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Tag: Advanced Analytics

Azure Data Scientist Associate Certification For Your Employees: The What, Why And How

Posted on by Imarticus

Understanding the Importance of an Azure Data Scientist Associate Certification 

The demand for skilled data scientists has skyrocketed and companies are relying on data analytics to make informed decisions to gain a competitive edge. As an employer, understanding the importance of having a proficient data science team to drive growth and innovation is crucial.

One effective way to equip your employees with the necessary skills is through the Azure Data Scientist Associate certification. In this blog, we will explore what this certification entails, why it is beneficial for your organisation, and how to go about obtaining it.

What is Azure Data Scientist Associate Certification?

Azure Data Scientist Associate certification is a globally recognised credential offered by Microsoft. It validates an individual’s expertise in using Azure technologies for implementing and managing machine learning (ML) models. This certification equips professionals with the knowledge required to use Azure tools and services effectively.

Why Invest in Azure Data Scientist Associate Certification for Your Employees?

  1. Access to cutting-edge technology: By certifying your employees as Azure Data Scientists Associates, you provide them with access to Microsoft’s advanced cloud-based platform – Azure. This enables them to leverage powerful tools such as Azure Machine Learning Studio, Cognitive Services and HDInsight for enhanced productivity in their data science projects.
  2. Enhanced business insights: With certified data scientists on board, your organisation can extract insights from vast amounts of raw data. They can develop predictive models that help identify patterns, trends and opportunities crucial for making informed business decisions.
  3. Increased efficiency & cost savings: A certified team can streamline your organisation’s processes by automating repetitive tasks and optimising resource allocation through insightful analytics. This leads to improved efficiency and cost savings across various departments.
  4. Competitive advantage: Having a team of certified data scientists gives your organisation a competitive edge.It delivers more accurate predictions and helps personalise customer experiences.

How to Obtain Azure Data Scientist Associate Certification?

  1. Prerequisites: To pursue the Azure Data Scientist Associate certification, your employees should have a basic understanding of data science concepts, and programming languages such as Python or R. Moreover, the individual needs to have a little bit of experience in working with Azure technologies.
  2. Hands-on experience: Practical experience is the key to mastering data science techniques. Encourage your employees to work on real-world projects by utilising Azure tools and services.
  3. Exam preparation: Familiarise your team with the exam pattern and practice using sample questions and mock tests. This will help them become comfortable with the format and identify areas they need to focus on during their preparations.

Driving Employees’ Success with Azure Data Scientist Associate Certification

Investing in Azure Data Scientist Associate certification can have a profound impact on your organisation’s success in today’s data-centric world. By equipping employees with cutting-edge skills and technology you set your company up for future growth. If you want to empower your team with these essential skills, consider enrolling them in one of the Azure Data Scientist Associate certifications.  If you need more details on this course please visit here.

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

Posted on by Imarticus

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.

Why Artificial Intelligence is Invaluable for Weather Forecasting and Disaster Prediction

Posted on by Imarticus

For most people, weather forecasts are simply indicators of whether they need to carry an umbrella or throw on a coat when they go outside. However, for many industries and types of individuals, weather changes and patterns have a direct impact on their lives and livelihoods.

Agriculture, for example, benefits from accurate weather forecasting because farmers can make better planting and harvesting decisions. For governments, weather forecasts factor in their budget plans and disaster relief fund allotments. Businesses that rely on clear weather (or rough weather) depend on weather forecasts to drive several of their operational processes.

From all this, it is easy to gather that accurate weather and disaster forecasting carries much more weight than we think. Artificial intelligence augments the accuracy and reliability of weather forecasting, especially given that so many details fluctuate every day and with every geographical location. It is a great fit, given the volume of data is nigh impossible to sift through with manual labor alone.

In short, the future of artificial intelligence will also see its increasing use in the weather and natural disaster forecasting domains. Here are  a few more reasons why:

  • Managing several sources of weather data

There are currently more than one thousand weather satellites orbiting the  Earth, each sending back weather data dumps to various collection points. These data dumps are a mix of information about temperatures, cloud patterns, winds, and pollution levels. Then there are thousands of government and private weather stations around the world, each conducting their own real-time research on weather and climate.  It is nearly impossible to sift through all this data manually, but AI algorithms can do it in a matter of hours.

  • Sifting through multiple data categories

Suffice to say that the amount of data generated from satellites and personal weather stations is too much to fathom, and impossible for humans to sift through. However, Artificial Intelligence training can be applied to segregate and classify data from dumps, as well as to pull out key insights for analysis. This is a preliminary process in the weather prediction model, wherein AI segregates data based on indicators, flags significant shifts or patterns, and keeps data classified such that predictions are made as accurately and as scientifically as possible.

  • Preparing for potential disasters

Beyond real-time predictions, AI is also used to identify patterns and prepare for natural disasters in advance, off the back of previous circumstances. It may also split this data between geographies, allowing disaster management teams to evaluate which areas will be hit the hardest and prepare for that. This data is also invaluable for civil engineering teams, architectural firms, and city planning teams who need to take weather into account when mapping out residential and commercial areas.

  • Sending out warnings

Apart from predicting natural disasters, AI can also be leveraged to send out warnings to potential danger zones. This is invaluable when it comes to saving human and animal lives and generally preparing areas for the worse. Warnings can be sent out through media alerts, push notifications, and citizen broadcasts; whatever the method of delivery, AI is vital to sending such notices out in time and to the right people to curb panic and facilitate seamless planning.

Artificial Intelligence Training for Weather Forecasting

Weather forecasting teams and companies need skilled AI scientists and engineers to apply theory to practice in real-time. They need AI professionals who can create automated setups to free human minds for higher-order thinking; they also need pros who are fast on their feet and adept at creative problem-solving.

Using AI for weather forecasting is a whole new ball game – one on which many lives depend.

Edge Vs Cloud: Which Is Better For Data Analytics?

Posted on by Imarticus

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!

5 Reasons to Learn Hadoop!

Posted on by Imarticus

Big Data Analytics is ruling the world. Organizations across the world have realized the potential of Big data analytics to push their business decisions to be more informed and data-driven. Data analytics has become imperative in terms of uncovering the hidden patterns, deriving correlations, understanding business information, and learning the market trends.

Hadoop is open-source software that facilitates the storage and processing of a large amount of data. It is scalable and reliable and can be used on distributed computing that does not share any common memory or discs. So, is it good to learn Hadoop? Let us look at the top five reasons to learn Hadoop.

  1. Bright Career Prospects

More than 90% of the companies have invested in big data and they are in the hunt for talents to manage the data for them. This unveils a big career path ahead for big data and Hadoop trained professionals. If you are looking for a lucrative career in big data, you should get Hadoop training to brighten up your future employment prospects.

  1. Many Choice of Profiles

There are many different profiles related to Hadoop depending upon your proficiency, learning skills, and experience. You will be amazed at the designations available – have a look at some of them:

  • Hadoop Admin
  • Hadoop Developer,
  • Data Engineer
  • Data Analyst
  • Data Scientist
  • Big Data Architect
  • Software Engineer
  • Senior Software Engineer
  1. Constant Increase in the Demand

Big data and its applications are ever-increasing, and this works in favor of Hadoop professionals too. Big data has now become the basic requirement for effective business strategy formulation and hence, the companies are on a constant lookout for talents who can collect, process, and interpret data. The demand is only going to increase in the coming years. Getting Hadoop training will help you to be future-ready.

  1. Accelerated Career Growth

As mentioned earlier, there are many different profiles associated with Hadoop. Depending upon your skills, experience level, and your willingness to learn, you can easily move up your career ladder and secure a more challenging and rewarding position.

The fact that many global market leaders are big recruiters of data professionals the scope of data science-related jobs is as vast as the sea. Also, unlike many other jobs where the supply of talents is far exceeding the demand, there is a serious shortage of skillful professionals in data analytics. This increases the chances of employability by many folds.

  1. It Promises Good Pay

The fact that Hadoop is the leader in big data job postings gives you a taste of the situation. There is a serious lacuna in terms of good talents, and companies are ready to pay fat salaries for the right talent. All you need to do is to sharpen your skills and keep yourself updated all the time.

Conclusion

You now know the top reasons to learn Hadoop. Ease of learning and high demand makes it a hot pick among aspiring data professionals. Hadoop skills will earn you brownie points and help you get your dream job.

Should You Start With Big Data Training Or Learn Data Analytics?

Posted on by Imarticus

While the difference between Big Data and Data Analytics isn’t huge, it’s an important one. Career streams in Data Science can diverge into two separate branches based on which one of the above you choose. 

Both Big Data and Data Analytics focus on the same thing – processing large chunks of data for valuable insights. But the methodologies and tools used for the same are different. Therefore, you can choose to specialize in one career stream and continue progressing in it, or you can learn both. 

In order to make a clear distinction, let’s define both the streams separately. 

What is Big Data?

Big Data refers to the management and operations performed on extremely large data sets (one data set is often greater than 100GB) in order to extract patterns, trends, and insights that can power business decisions. Therefore, you have to cultivate expertise working with a large amount of data, and meet specific challenges that it presents. 

 

  • What all you learn in Big Data Analytics Training?

 

  • Data optimization techniques. 
  • Finding relationships and patterns. 
  • Big Data tools such as Hadoop, MapReduce, etc.
  • Compiling, sorting, and processing data using Python, R, etc.

What is Data Analytics?

Data analytics deals with obtaining relevant and very specific information out of smaller data sets. Where a Big Data analyst will sort through millions of rows of data, a Data Analyst will work on finding the statistical parameters in a given range. It involves reporting of elementary but well-defined parameters than can be analyzed for business value. 

  • What is taught when you learn Data Analytics? 
  • Applying statistical principles to data sets. 
  • Compiling and disseminating information from data sets. 
  • Generating goal-oriented reports for decision-makers. 
  • Working with analytical techniques for smaller data sets. 

Which one to learn first?

  • Direct Path 

At Imarticus, we have made both the options available to the students. They can dive straight into Big Data Analytics Training which will prepare them for Big Data from grassroots levels.

Learning Big Data directly ensures that the students are absorbing the concepts they’ll use in professional places directly. They’ll be trained on various tools used in Big Data so that they can come to speed with the industry scenario. 

Same is the case with Data Analytics. Students can learn Data Analytics directly and make it as their career objective. In case they don’t want to get into Big Data, this is an excellent Data Science alternative for them. 

  • Progressive Path

The second option available to students is to specialize in both Big Data and Data Analytics but in a progressive way. They’ll study Data Analytics first and then shift their focus to Big Data. 

Although there is a significant difference between the two career streams, the underlying concepts remain the same. Therefore, someone with the knowledge of Data Analytics will not find it difficult to make the switch. Rather, they’ll be able to advance their career at will. 

Conclusion

As Data Science flourishes as a career, Big Data and Data Analytics continue to be the two best career streams in the market. Our courses cater to both the streams exclusively, as well as, in an integrated way for students to plan their career logically.