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Data Analytics- The Key to an Unprecedented Future

In this technology-driven world, where we are informed about transformations on a daily basis, these transformations are all occurring in the field of ‘Data’. The incredible amount of information being created every second is as valuable as gold and it will undoubtedly continue to rule the future. One such domain that leverages the use of data is Data Analytics.

SO, WHAT IS DATA ANALYTICS?

Data Analytics is an approach that is used to predict results by monitoring and modeling data collected from careful analysis. It is the study where the outcomes result in the enhanced performance of business operations.

Due to rapid advancements in the business sector, organizations have been relying on technologies to enable the decide on strategic plans, which may lead their path to growth. Keeping in mind such demands, researchers have developed this field of Analytics- a comprehensive algorithm based on predictions and conclusions derived from the data. As data is a limitless entity- with tonnes of it being generated each day, data analytics remains a practice that will grow exponentially in the future ahead. That’s the reason why most of the companies recruit the candidates who’re having the knowledge of Data Analytics.

THE 4D PROCESS OF DATA ANALYTICS

Let’s take a look at the extensive process of this ingenious technology

DATA COLLECTION

Once we have defined the requirements, data is collected keeping in mind the outline specified in the first phase. There are several mechanisms which can be employed to gather data. The analysts can opt for research, interviews, documentation or any other specified format. The aim is clear: collect data from all information sources and fulfill the requirements as defined.

DATA PROCESSING

Data collected from different sources is in its raw form and must be converted into a suitable format to conduct the analysis. The data must be organized to generate it into a structured format. We can organize the data in the form of rows and columns for effective interpretation.

DATA CLEANING

After the aforementioned steps, the data is processed and structured. There might be some underlying errors or redundancies or inadequate information. For instance, while entering the collected information in a tabular format, human errors in the form of misspelled words can have an entirely different meaning from the original data. Thus, thorough identification, checking and inspection must be undertaken to evade any conflicting data.

After the 4D’s of the Analytical process defined above, the data is analyzed through various techniques such as exploratory data analysis, data visualization as well as descriptive statistics. In the final step, data modeling makes use of novel algorithmic models to generate quantitative results.

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THE FUTURE AS A DATA ANALYST

What would the scenario be if Data Analytics hadn’t come into the picture?

A simple answer- Failure! The rate at which organizations would face inescapable negative outcomes would be tenfold.

Harnessing value from data and generating insights is the key to the bright future of business. Individuals possessing such skills are in high demand and are seen as a valuable asset. If you are good with numbers, possess logical and cognitive thinking, can make inferences from figures and generate insights, you can undoubtedly have a great career as a Data Analyst.

Analytics is an emerging field showing bits of development at a tremendous rate. Its potential is yet to be truly discovered, but one thing is certain, it will assume an essential role in the trailblazing transformations of the future!

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Data Analytics Work Prospects

Data analytics is such a huge field with an ample number of fields within it, provides multiple job options that are highly paid. Maybe you are eager to learn a new skill set in data analytics, but before that, you are more likely to be inquisitive regarding earning capabilities of the respective positions. This will give an actual boost to your leaning knowing how your real and fresh skills will be remunerated.

A lot of employers have been hired for these positions in data analytics and it’s currently still going on. Many people dream of working in the data science field, especially data analytics, so let’s look and pay some heed at some of the job prospects of the data analytics industry. Below is a list of some job titles that are highly valued in the world.

IT Analyst: If any problem arises in the Information Technology department, these system analyst design use systems to make solutions and try to eradicate such problems. The technical level of expertise differs in position and that is what creates multiple options for specialization according to the personal interest and industry. Some analysts make use of already present third-party tools for testing software in a company while the others create new tools from their knowledge and experience in the data analytics business. To view more about the role of an IT Analyst in the field of Data Analytics Courses Click here.

Healthcare Analyst: The primary task of a healthcare data analyst is to improve the health of many people by assisting scientists and doctors search for solutions to our daily basis problems. A great amount of data is generated through medical testing, labs, clinics, and hospitals that need to be channeled in the right direction. Also, the restrictions are increased on the storage, processing, managing techniques of the data which increases the demand for more efficient data analytics.

Operation Analyst: These analysts are generally present inside organizations and sometimes work as consultants. They mainly concentrate on the internal business process like product development and distribution, reporting systems, and smooth functioning of business operations. Operation Analysts are present pretty much everywhere from postal service providers to grocery chains.

Quantitative Analyst: A quantitative analyst is one of the highly desired and demanded professional specifically in finance. Quantitative analyst utilizes data analytics to look for appropriate financial investment options and risk handling issues. They have the flexibility to create their own trading models to estimate stock prices, exchange rates, commodities, and many more. Even many analysts in the industry go for their own firms.

Project Manager: The project manager uses data analytic tools and keeps an eye on the team’s progress, efficiency, and change processes to elevate the overall productivity of the team. At the least, the product manager must know the working of data analytics and sometimes more. These are often present internally in business corporations and commonly in consulting departments.

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ExcelR – Data Science, Data Analytics Course Training in Bangalore

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Data Analytics: Why’s it so important?

Companies and business have to collect a large quantity of data to see how their products or services are doing in the market and how they can improve and create more impact on the market. They would also collect data on gaps in the market so they can create products. The process of analyzing, sorting through and going through all the data and forming a conclusion on how to progress their business is called Data Analytics.

Benefits of Data Analytics

Companies are often under constant pressure due to the changing needs of people and competition from other businesses, it’s the best way for a company to figure out what is missing from the market and what might be required to boost their business. This is why a lot of companies are constantly asking for feedback and ratings and also take details like email and phone numbers; it’s all to help boost their business.

There are teams such as the project management team which have the sole responsibility of finding out gaps in the market, finding out what is currently preferred and delivering the relevant information to the company so the company knows where to put in their efforts and create a business plan for the future. Data analytics is a key part of creating business plans and seeing trends and how certain companies might perform in the future.

It can also help businesses improve their customer experience. Companies lookout for feedback through user and customer data to help them improve their services. Companies with bad customer experience can ruin their brand image and loyalty from customers. Customers often use star ratings and feedbacks and use the data provided by those to see what they can add or improve in their customer experience.

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The Downside of Data Analytics

The biggest concern of Data analytics is the invasion of privacy of the customers. Companies need customer information and data such as purchase history, what the customers might be looking for, their searches, etc. This is a major breach of privacy as companies are buying private information for their own gain.

Another downside of Data Analytics is it is time-consuming and expensive. Data analytics requires going through large quantities of Data to find relevant information and form conclusions using the data provided. This can be highly time-consuming as companies might have lots of data to go through. Sometimes, the tools used such as the applications might be difficult to use and would take time and money to teach their employees on how to use the software and use it correctly.

Data Analytics Course is more than just collecting and going through data, it requires planning on how to collect the data and how to form conclusions using the correct tools and collecting and filtering the information to the right places and determining which data is relevant and which is not. Data scientists will go through and analyze the information and data engineers would help create data sets.

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ExcelR – Data Science, Data Analytics Course Training in Bangalore

               49, 1st Cross, 27th Main,

               Behind Tata Motors, 1st Stage,

               BTM Layout, Bengaluru,

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Data Analytics Training And Skills That You Need In The Current Scenario

In this article, we will be talking about data analytic training and its needs in the current scenario; basically, what is data science and how can you choose it as your new career option?

What Is Data Analytics Training?

So, basically in this course, you will learn how to collect data from different resources and analyze it to make a decision or get to know about your project. You can also calculate the probability about how your project will perform in the market based on using the past data as a predictor.

You can also say that data science is nothing but the study of all the information about every basic thing, and then converting it into the form of data that can be analyzed and acted upon to improve the profit of your business.

History Of Data Science

Data Science has been in existence for a very long time. If you look back, then you will find that many companies, even our ancient kings in their taxation plans, also used to collect data, and develop plans and actions according to the collected data. While data collection is not a new thing, it has evolved and now has become a whole new profession.

Skills that are Necessary for Data Analytics Course

There are many types of skills that are necessary which will be taught to you in data analytics training. These skills include

MS EXCEL Earlier, Excel was widely used for data analytics, but now, given that the amount of data has been increased dramatically, it has become impossible for a person to analyze data solely in Excel. Now Artificial Intelligence (AI) and machine learning are used for that. MS Excel is used for smaller amounts of data, such as for someone’s personal business. Where there is no need for large amounts of data you can simply collect data from different resources then analyze it using Excel. It is not useful for big multi-national companies such as Amazon, Flipkart, etc., as they have to use machine learning and artificial intelligence.

AI AND MACHINE LEARNING To compute a vast amount of data several AI and machine learning tools have developed. It is impossible for an individual to deal with that huge amount of data without additional support.

As one example, if you have noticed that if you ordered anything from Amazon or any other site, or even if you are using any other app they ask for feedback on your experience. If you submit the feedback, then the program will automatically try to understand where is the fault in their application that causes you to cut stars. It will collect that data and then send it to the company. Once they have collected the data and analyzed it, you will get future updates and feedback.

Some Programming

You need to know about many kinds of data processing, such as:
Data engineering
Data visualization
Data pre-processing
Data transformation
Data cleaning

To work with these data processing applications, you need to know about programming languages
such as Python.

ExcelR – Data Science, Data Analytics Course Training in Bangalore

               49, 1st Cross, 27th Main,

               Behind Tata Motors, 1st Stage,

               BTM Layout, Bengaluru,

               Karnataka 560068

               Phone: 096321 56744

               Hours: Sunday – Saturday 7AM – 11PM

Data Analytics: The Future Of Dealing With Data

Since the time when the Internet has taken over the world, the importance of data and the rate at which data is increasing is at higher levels. Data can be organized as structured, semi-structured and unstructured. Handling and analyzing different sorts of data is the major challenge these days. Day-by-day, we can see that the amount of data emerging is drastically increasing, and the need to manage such vast amounts is very important. Choosing the right type of storage becomes the first priority in the field of Data Analytics
When you have to analyze data, it involves: • Studying the data.
• Storage medium.
• Characteristics.
• Security measures, and many more items
.

Big data consists of large data sets which are not possible for the traditional database systems to handle. Data storage techniques now used for data include clustered networks and object-based storage. The ability to store large amounts of data is what is necessary for business executives to use big data. Unique techniques are needed to analyze big data.

We need to have knowledge about methodologies, adopt technologies and ensure to improve skills with technology. In the earlier years, executives relied entirely on structured data. Later on, unstructured data were also provided with methods to handle and analyze this kind of data easily. It poses opportunities and challenges for the business.

Let’s just consider a simple example of an Excel sheet that holds different values, such as customer details, order details, inventories, etc. As it contains different kinds of information in one sheet, it has to be structured in such a way that it is easy to access whatever information is required from it.

Analyzing data is like handling business and technology together, which is a great challenge. Proper analysis of data reduces risk. Analytics architecture refers to infrastructure, tools, and practices that lead to access and analysis of information to enable easier decision making for businesses.

Let’s take a look at the big data analytic steps

Data extract and feed: Collect the data that you want to store and feed it to the storage device.
Discovery and visualization: This involves knowing about what sort of data it is, sorting it if it is unsorted, and saving it. There can be two types of data – public data and confidential data. Public data is the data that can be shared across the wider public. Confidential data is only accessed by a few authorized users. To develop the security of information, authentication, authorization, and validation processing software must be stored. To manage so much of data requires visualization of how you can store your data and develop the respective framework for it.
Decision support: After you store and validate data, unnecessary data has to be removed and a decision has to be taken as to which operations have to be performed on which data and so on.
Data and applications: Once decisions are made you are ready to apply these data into the areas where you want it to be used.

For more information regarding WHY DATA COLLECTION IS USED IN DATA ANALYTICS?

ExcelR – Data Science, Data Analytics Course Training in Bangalore

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               Behind Tata Motors, 1st Stage,

               BTM Layout, Bengaluru,

               Karnataka 560068

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Data Analytics: Technical Prowess And Curriculum

The blooming profession in Data analytics is distinctly fruitful because of a higher pay scale and a swift rate of development. Studies suggest a steady growth in the number of job opportunities in the analytical department in the near future, so much so that almost every organization has started showing an inclination towards it. However, there is a shortage in the fundamental skill set required for this sector. { Data Analytics courses }

Even if you possess numerous degrees, in this particular field, it might be considered as less important if there is an absence of technical proficiency.

What to Study?

Besides attaining the basic educational qualifications, you must have technical prowess in logical reasoning, statistics, and the methods of working with data applications. Unlike a data scientist, an analyst’s role does not require finesse in business acumen or communication.

At the primary level, you must possess a higher education degree either in mathematics, statistics or in computer science, finance and IT. A bachelor’s degree in the first two subjects followed by a masters degree in the latter two might be successful in securing a position in any company.

Additionally, a CDMP certification at a later stage with six months of rigorous training provides a candidate with extensive experience in the respective field. SAS certification turns out to be one of the mandatory training courses for data analysts, whereby, they are examined on their ability to extract, export, analyze, transform raw data, correct logical errors and present the correct ones.

Additional Qualifications

Other than the above-mentioned expertise, a candidate must pursue a number of courses to sharpen their computer skills. Due to the field being totally technically based, programming tools are constantly in high demand.

Languages such as R and SAS programming provide an in-depth grasp of data analytics for aspiring analysts. The most versatile programming tool, Python, is usable for every form of data analysis; data mining, Hadoop and SQL being another three languages to pursue. Along with these, an accurate knowledge of the administration and management of the analysis of past data assists in checking the growth of the data management field in the near future.

Institute Curriculum

The above-mentioned additional qualifications are not necessarily covered in a degree program. A resume displaying five different types of experience is more eye-catching. Globalization has brought a stark change in the education system of the world. Where people depended on the universities to play a key role in shaping the career, today, it has been rendered as one of the primary level knowledge houses.

The field of Big Data is still unclear to many; hence, experts have taken the responsibility to educate the public about its culture. Institutions have cropped up in no time, following a routine curriculum so that learning becomes interesting.

One of the winning techniques of teaching is recognizing real-world events and explaining them using data management tools. Other than imparting the knowledge of programming languages, they discuss the rules and regulations that help in the smooth operation of a business like Data Governance, Quality, and Privacy.

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ExcelR – Data Science, Data Analytics Course Training in Bangalore

               49, 1st Cross, 27th Main,

               Behind Tata Motors, 1st Stage,

               BTM Layout, Bengaluru,

               Karnataka 560068

               Phone: 096321 56744

               Hours: Sunday – Saturday 7AM – 11PM

 

WHAT IS DATA ANALYTICS AND WHAT IS YOUR ROLE AS A PROFESSIONAL

Data has become the new focus for the new generation. It has distinguished its presence in all aspects of tech mainly due to its dynamic manifestations. It is a part of the forms you submit online and calls you to make. Data also has a public format which is accessible to all. Companies and firms that have emerged recently have centralized their focus on becoming data intensive. Traditional business giants have only recently realized the data’s value and are currently hunting for professionals to dig into this particular market.

What does it take to be a Data Analytics Professional?

It is obvious that data analytics requires a colossal amount of data to process. The greater the amount of data, the greater the bulk of information which means greater accuracy. This means firms have developed a higher demand for data professionals. Knowing this, it is logical to expect a data analytics professional to work hard to develop skills that display accuracy and efficiency when dealing with numbers.

The data analytics professional should have a strong interest in numbers and should not hesitate when handling any collection of figures. Not only this, but they should feel extreme comfort developing complex databases and completing detailed processes through software support. This software could be SQL, which is used for database maintenance or Hadoop, which is used to manage large chunks of data. Both of these play a key role in the formulation of insight from any data. An individual in this position needs to be efficient in dealing with numbers and computers, but also with their judgment. They are required to have a rational and structured thinking pattern. They are expected to have a higher degree of awareness.

Data Analytics and its spread around us

As you may already know, data analytics is productive mainly due to the insights it can provide to a firm. As a result of this, it has been observed that, as firms and companies grow in size and in their operations, they tend to have to deal with a higher number of transactions. This increases the need to wrangle and develop processes to handle the vast inflow and outflow of data occurring within their systems which, naturally, intensifies their need for professionals to extract meaning from it.

Moreover, as more and more data-intensive firms appear, they continue to grow the demand for data analytics professionals. Added to this, is the fact that we need to consider the role of the various types of open source software. They help final analyses reach the masses. We may say that, due to this eruption in the use of data as well as of the firms who depend on it, our current market has led to the increased hunt for data.

Is securing a Job as a Professional in Data Analytics valuable?

For the data professional, a career in data analytics is fruitful and productive, especially when as compared to other jobs in the IT industry. This type of career provides individuals with a more significant role in a company. The earnings package is healthy and rewarding at the same time.

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ExcelR – Data Science, Data Analytics Course Training in Bangalore

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              Hours: Sunday – Saturday 7AM – 11PM

WHY DATA COLLECTION IS USED IN DATA ANALYTICS?

WHAT IS A DATA COLLECTION?

Data collection is a process in which data is accumulated and predictions are made. Why do we need data collection? All modern techniques are working to find solutions to real-world problems with the help of the data. To accomplish this, the data needs to be understood by the data analyst. To understand the data properly, it is important to collect that data in an organized way. That’s why the data collection method is used.

When a data analyst collects data, he must know which type of data he is collecting. Data is classified into three types, based their structure. These three types are as follows

Structured Data – Structured data is that data which is stored or arranged in a proper format like spreadsheets,
tables, etc.
Unstructured Data – Unstructured data is that data which is not stored or arranged in a proper format. For example,
any text, sound, video, etc.
Semi-Structured Data – Semi-Structured data is the combination of both, structured as well as unstructured.

The type of data is decided by the data itself. The method for collecting data and the operations to be
performed on the data is selected.

DATA COLLECTION METHODS

Data collection methods are decided based on which data is collected and what problem is to be solved. There are two types of data collection methods and these are listed below
Primary Data Collection
Secondary Data Collection

WHAT ARE PRIMARY DATA COLLECTION METHODS?

The primary data collection methods are used for collecting primary data. Primary data is that data which is being collected for the first time. Primary data is collected for new and unique problems. No research is performed prior to this data collection. That’s why new and fresh data is collected for it. The results we get from primary data collection methods are accurate as well as less time-consuming. Here, the data is gathered by the analyst itself.

WHAT ARE SECONDARY DATA COLLECTION METHODS?

The secondary collection methods are used for collecting secondary data. Secondary data is that data which is not gathered by the analyst itself. Someone else gathers this data for the data analyst. The data which is gathered here is not new. It has already been gathered and researched. Secondary data is also known as second-hand data because this type of data is already available. It has been said that secondary data is more cost-effective since this data is already available.

The sources from which we get the secondary data are as follows
• Sales reports
• Customer details
• Feedback from customers or others
• Financial statements
• Company information
• Management information system

These all are internal sources. External sources are as follows
• Government census
• Business journals
• Business magazines
• Internet
• Information from other government departments
• Social books
• Libraries

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ExcelR – Data Science, Data Analytics Course Training in Bangalore

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Why You Should Choose Data Analytics as a Career

Data Analytics is emerging as a relatively new field for professionals in any business organization. It is not only attractive to those in the corporate world, but to those interested in this field or for attaining a tool that can bring more success to their specializations.

Why the sudden rise in demand for Data Analytics?

Let us investigate and delve deep into the factors that have led to this global increase. As we all know, there is a good amount of increase or explosion in digital footprints, there are more social media platforms arising, every other person now has access to a smartphone, and so every action performed on these devices is being recorded somewhere within the arena of social media, i.e. a digital trace. So our access to the internet and social media websites has led to the explosion in the availability of data.

Until around 2009, 0.79 zettabytes of data had been generated, which is predicted to have increased to 35.0 units of the same data in 2020. This means an explosion of data several hundred times more than what it was before. It is, therefore, no surprise that the availability of data that is being generated, would definitely encourage businesses and their enterprises to understand and extract the value of data. Where we might be lacking, however, is the fact that our education system has failed to update it to include the enormous amounts of data that becomes generated every day. This makes students unable to get themselves equipped with sufficient knowledge of recent data. At the same time, in businesses where the management was slow to react to the huge availability of data, they did not train or equip their employees with the right skill sets or prepare them for the upcoming demand.

Students Can Look for Future Career Prospects in Data Analytics

Undergraduate and/or Postgraduate Students can still build the requisite skill sets in Data Analytics and build a lucrative, high-paying career for themselves. There is a long way to go into the field of Data Science and even research. For those who are midway in their career and want to survive the next 20 years, Data Analytics is the future for this progression. Times could become highly challenging if you don’t equip yourself with sufficient knowledge in the field of Analytics.

IBM has predicted the demand for Data Scientists is likely to increase in the job market by the financial
year of in 2020.

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ExcelR – Data Science, Data Analytics Course Training in Bangalore

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Data Science and Healthcare sector

Technology has helped medical and healthcare sectors to evolve and revolutionary changes have been possible due to these changes. Computerized medical records, lead generation for new drug molecules, exploration of genetic data to diagnose deadly diseases all have been possible due to technological advancements and data science is one such field that has made the healthcare sector to move to a whole new level.

Data science has helped to deal with piles of structured and unstructured data to fetch out meaningful data for healthcare experts and it is therefore very important for the two sectors to advance together. This, in turn, would not just help the medical sector to provide better service and find treatment for deadly diseases, but also help society by bringing the cost of medicine and treatments down.

Data Science Application in healthcare and medicine sectors:

Medical Imaging: A lot of research has been done in this area. Imaging techniques like MRI, CT scan, X-ray imaging and mammography etc. are using deep-learning to tackle with differences in various resolutions, modalities, and dimensions of images. Data science can be used to increase the accuracy by comparing the trends from previous studies and help in developing better treatment options. Hadoop analytical tool is used to discover alternatives for tasks such as lung texture categorization.

Genetics and genomics: Research work in genetics and genomics has helped design advanced and more personalized treatment. Data science helps in handling the huge data stored in one’s DNA and its impact on health, find connection between diseases, genetic makeup and drug response. This data is integrated to have a deeper understanding of various genetic responses towards drugs. Data science has made it possible to get results faster and accurate.

Drug Discovery: It is a very complicated and time taking process involving huge time (as long as ten to twelve years) and financial investment. Data science has significantly shortened this by forecasting the success rate of a drug molecule based on biological factors, pharmacokinetic and pharmacodynamic response using mathematical and simulation models in place of laboratory testing. This saves both time and money.

Accurate Prognosis and Diagnosis:  The failure to diagnose a disease results in as high as 5% to 10% deaths in U.S alone. The enormous amount of health data has to be dealt with making it difficult for humans to give accurate results every time. This problem inspired a deep-learning start up called Enlitic to design a program to process such large data and compare it with an extensive database of pathological reports, studies and patient history, giving accurate diagnostic results quicker.

Customer data Management:  Gone are the days when you used to encounter big registers in hospitals keeping patient records. Machine learning tools such as optical character recognition and support vector machine has allowed to create comprehensive record inventories of medical data which is easy to access and more promising and organized form to refer to.

Better understanding of the industry:  Data science is helping to impart better management of knowledge and bring fruitful results for the betterment of society. It has made it possible to effectively gather, store, integrate and distribute this knowledge in order to help health care organization achieve their targets.

The possibilities for better utilization of Data Science are always open as the data is expanding day by day and technology is advancing every second. The future in Data science is quite promising and with enormous opportunities for the healthcare sector and technology enthusiasts.

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Healthcare and medicine sector is just one of the many applications of data science. Data science certification can help manage every business better. The best institute for data science certificate course globally is now available in Jeddah too. Contact our customer support cell to get more information about course duration and everything else.

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ExcelR – Data Science, Data Analytics Course Training in Bangalore

               49, 1st Cross, 27th Main,

               Behind Tata Motors, 1st Stage,

               BTM Layout, Bengaluru,

               Karnataka 560068

               Phone: 096321 56744

               Hours: Sunday – Saturday 7AM – 11PM