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Tag: data analytics online training

Learn Computer Vision: What is Hyberbolic Image Segmentatioan?

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Learn Computer Vision: What is Hyberbolic Image Segmentation?

Currently, we carry out optimization at the pixel level in Euclidean embedding spaces for segmenting images. We do this through linear hyperplanes (2D visualization). Keep reading to clear your concept for the Artificial Intelligence Course regarding a key alternative for image segmentation that is done in hyperbolic space. 

Computer Vision is one of the most exciting topics covered in Artificial Intelligence and Machine Learning Certification. This field allows systems and computers to retrieve important information from digital visuals like images and videos. Based on the received data, computers process information to make suggestions to the user. 

If we were to simplify the concept, computer vision tries to make computers view images and videos the way humans do. Today, the advancement in deep learning and neural networks has made these systems exceed human performances in some aspects like object detection. 

Today, spherical and Euclidean embeddings dominate the most-used tasks of computer vision like image retrieval and image classification. 

On the other hand, Hyperbolic Image Segmentation is one of the latest standards for segmenting images. It offers multiple practical benefits like: 

  • Uncertainty estimation
  • Boundary information
  • Zero-label generalization 
  • Increased performance in embeddings of low-dimension

Why do we use Hyperbolic Image Embeddings? 

In Natural Language Processing (NLP) tasks, hierarchies are ubiquitous. The widespread presence of these tasks motivates the use of hyperbolic spaces in this field. This is because hyperbolic spaces inherently embed tree graphs and other types of hierarchies with minimum distortion. 

While retrieving an image, you will notice that an overview picture of something can be mapped to the closeups of many unrelated pictures. These pictures might have a wide range of dissimilarities in their details. 

Furthermore, let’s consider classification tasks. For such tasks, an image that contains representations from many classes is generally connected to images that possess the representatives of those classes in insulation. Thus, the process of embedding such a dataset, which contains composite images, into a continuous space is said to be similar to hierarchy embedding. 

There are also some tasks where generic images are used. These images could be related to obscure images because they lack much information. For example, if face recognition software is run over an image that contains a blurry face, the software could match the unclear image with the high-resolution images of many different people. 

There are several inherent hierarchies in NLP that go beyond to reach the visual region. For instance, you can use hierarchical grouping to visually represent different species of plants. 

Collectively, using hierarchical relations in AI increases the demand for hyperbolic spaces for output embedding. As the volume of Euclidean spaces expands, the resulting expansion is polynomial in nature. However, the expansion of hyperbolic spaces is exponential. This results in the generation of continuous tree analogues. 

This information makes it possible to conclude that the unrevealed hierarchy of visual information can be captured by the expanding hyperbolic output embedding. 

Get an Industry-Ready Artificial Intelligence and Machine Learning Certification

During the certification course you will get the insights of the artificial intelligence and machine learning concepts, latest trends and much more to achieve success in the field.  You can become a part of this global institute by joining our centers in cities like Mumbai, Pune, Lucknow, Chandigarh, Hyderabad, Kolkata, etc. Get in touch with our team by emailing info@imarticus.com to know more about the programs. 

The best prediction models that win big this IPL

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The best prediction models that win big this IPL 

The Indian Premier League (IPL) is the most popular and exciting cricket league in the world. There will be ten teams competing against each other this IPL season 2022 for the trophy. Since the start of IPL in 2008, it has grabbed the attention and interest of people from all over the globe. The high level of uncertainty of the matches, and last-minute wins and losses have only increased the viewer count for this cricketing event over the years. And who doesn’t like knowing which team will win that particular match, or even the entire season in advance? Between the matches, you might have also seen a scoreline at the bottom of your television screens showing the winning probability of the two teams. You’d have also seen the probability changing every over for the teams. That is based on the number of runs scored and wickets taken in that particular over. All these predictions are made with the help of data analytics, deep learning, and machine learning. 

Humans cannot analyze extremely huge sets of data, and that is where data analytics and machine learning come into the picture. Predicting the result of an IPL match is a massive work. It includes consolidating the data, analyzing it, and then predicting the result through the number of runs scored, wickets taken, wins and losses of any particular team from the past seasons, and so much more. Imagine the amount of data one would have to analyze for that! Data Analytics makes the work a lot easier.

Data Analytics and Data Science are not rocket science. It only gets easier and more interesting as one starts pursuing it. Doing a data analytics course would help any individual predict the wins of the IPL matches forever! Most of the data science and data analytics courses can also be done online, in the comfort and convenience of one’s own house. Learn Data Analytics online with Imarticus.

STEP-BY-STEP IMPLEMENTATION OF PREDICTION MODEL:

Step 1: Data extraction!

Extract a data sheet that contains all the details of every IPL player from as many seasons as possible.

Step 2: Data cleaning and formatting

Keep the required data sets only.

Step 3: Encoding the categorical data to numerical values.

Encode the raw data into numerical values that make sense to the computer.

Step 4: Feature Engineering and Selection

Divide the data into train sets and test sets before using a machine learning algorithm. Also, scale the data before processing it to make the model less complicated.

Step 5: Building, Training & Testing the Model

Building the correct prediction model using a computer language is crucial. The model can use functions like Sequential and mean squared error, and algorithms like Adam Optimizer, etc. The prediction models can be of a wide range, and multiple kinds of functions and algorithms can be used.

Step 6: Prediction

Create a data frame that shows the actual values and the predicted values. If done correctly, the model will predict the results of the IPL matches at maximum accuracy. It will give almost similar scores. To find out the difference between the actual and predicted scores more accurately, performance metrics will show the error rate using mean_absolute_error and mean_squared_error. 

 Imagine how much time it would have taken us to do all this! But as it can be seen, the above steps in a procedural manner can simplify problem-solving and are generally preferred in the industry. Predicting an IPL match can be as easy for you too! Learn Data Science and Data Analytics courses online with Imarticus. 

Does data analysis using SQL for software libraries?

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Introduction

If you love tackling complex algorithms, solving tricky mathematical problems, or are simply attracted to your computer or similar electronic/digital devices by nature of being a geek, then the life of a data scientist is what you should be running after.

One of the most sought-after jobs in the 21st century, becoming a data scientist requires some basic knowledge of core concepts of big data training. These concepts are discussed briefly in the article below.

What is Data?

Fundamentally, data refer to bits and pieces of information that are stored and formatted in a manner that is consistent with the rest of the data for a particular purpose. Data can be found to exist in any format, whether it be on paper, in figures or as bytes in an electronic device, or even as nuggets of information in a person’s brain. However, in the era of the 4th industrial revolution, any mention of data generally denotes the kind that can be transmitted electronically.

Since data has become the lingua franca in the IT industry, it is imperative that we also explore the many dimensions of data through its many phrases old and new:

  • Structured data

Data existing in a fixed field such as spreadsheets and datasets are referred to as structured data

  • Unstructured data

Information or data that is not arranged in a column-row format

  • Raw data

Information yet to be analyzed and formatted

  • Metadata

A data set that has been summarized

  • Big data

A huge corpus of both structured as well as unstructured data that cannot be processed in the traditional manner of software technologies and database

  • Big data analytics

The method of organizing, collecting, and synthesizing data to explore useful information

  • Data mining

Data applications are used to figure out patterns that cannot be seen with the naked eye from a group of data in order to predict the behavior of something in the future.

How is SQL Relevant for Data Science?

SQL or Structured Query Language is a type of programming language used in standard databases for data management systems. It was conceived in the 1970s by IBM and has now become a standard process recognized by International Organization for Standardization (ISO) and American National Standards Institute (ANSI).

This programming language is employed for better management of relational databases and performance of operations on the data contained within them.

When we speak of SQL for database management, we speak of language’s capability to engage with more than one database at the same time. SQL is highly popular for data science since it is highly flexible and offers a great accessible learning curve with respect to complex depth. This enables users to create advanced data boards and tools for analyzing data.

To create and engage with databases efficiently, SQL has been utilized in a variety of tools, each with its niche market and focus. This includes big names such as Microsoft Access, PostgreSQL, and MySQL.  

Nowadays, big data is a huge concept, and learning how to use SQL for big data analysis can be a great skill that will open up big opportunities for you worldwide.

Benefits of a Big Data Training

To understand how a big data analytics certification course with a SQL specialization is helpful, let us look at the points below:

  1.     Big data training as part of business analytics courses will be a significant skill for top organizations
  2.     Data specialists are in high demand, and a big data analytics certification with SQL specialization will be a wise career choice for you for a guaranteed upward trajectory in terms of progress.
  3.     The role of a data specialist is a high-paying job
  4.     You can work across industries such as data quality, higher education, sales, financial institutions, marketing, business intelligence, and more!
  5.     Your big data training and SQL skills will put you at the forefront of important decision-making groups in your organization.

Conclusion

In the information era, if you want to carve a place for yourself in the digital world big data training with SQL specialization will be highly beneficial for you.

Here’s how you can excel in a data analytics course with placement

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Here’s How You Can Excel In A Data Analytics Course With Placement

In the digital age, most companies use advanced technology in their business which, in turn, creates a lot of data in the form of several digital footprints. Now humanly, it is not possible to comb through such digital footprints and find trends or patterns which could benefit the business.

Here, the concept of data analytics comes into play which can dig deep and provide meaningful insights that will not only help to find trends but will also help the business to grow. And, this has made business analytics courses one of the sought-after training courses for students globally.

Types of Data Analytics

There are 4 types of data analytics:

Descriptive Analytics – With the usage of key performance indicators (KPI), it answers particular questions like ‘what happened?’ and ‘what is happening?’ and gauges the success or failures of methods implemented in the business.

Diagnostic Analytics – To easily summarise, it is considered the upgraded version of descriptive analytics. It goes further digging into the raw data and provides information about ‘why it failed?’ or ‘why it succeeded?’

Predictive Analytics – From the name, it can be understood that it predicts the future outcome of any initiative by finding key patterns or trends. It also sheds light on the fact further it will again happen or not.

Prescriptive Analytics – Heavily dependent on machine learning, this process collects data from the predictive analysis and provides insights on ‘how to get the work done?’ and is a great way to avoid rash decisions. 

What advantages would you have with a certification in data analytics?

The processes that are involved in data analytics are data mining, data management, statistical analysis, and data presentation. After learning these processes, your abilities would include: 

Firstly, the omission of guesswork which, in turn, will help to plan proper designs for various business models.

Providing tailor-made customer service is one of the key strategies for a successful business. With its cutting-edge technology, it would analyze the interests and concerns of the customers and would recommend in the same manner creating a trustable customer-company relationship.

With proper information on the table, it would benefit one by cutting the budget and saving valuable time. Both of these precious elements can be invested in other places for further development.

The leads which were once lost in the tons of data now can be easily converted into potential customers, and also, it is one of the most demanding professions in the world according to reports by Forbes.

The sectors that have implemented data analytics are: 

Retail Sector – By using data analytics, retailers understand the trends and the needs of their customers. And then, supply them with their want hence increasing their profit.

Financial Sector – There has been an extreme rise in loan scams and frauds globally. In the financial sector, data analytics has been a blessing that has helped to curb these scams to a great extent.

Logistics – Data analytics helps to provide efficient safe routes which, in turn, helps the companies to deliver desired materials in less time conveniently.

Healthcare – It has had a great impact on the healthcare sector, as it not only helps to develop new methods for preparing drugs but also helps in accurate diagnostics of patients and thus providing them with proper treatment.

Conclusion

With the advancement of technology, a career in data analytics seems to be a smart choice, and for doing so, you can definitely check out data analytics certification courses online designed by Imarticus Learning. The course is implemented with real business projects, case studies and mentorships which will help you excel in the corporate world.

Tech Talks: Use of Data Analytics in improving working Capital Management

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What is working capital management?

Working capital is an organization’s utilization of money to cover its daily needs, such as paying for raw materials, supplies, and salaries. The term can also be applied to individuals. Working Capital Management is defined as “managing cash flow so that it fulfills all the business needs”.

For example, if you have $10 in your wallet but need $100 worth of groceries today, your working capital would be negative $90. Working capital management takes care of the flow of funds within the organization. It ensures that funds are available to meet short-term obligations without having to borrow or sell assets. It’s essential for all businesses because it affects the growth and the profits of the company.

Without sufficient working capital, companies will fail before utilizing their full potential. Working capital management is a critical function for every company. Whether you are operating in the manufacturing or service industry, managing your working capital will impact your ability to grow and succeed. 

How does it help the organizations?

Data analytics can help organizations measure how much money they need for their working capital based on their current situation. This way, they can improve their working capital management by minimizing risks such as overinvesting or underinvesting in one area while neglecting others.

There is a need for both MSMEs and large manufacturers to remotely manage their supply chain, cash flows, etc. This has led to a rapid and massive shift away from manual processes. This is where automation comes into play – Accounts Receivable Automation (ARA) was developed as an alternative solution by many companies who needed more control in this area of their business while still managing all aspects with less workforce. These systems allow businesses to deal directly with suppliers and it drastically cuts down processing between payments and delivery. 

Application in the real world 

In the past few years, the use of Data Analytics has been steadily increasing as a way for organizations to understand their customers better and identify trends. 

In today’s world, data analytics is indispensable as it facilitates the efficient working of an organization. The proper recording and analysis of every activity related to the manufacturing cycle of the products help in having visibility of the processes. 

One particular area where Data Analytics can be applied is in examining customer payment patterns, such as when customers pay or don’t pay on time. For example, one company found that because of the customers who paid late, they were losing roughly about $21 million annually due to delayed payments from other clients. It also examined the reasons why people pay late. They came up with several insights like cash crunch during month-ends, etc. that stopped them from making these payments. 

Using a company’s balance sheet and cash flow statement, a financial analyst can determine when the business has excess funds and also the times when they need more money. This analysis can then be used to establish an appropriate financing strategy that balances the company’s needs with its ability to repay the debt over time. Data Analytics makes the entire process smoother and better.

Conclusion

To maintain change, it is imperative to differentiate between noise and signal. This is done by developing measurable, granular  KPIs that are monitored strictly. Carefully analyzing historical data can provide valuable insights into managing networking capital by quickly finding and dealing with emerging issues.

Contact us today if you want to be well equipped when it comes to dealing with such situations. With a digital analytics course, implementing these tactics in your business becomes easier.

With a data science course, you become aware of the techniques that go into it. The course comes along with a placement opportunity so that you’re all set to apply your business analytics knowledge in managing operations.

Debate the usefulness of various tools in data analytics

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To perform data analysis, professionals use tools & software that ensure the best results. There are many of them in the market, but a few are on the top list for data analysts that need to be chosen wisely to extract the best benefit. 

This article discovers the best data analyst tools and highlights key features of each based on different types of analysis processes. But first, here’s a basic definition & introduction.

What Are Data Analyst Tools?

Data analyst tools are applications & software that data analysts develop & perform analytical processes to make better, informed business decisions while keeping costs low & profits high.

Top Tools Used by Data Analysts?

1.) Business Intelligence Tools

BI tools are the most represented means to perform data analysis. Specialized in business analytics, they prove beneficial for every data analyst to monitor, analyze, and report essential findings. Self-service, advanced SQL modes and predictive analytics make them easily adjustable to every level of knowledge without immense IT involvement. 

DATAPINE covers most requirements of beginners & advanced users.

Key Features:

Visual drag-&-drop interface

Powerful predictive analytics & interactive charts & dashboards

Intelligent alarms triggered for anomaly

2.) Statistical Analysis Tools

There exist multiple programming languages that make work easier & effective. With the expansion of languages, some rules and scenarios need special attention for statistical data analysis & modeling.

R PROGRAMMING is the most popular tool for data analysts.

Key Features:

An ecosystem of more than 10k packages & extensions for data analysis

Statistical analysis, modeling, & hypothesis testing

The active community of researchers, statisticians, & scientists

3.) General-Purpose Programming Languages

Programming languages help to resolve a variety of data problems. Tools like C#, Java, PHP, Julia, Ruby, and Python are many reliable solutions.

PYTHON is the most trusted tool.

Key Features:

Open-source solution with simple coding processes

Easy integration with other languages

Advanced analysis processes through ML & text mining

4.) SQL Consoles

SQL is a programming language used to manage data held in relational databases, effectively handling structured data for analysts. Highly popular in the data science community, these tools are used in various business cases & data scenarios. 

MYSQL WORKBENCH is the most popular one.

Key Features:

  • Unified visual tool for data modeling, administration, SQL development, backup, etc.
  • Instant access to the database via Object Browser
  • SQL Editor offers color syntax highlighting, reuse of SQL snippets, & execution history

5.) Unique Predictive Analytics Tools

Predictive analytics is an advanced technique to combines data mining, AI/ML, and predictive modeling to predict future events. It is an essential data analysis tool as it simplifies predictive analytics processes. 

SAS FORECASTING is an analysis software helpful for predictive analytics processes & helps analysts to predict future scenarios.

Key Features:

  • Automatic forecasting for entities or products, including hierarchical forecasting
  • Scalability & modeling to create an ensemble
  • An unlimited model repository including time-series & casual methods

Apart from the tools mentioned above, there are industry-specific analytics tools & data science platforms that assist professionals. A business analytics course can help you better understand what data analytics is!

Build a New-Age Career with Imarticus:

Imarticus Learning offers Data Science Courses, the most extensive, industry-approved experiential learning program ideal for Data Analysts. Industry experts have developed the programs to help students learn real-world applications of Data Science from scratch & build robust models to generate valuable business insights and predictions. Take a giant career leap with this analytics program.

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

7 ways how big data is used in social media

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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. business analytics certification course

  • 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. It also offers a Data Science Prodegree course is also for future data scientists. Start your big data course and learn Python and Hadoop programming!

9 trends in the data visualization job market

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Do you love to solve problems by going through huge amounts of data? Do you dream of coming up with strategies and tactics that could take businesses and companies to new heights? If the answer to these questions is yes, then a data analytics course is rightfully suited for you.

When you enroll for a data analytics certification you will oftentimes come across the term data visualization. Before we understand data visualization and what some of the hottest topics trending are, let us quickly browse through the basic idea of data analytics. 

business analytics certification courseWhat is Data Analytics?

Data analytics is the process by which one analysis raw data to draw conclusions about the acquired data. Most of the processes and methods that go into the science of data analytics have been automated into algorithms that analyze data quickly and efficiently for ready human consumption.

The word data analytics is an all-encompassing term that includes diverse data types. The technique of data analytics is useful to reveal metrics and ongoing trends that would at other times be lost in the vast ocean of existing information. The ocean accumulated can aid in optimizing processes for greater efficiency and productivity.

There are 4 types of data analytics explained briefly for your better understanding:

  1. Descriptive Analytics:

Evaluates and describe what has occurred over a fixed time

  1. Diagnostic Analytics:

Focuses on the why with greater emphasis on the diversity of data inputs

  1. Predictive Analytics:

As the name suggests, it tries to predict what is going to happen in the coming term

  1. Prescriptive Analytics:

Makes a suggestion about the possible course of action

9 Trends in Data Visualization Currently

Once you have acquired and analyzed your data, you need to be able to graphically and pictorially represent it in a way that is easily understandable. Data visualization presents you with the technologies and tools required to analyze a massive corpus of data. Here are a few ongoing trends in the field of data visualization that can help you get greater clarity on things:

  1. Big Data

This kind of data visualization will need high-performing devices that will be able to gather data, process it, and transform it into graphical representations that people can quickly draw insights from.

  1. DataOps and Self-Service BI

The term coined by Gartner enhances the quality and reduces the cycle time that goes into the operational aspect of data management. Together, these two tools aid in the smooth flow of information/data across pipelines over the entire supply chain system.

  1. Hybrid Enterprises

After the pandemic hit the world, more and more businesses are opting for cloud-based computing which signals the fact that there is an emergence and rise of product features that is hybrid in nature.

  1. Data Storytelling

This is a crucial angle of business intelligence wherein data analytics helps to revolutionize the art of data storytelling. As organizations strive to build a thriving culture, data storytelling methods enable conversations to be nurtured around data.

  1. Adaptable BI Ecosystem

Developers have made it easy to personalize experiences with respect to a company’s flow of data. Analytic apps have enriched the system reducing the time required for data-based actions over BI loops. With the help of the BI ecosystem, the workflow would run more smoothly making it feel as if it were a unified system.

  1. Agile Strategies

As data is moving at a break-neck speed, firms and organizations would need to re-evaluate how they can strategize data to make it more agile and flexible. Cloud computing can be highly useful in this regard.

  1. Non-Technical Professionals

Non-Technical Professionals also referred to as Citizen Data Communicators, are scientists who can communicate with data effectively and easily. Post-2021 will see an epic rise in data scientists as data visualization becomes the norm and the universal language across business ventures.

  1. Operationalized Data Lake

Data Lake is a popular concept that acts as a storage area for enterprises. A data lake is highly preferred because it offers an economical way of storing a massive amount of data similar, yet, different from the cloud.

  1. Personalized Conversations

Updated NLP systems help data owners to engage with data in a more economical and direct manner.

Benefits of Data Analytics Course

Now that we have gone over in a concise manner what is data visualization, data analytics, and the various trends of data visualization, let us see how a business analytics course can be helpful in the current job market:

  •         It is highly in-demand
  •         The job opportunities are immense
  •         Big data analytics is very important these days
  •         You can explore new revenue streams
  •         Opens up wider freelancing opportunities

These are the various ways data analytics certification can help you in your professional life.

8 benefits of Power BI for businesses

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Businesses of all sizes are starting to realize the importance of data. To make informed decisions, you need access to accurate and timely information. It is where Power BI comes in. It allows you to visualize your data in various ways. This blog post will discuss eight benefits of Power BI for businesses. 

Power BI is a business analytics tool that enables you to connect to your data, visualize it, and share insights. With Power BI, you can get the most out of your data to make better decisions for your business.

Here are eight benefits of using Power BI:

Benefit # 01: Rich, personalized dashboards:

With Power BI, you can create rich, personalized dashboards with data visualizations unique to your business. You can include data from various sources, including Excel files, SQL Server databases, and cloud-based services like Salesforce and Google Analytics.

Benefit # 02: Seamless Integration:

Power BI integrates seamlessly with Excel, making it easy to connect to your data and get started quickly. You can use Power BI Desktop to create reports and dashboards that you can share with others.

Benefit # 03: Q&A functionality

With Power BI, you can easily ask questions about your data and get answers in return. This functionality is beneficial for businesses that want to make sure they get the most out of their data. 

Benefit # 4: No memory or speed constraints

 It means that you can quickly analyze large data sets without having to worry about your computer running out of memory or processing power. 

Benefit # 05: Collaborative features

With Power BI, you can easily collaborate with other team members. 

Benefit # 06: Drag-and-drop functionality:

Power BI also offers drag-and-drop functionality, making it easy to create visualizations and reports. 

Benefit # 07: No specialized technical support is required:

Power BI does not require any specialized technical support. It means that you can quickly get started with Power BI without having to worry about whether or not you have the right skillset. Additionally, Power BI is easy to use so that you can get started quickly and easily.

Benefit # 8: Supports Advanced Data Services:

Power BI also supports advanced data services, which means that you can easily connect and analyze your data. It is constructive for businesses to get the most out of their data.  

 If you are searching for the best technique to ensure you are getting the most out of your data, then Power BI is a tool you should consider.

Discover data analytics course in India with Imarticus Learning

This data visualization program is by industry specialists to help students master real-world Data Science applications from the ground up and construct challenging models to deliver relevant business insights and forecasts. 

Course Benefits For learners:

 

 

Wondering how to enter the data-driven in 2022? Learn Hadoop Online

Posted on by Imarticus Learning

The world is changing, and the data-driven economy is becoming more critical every day. If you are willing to be a part of this economy, you need to learn Hadoop. This powerful open-source platform can help you manage and analyze large amounts of data. In this blog, we will discuss the basics of Hadoop and how you can learn Hadoop online.

An overview of Hadoop

Hadoop is a powerful tool for analyzing and storing vast amounts of data. It’s an open-source project started by the Apache Software Foundation. 

Hadoop has two main components: HDFS (Hadoop Distributed File System) and MapReduce.

HDFS stores large files across a cluster of nodes, whereas MapReduce processes data in parallel.

Many large companies also use Hadoop, such as Facebook, Yahoo, and Google. It’s an excellent tool for analyzing big data sets.

Here are a few benefits of learning Hadoop:

  • Hadoop is an open-source platform, which means it’s constantly improving with the community’s help.
  • It’s a very versatile tool that you can use for various tasks such as data mining, web indexing, and log processing.
  • Its scalability makes it perfect for big data projects.
  • You can use Hadoop on various devices, from laptops to servers.

So if you want to make a career out of data-driven, learning Hadoop is a great place to start. And with so many online resources available, it’s easier than ever to get started.

How to learn Hadoop online: some tips for getting started?

Data is increasingly essential in today’s business world. It is vital to have a strong understanding of data and use it effectively to stay competitive. Hadoop is the most popular tool for managing and analyzing large amounts of data. If you’re looking to enter the data-driven world in 2022, learning Hadoop is a great place to start.

But where do you start? Learning Hadoop can be overwhelming, especially if you’re new to the topic. There are dozens of online courses and tutorials, and it can be challenging to know which one is right for you.

 Here are some tips for getting started:

It will be the best option if you decide what you want to learn. Hadoop is a complex tool, and there’s a lot of material to cover. It’s essential to focus on one or two specific topics first, rather than learning everything all at once.

Once you’ve decided what you wish to learn, do some research and find the best course or tutorial for you. There are many different options available, so it’s crucial to find one that fits your needs and learning style.

Discover data analytics courses with Imarticus Learning

This data visualization program is by industry specialists to help students master real-world Data Science applications from the ground up and construct challenging models to deliver relevant business insights and forecasts. 

Course Benefits For learners:

    • Learn data analytics and machine learning fundamentals and the most in-demand data science tools and methodologies to become job-ready.

    • Study data analytics certification courses by participating in 25 in-class real-world projects and case studies from business partners.

    • Business analytics courses are for recent graduates and early-career professionals who want to advance their jobs in Data Science & Analytics, the most in-demand job skill.