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Tag: big data analytics course

How big data Analytics can help in improving Network Security

Posted on by Imarticus Learning

Every day, the information generated and stored by companies worldwide is increasing in volume and complexity. With this arises a need for tools to protect this vulnerable data from data breaches and exploitation by cybercriminals.

Can big data analytics strengthen network security and save the day? Read on to find out.

What is big data analytics?

Big data refers to highly variable, complex data generated at a high volume and high velocity. The analysis and interpretation of big data are known as big data analytics.

With the boom in digitalisation and cloud computing, hundreds of terabytes of data are generated and utilised daily. Extracting useful information from data of this size is tedious with traditional SQL (Structured Query Language) tools. Here’s where big data analytics comes in. 

Frameworks like Hadoop and databases such as MongoDB, Cassandra, Vertica, and Apache HBase make complex data management quicker and simpler. Searching for specific events within large datasets can be completed by such frameworks in minutes.

Applications of big data analytics

Big Data Analytics Courses

Big data analytics helps store and maintain data, detect patterns and trends in real-time, predict future events or problems, and nullify abnormal or malicious activities in vast networks.

Some sectors where big data analytics finds wide applications are:

  • Finance and banking
  • Business and e-commerce
  • Public welfare and governance
  • Healthcare 
  • Communication
  • Energy
  • Entertainment and media

Network security and its challenges

Protocols, policies, and practices designed to keep data safe from cyber attacks and security breaches are part of network security. It aims to detect, prevent and nullify misuse, denial of access, unauthorised modification, or data extraction.

Network security uses physical and digital methods to secure confidential and sensitive data. Biometrics, firewalls, VPNs, and sandboxing are various types of network security. 

As databases expand, monitoring big data with traditional SQL tools becomes unreliable. Downsides of traditional analytics include frequent false positives, cyber attacks, privacy breaches, phishing, malware, and other network security threats.

Other challenges faced by network security are:

  • Breaches at admin or high clearance levels
  • Lack of vigilant monitoring in open network structures
  • Inability to process high amounts of data in real-time
  • Deliberate or accidental breaches by humans or bots
  • Stealing confidential data through hacking or identity theft

How big data analytics can be the solution?

Storage and maintenance of big data

High-volume non-relational data can be collected and stored on NoSQL databases such as Apache HBase, Cassandra, and MongoDB. These databases use platforms like Hadoop to organise variable and unstructured data.

Identification of anomalous patterns in real-time

Employees are responsible for a majority of network security breaches. These leaks may be accidental, but deliberate breaches may be minor and go undetected for long periods.

Big data analytics can monitor real-time changes in regular data flow patterns. Data from sensors monitoring user credentials can detect unauthorised log-ins and reveal botnets or APTs (Advanced Persistent Threats).

Predictive assessment of network integrity

Tools used in big data analytics can perform predictive analysis of the errors that can occur in the system. NoSQL frameworks can reveal the pitfalls in network security, which can then be patched before hackers discover them.

Prompt retrieval of information from networks

A case study by Zion Bancorporation was able to draw a comparison between the processing speed of data by traditional and big data analytics. Traditional SIEM (Security Information and Event Management) tools took 20 minutes to 1 hour to process data collected over a month. It took a Hadoop-based tool only about a minute to do the same. 

This case study shows the ability of NoSQL tools to process data at high speeds that could detect and barricade gaps in network security.

Analysis of datasets to identify past breaches

When breaches in the system occur, companies often fumble to find what exactly caused it. The sheer volume of big data often makes finding a fault in the network a hassle. With big data analytics, a detailed assessment of stored data can be performed within minutes, assuring prompt responses to cyber attacks.

Conclusion

Advancements in big data analytics have the potential to become an indispensable tool in bolstering network security. There is a rising demand for big data analysts to protect and secure sensitive data.

A career in big data analytics and network security is one click away with the advanced certification programme in cybersecurity from IIT Roorkee. The practical approach of this course aims to equip you to deal with cyber attacks and protect vulnerable networks. It is also a comprehensive guide to ethical hacking, social engineering, encryption, and data forensics.

Enrol now to begin an exciting career in cybersecurity today!

Sports Analytics and Big Data: A Guide to Analyst Training

Posted on by Imarticus Learning

Big data and data analytics are becoming widely popular terms in today’s technologically dominant landscape. Numerous organisations and sectors are embracing these emerging technologies to make fruitful use of data. The sports industry is also leveraging big data analytics to revolutionise sports.

Data analytics and sports might seem completely unrelated. But, data analytics has proven useful in improving game quality, fan experience and player safety. Nowadays, sports analytics are taken very seriously by professional teams, managers, coaches and players.

 

Data analytics jobs are in huge demand these days. These jobs pay well and provide several opportunities to aspiring data scientists. If you are a sports fan, you can now have a rewarding career as a data analyst. Sports analytics is the perfect career choice for you, allowing you to pursue your love for sports. However, since data analytics is a technical field, you need to have proper data analyst training.

How is sports analytics changing the game?

It helps coaches make decisions regarding the recruitment of athletes.

Player recruitment is an important part of any sport, particularly at the professional level.

Many modern sports franchises are leveraging data analytics to recruit the right players for their teams.

Data is being used to identify and hire talented but undervalued players.

Universities are also using data analytics to discover potential upcoming athletes. This allows them to understand where to invest their time and efforts.

It helps broadcasters create a better viewing experience for fans.

Sports commentary makes watching a sport more fun. The broadcasters provide stats and facts for viewers to understand the importance of each event. This makes the viewing experience more compelling. This is made possible by big data analytics.

Big data is also being used to increase live attendance in stadiums by satisfying fans and enhancing their overall experience of watching a game live. This can be done by analysing which games have better chances of selling swiftly and prompting frequent audiences to buy tickets.

Another way to compel audiences to watch games live is to improve the supply of items that are in high demand. This can include merchandise, eatables, etc., which can improve the viewer experience.

Big data can also be useful in tackling the factors which affect the viewer experience negatively. For instance, it can save viewers from the struggle of finding a parking spot by carrying out traffic analysis.

It is used to create better sporting strategies.

Strategy plays a significant role to win a game. Be it an individual sport or a team sport, players cannot compete without a strategy in mind.

Coaches make use of big data to create personalised winning strategies for each player and the team as a whole. For example, Liverpool FC’s coach used data analytics to compete against opponents and emerge as winners of the Premier League.

It helps in live data collection.

Data like speed, distance, mileage, etc. is difficult to gather manually. However, with wearable devices operated using artificial intelligence and machine learning technologies, vital data can be collected in real-time.

The devices are worn by players or attached to their clothes to keep track of player performance and fitness variables like heartbeat, speed, etc.

This data can help coaches in preparing an ideal fitness plan for players and improve their safety.

RFID tags are also attached to players or their sports equipment to collect crucial data.

It allows better judgment.

Many times, a situation occurs where the referees might find it challenging to take a decision. Thus, they might end up taking the wrong decision. This impacts the whole game and demoralises the players.

With big data and analytics, sports authorities now make use of devices that can track data which is difficult to be observed by the human eye. For example, it can give information on a strike or a ball hit that could be missed by referees.

Sports analytics is a relatively new field and the sports industry is still in the process of optimising its applications. Thus, it has a huge scope for research and development.

However, data analyst training is necessary to enter this job market. Our team at Imarticus Learning has designed an data Analytics and Machine Learning course to kickstart your data analyst career. Our Postgraduate Program in Data Analytics and Machine Learning comes with guaranteed interview opportunities and extensive career services. Graduates and professionals (with up to 5 years of experience) having a technical background are eligible to apply.

Sports analytics is an upcoming and interesting area of work. Join the big data revolution to make sports more fair, entertaining and competitive.

Machine Learning at work: The future of the workplace in 2022

Posted on by Imarticus Learning

Automated algorithms that learn from experience and data are known as machine learning (ML) algorithms. In order to generate predictions or judgments without being exclusively coded or overseen by humans, machine learning algorithms generate a model based on training data. The discipline of machine learning gains new techniques, theories, and application fields from mathematical optimization research. 

When it comes to machine learning, input such as training data or knowledge graphs is used in the same way that the human brain develops information and comprehension. Defining entities allows for deep learning. The Edutech industry nowadays is filled with Machine Learning and Artificial Intelligence courses.

There are several ways to start the machine learning process, such as through looking at examples, direct experience, or even teaching. Data is examined for patterns that may be used to draw conclusions from the instances given. With machine learning, computers can learn on their own, with no help from humans, and then modify their behavior as a result. 

Types of Machine Learning Algorithms

Machine Learning algorithms can be mainly distributed into three categories, they are:

  • Supervised Learning: Supervised machine learning systems use labeled samples to predict future occurrences based on what has been learned in the past. The learning technique uses a known training dataset to build an inferred function that predicts output values based on that dataset. After sufficient training, the system is capable of providing objectives for any new input. It is also capable of comparing the model’s output with the planned, proper output in order to identify problems and make modifications as necessary.

  • Unsupervised Learning: To train an unsupervised machine learning algorithm, you don’t need to classify or label the data. It is possible for systems to infer a function from unlabeled data in order to explain a hidden structure. Throughout the whole process, the machine does not know the right answer. Instead, it uses datasets to make predictions about the result.

  • Reinforcement Learning: A Reinforcement learning algorithm is a way of teaching a computer to do something by causing it to do an activity and then looking for faults or rewards. “Trial and error” and “feedback” are two of the most important aspects of this kind of learning. 

Workplaces Where Machine Learning is Being Extensively Used

Businesses across many industries are already using machine learning to boost innovation and improve operational efficiency

  • Cyber-security firms
  • Finance sectors such as banks and FinTech enterprises
  • Healthcare industry
  • Detection of fraud
  • Retail businesses

Why this Course?

Acquire a firm grasp of the fundamentals of data analytics and machine learning, and learn how to master the most used data science tools and methodologies, to position yourself for employment. Post Graduate Program in Data Analytics & Machine learning by Imarticus comes with a guaranteed placement opportunity. 

In this machine learning course, you’ll learn exactly what the world’s leading employers of data scientists are looking for in their employees. As part of the curriculum, you’ll work on Capstone Projects, real-world business cases, and mentoring from industry leaders that matter.

Conclusion:

While many might say that employment is being lost to AI at a far faster rate than in prior industry-changing events, the numbers so far do not stack up. Rather than eliminating employment, artificial intelligence in the workplace is enhancing people’s skillsets, and hence their compensation, across a broad variety of sectors.

So without any delay, get your machine learning certification now and pave your own path to becoming a successful Data Science Professional with Imarticus’ Post Graduate Program in Data Analytics & Machine learning.

What is Business Analytics All About?

Posted on by Imarticus Learning

Business Analytics Definition

The importance of Business Analytics stems from the fact that it is the method by which firms analyze historical data using statistical methods and techniques to generate new insights and enhance tactical decision-making.

Since data-driven firms see their data as a business asset and actively seek methods to transform it into a competitive advantage, an increasing number of employees are taking data analytics and machine learning courses and acquiring a business analytics certification.

Data quality, trained analysts who understand the technology and the business, and a dedication to leveraging data to uncover insights that influence business choices are all essential components of business analytics success.

What is Business Analytics?

Business analytics is a data managing solution and a subset of business intelligence that involves analyzing and transforming data into valuable information, identifying and predicting outcomes and trends, and making better, data-driven business choices using methodologies such as data mining, predictive analytics, and statistical analysis.

The key elements of a conventional business analytics dash are as follows:

  • Data Visualization: For easy and rapid data analysis, visual representations such as charts and graphs are offered.
  • Optimization: after identifying patterns and making forecasts, firms may use simulation tools to test best-case scenarios.
  • Predictive Analytics: predicting business analytics uses a number of statistical approaches to building predictive models that extract data from datasets, discover trends, and offer a score for a variety of organizational results.
  • Forecasting: examines historical data from a given time period to make educated predictions about future occurrences or behaviors.
  • Association and Sequence Identification: identifying predictable behaviors that are conducted concurrently or sequentially with other acts
  • Text Mining: examines and organizes huge, unstructured text collections for quantitative and qualitative analysis 
  • Data mining for business analytics: data mining for business analytics sifts through large datasets to uncover patterns and connections using databases, statistics, and machine learning.
  • Data Aggregation: data must be acquired, structured, and filtered before being analyzed, whether through provided transactional records or data.

Why is business analytics important?

Business analytics has a lot of moving pieces, but it’s not always evident why it’s vital to your company. To begin with, business analytics is the instrument that your company requires in order to make informed judgments. These decisions are likely to have an impact throughout your whole organization, assisting you in increasing profitability, market share, and possible shareholder returns.

There’s no doubting that technology has an influence on many enterprises, but when utilized appropriately, BA may have a beneficial impact on your business by giving you a competitive advantage in a variety of ways.

While some firms are unclear what to do with vast volumes of data, business analytics combines data with actionable insights to help you make better business decisions.

Furthermore, because this data may be provided in any manner, your organization’s decision-makers will be well-informed in a way that suits them and the objectives you set at the start of the process.

Conclusion

If you are aware of the importance of Business Analytics and are interested in obtaining a business analytics certification, then you should subscribe to our data analytics and machine learning course given at Imarticus.  

Related Article:

https://imarticus.org/what-are-the-benefits-of-business-analytics/

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.

What Are the Characteristics of Big Data?

Posted on by Imarticus Learning

Big data is the next big wave that is shaping the corporate sector today. Big data gives an idea about the size of data but there are various aspects associated with it. It is also driven by various other factors apart from the size of data such as the sources of data, various formats in which it is available, chunking and extraction, etc.

Big data has managed to find space in all sectors of the market – technology, retail, telecommunications or any other broadly recognized field. It makes use of the available data to derive conclusions.

Need for Big Data

Organizations have huge data resources in an unstructured format. Mostly this data is stored in various devices and is never brought to any use. Data can prove to be a mega resource for the growth of any company as it can equip the company with numerous insights thus acting as the steering wheel of the company. Traditional tools such as Excel are not that efficient in extracting information and putting it to any relevant use. Big data comes into the picture here.

When you have a huge amount of data, it needs to be sorted and then classified under various heads so that the important fields can be easily recognized and brought to use. This space is getting bigger with every passing minute as we are becoming more and more data-oriented.

The volume of data is huge. With the increase in the number of internet users, more data and information are coming into circulation and this has given rise to the value data holds today. This data is produced through various channels like search engines, social media networks, business informatics, etc. It makes use of various tools to summarize information.

Learning Big Data and Hadoop can pave a great career path for someone who wants to have a career in data analytics.

Characteristics of Big Data

 The 4 Vs of Big Data characterize big data. Data needs to be classified and organized for better understanding. The 4 Vs of Big Data are:

  1. Volume
  2. Velocity
  3. Variety
  4. Veracity

These characteristics form the essence of Big Data. It gives insights on how the data should be dealt with and how can the insights from that data can be put to good use.

  1. Volume: Volume defines the size of the data which in today’s time is exploding and increasing exponentially. To be precise, this defines the quantity of data available for the extraction of information. Based on the volume of data, various tools are applied for the segregation of information.
  2. Velocity: Velocity refers to the speed in which the data is processed. The speed of data processing plays a very important role in Big data as a lot of data has to be analyzed and insights have to be drawn within a stipulated time frame, thus making the velocity of data an important feature of Big Data.
  3. Variety: Variety refers to the various types of data from which the relevant information has to be extracted. It is important as data collected from different sources are diverse in many aspects. Big Data makes use of various tools to integrate the diversified data and draw insights for the business.
  4. Veracity: Veracity refers to data accuracy and its relevance with the business information we require or the business decision that has to be made. Veracity helps in the identification of relevant information and hence saves a lot of time.

Conclusion

Big Data today has various dimensions and has opened a new world for data harvesting and extraction. With the help of the Big Data Analytics course, one could gain expertise and in-depth insight into the field.

How Big Data Analytics Course Help to Achieve Better Data Management In Banking?

Posted on by Imarticus Learning

What is Big Data Analytics Course?

Banks create a huge amount of data regularly. The speed of data creation is slower than the speed of processing this information. The Big Data Analytics course can help the banks to diversify the data into Big Data that can be stored in a divided manner for better understanding and longevity.

Big Data Analytics Course focuses on the Collection and organization of the data and its conversion into such information that is worth analyzing and studying to draw meaningful conclusions. It educates about the ways to handle Big Data that cannot be used making use of the traditional methods.

Companies require specialized personnel of Big Data Analysts specifically for this job. Jobs in this particular field are shooting because of the usage of the internet and technology at large. This amalgamation of Finance and Technology can give rise to Fintech (Financial Technology)

What are the sources of Big Data?

Analysts can find Big Data whenever they want to make use of it. Some of the most important sources of Big Data are mention below:

  • Sensors- Used in Cars, Industrial machinery, Space, Technology and CCTV Footages, etc.
  • Social Networking Site- Facebook, Twitter, Instagram, Google, etc.
  • Transportation Services- Data from Aviation, Railways, Shipping, etc.
  • Online Shopping Portals- Data from Amazon, Flipkart, Snapdeal, eBay, etc.
  • Institutions- Data from Hospitals, Banks, Software Companies, Educational Institutions, etc.

Characteristics of Big Data

Big Data has been characterized by 3Vs. All the Vs stand for the following:

  • Volume- Data in Tera Bytes, Zeta Bytes, Giga Bytes, etc.
  • Velocity- The speed at which the data grows fast.
  • Variety- Includes the unstructured and Semi-structured data.

Advantages of Big Data Analytics Course in Banking

Big Data Analytics Course has been proved advantageous in numerous fields and industries but the Banking Sector has been able to make the best use out of it so far. The following points show how Big Data Analytics Course can help Banking Sector to achieve Better Data Management:

  • Boosting the Overall Performance

data analytics courses in IndiaAs far as the performance is concerned, both the employees’ and the bank’s performance can be analyzed through Performance Analytics. The Big Data Analytics Course helps to ascertain the loopholes in the performances that can be corrected in the future course of action.

  • Providing Personalized Banking Services to the Customers

The deposit or withdrawal of money in a bank account or the usage of bank cards at shopping sites, all are activities or information of the customers that a bank has. By using this information and the tools from the Big Data Analytics Course, banks can design some personalized services for their specific customers. This can benefit the banks by the way of increased customer loyalty.

  • Managing the risks to the Data

With a discreet vision of the market, banks can regulate their policies or can bring changes in their framework. If the return from the market keeps running low, after analyzing, banks can raise the loan interests for the customers in that respect.

To avoid frauds, banks can turn down or withdraw payments from questionable Investments in the market.

  • Sentiment Analytics

Under this, the banks analyze the data through social media and understand the patterns and behaviors of the customers on social media platforms. This helps to know the sentiments of people about a brand, firm, Company, or product.

Conclusion

Anyone aspiring to be a Big Data Analyst must take up a  Big Data Analytics Course. Considering the current scenario where every company deals with its data through Information Technology, the use of Big Data Career is on the rise.

Related Article:

How To Upskill Your Career In Big Data Analysis

 

How A Big Data Can Be Used In Retail Banking?

Posted on by Imarticus Learning

 

Like in all successful business ventures, the field of banking is no exception. Big Data drives decisions. The successful use of such large-volume data-based applications already exists and is hugely popular too. Retail banks are big data-driven with nearly all its processes being already supported by such data to deliver business value to their customers.

Their advantages and competitive value is data fueled and depends on the insights provided by the most effective use of such data. It is surprising that in spite of having had access to such large databases for over a decade now, Retail Banking is yet to exploit the numerous benefits uses of big data in retail-banking can bring in.

A data analyst Retail Bankingintern or freshman makes a handsome payout package and the range of the salary depends on the skill-set, certification, and experience. The skills required can vary depending on the employer and industry. As they climb the ladder the promotions depend on continuous skill up-gradation, managerial and leadership skills. Hence, soft-skills and personality development are also important attributes.

Big Data transformation benefits:

With the move by customers to digital transactions many banks did invest substantial efforts in dedicated teams, advanced analytics, appointing data officers, and upgrading their infrastructure. The early adapters are the survivors and have evolved more competitive as new-age banks offering customer-need based services based on Big Data insights. There are many areas where banks are yet to ramp up their use of big data to reap benefits according to the Boston Consulting Group’s reports.

The three main abilities that are leading transformations are: 

  • Data: Multi-source multi-system huge volumes of data petabytes being available which include high definitions of detail and features.
  • Models and ML: The models are now more insightful thanks to the evolution of better ML software which enables decisions and predictions that are data-driven.
  • Software technology: The hardware-software clustering technique in software like Hadoop has proven to be big-data centric and allowing use of complex databases non-structured and structured in a cost-effective manner.

There are at least six areas in Retail Banking which focused and coordinated big-data programs can lead to substantial value for banks in the form of increased revenues and bigger profits.

IMPROVING CURRENT PRACTICES WITH POINT ANALYTICS: Applications of big data analytics for individual needs can be simple and yet powerful with the point analytics method.

TRANSFORMING CORE PROCESSES WITH PLATFORM ANALYTICS: Big data and point analytics can be used to improve customer risk assessment and for effectively tapping the marketing potential measures analyzed.

TRANSFORMING CORE PROCESSES WITH PLATFORM ANALYTICS: Big data applications can transform the collection process with step-by-step optimization to bring in a 40 percent savings in terms of writing off bad debts, with effective use of mining outdated customer information, their predispositions, and newer behavioral models.

BOOSTING IT PERFORMANCE: Big-data IT technologies should have need-based linear scaling to reduce costs. Data-intensive models, mining omnichannel customer experience, balancing data warehouse workloads and effective leveraging of data can help.

CREATING NEW REVENUE STREAMS: 

A European bank used new architecture, hybrid data-warehousing combining banking tech and big-data by clustering the Hadoop commodity servers. Budget savings were 30 percent with all functionalities!

GETTING THE MOST FROM BIG DATA: 

This involves these basic steps of infra and people management detailed below: 

Assess the present situation: Banks needs to bring in newer innovative applications as a differentiator from the competition where all organizational levels collaborate to contribute to the use and needs-based model.

Be Agile: The agile requirements of communication, collaboration, and contribution across all processes will help big data transform them.

Critical capability cultivation: If not implemented the cultivation of critical capabilities can hinder the big data transformation of processes. Limiting the capability to the vision essentials is recommended in all domains of big data capabilities.

The three domains of Big data capabilities that Retail Banking should question itself about are: 

  • The usage of data
  • The engine driving the data
  • The ecosystem of the data

Retail banks should necessarily explore and act on these domains effectively by using smaller discrete programs to take their strategy to execution.

Conclusion:

BIG business for all banks comes from effectively exploring Big Data. Such large institutions who cash in early will stay ahead of the other banks by adapting technology into the very fabric of their banks for its many benefits.

The future holds great promise for development in the field of Retail Banking and to make a high-paid scope-filled career even without experience. Start your Big Data Analytics Course at Imarticus Learning and take advantage of their assured placements and certification. All the best with your career in big data and retail banking!

For more details, you can also 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.

Why Working Professionals Must Learn Business Analytics?

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The business world is currently witnessing the worst catastrophe ever. Employees across the world are struggling to save their jobs. It is tougher to get a new job, even though there are opportunities around. But what decides who gets the prize?

Business Analytics CourseBelieve it or not, professionals who have undergone a business analytics course have a better chance of landing jobs when compared to those who don’t. Learning about analytics works for the advantage of the company as well as for the professionals.

Multi-skilled professionals score more!

The need of the hour is multi-skilled professionals since several companies are paring down their employees to cut their expenses. At this moment someone with multiple skills in a wider area becomes an asset.

The profile of a business analyst covers different areas such as data mining, research, technology, analysis, communications, etc. The analyst is the one who goes through data to come up with the right solution for a problem in business.

A business analyst is someone who identifies the pulse of the customer to find out what is expected and how to deliver the expected quality product. The analyst will need to go through an endless range of data to find out the key factors that work and then implement it most effectively.

It is the implementation part that requires the use of the different areas of expertise expected of a business analyst. Someone who is knowledgeable in this area is most likely to score above the other fellow workers.

Going with the tide

Professionals are taking full advantage of the current work from home scenarios. Not all of them are enjoying some time at home. Instead, they have enrolled themselves in several courses to upgrade them in their profession.

Learning analytics is definitely on the cards for several of them. Not only would it allow them to learn something new but also make them eligible to test some new waters.

Business Analytics CourseBusiness analysis is definitely one of the best options for freshers. Luckily for them, there are a number of business analytics online training options that they can choose from. Undoubtedly, it will be a new opening for them while helping them swim with the flow.

It’s part of the competition

From the companies’ point of view, the competition between them is getting tighter and the best way to get the edge is to find professionals who are capable of raising the bar. Getting the most skillful person wins half the game.

In order to fulfill such roles, the employees need their skill set polished and upgraded. The scope for a business analyst is higher since the finance as well as the business sectors rely hugely on stored data. The success of any business depends upon how the data is utilized in the best way that reaches the customers.

Bottom Line

The business analysis course makes way for improving knowledge for professionals. At the same time, it also helps them polishing their leadership qualities. This is one profile that qualifies for someone starting from scratch or for a well-established business. Both of them need the assistance of data analysis to grow their respective businesses.

What makes it more appealing is the fact that any professional from any background can become a business analyst and end up making crucial decisions that change the course of the businesses.

The Top Data Analytics Certifications in 2020 For Advanced Data Expertise!

Posted on by Imarticus Learning

The explosion of interest in technologies such as the Internet of Things (IoT), Big Data and Artificial Intelligence (AI) has stepped up demand for data scientists and analysts over the past few years.

Advanced capabilities in data analytics are just too critical to ignore at this point. In a matter of years, they’ve reached out from a niche sector to nearly every industry that generates any form of data. However, data analysts with the right mix of skills, experience and spirit of innovation are quite rare. This means businesses are well away from realizing the true potential of their data dumps and the insights that can be garnered from it.

Big Data Analytics Certification CoursesIf you’re interested in pursuing data analytics jobs sometime in the future, it is advised that sign up for big data analytics training opportunities within or through your company. If you’re a student or a fresh graduate, enrolling in a Data Analytics Certification Course is a sure-fire way to strengthen your core competencies and upgrade your skillset in the process.

Here are some of the top data analytics certifications you could choose from in 2020:

  • SAS Certified Data Scientist

To earn the SAS Certified Data Scientist certificate, you will need to pass all 5 SAS Certified Big Data Professional and SAS Certified Advanced Analytics Professional levels. They consist of two and three exams respectively. The exams test a student’s knowledge in big data preparation, programming, statistics, predictive modelling, text analytics and visual exploration.

Upon completing the exams successfully, the newly-certified professional can gain insights from big data using SAS tools as well as open-source options. The professional will also be skilled in creating machine learning business models to derive insights and influence decisions at a higher management level.

  • Microsoft CSE (Certified Solutions Expert)

This certification deals with data management and analytics. It consists of 12 exams; however, aspirants will first need to earn at least one out of the seven MSCA (Microsoft Certified Solutions Associate) certifications. The costs of the exams don’t include the training material, which a candidate can source directly from Microsoft.

This certification prepares you for building data solutions at an enterprise level and applying Business Intelligence approaches to big data. It also prepares you for the administration of SQL databases. After obtaining this certification, job roles such as database analyst, BI analyst and database designer are well within your reach.

  • IBM Data Science Professional

This beginner-level certification demonstrates the skills of an individual in the subjects of data science such as SQL, Python, data analysis, basic methodologies and open source libraries. The candidate will need to complete nine courses spread out over 12 hours per week for a total of three months. These courses will include practical experiments and assignments that contribute to the individual’s portfolio. The professional certificate earned at the end of the courses is branded with the IBM logo and add weight to any data science-oriented resume.

  • Cloudera Certified Associate Data Analyst (CCA)

The CCA certification requires that candidates pass the CCA159 Data Analyst exam, which is a set of 8 to 12 performance-heavy tasks on the Cloudera Enterprise cluster. Each task is allotted 120 minutes and involves analysing a problem and coming up with a technical solution that covers all bases and is highly precise. The Data Analyst training course from Cloudera helps candidates prepare for this specific exam.

By successfully completing this exam, SQL developers will be able to demonstrate core competencies in Cloudera’s CDH setup through using Hive and Impala.

Conclusion

Data analytics allows industries to revolutionise their business operations and implement insights gained from sifting through previously unstructured big data. Data analysts with any of these certifications in their kitty can expect to rise to the top of the CV pile.