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Tag: data analytics courses

AI, Data Science, Machine Learning Terms You Need to Know in 2022!

Posted on by Imarticus Learning

In the present paradigm of technical knowledge, it is imperative to be aware of certain concepts to survive and thrive. Whether you are pursuing a career in artificial intelligence (AI), have a cursory interest in data analytics, or simply wish to broaden your horizons, here are some artificial intelligence, data science, and machine learning terms you need to know in 2021. Read on…

  1.     Natural language processing: 

Both humans and computational devices use their own modes of language to communicate and share ideas to the extent of imparting and debating on the information. The languages, however, are different in their basic forms and formats. Using natural language processing, or NLP, artificial intelligence can decipher many human languages to suit specific functions that may range from the academic study of linguistics to providing utility to hearing-impaired people.

  1.   Data warehouse: 

A data warehouse, as the name suggests, contains a large ensemble of data pertaining to businesses and learnings from past successes and failures to provide better services. One who is not entirely proficient in data architecture may yet take the advantage of data warehouses to gather business analytics courses and make far better decisions. This method allows one to find new ways to process old data and change future iterations of that data with his/her actions. 

Career In Data Analytics   Data journalism: 

This is a mode of journalism that is slowly gaining greater prominence and is proving its necessity in combating the ever-growing trend of fake news. In this form of news reporting, one focuses on proving his/her assertions through the collection and presentation of reliable data. This may be done through human and/or AI collection and calculations. Soon, we may be able to have a collated base of data obtained through AI learning. This will make it very hard for individuals and/or groups to spread misinformation.

  1.   Deep learning:

This uses artificial intelligence to construct structures that mimic the human neural network – starting from simple problems to finding layers of hidden information. Meanwhile, it makes errors and learns from them with the program often ending up with a different solution than what was expected by its programmers and set parameters. Using this process, we can identify and solve possibly any real-world problem. The degree of human supervision in this process can be ascertained at various levels of this process.

  1.   Cybersecurity: 

Both defenders and attackers of databases are getting smarter, escalating the never-ending battles between cybersecurity and hackers. Often, the strategies used by either group are similar to the point of being indistinguishable. Here, any large organization employs AI and/or deep learning to be one step ahead of the threats that plague them.

The above-mentioned terms are only the tip of the iceberg when it comes to talking about new technology-related topics. Hopefully, they have provided you with new avenues to look into as per your interests, or at least recapitulated some of the basic terminologies.

What Are The New Advancements In Data Analytics?

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Data analytics is a field which witnesses a continuous revolution. Since data is becoming increasingly valuable with each passing time, it has been treated with great care and concern. New tools, techniques, theories, and trends are always introduced in the data analytics sector to cope with the constant changes in the industries and societies. You can opt for a sought-after data analytics course to get a deeper understanding.

In this article, we will go through some of the latest data analytics opportunities which have come up in the industry.

The Intelligent Data Mesh

The intelligent data mesh has been termed the next revolution in healthcare and medical diagnostics systems in the coming years by the Gartner Top 10 Strategic Technology Trends in 2018.

The “Intelligent Data Mesh” has been described by Gartner as a meeting point of the physical and digital worlds where humans, machines, data, and services have been entwined together into a mesh.

The purpose is to gather the benefits offered by all these individual entities into a single unit to find solutions to complex issues thought to be insolvable until now.

One major industry expected to benefit most from this system is the healthcare industry where Intelligent Data Mesh is being hailed as a game-changer in enhancing patient care.

Blockchain

Blockchain continues to be an exciting technology even in 2018 and is expected to remain so for at least another decade. New advancements are being made almost daily regarding this technology as blockchain finds wider uses in various industries with time.

It will not be wrong to describe blockchain as one of the greatest data analytics opportunities. The concept of blockchain started with the idea of a decentralized digital currency which came to be known as Bitcoin in the market.

However, even though the controversy regarding the currency rose the concept of a decentralized and open-source peer-to-peer modeled technology for storing and analysing data. The concept of blockchain is now applied in a wide range of industries with its use predicted to keep rising soon.

Artificial Intelligence

Artificial intelligence is one such data analytics opportunity which is finding widespread adoption in all businesses and decision-making applications. As per Gartner 2018, as much as 41 percent of organizations have already adopted AI into some aspect of their functioning while the remaining 59 percent are striving hard to do the same.

There is considerable research going on at present to incorporate artificial intelligence into the field of data science too. With data becoming larger and more complex with each passing minute, management of such data is getting out of manual capacities very soon. Scholars have now turned to AI for storing, handling, manipulating and managing larger chunks of data in a safe environment.

Augmented Reality

Augmented Reality is an interesting new technology coming up in recent years. As a source which facilitates the interaction between machine and humans in a unique manner, AR has the potential to be a game-changer in the field of data sciences making it another top data analytics opportunity in the future.
best big data analytics course

AR can provide for the development of simpler UIs for the man-machine interaction when merged with AI allowing the users to store and interact with data in a completely new manner.

Imagine going to an island where all your data is stored in different treasure chests and you are provided with customised keys to access the chests with your data in it. These things may be possible in the future because of the use of AR in data analytics. 

Imarticus Learning offers select best data analytics courses that not only boosts your skillset but also your career as a whole. 

Frequently Asked Questions

What is a data analytics course?

Data analytics involves examining raw data to extract valuable and actionable insights. These insights, once gleaned, serve as the basis for informed and strategic business decisions, contributing to the enhancement of overall business intelligence.

Do data analysts require coding?

Certainly, coding is a fundamental requirement when undertaking an online Data Analytics Degree. While highly advanced programming skills may be optional, mastering the basics of R and Python is crucial. Additionally, a solid command of querying languages such as SQL is indispensable for a comprehensive understanding of data analytics.

Is Python a mandate for data analysts?

Possessing a thorough grasp of Python programming proves highly advantageous for data analysts. Employers commonly anticipate proficiency in utilizing Python libraries to streamline various data-related tasks. Consequently, acquiring skills in Python emerges as a prudent and strategic career decision for aspiring data analysts.

Learn NLP: How are chatbots created?

Posted on by Imarticus Learning

Chatbot, conversational bot, Artificial Intelligence assistant, intelligent virtual assistant, conversational agent, digital assistant, conversational interface, we find endless names, some more accurate than others, to refer to this technology. Experts do not agree on which one is the best or what subtle differences there are between each one, but what is clear is that they are everywhere.

Conversational assistants answer countless questions and tasks, such as buying a train ticket, knowing the stock of a product in a store, buying movie tickets, ordering food at a restaurant, or checking the weather in your city with the mobile.

It is common to use Machine Learning and Natural Language Processing in Artificial Intelligence to create these chatbots, achieving that, based on examples, they are able to detect what the user needs through text and to maintain a conversation with concrete and coherent answers. With the CIBOP program from Imarticus, get an opportunity to learn more about chatbots and how Natural Language Processing with python can achieve this.  

Types of Chatbots

Although it is clear that these machines have the purpose of making our lives a little easier, there are different types of chatbots depending on the purpose they have:

  • Some assistants have the purpose of maintaining an unstructured conversation, imitating those of the people. A good example of this is BlenderBot, from Facebook, designed to be able to carry on a conversation as if it were a human: with its own personality, showing empathy, knowledge, feelings, etc. 
  • Others are designed for short conversations and are also capable of solving certain specific tasks. For example, Apple’s Siri, which is capable of following short dialogues and responding to tasks such as sending a message, setting an alarm, or searching for a song.
  • Another type is chatbots specialized in specific tasks for specific domains. These are tools that provide solutions to limited complex problems, such as booking a flight, ordering food, analyzing health problems, or, for example, buying a train ticket. 

Normally these chatbots use Machine Learning and Natural Language Processing techniques to provide solutions and respond to user needs. 

Within the Natural Language Processing techniques, they need the understanding of natural language (NLU) to understand what the user has said and to be able to respond to it (for this, they use the intentions, entities, and dialogue flows). On the other hand, using natural language generation (NLG) they are able to return answers prefabricated or custom responses through, for example, query databases.

Steps To Create a Chatbot

But the important question that arises here is how do you create a chatbot? There are platforms that help to design a conversational agent, analyze data from conversations, search databases, or train chatbots in a relatively simple way. Some of the many available on the web are Language Understanding (LUIS) of Microsoft, Google Dialogflow, or Watson Assistant IBM.

These tools are usually based on intentions, entities, and flows of dialogue to build conversational agents. By integrating Natural Language Processing with python, chatbots can be specialized in specific tasks depending on the demand. We, at Imarticus, offer Natural Language Processing courses to learn and create chatbots.

Is a Chatbot the Same as a Virtual Assistant?

Some specialists believe that what differentiates a bot from a virtual assistant is the high degree of customization of the latter. In this way, while the chatbot is the face of a company, to whose codes or particularities the user has to adapt to achieve their goal, it is the personal assistant who adapts to the user and not the other way around. 

How Do Business Analytics & Data Analytics Differ? What Are Their Applications?

Posted on by Imarticus Learning

Both business and data analytics is about processing data and gaining insights from data.

The terms business analytics and data analytics might be used interchangeably in organizations on a small scale. On the other hand, big firms hire both business analysts and data analysts to perform well-defined functions, thus making it very necessary to understand the difference between the two.

With Imarticus’s, you can choose between the best data analytics courses and business analytics courses in India. Read on to have a deep insight into the two important career paths and their respective applications.

best data analytics courses in IndiaBusiness Analytics

Business analytics course focuses on business impact when analyzing data and the actions that should result from the insights. For example, it provides answers to the following questions, “Should a company develop a new product line?” Or, “Should it prioritize one particular project over another?”

Business analytics combines various capabilities, tools, and applications to measure and improve the effectiveness of core business functions such as marketing, customer service, sales, or IT.

Business analysis (also Business Analytics) is the iterative exploration of a company’s data. There is a statistical analysis method used to bring information to light that can help to stimulate innovation and business results. Companies that rely on analysis consider big data to be a valuable asset of the company: data helps to advance business planning and forms the basis for future strategies. Business analysis helps these companies get the maximum value from this gold mine of insights.

Sufficiently large volumes of high-quality data are required for business analysis. Companies that want to achieve the most accurate results possible have to integrate and compare the data from different systems. A decision is then made as to which subsets will be made available to the business.

Data Analytics

A Data analytics career involves combing through massive data sets to identify patterns and trends, create hypotheses, and support business decisions with data-based insights.

For example, data analytics seeks to answer questions such as, “What impact do geographic factors or time of year have on customer preferences?” Or, “What is the likelihood that a customer will defect to a competitor?” In practice, data analytics encompasses many different techniques and approaches. It is also known as data science, data mining, data modeling, and big data analysis.

In data analysis, raw data is collected and examined to conclude it. Every company collects huge amounts of data, such as sales figures, market research, logistics, and transaction data. The real benefit of data analysis is in identifying patterns in a data set that can indicate trends, risks, and opportunities.

Data analysis enables companies to use this knowledge to change their processes to make better decisions. In practice, data analysis can help, for example, to decide about the next product developments, to develop customer loyalty strategies, or to evaluate the effectiveness of new medical treatments.

Applications of Business Analytics and Data Analytics

Since Business Analytics and data analytics are both based on big data tools, they have several applications in various industries across the globe. Some of them are mentioned below:

  1. Digital Advertising
  2. Energy Management
  3. Medical Applications
  4. City planning and mapping
  5. GPS tracking
  6. Transportation
  7. Risk detection and management
  8. Security
  9. Transportation
  10. Traveling
  11. Customer interaction
  12. Internet Browsing
  13. Expenditure Management

Conclusion

Business and Data analytics share the same overarching goal: use technology and data to drive business success. We live in a data-driven world where the amount of information available to businesses is growing exponentially. Both functions in combination can help companies achieve maximum efficiency and gain some useful insights.

How Data Analytics is Reviving Up the Digital Economy

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Today, data analytics is a booming industry. It has been estimated that the data analytics market will grow to $187 billion by 2025. These numbers are staggering and they just show how much potential this industry holds for those willing to take on it as a career.

Data analysts have an essential role in our digital economy because they dig out insights from raw data to help companies make better decisions. To succeed as a data analyst, one must be adept with programming languages such as Python, R, SAS, or SQL, among others, and know statistical analysis techniques.

Data analytics course is used to make decisions about important marketing campaigns, product development, and customer service. Data analysis techniques include descriptive statistics, inferential statistics, hypothesis testing, cluster analysis, exploratory data analysis (EDA), causal modeling, and regression modeling.

Use of Big Data and Analytics

The definition of “digital economy” has been blurred by its widespread use but in general, let’s understand what it means. The digital economy uses technology to create, capture, store, manage, transmit and exchange information. One example of this includes using a cell phone app to order food online.

Here, the companies have created an online interface for customers to easily find restaurants that deliver in their area and then order by clicking on what they want. This eliminates the need for human intervention because all orders are taken or placed through the device. This means that the entire process has become hassle-free.

Another most common example where big data is used is retail stores. They use it to analyze their customers’ shopping habits, so they know what products to put out on display during a particular time frame throughout the day. This helps them in maximizing profits while also making sure that every customer gets a personalized experience when they visit the store.

In the same way, most companies use big data to analyze and find patterns in the available data. They leverage this data to derive insights that offer a unique experience to their target group. Companies dig deeper into these insights to identify untapped business opportunities that they can explore. Companies can identify a niche and expand into that area to fill the need gap.

They also have many other options for growth, such as partnerships, joint ventures or acquisitions within their target industry verticals; these strategies allow them to survive and thrive in changing markets while often expanding business operations. While exploring new business areas, companies always study the data available to forecast the project’s profitability.

Use Case of Big Data and Data Analytics

Data analytics is an essential tool when it comes to modern marketing & growing the digital economy. Without it, a company’s decisions would be made without any data to back them up, and you could miss out on some excellent growth opportunities!

best data analytics courses in IndiaThe good news is that there are many different types of data analysis techniques available, so you can find one that works best with the kind of information you have at hand.

If this sounds like something worth investing in, you can consider enrolling for several online courses on Data Analytics from Imarticus Learning. You will learn how to analyze data yourself and come up with insights or use a computer program such as Microsoft Excel and several other data science tools to analyze data.

Conclusion

The digital economy is expanding day by day, and it might replace traditional economies in the future. Artificial intelligence (AI) powered tools manage data in this new environment, while manual tasks remain essential for understanding the numbers.

However, businesses rely heavily on consumer insights obtained through machine learning algorithms. These algorithms are decoded with the help of analysts who provide valuable input during the decision-making processes.

Here Is How You Can Become A Future-Ready Data Analytics Professional!

Posted on by Imarticus Learning

Employers of all kinds are fast recognizing data science as one of the most in-demand skills. Businesses, government agencies, medical institutions, and charitable groups are all hiring data scientists at a rapid pace.

Do you want to enter the workforce or improve your data science skills? Now that data scientists are in high demand; there are plenty of opportunities to study the craft.

Enroll In Any Imarticus Online Data Science Courses

Online learning is the most cost-effective and trustworthy method of instruction. Before you enroll in a course, verify that it covers the topics you’re interested in and that it is appropriate for your ability level.

Data analytics courseIndustry specialists together created the Imarticus postgraduate data analytics certification program to help you understand real-world Data Science applications from the ground up and build powerful models to deliver relevant business insights and predictions.

This program is for recent graduates and early-career professionals interested in pursuing a data science and analytics career, the most in-demand job skill. With this data science course with a job assurance guarantee, you may take a considerable step forward in your career.

 

How Do I Register For The Data Analytics Course?

This data analytics certification is just what you need if you’re an inquisitive spirit who wants to dominate the digital business by studying the latest and most in-demand data science abilities.

Once you have completed your counseling session and understood this course is right for you; you can then proceed to apply for the data science course in three easy steps:

  • Enquire
  • Enlist in a counseling session
  • Enroll

What Is the Program’s Duration?

The PG Program in Data Analytics is a full-time program that lasts six months (on weekdays). Each week, you will have 4-hour-long lessons from Monday through Friday.

What Is a Hackathon? How Does It Assist?

While enrolled in an Imarticus course, you will be assigned a Course Guide/Mentor who will assist you academically and advise you on the best career route for you.

Your Course Guide will inspire you to finish assignments, attend classes, and get the most out of their PG program in data science with placement.

Moreover, your Course Guide or Mentor will also be playing a role in the following activities:

  • Keep an eye on your grades and academic progress.
  • Provide you with insider information to help you land the job of your dreams.
  • Encourage you to do better on projects and homework in class.
  • Make lasting friendships that will help you get through the course and beyond.

The hackathons at Imarticus Learning allow you to compete against a large number of students and stand out from the pack. They are regarded for instilling critical thinking skills in Data Analytics and pushing them to achieve at a higher level.

Take Away

It takes more than being able to evaluate large amounts of data to become a Data Analytics Professional.

You must also be familiar with the organization’s business processes and identify ways in which your participation will impact, which is now available with Imarticus online data science courses.

#ImarticusBlogLikeAPro – Write about your favourite Tech topic and take home rewards worth INR 10,000

Posted on by Imarticus Learning

Love Data Science, Machine Learning, or Data Engineering? Here is your chance to express your thoughts on the field of technology of your choice. While professionals can discuss the various tools, techniques, and best practices involved with their daily work, business leaders can speak of industry experience. Candidates are free to write about the current state of the discipline as well as the future potential of the field.

One can even choose to write about the effects of certain technology and the incredible benefits society and businesses enjoy due to these fields.

data science and analytics blogging contestThe Imarticus Blogger of the Year encourages budding bloggers or younger candidates to participate and write about the topic of their choice as well. You can participate in this Data Analytics and Data Science blogging contest by applying here.

What is Imarticus Blogger of the Year?

The main focus behind this contest is for providing enthusiasts with the opportunity to express what they wish to share about the field of their interest. Blogs can also speak about research, new technology, and interesting case studies of all the relevant fields.

Fundamentally, Imarticus has come up with this contest to provide some exposure to bloggers and professionals who are involved with Data Science and other disciplines that work with data. Candidates are also authorized to discuss core concepts such as Deep Learning or talk about diverse fields such as Data Analytics.

The Submission and Selection Process

The submission process followed by the selection process for this Data Science blogging contest can be divided into 6 simple stages. Let’s understand what exactly you must do in order to successfully submit your blog and how exactly the winners of the contest are decided.

Stage 1: First, participants are required to register themselves and opt-in for the contest. Then, you must write a high-quality blog related to the authorized topics or fields and then send the piece over to blog@imarticus.com before the 31st of August. Submissions close on the 31st of August, 2021 at 11:59 PM IST.

Stage 2: During this stage, the editorial panel will be evaluating the blog based on its creativity, originality, and depth of the subject matter.

Stage 3: Here, every blog that has met the minimum requirements and passed Imarticus’s quality standards shall be uploaded on the platform (blog.imarticus.org) by the 11th of September this year. The writers will be credited for their blogs and the links for their respective blogs will be shared with them by Imarticus.

Stage 4: In this stage, writers are recommended to share their blogs on their personal social media accounts with the mandatory hashtags. Writers are also advised to share their pieces with friends and family in order to gain more traction.

Stage 5: At this point, all the blogs will be evaluated on the basis of audience engagement and the score provided by the editorial panel. 25 of the best blogs shall be shortlisted to be promoted by Imarticus on all social media platforms till the 30th of October, 2021.

Stage 6: This is the final stage where the blog with the highest amount of cumulative shall be selected as the winner of the contest.

Conclusion

The winner is crowned the Imarticus Blogger of the Year and handed down a gift voucher of 10,000 rupees. Imarticus Learning is also offering gift vouchers of up to 7500 rupees for the other top 10 bloggers. Not just that, participants who rank within the top 20 get the opportunity to be featured in Imarticus’s hall of fame. So, why wait? Register yourself and start blogging from today.

What is the Technical Data Analyst Skillset for The Digital Enterprise?

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As data science is regarded as the “new oil” for the technology industry, there is a surge in the demand for skilled professionals. Data Analytics is finding its application in nearly all aspects of life, from education to healthcare, social media, e-commerce, and fintech, to mention some. As a professional, data scientist plays a pivotal role in the business ecosystem and influence business decisions.

data analytics coursesAnalytics, in general, has three important components – business context, technological intentions, and data science.

Data science is a broad term that encompasses different expertise like research – both operations research and statistical research, machine learning algorithms, and deep learning.

No matter which aspect of data science are you planning to focus on, you need to understand the key skills that make up a good data analyst. Read on to learn about the skills you need to excel in a data analyst career.

MS Excel

One of the most widely used app for data visualization as well as financial, statistical, and mathematical calculations, excel is the basic tool one needs to master while looking for a career in data science. The pivot tables are one of the most popular tools used in data analytics. Charts are excellent tools for data visualization. Slicers and filters allow organizing data while the formula-based techniques are great for creating aggregate reports, while other features like Vlookup help process a large number of data bits into a well-organized structure, enabling easy comprehension.

Data Warehousing

In simple words, data warehousing involves the collection of data, organizing them, and storing them. Data analysts and projects managers are usually responsible for data warehousing, where they handle the safekeeping of the data, ensure that the privacy rules are strictly adhered to, and monitor them. Data analysts should be able to integrate the data collected from different sources, analyze and create analytical reports, and support decision-making.

Data Retrieval Skills – SQL

The Structured Query Language (SQL) is used to connect with the database. Whether updating a database or retrieving data from it, SQL is the standard language used by Relational Database Management Systems (RDMS).  SQL skills are mandatory if you wish to have a career in Big Data. However, learning SQL is simple, even non-technical people can learn it. It has become a basic skill used in many organizations to create, maintain and modify database structures and storing the collected data.

Coding and Programming Skills

Data analysts should master coding and programming to practice their knowledge. You don’t need to be a hard-core programmer, but you need to know the basics and should be comfortable with coding and programming to excel in this role.

While the primary responsibility of a data analyst is to analyze the data and derive meaningful insights from the, understanding the concepts such as functions, loops, if-else, and programming logic would be an advantage while dealing with a large amount of data.

Artificial Intelligence (AI) and Machine Learning

AI and machine learning help enhance data analytics. When you prepare a machine learning algorithm and provide it with a model, it gives you an output based on the model provided. For instance, if you train a machine learning algorithm and provide it with a predictive model, it will provide you with predictive analysis based on the data feed.

There is no absolute definition of skills needed to excel as a data analyst. Most data analyst courses cover the basic skills needed to secure a data analyst job. There is a buzz about big data and its application. Almost all industries are espousing data analytics. As technology improves, there could be overlaps in the job role, or the job roles could encompass wider aspects of data science. The better skilled you are the more are the chances to excel in a data analytic job.

All You Need to Know About Hadoop!

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Hadoop is an open-source software framework to store data and running applications on clusters of commodity hardware. It provides massive storage for different data types, enormous processing power, and the ability to handle virtually limitless concurrent tasks or jobs.

Hadoop programming is a vital skill in today’s world for people looking to build a career in Data Science. Hadoop processes large data sets across clusters of computers using simple programming models called MapReduce jobs.

Importance of Hadoop for Organizations?

  • The ability to store & process enormous data quickly makes Hadoop development a much-needed thing for organizations.
  • Hadoop’s distributed computing model processes big data in no time. With more computing nodes, you have better processing power.
  • Hadoop is equipped with fault tolerance and guard against hardware failure. If a node goes down, tasks are automatically redirected to other nodes to ensure that distributed computing doesn’t fail.
  • You can quickly scale your system and handle more data simply by adding nodes.

How is Hadoop Used?

Hadoop development is used in a variety of ways. It can be deployed for batch processing, real-time analysis, and machine learning algorithms. The framework has become the go-to technology to store data when there’s an exponential growth in its volume or velocity. Some common uses of Hadoop include:

Low-cost storage and data archive

Hadoop stores and combines data such as transactional, sensor, social media, machine, scientific, clickstreams, and the modest cost of commodity hardware makes it more likable. The low-cost storage lets you keep data and use it as & when needed!

Secure for analysis & discovery

Since Hadoop was designed to deal with massive data, it is efficient in running analytical algorithms. Big data analytics on Hadoop can help organizations operate efficiently, uncover opportunities and derive next-level competitive advantage. This approach provides opportunities to innovate with minimal investment.

Data lake

Data lakes back up data stored in original form. The objective is to offer a raw view of data-to-data scientists and analysts for discovery and analytics. It helps them ask new questions without constraints. Data lakes are a huge topic for IT and may rely on data federation techniques to create logical data structures.

IoT and Hadoop

Hadoop is commonly used as a data store for millions of transactions. Massive storage and processing allow Hadoop to be used as a sandbox to discover and define patterns monitored for instruction.

Build a Career in Data Science:

Data analytics is a lucrative career and is high in demand and low in supply. It’s a field requiring plenty of expertise to master. But what if you have the ambition but lack the know-how? What do you do?

Data science courses or Data Analytics courses can help you gain better insights into the field. For a person to be technically sound, education, training, and development are the foremost steps.

Data Science Course

Imarticus Learning offers some best data science courses in India, ideal for fresh graduates and professionals. If you plan to advance your Data Science career with guaranteed job interview opportunities, Imarticus Learning is the place to head for today!

The certification programs in data science are designed by industry experts and help students learn practical applications to build robust models and generate valuable insights.

The rigorous exercises, live projects, boot camps, hackathons, and customized capstone projects will prepare students to start a career in Data Analytics at A-list firms and start-ups throughout the program curriculum.

The industry connections, networking opportunities, and data science course with placement are other salient features that draw attention from learners.

For more details on the transformative journey in data science, contact Team Imarticus through the Live Chat Support system and request virtual assistance!

10 Most Popular Analytics Tools In Business

Posted on by Imarticus Learning

The increasing importance and demand for data analytics have opened up new potential in the market. Each year, new tools and programming languages are being launched aimed at easing up the process of analyzing and visualizing the data.

While many such advanced business intelligence tools come up in paid versions, there are great free and open-source data analytics courses and tools available in the market too. Read on to find out about the 10 best and most popular data analytics tool for business right now.

1. R Programming
R is the most popular programming language cum tool widely used by experts for the purpose of data analytics and visualization. The tool is free and open-source in nature and allows the users to alter its code set for clearing bugs and updating the software on their own.
2. PYTHON
Python is an open-source and free OOP based scripting language popular in the data analytics market since the start of the 90s. Python supports both structured and functional programming methods and is very easy to learn and operate upon. Python is expert in handling text-based data.
3. Tableau Public
Tableau Public is another free software and business intelligence tool which is capable of connecting all kinds of data source be it Excel-based data, Data Warehouse or web-based data. Tableau creates maps, graphs and dashboards with real-time updates presenting on the web. The data can be shared over social networks too.
4. SAS
Sas is a leading analytics tool and programming language specifically developed for the purpose of interacting with and manipulating data by the SAS institute in 1966 with updates presented during the 80s and 90s. Data present in SAS can be accessed, analyzed and managed easily from any sources and is capable of predicting behaviors of customers and prospects along with recommending optimized communication models.
5. Excel
One of the most popular and underrated data analytics and visualization tool in the market, Excel was developed by Microsoft as part of their MS Office and is one of the most widely used tools in the industry. All kinds of data analytics tools still require Excel to work in some kind of way and it is very easy to be learnt and operated.
6. KNIME
KNIME is a leading open source and integrated analytics tool developed by a team of software engineers from the University of Konstanz in January 2004. KNIME allows the users to analyze and model the data through visual programming integrating components of data mining and machine learning via its modular data pipelining concept.
7. Apache Spark
Developed in 2006 by the Berkeley’s AMP Lab of University of California, Apache is a fast large-scale data processing, analysis, and visualization tool capable of executing applications around 100 times faster in memory and 10 times faster on disk. It is popular for data pipelining and machine learning models development allowing it to double up as business intelligence tool.
8. RapidMiner
RapidMiner is another powerful data analytics tool which can double up as business intelligence tool owing to its capability to perform predictive analysis, behavioral analysis, data mining, etc. The tool can incorporate with any other data source types such as Excel, Microsoft SQL, ACCESS, Oracle, Ingres, IBM SPSS, Dbase, etc.
9. Google Analytics
A freemium and widely recommended product for data analytics, Google Analytics is a perfect offering from Google for the Small and Medium-scale enterprises who don’t possess the technical knowledge or the means to gather that knowledge in the present course.
10. Splunk
Splunk is an analytics tool mostly directed to searching and analyzing machine-generated data. The tool pulls up all text-based log data and provides the means to search through it for gathering any relevant or required data.