze the trends by developing requisite algorithms is a Data Engineer.

The entire task of Data Mining, maintaining and extracting trends from different data sets in an organization is completed by a team of Data Engineers. Ultimately, the Data Engineers provide reliable infrastructure to maintain big data.

Skills required to be a Data Engineer

A Data Engineer must have deep understanding of SQL, Extract Transform Load, Apache Hadoop, in depth knowledge of Python, Java, Scala, Kafka, hive, storm and many more.

Big Data EngineerEnterprises now a days prefer the employees with the experience of working on the cloud platforms like Amazon Web Services etc. Sound knowledge of Data warehousing and Data modelling is also given a lot of preference these days.

The required skills and preferences may affect the salary of an Data Engineer by 10%-15%.

A Data Engineer deals in Big Data, the person should be proficient in the documentation skills and must also be good in his/her verbal and Non-verbal communication skills.

How to Become a Data Engineer?

Applied Mathematicians, Engineers, People holding Bachelor’s degree in Computer Sciences or related IT field find it easier to become a Data Engineer. The aspiring candidates then go for a Big Data certification course to have in depth understanding of required technological skills to be a Data Engineer.

Roles and Responsibilities of a Data Engineer

The generic tasks that a Data Engineer has to perform include:

  • Aggregation and Analysis of given data sets
  • Development of Dashboards and reports
  • Development of tools for business professionals
  • Providing improved techniques to access the Big Data

Three main domains in which a Data Engineer works are: Generalist, Pipeline centric, Database-Centric Generalists are the Data Engineers who processes, manages and analyses the data.

Big Data EngineerPipe-line centric Data Engineers work in coherence with Data Scientists to utilize their collected Data. Database-centric Data Engineers manages the Data-flow and database analytics.

Along with the technical skills, a Data Engineers must have some soft skills as well to communicate their analysis. Some of the key responsibilities are:

  • Acquisition of Data
  • To match their development constantly with the business requirements
  • Consistent improvement in the data reliability, efficiency and Data Quality
  • Development of predictive and prescriptive modelling

The key responsibilities vary from organization to organization.

Data Engineer: Employers and Salaries

Some of the top companies where Data Engineers are highly paid are:

  • com Inc
  • Tata Consultancy Services Limited
  • IBM Private Limited
  • General Electric (GE) Co
  • Hewlett-Packard
  • Facebook

Factors affecting Salaries of Data Engineers 

Experience:

Average Experience as a Data Engineer Average Pay-Scale based only on Experience
Entry level ₹400,000 approx.
1-4 years ₹739,916 based on 317 salaries
5-9 years ₹1,227,921 based on 179 salaries
10-19 years ₹1,525,827 based on 49 salaries

Job Location:

The Data Engineers working in the prime locations like Gurgaon (Haryana) earns 27.3% more average salary, in Hyderabad (Andhra Pradesh) 13.7% more average salary, in Bangalore (Karnataka) 12.5% more average salary than in locations across the nation.

The average salary of a Data Engineer in Mumbai, New Delhi and Chennai are relatively lesser than average salary across the nation.

The Growing Need of Data Storytelling as Salient Analytical Skill!

Posted on by Imarticus Learning

Data storytelling is a methodology used to convey information to a specific audience with a narrative. It makes the data insights understandable to fellow workers by using natural language statements & storytelling. Three key elements which are data, visuals, and narrative are combined & used for data storytelling.

The data analysis results are converted into layman’s language via data storytelling so that the non-analytical people can also understand it. Data storytelling in a firm keeps the employees more informed and better business decisions can be made. Let us see more about how data storytelling is an important analytical skill & how it will help in building a successful Big Data Career.

Benefits of Data Storytelling

The benefits of data storytelling are as follows:

  • Stories have always been an important part of human civilization. One can understand the context better via a story. Complex data sets can be visualized and then data insights can be shared simply through a story to non-analytical people too.
  • Data storytelling helps in making informed decisions & stakeholders can understand the insights via Data storytelling and you can compel them to make a decision.
  • Data analytics is about numbers and insights but with data storytelling, you make your data analytics results more interesting.
  • The risks associated with any particular process can be explained to the stakeholders, employees in simple terms.
  • According to reports, more data is produced from 2013 than produced in all human history. To manage this big data and to make data insights accessible to all, data storytelling is a must.

Tips for Making a Better Data Story 

  • If you are running an organization, make sure to involve stakeholders/investors in data storytelling. This helps in increasing clarity in communication and they do not find a lack of information.
  • Make sure to embed numerical values with interesting plots for a data story. Our brains are designed to conceive visual information faster. Only numerical insights will make the data story boring and more complex to understand. The data insights should be conveyed in a layman’s language through a data story.
  • Data visualization should be used for data storytelling but it should not hide the critical highlights in the data set.
  • Make sure you imbibe all the three aspects of data storytelling which are visuals, data & narrative. The excess of any attribute can hamper the effectiveness of your data story.
  • The outliers/exception in the data set should be analyzed and included in your data story.

The Growing Need for Data Storytelling 

New ways of data analytics like augmented analysis, data storytelling, etc. are surging a lot in recent days due to the high rate of data production by firms/businesses. One can learn analytical skills from a Data Analytics course from Imarticus Learning. To build a successful Big Data Career, you will need to learn these new concepts in data analytics.

big data analytics courses in IndiaConclusion 

Imarticus Learning is one of the leading online course providers in the country. You can learn key skills via a Data Analytics course from industry experts provided by Imarticus Learning. Start learning data storytelling now!

How To Advance Your Business With Analytics & Build The Right Team?

Posted on by Imarticus Learning

In 2020, data is a goldmine of information, and if you can collect and analyze the right data sets, a lot can be achieved in a short period of time.

As companies around the world, start recognizing and collecting more data points from their customers, it is crucial than ever before to have a data analytics team, which can not only process and analyze the collected data but also emphasize sharing key insights which will assist you in advancing your business.

LinkedIn, the number one job search portal reported that 2020 saw a 25% increase in professionals who are seeking a Big Data Career in data science and analytics.

Bi Data CareerWhile this clearly indicates that the importance of data scientists is on a steady rise, it also indicates that companies need to better analyze the capabilities of each individual domain to choose the right man for the job.

How to Choose and Build the Right Data Analytics Team for Your Company?

One of the first and most crucial aspects to understand and embrace is the fact that in 2020, data scientists come with a variety of different skill sets, and thus it is essential to recognize each of the skills and categorize them into the functions best suited for.

While building an analytics team for your organization, you can follow either of two different approaches.

  1. The Direct Method of Segmentation
  2. The Indirect Method of Appreciation

The Direct Method of Segmentation

The concept of the direct method of segmentation is based on the ideology that each data scientist depending on their skill set can be grouped into either of three different designations and then hires can be made based on deciding which skill is required first.

  1. Data Engineers: Data Engineers are the crux of any data analytics team you want to design. The main skill sets you should look for in a data engineer include, ETL (Extraction, Transformation, and Load), Data Warehousing, data processing, and other similar roles.The fundamental job of a data engineer can be summarized as preparing the data for further analysis by data scientists and analysts, who form the rest of the team. They generally have a degree in Big Data Analytics Training.

    Big Data Career

  2. Data Analysts: Using the data prepared by data engineers, analysts extract critical information and decisions which are helpful in solving problems and contribute to advancing business decisions within the organization.
  3. Data Scientists: Data scientists form the last hierarchy of the team and are mainly responsible for crafting and perfecting algorithms using either Machine Learning or Artificial Intelligence to make compelling decisions from unstructured data sets. While a data scientist can easily be tasked with the responsibilities of both analysts and engineers, in big teams these designations are separated for better utilization of time and resources.

The Indirect Method of Appreciation

The indirect method of appreciation is based on the concept of recognizing people who have a broad range of skills, but also in-depth knowledge in a few key areas. This method of hiring can be understood using the “T-Shaped” skill concept, where the horizontal bar of the T represents the broader knowledge set of the hires, and the vertical bar represents the specialized knowledge in key areas.

The overall aim of this methodology is always to find the right set of people, who have the expertise and the knowledge to get the work done in a timely manner.

Conclusion

Building the right data analytics team for your business can not only contribute to its immediate success but also long-term growth. Thus always make it a point to invest the right amount of resources and figuring out which methodology of hiring works best for your business.

Edge Vs Cloud: Which Is Better For Data Analytics?

Posted on by Imarticus Learning

What is Edge Computing?

Edge computing is a segregated topology which serves to bring processed information closer to the device that is gathering the data rather than relying on a central unit which would be located much farther away.

What is Cloud Computing?

Cloud computing involves the process of delivering important information and services such as storage without the need for involvement of active management.

Which Out of the Two Is Better For Data Analysis?

In today’s world where AI has become an extremely important part of our lives, developers are looking to merge the devices we use on a day-to-day basis with artificial intelligence to make running businesses easier for organizations.

In such cases, we must look at the various computing methods that can make this possible in an efficient manner. Here, you would think that cloud computing would hold an important position in making the most suitable and ideal decisions. Platforms which are based on cloud allow developers to quickly create, deploy and handle their applications.

These would include playing the role of a platform of data for applications, application development which would help bridge the gap between data and users, and so on. It is popular for its flexibility with data storage and the ability to perform analysis processes.

On the other hand, edge computing allows applications and various other analytical and service processes of data to be done away from a central data unit, bringing it nearer to end-users. It allows the processing to take place within the locally available resources, thus bringing it a step back from the intricately planned cloud model where data processing happens in specific data centres.

Let us dive into this further in detail.

Cloud vs Edge Computing: Latency Problems

Cloud computing is used extensively across various organizations and companies for data analysis. However, there may be situations where a business may face problems in collecting, transporting and analysing the data given.

Edge and cloud computing for Data AnalyticsWhen data is transferred to a remote cloud server, it allows the user to perform various complex algorithms with machine learning and thus predict the maintenance needs of a particular section. This is then forwarded to a dashboard on a personal system where one can determine what decisions are to be made further. This is all done comfortably from home or the office.

This is great, however, as one begins to increase the intensity of operations, one may begin to run into issues such as physical limitations on the bandwidth of the network and thus also latency issues.

Edge computing does a great job at reducing latency issues by involving a local server, maybe even on the device itself. The only difference here is that the issue with latency is solved at the expense of the processing power offered by cloud computing methods.

Businesses, with edge computing, are now being able to decrease data volumes which would need to be uploaded and stored in the cloud. This thus makes the process of data analysis less time-consuming.

Edge computing may still interact with other website applications and servers. It includes physical sensor thus allowing it to help run smarter algorithms and facilitate real-time processing which is used in smart vehicles, drones and smart appliances. It may not be as strong as a remote server, but it helps reduce the bandwidth strain that one would normally face with cloud computing.

Data Analytics CareerA big data analytics courses would help equip a person aspiring to work in the field of data analysis with all the information that would be necessary. A big data analytics career is a good option because it is an ever-expanding field with a large number of opportunities!