List of top analytics areas and their differences 1. Analytics in Business Advertising Analytics Brand Analytics Promotion Analytics Business-to-business marketing Analytics Social Media Analytics Tracking Studies 2. Demographic Analytics Consumer Analytics Concept Testing Data Mining Customer Satisfaction Study Analytics Demographic Analytics Employee Satisfaction Analysis Text Mining Ethnographic Analytics Media Testing Opinion Polling and Predictive Analytics Usage & Attitude Studies Segmentation Analytics Semiotic and Cultural Analysis 3. Product Analytics Packaging and Design Effectiveness Analytics New Product Development Pricing Studies Product Testing Scenario Planning  Also Read Top IT Skills You Need to Become Data Analyst

SAS visual analytics is a completely new architecture from SAS. It has the capability to manage large amounts of data and bring it into memory to analyze it, explore it and publish reports.  Although the data amounts are massive — up to 1.1 billion rows of data, the SAS LASR Analytic Server, to use its full name, was designed to be intuitive to users without an advanced degree in computer science. A report from Simply hired. All about SAS analytics Server - The SAS Analytic Server begins with an eight-blade server with 96 processor cores, 768 gigabytes memory and 4.8 terabytes (TB) of disk storage.  The upper end of the reference configurations is 96 blades with 1,152 cores, 9.2 TB memory and 57.6 TB of disk storage, enough disk space to store the entire Library of Congress six times. Where to Learn SAS Visual Analytics Also read:    Modelling with SAS a detailed video course to get instant benefit  The real SAS Visual analytics benefits The speed of in-me

The IBM Netezza data warehouse appliance is easy-to-use and dramatically accelerates the entire analytic process. The programming interfaces and parallelization options make it straightforward to move a majority of analytics inside the appliance, regardless of whether they are being performed using tools from such vendors as IBM SPSS, SAS, or Revolution Analytics, or written in languages such as Java,Lua, Perl, Python, R or Fortran. Additionally, IBM Netezza data warehouse appliances are delivered with a built-in library of parallelized analytic functions, purpose-built for large data volumes, to kick-start and accelerate any analytic application development and deployment. The simplicity and ease of development is what truly sets IBM Netezza apart. It is the first appliance of its kind – packing the power and scalability of hundreds of processing cores in an architecture ideally suited for parallel analytics. Instead of a fragmented analytics infrastructure with multiple systems

There is a big question to be successful in analytics career. I am giving most popular ideas to be successful in this analytics career. Gettyimages.in There are absolutely three... The tip number one is you need to be familiar with different types of analytics. Web Analytics Financial Analytics Marketing Analytics Education Analytics Retail Analytics Health Care Analytics The second one is you should have right kind of training at less expensive way. In the booming Analytics Industry, one needs right course of training and experience, and working experience of case studies on selected analytics area, really boost your career. I am sure Udemy is offering lot of courses . The third rule and idea is correct mindset Assertive Communication   BE PROACTIVE - Be Proactive is about taking responsibility for your life. You can't keep blaming everything on your parents or grandparents. Proactive people recognize that they are "response-able." They don'

The Introduction to Analytics course is a set of 12 videos that take you through the basics of data analytics and the language of SAS.You should do this course if you are interested in data analytics and would like to pursue it as a career or if you have heard about the buzz it is creating and just want to know what it is all about. Each video is 5 to 30 minutes in length. If done in one go, it should take you about two hours to cover the course. The course is divided into three sections. The first section 'Introduction to Analytics' is a set of 6 videos. Here is a snapshot of the 6 videos: 1. What is analytics? This module explains the basis of analytics. 2. Why is analytics popular? This module talks about why analytics has become a big buzzword in business circles and the advantages of analytics. 3. Analytics applications? Here you will be introduced to the various analytics applications popularly used today. 4. Analytics technology Link to free course L

sas career options Many people think analytics is about gathering data using software tools and creating dashboards and reports. However, analytics is much more. Analytics goes beyond data; its primary goal is to enable business decisions based on that data. This involves working with stakeholders to understand the gaps in the business and using this knowledge as a guide to manipulate data, derive useful insights, and make recommendations – all key actions to increase revenue and lower costs. Wherever you sit in your organization, what’s most important is the bottom line. And so whether you lead business or IT units or are in the trenches, the analytics profession has likely crossed your mind. What does it entail? Who are true analysts? How does one become an analyst? Those of you specifically in a data management, data warehousing or business intelligence role may wonder how to further develop your analytics career. On the surface, an “analytics career” can be quite broadly d

Energy Analytics is a new area started recently. The below are the key points in analytics. According to Siemens, Outsource your energy data management as a service and benefit from regular analyses performed by our energy experts. Evaluations show that this service makes it possible to easily achieve savings potential of as much as 5 percent. Headlines of energy analytics Market Forecast: Energy Data & Analytics Energy Data Management & Analytics Business Skills Big Data in an Utilities Environment: Real Case Studies Advantages of Big Data & Analytics: Smart Metering & Cloud Computing Innovative Solutions & Technologies Data Analytics in a Smart Grid Perspective - DONG Energy Case Study Big Data in the TSO Business Achieving Benefits From Smart Meters Deployment With Advanced Analytics - Return on Experience From Large Scale Deployments in the US Smart Grid Cybersecurity, Frameworks and Standards Adopting Open Source Software in Energy Anal

Accenture Plc., which has 40% of its global big data analytics team based in India, plans to tap the country’s high-end analytics market in anticipation of an increase in demand driven by the government’s Digital India programme.  The National Democratic Alliance (NDA) government’s Digital India initiative is aimed at delivering government services electronically in urban and rural areas, cutting out human intervention and closing the gap between digital haves and have-nots.  It will act as an umbrella plan to integrate and synchronize all digital initiatives including the national broadband plan and the domestic manufacturing policy. “Big data analytics will play a major role in the whole digital disruption that is happening in India today,” said Arnab Chakraborty, managing director for advanced analytics at Accenture Digital.  “Mobility has leapfrogged in India and the whole ‘Make in India initiative’ has caught up strongly in the last six to eight months with the new leadership co

Every industry has its own particular big data challenges. Banks need to analyze streaming transactions in real time to quickly identify potential fraud. Utility companies need to analyze energy usage data to gain control over demand.  Retailers need to understand the social sentiment around their products and markets to develop more effective campaigns and promotions. Analytics solutions help organizations take control of big data and uncover the insights they need to make the best decisions. IBM has Analytics Solutions in various lines: Banks: Apply analytics to improve customer experiences and operational efficiency, and integrate risk into daily decision making. Communication:Uncover insights about customers, network performance and market trends to make better business decisions. Retail: Build lifetime customer relationships by meeting demands for innovative products while containing costs. Education: Make more informed decisions to improve student performance an

The word predictive analytics is to increase competitive advantage and at the same time to suggest the best value products to end customers. Data analytics Why you need to go for predictive analytics The reasons are Growing data Cheaper computers and servers Easy to use software Tough economic conditions More: Case Study on data analytics The predictive analytics helps in the following areas: Detecting fraud Improve marketing campaigns Reduce risk Improving operations Growing data analytics creating many job opportunities. Where You Need to Learn Do PG or post graduation in data analytics Attend Class Room Coachings

You may already know that SAS is king in visual analytics. The real features that SAS provide you can learn here. I have collected some features on SAS analytics.  SAS Visual Analytics is an easy to use, web-based product that leverages SAS high performance analytic technologies and empowers organizations to explore huge volumes of data very quickly in order to see patterns and trends, and to identify opportunities for further analysis.  "SAS Visual Data Builder enables users to summarize data, join data, and enhance the predictive power of their data. Users can prepare data for exploration and mining quickly and easily." The highly visual, drag and drop data interface of SAS Visual Analytics Explorer combined with the speed of the SAS LASR Analytic Server accelerates analytic computations and enables organizations to derive value from massive amounts of data.  This creates an unprecedented ability to solve difficult problems, improve business performance, and m

Hey, welcome to data analytics. Here I have given a few questions and answers. The below five questions helps you to understand very basics on data analytics. These are very basic questions. But, for laymen, they can understand easily and also good for interviews. Stocksnap.io Questions on Business Intelligence What is Business Intelligence?  It is a well-established process in the business world whereby decision makers integrate strategic thinking with IT  What is Web Analytics?  It is the collection, analysis, and reporting of Web site usage by visitors and customers of a website in order to better understand the effectiveness of online initiatives and other changes to the website in an objective, the scientific way through experimentation, testing, and measurement.  What is academic analytics?  This is the area analytics applied in academia  What is action analytics?  This is the area of analytics to produce actionable intelligence  What is the process of

#Data Analytics Engineers - Salaries and career options: Some key skills which are very crucial for an employee these days are the ability to communicate disruptive ideas, multi-disciplinary skills with appropriate depth of specialisation, learnability and resilience," said Prithvi Shergill, chief human resources officer, HCL Technologies, the country's fifth-largest technology company. So, an engineer with 2-3 years' experience working in data analytics can expect a salary of Rs 4-6 lakh in India and $70-80,000 in the US or Europe.  But without big data skills, the same engineer can earn only Rs 3-5 lakh, according to Anurag Gupta, chief operating officer at Magna Infotech, an IT staffing firm. Experience of 5-6 years in data analytics can help an engineer command a salary of up to Rs 10-12 lakh in the domestic market, according to Kamal Karanth, India MD of staffing firm Kelly Services. Firms like IPsoft, Happiest Minds and Mu Sigma, operating in automati

There is no surprise in India, all companies started building Data analytics team and infrastructure. With a lot of Indian companies building their data analytics team, the requirement in the domestic market for this skill will increase over the next couple of years. The requirement for Data Analytics  There will be an increased demand for data analytics professionals. Industry experts believe that currently big data and analytics is one of the top three skills in demand in India.  Organizations are looking at their internal set of data to understand the business better – as a result, there will be an explosion of various job opportunities in this area. Top three segments where huge demand for data analytics engineers are Data Science, Statistics, Technical specialists with multiple skills. Top Demand Roles in Data Analytics Some of the requirements are for tech personnel, statistician, econometrician, data scientist, analytical consultant, functional consultant, etc

Top criteria you need before you start analytics in the Human Resource department. I am sure you need many approvals to start analytics in HR. The risks involved to start analytics in the Human Resource department You must comply with the legal requirements in which you operate as it relates to the use of people data. The reason is the analytical insights should reflect the cultural and social marks of your organization. You need to get involved all stakeholders involved and what the cost of what you're doing is relative to the benefit of doing it. Use analytics through accountable processes, one of which should be acknowledging that using predictive analytics with the workforce has the potential for negative impact, not just positive impact, Walzer said. Engage the legal department to make sure you understand any implications before you've done something, not after the fact. Assess whether the use of analytics involves sensitive areas, which it often will, Walzer sa

Can you do analytics—either kind—using Excel? Sure. Excel has a large array of tools that bear directly on analytics, including various mathematical and statistical functions that calculate logarithms, regression statistics, matrix multiplication and inversion, and many of the other tools needed for different kinds of analytics. But not all the tools are native to Excel.  For example, some situations call for you to use logistic regression: a technique that can work much better than ordinary least-squares regression when you have an outcome variable that takes on a very limited range of values, perhaps only two. Odds ratios are the workhorses of logistic regression, but although Excel offers a generous supply of least-squares functions, it doesn’t offer a maximum likelihood odds ratio function.  The below list makes you strong in Excel analytics Microsoft Excel 2010 Data Analysis With Functions Analytics with Excel - Book Cloud data analytics from Excel Quantitative Analysis

Python language is popular in many areas. Below are the real uses of Python. Surely, many developers confuse to tell all the applications. So I have listed for quick reference. 8 Python Real Application Uses #1. Python Application use -  Python Scripts  Very much popular in scripting. You can write scripts in many areas. Like Interfaces, to access files, to access database, and to access the application programs. #2. Python Application use -  Python in Web Development  You can use Python in Web development. There are powerful Python frameworks present such as Django, Flask, Web2Py, and Pyramid. You can develop an application in IoS/Android/Windows. #3. Python Application use -  Graphical User Interface You can develop applications, which need access to GUI. The libraries and APIs like Tkinter, PyQt, PyGTK, and wxPython allow developers to develop GUI-based apps with simple or complex interfaces.  #4. Python Application use -  System Programming You can use it in System Programming. Li

The following skills are must for every data analytics engineer to be successful in e-commerce companies. Many software engineers are struggling to get this information. I am giving here both technical and general skills. 1# Technical Skills for Analytics Career What skills they need to be successful in their analytics career. The skills required to get an entry into the Analytics job are here for your reference. I have told in my previous posts that there are many branches in analytics . You need to apply domesticated techniques to extract actionable knowledge. 2# General Skills I have selected the following mindset and skills that you need to get a job in data analytics. These are proven skills. Set a clear goal to acquire these skills. Strong interpersonal, oral and written communication and presentation skills;  Ability to communicate complex findings and ideas in plain language  Being able to work in teams towards a shared goal;  Ability to change direction quickl

AIM is one of the India's best analytics Magazine. The features it covers Articles, Latest trends in analytics, Start-ups in India, various job opportunities. Various government initiation on Big Analytics is plus point in this Magazine. Read AIM here. Analytics Tips for E-retailers:  This is one of the good article from CIO. Click to read Also read Google Analytics for Web Marketers and Tips AWS Basics for Software Engineers

SAP HANA converges database and application platform capabilities in-memory to transform transactions, analytics, text analysis, predictive and spatial processing so businesses can operate in real-time. I have listed the areas on available careers for SAP HANA. SAP HANA  It has completely transformed the database industry by combining database, data processing, and application platform capabilities in a single in-memory platform. The platform also provides libraries for predictive, planning, text processing, spatial, and business analytics -- all on the same architecture. This makes it possible for applications and analytics to be rethought without information processing latency, and sense-and-response solutions can work on massive quantities of real-time data for immediate answers without building pre-aggregates. Simply put -- this makes SAP HANA the platform for building and deploying next-generation, real-time applications and analytics Operational Analytics With tr