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Tag: Big Data Analytics online training

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.

Python for Data Science: 5 concepts you should remember

Posted on by Imarticus Learning

Python for Data Science: 5 Concepts You Should Remember

The cheat sheet is a helpful complement to your learning since it provides the fundamentals, which are organized into five sections, that any novice needs to know to get started on data analytics courses online with Python. When learning data science, you should also have python training. Here are the main concepts. 

5 concepts in Python for Data Science

  • Variables and data types: Before you begin learning Python, you must first understand variables and data types. That should come as no surprise, given that they form the foundation of all programming languages.

Variables are used by the computer program to name and store a value for subsequent usages, such as reference or modification. You assign a value to a variable to save it. This is known as variable assignment, and it entails setting or resetting the value stored in one or more places identified by a variable name.

    • String instruments: Strings are a fundamental building component of computer languages in general, and Python is no exception. When it comes to dealing with strings, you’ll need to understand a few string operations and procedures.

 

  • Lists: Lists, on the other hand, will appear to be more useful right away. Lists are used to keep track of an ordered collection of elements that may or may not be of distinct sorts. Commas divide the elements into a list, which is encased in square brackets.
  • Tuple: A tuple is an ordered collection of immutable objects. Tuples are lists of sequences. Tuples and lists vary in that tuples cannot be altered, although lists may, and tuples use parentheses while lists use square brackets.

 

  • Dictionaries and Libraries: Python dictionaries allow you to link together disparate pieces of data. In a dictionary, each item of data is kept as a key-value pair. Python returns the value associated with a key when you specify one. All key-value pairs, all keys, and all values may be traversed. When you’ve mastered the fundamentals of Python, though, it’s time to move on to the Python data science libraries. You should look at pandas, NumPy, scikit-learn, and matplotlib, which are the most popular.

Installing Python

If you haven’t already, you should install Python now that you’ve covered some of the fundamentals. Consider installing Anaconda or another Python distribution. It is the most popular open data science platform, and it is based on Python. The most significant benefit of installing Anaconda is that you have immediate access to over 720 packages that can be installed via conda.

However, a dependency and environment manager, as well as Spyder’s integrated development environment, are included (IDE). As if these tools weren’t enough, you also receive the Jupyter Notebook, an interactive data science environment that lets you utilize your favorite data science tools while easily sharing your code and analyses. In a nutshell, everything you’ll need to get started with Python data science!

After you’ve imported the libraries you’ll need for data science, you’ll probably need to import the NumPy array, which is the most significant data structure for scientific computing in Python.

Conclusion

Here at Imarticus, we offer python training and tools to learn data science via our data analytics courses online. Come visit us today and start your career in data science online. 

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Python Coding Tips For Beginners

Python For Beginners – What Is Python And Why Is It Used?

Why is Python of Paramount Importance in Data Analytics?

Python Developer Salary In Terms Of Job Roles

Does a Tableau Certification Really Matter? Here’s What You Need To Know

Posted on by Imarticus Learning

Tableau presently offers five different certifications. Here, we’ll go through each one, their distinctions, and the necessity of combining them with the Data analytics & machine learning course that we provide at Imarticus in order to get a decent Tableau certification salary.

What does it mean to get certified in Tableau?

Certification in any tool is a method to add proof of your abilities to your resume. As we develop in our usage of the tool and strengthen our analytical skills, the different levels assist in establishing a step-by-step approach to building our framework of possibilities with it.

Once we’ve determined it’s time to earn a Tableau Certification, we must follow what Tableau refers to as Exam Guide Prep, which is a series of instructions or recommendations that must be followed before taking any of the three levels of Tableau Desktop or the two levels of Tableau Server. 

big data analytics courseCertification Format

You may download the certification preparation guide from various pages to go over the different points on what is actually necessary to consider so that you can walk into the test with the best possible preparation. This is a $250 fee-based certification that is valid for two years from the date of purchase.

First and foremost, the test consists of a total of 36 questions that must be answered in two hours. These 36 questions might come in a variety of forms:

  • Practical questions (or Hand-On Questions): These are questions in which the statement indicates which file we must utilize (it’s in a folder on the virtual machine’s desktop) as well as the analysis’s real query. The use of level of detail (or LOD) expressions, table calculations such as the difference from, the difference in percentage from a ranking, or even a moving average calculation, and, finally, answering the question using different graphs such as a Bullet Graph, a Pareto Chart, or a Box and Whisker Plot are all examples of these types of questions. 
  • Theoretical questions: These may be divided into two categories: those in which we are asked if the argument is true or wrong, with only one potential answer, and those in which we are asked to choose all that apply, in which case more than one answer is required to respond correctly. These questions cover a wide range of topics, including how to use various data sources in Tableau Desktop, different types of computations or ways to integrate data in the tool, and various actions and tools that may be used to get the most out of Tableau’s interactivity.

The minimum passing score for the certification is 75%, which must be achieved on both types of questions that a candidate may encounter on the test. However, not all questions on the test are equal in value; those with practical substance will be the most useful, followed by multiple-choice questions with theoretical information, with true/false and single-choice responses coming in last. It’s worth noting that Tableau requires all potential options to be picked for the right response, thus a partially accurate answer will not get you any points.

The test is 2 hours long in total, although it is advised that a longer time be allocated in case there are additional duties relating to the virtual machine’s configuration and for the proctor to ensure that everything is in order before beginning the exam.

Skills to be Assessed

These four abilities will be examined during the exam, according to the Tableau Desktop Specialist study guide: 

  • Connect to and prepare data 
  • Exploring and analyzing data 
  • Sharing information 
  • Understanding Tableau concepts

Conclusion

Data literacy is more critical than ever before. There’s always something new to learn at college, whether you’re a freshman or a senior. Here at Imarticus, we encourage all our students taking the Data analytics & machine learning course to learn Tableau to access the Tableau certification salary in the market today. Come and visit us at Imarticus to learn more about Tableau Certification.

How big data analytics is used in social media

Posted on by Imarticus Learning

Almost all of us are familiar with social media in 2021. There are many social media platforms run by different companies in the world. Not to forget, the large number of social media users that have been added in recent years. With the rise of social media, the amount of data produced by different platforms is unmatchable. The likes, shares, and comments across social media platforms contain information regarding user behavior.

It is why business organizations are using big data analytics to make the best use of the data available on social media platforms. Big data analytics is widely used in social media to shape marketing strategies and much more. Read on to know seven ways how big data analytics is used in social media.

  • Omnichannel presence

Many business applications and websites have a social media integration. Customers can log in to a business application using their social media credentials. It helps businesses to collect customer data from social media platforms and use them to provide better services. You can get access to social media posts, browser history, and much more. Since customers have an omnichannel presence, you can collect data from all sources to know more about the preferences of customers.

  • Real-time activity monitoring

Social media is a place where you immediately get to know when someone has liked a post or shared a product link. Businesses monitor the activity of customers on social media to know about their current mood. If a social media user is liking your product posts, you can show them an email quickly to convert them into a customer. No other platform can inform customer preferences in real-time other than social media. Big data is used extensively to collect real-time activity reports on social media.

  • Forecasting

When big data is mixed with modern-day technologies like ML and AI, it can predict customer preferences. Based on customer habits on social media, AI/ML algorithms predict their demands. Businesses then focus on releasing new products/services as per the future demands. For example, when a customer buys something online, the chances of them buying similar products increase.

  • Security

Data vendors cannot illegally transfer customer data to the wrong hands. When customers share data on social media, that data can only be used for business purposes. Your social media data cannot be placed in the wrong hands by business organizations. Big data is used for enhancing the security of social media platforms based on customer suggestions.

 

  • Campaign monitoring

 

Marketers run social media campaigns to boost their ROI (Return on Investment). Using big data, marketers can know how well a social media campaign has performed. Young aspirants can go for big data training to know more about how to run social media campaigns and study high-end analytics.

 

  • Product pricing

 

When a firm launches its product on social media, customers give their valuable opinions. Social media is widely used to determine whether customers are satisfied with the pricing of a product or not. Big data training includes data collection from social media channels and how to analyze them.

 

  • Ad creation

 

Social media is used to collect info about customer preferences. Based on that info, targeted, and personalized advertisements are displayed on social media channels. Technologies like Hadoop programming and Python programming are also used by big data analysts in social media.

Young aspirants can go for the big data analytics programs launched by Imarticus Learning. Its PG Program in ML and Data Analytics can help working professionals in getting a raise. Start your big data course and learn Python and Hadoop programming!

What is the best approach to Data Analysis in 2021?

Posted on by Imarticus Learning

As time goes by the approach to any form or strategy of business changes. Even more so when it comes to data, as it is ever-changing. Throughout the years, data analytics has grown from a peripheral part of a business to an integral part of it. It is a prominent tool to not only extract and decipher past records but also predict and develop future strategies.

However, it comes with a little difficulty as most of the time, data analysts have little idea of how to execute a business. And in the same way, executives have little idea of how data analytics works and how to use it.

This is why it is important to have a basic idea of what is business analytics to give you that little edge over others. The best way to do this would be with a data analytics certification course, similar to what Imarticus Learning is providing with their new PG program. However, to make it easier, here we have compiled two successful ways that data analytics can be approached in 2021.

For the long run

In many cases, companies invest a lot in data analytics and focus on building business value around it. This includes training the employees about data analytics, developing company systems and syncing them with data analysis, and finally, discussing more data analysis initiatives. This requires a complete transformation of business values and systems.

Tackling clearly-define high-grade problems

One of the best ways to approach data analysis is by defining a high-grade problem with clear goals. When it comes to high-grade problems, the issue remains in the volume or layers in which a problem is divided. Each of these layers interplays and pile up to end up being a high-grade problem.

So, with data analytics, you can target a small subset of the problem and by getting the numbers right, you can take a sustained route towards growth. Unlike the investment model where too much time could pass before results show, this model seems clearly the best approach in 2021.

Things to keep in mind

One thing to always keep in mind is that data analytics merely show a record of how the past has been and how the future can turn out to be. It is not always possible to meet the exact expectations as the results depend on a variety of things. There are a few other things that everyone should keep in mind when it comes to data analytics, such as:

  • Data analytics need to be made a part of the main wing of a business as without being in the loop it would be impossible for the analytics team to be of any help.
  • Problems should be specified and addressed together with the executives. This will make the process of extracting and deciphering data as well as developing a proper plan to address the specified problems a lot easier on both sides.
  • Executives should have a basic knowledge of what is business analytics. It will make the communication and understanding between the two teams smoother.

 Conclusion

 The process can create disruptions as data tends to create transparency. It can be uncomfortable to face the shortcomings of a business, however, this is also the best way to form strategies to overcome those shortcomings. This is why companies need experts on their teams to help them along the way. You can enhance your skills with a proper data analytics certification course or a PG program if you wish to pursue this career.

Why Is It So Easy To Get an Analytics & AI Certification, But a Struggle To Get The Right Job Interview Opportunities ? Imarticus Has an Answer

Posted on by Imarticus Learning

The data science domain is expanding. With it, the demand for AI, ML, NLP, and other data science careers is also rising. Reports suggest that there were 2.9 million data science job openings in 2019 alone and by 2026, this number is going to go up to 11.5 million.

Despite this huge demand, getting a good job in the AI and Analytics field is a real struggle. Many aspiring people do many courses in different branches, be it computer visions or deep learning. But when it comes to securing a job, a lot of them find it extremely difficult.

Key Reason for Difficulty in Job Seeking

While most of the people blame it on the companies that they don’t understand what they want, the harsh reality is that most of the aspirants in this sector lack the important skills required to get a good job interview opportunities. Many candidates are not competent enough to grab a job role in data analytics or the AI field. Data science is a complex field that requires an extensive amount of knowledge and a high-level skill-set.

Though many students do certificate courses from various institutions online, most of the time they do not get taught and trained the right way. It results in students missing out on important concepts, analytics, and numerical aptitude. When these students go out in the real world, they end up facing rejection on their job applications.

The thing is if students want to build careers in the data science field, they need to be exceptionally well at it. For this, they need to pursue a course that builds their foundational and advanced knowledge in-depth and provides them with the crucial practical skills required in the real world.

Imarticus Learning PGA – The right way

Imarticus Learning offers a Post Graduate Program in Analytics & Artificial Intelligence that teaches the in-demand tech skills in the job market at present. The teaching method involves engaging videos, live sessions, and exercises.

data analytics courses in IndiaIndustry professionals having years of experience in this field deliver ML, computer visions, and deep learning training projects.

Upon completion of the post-graduate program in data analytics course, Not only but students also get guaranteed job interview opportunities and placements in leading companies.

The PGA course covers theory and practical learning on the following subjects:

  • Data Science Fundamentals
  • Deep Learning
  • Machine Learning
  • Computer Visions
  • Natural Language Processing
  • Placement Preparation

During the entire duration of the course, students are provided with a mentor who supports them in their academic journey and guides them in their career path. The mentor performs the following roles:

  • Motivate and Inspire to engage in projects and their completion.
  • Monitor your grades and potential.
  • Build meaningful connections that will help you beyond the course.

By studying the PGA course with Imarticus Learning, students get complete support and guidance throughout their learning process. The career services provided with this course include:

  • Guaranteed Jobs
  • Resume Building
  • Interview Preparation
  • Profile Enhancement

Job Roles after PGA Course

Upon successful completion of the Imarticus Learning PGA course, you can find suitable careers in:

  • Business Analyst
  • Artificial Intelligence Engineer
  • Data Analyst
  • Machine Learning Engineer
  • Data Science/Machine Learning Consultant
  • Web & Social Media Analyst
  • Machine Learning Architect
  • Data Scientist

The salary packages in these job roles are quite lucrative. The average pay scale in Analytics & AI roles is around INR 5 LPA to 25 LPA.

Joining Procedure of PGA Course

Candidates interested in Analytics & Artificial Intelligence can join this program for building a successful career in this field. The joining procedure is simple and includes three steps:

  • Detailed Profile Check
  • Online Tests
  • Enroll

The prerequisites for the course include a graduation degree with a minimum of 60% and a basic understanding of mathematics and programming. With data analytics course, students can get the best education and make their dream of becoming a professional in this field come true.

What are the Big Data Use Cases That Explains Digital Transformation?

Posted on by Imarticus Learning

The business world is transforming. Big data is influencing business decisions, marketing optimizations, business predictions, and day-to-day operations. Big data is expected to reach $300 billion by 2023. Though the term ‘Digital Transformation’ is loosely used as a euphemism for the modernization of data infrastructure, the true meaning of the word is becoming vital for the business world.

big data analytics courses in IndiaIt is true that the organizations are increasingly becoming data-centric, that they allot nearly 40 percent of their technology budget for digital transformation.

Data analysis courses facilitate faster, better, and smarter business decisions helping to achieve better operational efficiency, building a loyal client base, and leading to higher profit. Let us have a look at some use cases that explain this transformation.

Banking

Banks need to deal with a massive amount of financial information. Big data is used in banking to structure the collected data, organize them based on the business needs and analyze the data to derive insights on spending patterns, storing historical data like the transactions, payment records, etc. It also helps in identifying fraudulent actions and ensuring preventive measures are in place to block such frauds.

Another area of big data application is to prevent identity thefts. The banks use big data to keep a tab on customer actions. For example, if a customer follows regular spending patterns using the credit card such as groceries and general shopping, and all of a sudden there is a change in the behavior, such as spending money in gas stations and convenience stores from multiple locations across the city, the bank suspects something unusual. In this case, the bank may try to call the customer and find out if the card is stolen.

Online Shopping

Imagine you visited an online store and added some products to your cart. On second thought, you abandoned the cart and signed out without making any purchase. After some time, upon casual browsing, you see an advertisement for the same product you had planned to purchase.

The next day, you receive a mail with a discount offer for the same item. Targeted advertising is another application of big data. Online stores watch every movement of their customer from the moment they enter the store. They keep track of the pages you visited, the product you intended to buy by adding to the cart, etc. They use all this data to show targeted ads with irresistible offers that finally nudge you to purchase them.

Travel and Logistics

Big data facilitates the smooth running of logistics and transportation. It helps in scheduling flights, to show the booking status and seat availability, seasonal fluctuation in booking, etc. It also collects data regarding weather across the globe to predict flight delays. Big data is also used to analyze user trends and to predict the number of airplanes needed to cater to the increasing number of passengers. It could also be used to perform competitor analysis.

big data analytics courses in IndiaGiven above are some use cases that explain the application of big data in various industries. Apart from this, education, healthcare, digital advertising, environmental studies, human rights, etc. are some prominent areas that use big data to streamline their actions.

If you aspire to start a career in big data, this is the perfect time to enroll in a big data analyst course and secure a lucrative career.

Augmented Analytics: The Future of Data & Analytics!

Posted on by Imarticus Learning

Augmented analysis simplifies data analysis and helps in getting insights. It is used by firms/companies to forecast better and to automate data analysis processes. Augmented analyses use enabling technologies like AI and machine learning to help speed up and automate data analysis processes.

You can manage your business data up to an extent when the data generated is large one needs augmented analysis to manage big data sets and extract insights. Less time will be spent on understanding the data with the help of augmented analysis. Let us see how augmented analysis is the future of data & analytics.

Benefits of Augmented Analysis

The pros of augmented analysis are as follows:

  • You can automate data analysis processes like data cleaning, forecasting, data management, etc. with the help of augmented analysis.
  • Expert developers can build better business models with the help of insights via augmented analysis. The accuracy of predicting trends and business opportunities increases.
  • Data preparation under which the data is classified and arranged in a structured manner is a very tedious chore. This process can be automated via augmented analysis.
  • The augmented analysis will help you in cost optimization as it will help you in using less human labor and more automation. Your developers can provide you with more insights with the help of augmented analysis.
  • Risk identification and management can be done properly with the help of augmented analysis.
  • Data insights can be represented in natural language statements to people who are not into data analysis. In organizations, augmented analysis helps in conveying business insights and forecasting results to all employees.
  • Data security and privacy can be managed better via augmented analysis. Any anomaly in the data set can be identified immediately and can be managed. It also helps in adhering to data regulations laid by the regulatory authorities.

 Why Big Data Management is Necessary?

The augmented analysis helps in big data management as such a large amount of data cannot be managed manually. Big data management is done by firms to know about market trends, opportunities, market volatility, etc. It also helps in knowing about the buying habits of customers/clients. You can target an audience of any particular age group, locality, gender, by using data insights. You will stay ahead of your competitors and grab potential opportunities.

big data analytics courses One can learn big data management via Big Data Analytics Course from a trusted source like Imarticus Learning.

Data Analytics Future

The latest technologies like machine learning, deep learning, AI, etc. are shaping the way the data analytics industry worked.

Companies and firms are producing more data every day and a lot of businesses are shifting online, to manage this data augmented analysis is being adopted by firms. Developers can boast a more successful Big Data Analytics Career via augmented analysis as it will increase their analytics ability.

Conclusion

Data analytics is one of the fastest-growing sectors recently. Companies and firms are investing in data analytics to gain profits in the long run. One can learn from Big Data Analytics Courses available on the internet to build a successful Big Data Analytics Career. Grab your online course now!

Understanding the Role of Data Analytics for E-Commerce Success!

Posted on by Imarticus Learning

The demand for data analytics in e-commerce is on the rise. Data analytics is a key tool for understanding the behavior of your customers and prospects, allowing you to make data-driven decisions. Data specialists have a promising future in this industry, and companies are clamoring to find talented individuals who can help them better understand their consumers’ desires.

The increased use of data analytics will be pivotal for businesses that want to stay competitive in today’s marketplace. This blog post will discuss how data analytics can help e-commerce businesses succeed in today’s competitive marketplace.

Role of Data Analytics in E-commerce Success

Data analytics plays an increasing role in e-commerce success. Companies are looking for data analysts with the skills to help them gain a competitive advantage by understanding customer needs, behaviors, and trends within their industry. More specifically, companies rely on data analytics professionals to find:

  • Targeted customers that they would not have reached otherwise
  • Deep insights into areas such as product development and merchandising
  • Actionable information on factors such as click rates or session times across devices (desktop vs. mobile) can be used to improve online conversion rates

In addition, these new positions require individuals who can communicate effectively throughout a company’s hierarchy—from senior management down to marketing teams—to ensure all stakeholders have access to the information they need to make informed decisions.

Data is also playing an increasing role in offline commerce. The challenge of understanding customer behavior across channels has led to the increased demand for data analysts with a comprehensive view of all aspects, including brick-and-mortar retail and online sales and promotional efforts.

A growing number of companies are seeking specialists who can collect, cleanse, manage and analyze large amounts of disparate datasets at scale, helping them gain insights about their customers that were previously hidden or difficult to address.

Companies have access to more sources than ever before—from internal company databases such as CRM systems (customer relationship management) and ERP systems (enterprise planning) through social media platforms such as Facebook or Twitter. In addition, companies are finding new ways to interact with their customers and gather data through mobile apps and other interactive tools that enable them to obtain information about customer preferences (e.g., location) in real-time.

What kind of skills are required to succeed as a data specialist?

For a successful data analyst, it is important to have strong analytical skills. You need to ask the right questions and then find and understand relevant information (data). Some of the other qualities that are needed include:

  • Ability to work with large datasets
  • Attention to details
  • Analytical thinking rather than creative thinking
  • Ability to identify patterns
  • In-depth knowledge of statistical concepts like standard deviation, correlation, and regression analysis.

Have a promising data specialists career with Imarticus Learning

Big data analytics courses in IndiaImarticus Learning offers a data analytics certification course that prepares you for a high-growth career. Our unique combination of analytics courses helps students build strong foundational skills and practical industry exposure to build a successful career in the field of Data Science and Business Intelligence.

This course is developed and taught by industry experts with significant experience in analytics. It includes a range of topics, from basic statistics & probability theory to advanced machine learning techniques. This course will help you move forward in your career!

For more details, contact us through the Live Chat Support system or visit any of our training centers based in – Chennai, Mumbai, Thane, Pune, Bengaluru Hyderabad, Delhi, and Gurgaon

What is Big Data Analytics?

Posted on by Imarticus Learning

Big data can always be related to massive volumes of data generated directly by the company or acquired from various sources. However, in order to truly utilize the data to its full potential, companies are always in dire need of skilled data analysts or business analysts. Big Data Analytics allows companies to use this data to their advantage by gaining valuable insights from this data, thus making more effective data-backed decisions.

Data Analytics Certification Courses with placement in IndiaThe Big Data Analytics course positively affects sales, decreases risks, optimizes production, helps understand customer behavior, and even allows companies to come up with more efficient marketing strategies.

This is all possible due to Big Data, Data Science, and analytics. Budding analysts are always advised to join well-orchestrated Data Science courses in order to acquire the skills required to effectively analyze the data.

The true intention of Big Data Analytics is always to promote more efficient use of resources and getting better results through data-centric suggestions from business analysts. It is highly recommended to opt for reputed business analytics courses in order to productively contribute to this field. Constructing data-based predictions is the key to making businesses run adequately.

Why is Big Data Analytics Important?

Fundamentally, Big Data Analytics allows companies to discover patterns, trends, and correlations from massive amounts of data. This allows companies to use the newly acquired information to their benefit, thus making more effective business decisions, increasing profits, and satisfying customers better.

From helping in marketing campaigns to improving production capabilities, Big Data Analytics can help with any sector that generates data. Analytics help companies make customers happier through understanding customer behavior and trends better.

Fintech courseData scientists and analysts help companies massively cut down operational costs by resources much more effectively. Decades back, even though “Big Data” did not exist, business analytics did. However, it was not a cost-effective process and demanded a lot of time.

Big Data Analytics online training allows data scientists and analysts to make this process much faster. With the advent of AI in analytics, Big Data Analytics can almost provide real-time insights from incoming data.

What is Data Science?

The value Data Science brings to business analytics is immense. It is responsible for everything in the process. Starting from sourcing the data and modeling it to the time that the required insights are acquired, Data Science powers it all. Data Science allows companies to effectively source transactional or raw data from various sources and then process it. This is where analysts come into play, they analyze this data and then help companies visualize the data.

What are the requirements to become a Data analyst?

How to become a data analyst? It might sound complex but it is truly not that hard. There are many good Data Science courses or business analytics courses available online and offline that one can choose from. However, care must be taken when choosing a Data Science course in India or outside, ensuring that you get the best out of your time and energy. Any good Data Science course in India will cover the fundamental skills required to become a data analyst and prepare students to effectively use analytics tools when working with Big Data or data in general.

 

Conclusion

Big Data makes it possible for organizations to store and harness the capabilities of massive amounts of both structured and unstructured data. Even though what Data scientists do is similar, Big Data Analytics requires exceptional skills when working with large volumes of data. A good Data Science course in India is highly recommended such as this PG program in order to gather the required skills to work with Big Data and Analytics.

Related Article:

What is the difference between data science and data analytics?