What is Data Analytics? Master’s in Data Analytics with the best data analytics training institute in India

Data Analytics refers to our ability to collect and use all the data (real-time, historical, structured, unstructured) to generate insights that informed fact-based decision-making. Data Analytics sanctions organizations to digitally transform their business and culture, becoming more effective, innovative, and forward-thinking in their decision-making.

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data to describe, predict, and improve performance. To ascertain robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more.

Why data analytics is important?

• Huge job opportunities: The demand for data analysts is on a hike, the demand is rising, and more organizations are hiring data analysts. As the desideratum for jobs is growing, more people are gravitating towards this profession. Withal, more and more businessmen are probing for world-class analysts as this is how they will visually perceive a way to make a profit. This explosion of data and analytics is going to be an assistance in demand ascend along with a high growth rate.
• Helps decision-making: Data analytics is the key to driving productivity, efficiency, and revenue growth. The results from analyzing data sets are going to tell an organization where they can optimize, which processes can be optimized or automated, which processes they can get better efficiencies out of, and which processes are unproductive and thus can have resources dedicated away. In this way cost efficacy is incremented as areas that are hoarding a company’s finances unnecessarily can be identified and decisions can be made around technologies that can be put in place to minimize operational and production costs.
• New innovations: Data analyses give you a rough conception of the future trends in consumer behavior that will enable organizations to make futuristic inventions on their products. This way, they can engender products and engender accommodations that put them on top of their industry. With these innovations, they can maintain a sharp edge advantage over their competitors. The good thing with these innovations is that they can patent them and reap the best from them while at the same time being way ahead in their profits.
• Data analytics for students: Data Analytics is no more a surprising concept for a student to study as there is an explosion of tech implements that are available to make sense and withal the industry is growing rapidly. Tech tools are available to make sense of institutional data which will avail in further innovation.  There is the desideratum of more and more institutional research and there is so much scope for exploration that the budding curiosity has additionally made this field an Haute course.
• Data analytics for a professional: A professional may withal need analytical skills to understand his work patterns. The thing is people will definitely have to utilize analytics in their profession as this can avail them to prioritize what work that they can do or what is that they can do about the perpetual scenario. Work processes need analytical skills and therefore organizations are hiring a big bunch of analysts that can avail them in working preponderant.

       Who is using data analytics?

• Healthcare: Big data is a given in the health care industry. Patient records, health plans, insurance information, and other types of information can be difficult to manage – but are full of key insights once analytics are applied. That’s why big data analytics technology is so consequential to heath care. By analyzing large amounts of information – both structured and unstructured – expeditiously, health care providers can provide lifesaving diagnoses or treatment options almost immediately.
• Life Sciences: Clinical research is a slow and expensive process, with trials failing for a variety of reasons. Advanced analytics, artificial intelligence (AI), and the Internet of Medical Things (IoMT) unlocks the potential of improving speed and efficiency at every stage of clinical research by delivering more intelligent, automated solutions.
• Banking: Financial institutions gather and access analytical insight from large volumes of unstructured data in order to make sound financial decisions. Big data analytics allows them to access the information they need when they need it, by eliminating overlapping, redundant tools, and systems.
• Manufacturing: For manufacturers, solving problems is nothing incipient. They wrestle with problems on a circadian substructure - from complex supply chains to kineticism applications, to labor constraints and equipment breakdowns. That's why big data analytics is essential in the manufacturing industry, as it has sanctioned competitive organizations to discover new cost-preserving opportunities and revenue opportunities.
• Retail: Customer accommodation has evolved in the past several years, as savvier shoppers expect retailers to understand precisely what they require when they require it. Big data analytics technology avails retailers to meet those injunctive authorizations. Armed with illimitable amplitudes of data from customer loyalty programs, buying habits, and other sources, retailers not only have an in-depth understanding of their customers, they can additionally presage trends, recommend new products – and boost profitability.

Why should you pursue a career in data analytics?

• You can easily gain the skills required for the job: Having a natural desire to find solutions, regardless of your present vocation, will make it more easier and interesting for you to learn how to utilize analytical implements that are in demand and to specialize with time.
• There are multiple opportunities in many domains: Data Analytics attain cost-effective solutions and improve decision-making power in multiple development areas, including healthcare, manufacturing, education, media, retail, and even real estate. You will have an opportunity to select from a variety of industries that match your skills and interests.
• There are many high-paying jobs in the profession: The monetary benefits of shifting to a Data Analytics profession can prove to be better than those of other IT professionals. Students and young people who are logically driven, computer-savvy, and excellent communicators looking to make an above-average income while working for fixed hours should look into Data Analytics as a career move.
• It sanctions you to wield decision-making power: Data analytics is an integrated value to any organization, sanctioning it to make apprised decisions and providing an edge over competitors. With more and more companies depending on data specialists, you’ll work with the key person of the organization to streamline decision-making layers from top to bottom and coordinate with local levels to act on insights.

About Sankhyana: Sankhyana Consultancy Services (Biggest SAS Authorized Training Partner in India) is India’s Premium and best data analytics training institute in India offers the best classroom, online/ live- web, corporate & academia Training on SAS & Data Management tools. Our programs feature instructor-led classroom and real-world projects to ensure you get hands-on experience and relevant skills.

About SAS: SAS is the leader in analytics. SAS is the no.1 advanced skill to have in this data-driven world.

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Creating SDTM domains with SAS: A guide for Clinical SAS Programmers

As a clinical programmer, there are many paths available. The main goal is always to access the data, manipulate and transform it, analyze it, and report on it. A programmer can specialize in data management (DM) programming and spend most of the time cleaning the data through edit checks and the engendered of patient listings and profiles.

Another task of the DM programmer is to transform the data from its raw format into a standard format. This standard format could be the CDISC Study Data Tabulation Model (SDTM) that is requested by regulatory agencies such as the FDA for submission of an incipient compound, or it could be a sponsor’s own standards. In the process of transforming the data, the DM programmer must make sure that the output conforms to the standard and is compliant as well as valid.

Thus, another aspect of the job is to write programs to check the data against the standard and run the programs whenever an incipient study is about to be analyzed. Determinately, when all the data has been transformed, the DM programmer must engender a convey file that will be sent to the regulatory agency that will review the submission data. The second type of clinical programmer is the statistical programmer (STAT) who takes the data that is cleaned and transformed by the DM programmer and engenders tables, listings, and graphs (TLG) for the clinical study report (CSR). Sometimes the data is taken from its raw state and transformed directly into TLGs, but most often the STAT programmer engenders analysis data sets from which they can facilely engender the compulsory output documents for the CSR. The STAT programmer is withal tasked with engendering ad hoc reports when needed, yearly safety updates, DSMB reports, and integrated safety and efficacy summaries.

There is a significant transition occurring for many clinical programmers in data management (DM). Many DM programmers are evolving from engendering programs in Base SAS to the utilization of incipient implements and solutions to engender the data that is needed in an incipient drug application submission. What did the programmer do in the past to cleanse the data and how has that process transmuted? Now that the data is requested to be in a standard format, what types of programs, macros, and formats were habituated to transform the data? What is done now to make the process more facile, more efficient, and repeatable across protocols, compounds, and therapeutic areas? From the old methodology to the incipient implements, we will show how the transformation process can be transmuted and amended.

Implementing SDTM with Base SAS

One possible approach is to implement the SDTM data standard with Base SAS as the primary implement. In the simplest form, this involves importing the source data into Base SAS, transforming that data with DATA steps, SQL and SAS PROCS, and then preserving SDTM domains as aeonian data sets. For this instance of engendering the DM file, sort the three source data sets by patient identifier, and then merge them together. The remaining activity is to define each of the SDTM DM variables in a DATA step and preserve that DM file to the target LIBREF. As is the case with all legacy SAS work, we have at our disposal a code editor window and SAS documentation perhaps in hard copy as well as online.

Base SAS Approach – Challenges and Benefits

There are several challenges with the reliance on Base SAS alone to perform SDTM domain data engendered. A primary issue is the management of metadata, as there is no metadata provided with Base SAS alone. One thing to note about this program is that you require to inscribe in all the LENGTH and LABEL verbalizations to define the SDTM metadata for the final domain data sets. This type of metadata is tedious, prone to error, and liable to result in inconsistencies across SDTM domain metadata for a tribulation. You additionally have no authentic regulation of the target metadata and no genuine-time validation that your resulting domain is valid SDTM data. When utilizing this Base SAS approach, you additionally run into logistical and strategic issues with code maintenance and reusability of the SAS code. The Base SAS code itself can become arduous to read, which makes maintenance arduous. This kind of coding inclines to be “one-off” in nature – resulting in constrained reusability.

The primary advantage of the Base SAS approach – albeit some might genuinely consider it a disadvantage – is that you have no restrictions as to what you can do with your SAS code. You have the full arsenal of Base SAS and can utilize any SAS procedure, macro code, or SQL procedure code to solve the quandary of SDTM data conversion. Some programmers have taken the Base SAS approach to SDTM engendered work and have augmented it with commonly available implements such as Microsoft Access or Excel as a place to store and apply metadata. This augmented approach is better than Base SAS solutions alone because you have your target SDTM metadata in a more manageable source, and you can consider the effort remotely data-driven and less prone to metadata consistency errors.

Implementing SDTM with SAS Enterprise Guide

A second approach to SDTM domain engendered uses SAS Enterprise Guide, which features a graphical utilizer interface and some additional that facilitate the SDTM file engendered. The first step in this effort was to define a LIBREF called LIBRARY that would point to the aeonian format Catalog associated with the source legacy data sets. Next, simply drag and drop the source data sets into the SAS Enterprise Guide Process Flow window. With the data in the process flow, it’s picayune to apply PROC SORT SAS Enterprise Guide tasks to sort the data by patient identifier. At this point, the task of SDTM conversion joins the same process utilized with the Base SAS  solution, where the data is merged, SDTM variables defined, and the aeonian DM data set is preserved just as shown in the Base SAS solution.

SAS Enterprise Guide Approach - Challenges and Benefits

The selection of SAS Enterprise Guide as the primary implement to engender SDTM data results in homogeneous challenges to utilizing Base SAS alone. Metadata management is still destitute in this approach and all the variable lengths and labels are still manually typed into the program code. Again, there is no genuine regulation of the target metadata and no authentic-time validation that your resulting domain is valid SDTM data. Albeit SAS Enterprise Guide provides the auxiliary Process Flow GUI, the “Tasks” available that you can drop into your Process Flow are constrained to sorting, appending, and transposing the data. The next section shows how the SAS Clinical Data Integration solution distributes more available data management tasks in the form of what it calls “Transformations” in lieu of “Tasks.” There are some advantages to utilizing SAS Enterprise Guide to engender SDTM data over Base SAS alone. As with the Base SAS approach, there is always the full arsenal of Base SAS, and you can utilize any Base SAS PROC, SAS MACRO, or SAS SQL code to solve the quandary of data conversion. However, with SAS Enterprise Guide you get some additional assistance in the form of automated “Tasks” that you can drag and drop into your project. You can optically discern the PROC SORT-driven “Sort” task utilized in Exhibit 1 above, but there are other utilizable tasks for SDTM engendered such as the data splitter, data appended and data transposing (rows to columns and columns to rows) tasks that can be very subsidiary here. If we had a more arduous domain to engender, then these additional prepackaged “Tasks” could be included in the process flow and programming. Additionally, it is worth mentioning that with SAS Enterprise Guide 4.3, you get more of a true development environment in SAS than ever afore.

SAS Enterprise Guide 4.3 includes code completion facilities and interactive syntax guides found in other software development environments that you will dote as a SAS programmer. Because of the process flow view, the SDTM work lends itself to being more manageable and reusable long-term because the programming itself inclines to be less spaghetti code. Conclusively, just as with the Base SAS approach, the SAS Enterprise Guide approach could be utilized in conjunction with implements such as Microsoft Access or Excel to give you a minimal way of managing your SDTM metadata.

Implementing SDTM with SAS Clinical Data Integration

After exploring the engendered of SDTM files with Base SAS and SAS Enterprise Guide, it is now a good conception to optically canvass the “full monty” SAS approach to SDTM data engendered work utilizing SAS Clinical Data Integration. SAS Clinical Data Integration is an ETL implement built on top of SAS Data Management that includes concrete functionality to fortify clinical tribulations. To commence the same process in SAS Clinical Data Integration, drag and drop the SDTM DM domain from our metadata repository. That target domain already has defined the table and variable level metadata, and it additionally includes felicitous integrity constraints on the data. Now that the target is defined, drag, and drop the source data sets. The next step is to join the three source data sets via an SQL join, which is done by dragging and dropping the predefined “SQL Join” transform. The “Extract” transformation step you optically discern in Exhibit 2 is where the SDTM DM variables get defined in a process analogous to the Base SAS and SAS Enterprise Guide DATA step code in prior sections. Within SAS Clinical Data Integration, this is done within point-and-click driven PROC SQL code building steps. The final step is to insert the “Table Loader” transformation, which takes the SAS data set from the “Extract” step and saves the permanent DM data set.

SAS Clinical Data Integration Approach – Challenges and Benefits

Because SAS Clinical Data Integration handles many facets of SDTM data engendered, the challenges are minimal. Probably the most immensely colossal challenge for a SAS programmer is learning to give up slinging Base SAS code and learning to rely on the implement to do the work. Additionally, SAS Clinical Data Integration relies on SAS SQL under the hood quite marginally, so “old-school” Base SAS programmers may need to enhance their SQL skills. As with the Base SAS and SAS Enterprise Guide solutions, you can utilize any Base SAS procedures you require, but the key advantage of utilizing SAS Clinical Data Integration is in its competency to manage your metadata. Ergo, eschew inscribing a bunch of custom SAS code, because that constrains the tool’s competency to control the work. It can be a marginal adjustment to learn to work and program largely within the confines of the transforms available within SAS Clinical Data Integration. Metadata management is paramount, so scarcely of setup is required to define your target data metadata upfront.

SAS Clinical Data Integration provides the same kind of process flow view and drag-and-drop tasks/transforms that SAS Enterprise Guide provides. More importantly, SAS Clinical Data Integration manages the metadata for your SDTM work, a benefit that neither Base SAS nor SAS Enterprise Guide can provide alone. It controls the target SDTM metadata, so compliance with a defined SDTM standard is built into the workflow. It withal connects the metadata across SDTM data engendered so that you can analyze data for changes and updates and withal propagate a vicissitude across your SDTM data engendered.

Utilizing SAS Clinical Data Integration as intended with standard transforms essentially enforces remotely of consistency of process in engendering SDTM domains. This consistency along with the process view sanctions for SDTM engendered jobs to be more facilely maintained and withal sanctions for reuse of jobs. SAS Clinical Data Integration withal sanctions for “typical” SDTM generation tasks, such as study day (--DY) or ISO date (-- DTC) engendered, to be standardized into utilizer-inscribed transforms that can be dragged and dropped into future SDTM jobs.

Although SAS Enterprise Guide provides several mundane “tasks” that can be dragged and dropped into your process flow, SAS Clinical Data Integration provides a much more expansive list of transformations to cull from. Several of those are prodigiously handy in terms of engendering SDTM domains, including the sort, transpose, data joiner, lookup table, data extraction, and data loader transformations.

Finally, SAS Clinical Data Integration is integrated with the SAS Clinical Standards Toolkit associated with Base SAS software. There are pre-subsisting SAS Clinical Data Integration transformations that sanction you to validate SDTM data sets predicated on the SDTM metadata and withal to automatically engender a define.xml file – which is an astronomically immense benefit.

Conclusion:

There are multiple SAS approaches to the task of converting clinical tribulations data into the CDISC SDTM. This white paper presented a Base SAS approach, a SAS Enterprise Guide approach and a SAS Clinical Data Integration approach. The main distinction between approaches involved peregrinating from little implement support to heftily ponderous context-concrete implement support to accomplish the SDTM DM engendered task. It used to be that when clinical SAS programmers were confronted by a data transformation task such as SDTM conversions, we had Base SAS. Now, there is a better GUI implement, SAS Enterprise Guide, that avails with SAS code development. More recently, SAS Clinical Data Integration has emerged as an exhilarating incipient clinical tribulation and CDISC-cordial, industry-concrete implement. SAS Clinical Data Integration gives us a way to manage our metadata and process in a way that was not available afore, while still sanctioning us to write Base SAS code when needed. SAS Clinical Data Integration is critical in order to have largely metadata-driven transformation processes that can scale to perform numerous data conversions in a reliable and efficient manner.

About Sankhyana: Sankhyana (Biggest SAS Authorized Training Partner in India) is a premium and the best Clinical SAS Training Institute in India offers the best Online/Live-Web training on SAS and Data Management tools.

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The value of SAS Certifications – Upskill from the biggest SAS Authorized Training Partner in India

 The demand for data skills has been growing at an expeditious rate and will perpetuate to progress for years to come. According to the World Economic Forum (WEF), Data and AI will experience the highest annual magnification rate for job opportunities, at 41%. It’s no surprise that the desideratum for these skills is more preponderant than the faculty to consummate the requisites, hence the term “skills gap” that perpetuates to be a sultry topic throughout the job market.

How did we get here? Believe it or not, an article from the WEF estimates that by 2025, 463 exabytes of data will be engendered each day ecumenically. From the magnitude of data that people are engendering each day, there’s no question that organizations need talented individuals to digest this data, discover patterns, and make apprised decisions. With the quantity of authoritative ordinance in this space, it’s consequential to not only validate your capabilities but differentiate yourself. Achieving certification will set you apart and give you the power to succeed.

The following are 10 reasons to get a SAS certification.

1. SAS skills are in high demand: The overall magnification in SAS programming skills during a downward trend in national hiring is reassurance that the desideratum for SAS skills isn’t peregrinating anywhere. Another tech recruitment site, Dice.com, used Burning Glass to analyze jobs that demand SAS skills, so if you’re unsure what vocation path your hard-earned skills can take you to ascertain to check it out. They’re prognosticating a 4.4% magnification in SAS skills over the next 10 years.

2. Get granular or stay general: The SAS certification program spans a wide range of topic areas. From Artificial Astuteness and Machine Learning to Base Programming, you’ll be able to find the certification that is just right for you. Check out more details on the SAS Ecumenical Certification program. You can get commenced with specialist-level credentials and work your way towards a professional-level status.

3: Improve your employment opportunities: No certification can ensure that you will get a job, however, it could open the door to incipient job opportunities. Exhibiting a potential employer that you have inserted the time, effort, and dedication to earn a SAS credential can give you an advantage in the hiring process. Proximately 50% of certification holders deemed certifications availed them find an incipient job, enter an incipient vocation field and incremented their number of job interviews, according to Pearson Vue’s “Value of IT Certification” report.

4: Do you want to climb the career ladder?: Earning a SAS credential avails demonstrate that you are solemn about enhancing and amending your adeptness set and are disposed to invest in your personal magnification. A triumph-win for you and your employer. In a recent survey of Coursera learners who consummated SAS training, 84% of certification holders reported that achieving a SAS credential availed them advance their vocation, excel in their position, and do their job more efficaciously.

5: Demonstrate your true SAS skills and understanding: The Base and Advanced Programming exams are now performance-predicated, betokening you inscribe and execute genuine SAS code while you are in the exam. It’s an authentic world as it gets and a great way to demonstrate what you really know!

6: Digital gasconading rights: Upon passing your certification, you receive apperception with an online digital insignia that provides secure, electronic verification of your achievement. Utilize this insignia on your LinkedIn profile, boast to family and friends across Facebook, Twitter, Instagram, or wherever you optate your digital footprint to take you!

7. Sanction employers to facilely discover you: SAS certified individuals can be listed in the Directory of SAS Certified Professionals which can be accessed by employers to validate your credentials. "65% of certified individuals reported realizing the first benefit of getting certified within 3-4 months."

About SAS: SAS is the leader in analytics. SAS is the no.1 advanced skill to have in this data-driven world.

About Sankhyana: Sankhyana (Biggest SAS Authorized Training Partner in India) is a premium and the best SAS Training Institute in India that offers the best classroom & online/live-web training on SAS and Data Management tools.

www.sankhyana.com | info@sankhyana.com

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