Data Analytics is the science of examining raw data with the purpose of drawing conclusions from it. In today’s environment, where data driven business decisions are the norm, becoming familiar with data analysis tools and techniques is key to professional growth.

According to market sources, India will face a demand supply gap of 2,00,000 analytics professionals between 2016-19.

So, get trained in data analytics…Understand data driven application design, accelerate your learning curve, engage your customers well, improve your ROI and gain competitive advantage.

Sigmaway presents its Data Analytics training in Delhi, India, for those who think it is worth exploring a career in Analytics.

Gain from the experience of the best in class – With over 25 years of combined experience, our trainers have used the power of data analytics and generated huge benefits for clients.

Content: Hands on exercises on RapidMiner to understand Data Analytics tools and concepts; Case studies to emulate industry applications. Topics covered include Text Mining, Web Mining, Fraud Analytics, Decision Trees and Sentiment Analysis.

For details, contact: trainings@gosigmaway.com or visit: http://www.gosigmaway.com/events/analytics/24-data-analytics-training-delhi-india-7-8-january-2017  to register.

Date: 7th & 8th January, 2017, DELHI, India

Eligibility: No pre-requisites. Students need ID proof to avail discount

Certification: Training certificate provided on training completion.

Investment for Students: Rs. 3,000 + 15% tax

Investment for Professionals: Rs. 5,000 + 15% tax

Early bird discounts also available! And progressive discounts for Groups (conditions apply)

Nowadays, like other fields, big data insights affects strategies of digital marketers helping them to create effective campaigns. The discovery of various marketing technologies makes it clear that, companies are keen to invest in this. Sentiment Analysis tool is unethical but not illegal and is really helpful to find opinion –rich information to be acted upon. This “opinion mining” with the help of other tools can actually manage conversations about a brand when used with some other tools. But the drawback of it is we never know if the analysis becomes misleading and leads to the loss of a brand. There are many more such tools used. This proves the relationship between big data and digital marking has transformed into a more sophisticated one. Read more at:  https://channels.theinnovationenterprise.com/articles/where-big-data-marketing-meet

Sentiment Analysis is a text mining technique used to analyze the sentiment (unstructured data representing opinions, emotions or attitudes) about a particular product or topic, employing machine learning technique. Organizations across the world are extracting insights from the social data to understand how public feels about the product at a particular point in time. Sentiments are first categorized into negative or positive and then analyzed using various software like Hadoop. It can also be performed on excel. To know more, read the article at: https://www.brandwatch.com/2015/01/understanding-sentiment-analysis/

Predictive analysis helps in analyzing the choice and behavior of the viewers. This in turn helps in finding the target audience as well as optimizes revenue. Combination of system dynamics and agent based modeling can be used to predict the profits from the shows or movies. Proper techniques along with real time analysis can assess the content precisely and accurately.
Social media analytics or sentiment analytics allows the industry to understand the perception of the audience and the critical reviews that they give.
“Bag-of-words opinion analysis” approach can help in evaluating the tweets from various social media sites. Cost efficiencies can also be achieved with proper efficient use of data analytics.
Read more at: http://sites.tcs.com/blogs/agile-business/analytics-entertainment-industry

Even in this multichannel world, customers still prefer to call in and hence voice channel is critical to a business. These days’ businesses are coming to a standstill when it comes to listening to conversations that flow through their voice channel as they are stuck on sampling and high cost manual monitoring. A recent survey showed that it is important to link online marketing to offline sales.
Mining for the truths and hidden meanings from these conversations is crucial. Previously automatic data mining of the voice channel was unattainable. But now with conversation analysis it is possible. Being in the century of customers, the ability for the company representatives to understand the customer, be empathic and deliver fast solution is essential.
Thanks to conversational analysis or text mining, it is now possible to extract data from conversations, to automate and process this data in real-time and at scale. Thus listening to and analyzing conversations helps the representatives to build better relationships and in turn provide customers with the experience they expect.
Sigmaway provides text mining solutions for businesses. Contact us at contact@gosigmaway.com for a proof of concept.
Read more at: http://smartdatacollective.com/calljourney/304151/are-you-listening-your-conversations

Big Data Analytics is all about turning volume and variety of data into meaningful insights. When data is refined and combined, new patterns and ideas emerge, and one can take better decisions using these insights. Big Data is being used across almost all the sectors these days. In the Online Apparel Industry where success of next season's collection hinges on selecting the accurate designs, colors, fabrics, shapes, and sizes, Big Data can be a big game changer. Online apparel industry is mostly influenced by predictions that are based on identifying the most popular/liked parametric values (colors, fabric, style and many more) of the apparels. If you predict it right, it may bring a profitable season for you, else it may lead to heaps of discarded inventories. For many years, analysts and fashion reporters have tried to control these drifts. It is a great advantage to recognize customer preferences that will lead into high prospect ratio.

One of the ways to understand customers’ emotions behind the interactions made on social media sites and other forums is sentiment analysis. Sentiment analysis scans tweets, comments, likes, etc., for evidence of positive, negative, or even indifferent impressions to identify the overall trend of sentiments towards any entity. For example, possible positive expression on a personal level would ideally be like – “I like to wear plain cotton clothes in summers…” to an opinion projected in general - “I am looking for some cool blue apparels for my next vacations as it feels comfortable.”

Similarly, negative expressions may go like – “I am fed up of seeing bright yellow apparels all around in summers.” Or an opinion in negative tone may be like - “People look damn horrible in yellow.” A range of tools and methods are available to help determine customers’ sentiments; one of the ways to track is using the Twitter Sentiment APIs.

Big data is also extremely useful in a marketing capacity, using information like customer demographics and spending habits, in terms of how much they spend, on what and where. In addition to these habits, companies that invest in cloud computing studies can monitor how their existing marketing strategies are working - eye scanning data can be analyzed to see the effectiveness of billboards and other visual advertising. Every aspect of the business will change, from what color will be in next season to how to make clothing that fits different body types and how to optimize  supply chains.

Read more at: http://www.gogrid.com/news/2014/07/23/cloud-computing-public-cloud-big-data-how-big-data-will-change-fashion-industry

Big data has the automobile in its sights and the results will be good for both the vehicle and its owner. In the coming years we can expect to see both safer vehicles and car-to-car communications. You'll be advised of a needed repair before a problem and recall notices will be delivered through the car. Ford gathers data from over four million cars with in-car sensors and remote application management software. All data is analyzed in real-time giving engineers valuable information to notice and solve issues in real-time, know how the car responds in different road and weather conditions and any other forces that could affect the car. Ford is also installing numerous sensors in their cars to monitor behavior. They install over 74 sensors in cars including sonar, cameras, radar, accelerometers, temperature sensors and rain sensors. As a result, their Energi line of plug-in hybrid cars generate over 25 gigabytes of data every hour. This data is returned back to the factory for real-time analysis and returned to the driver via a mobile app. The cars in its testing facility even generate up to 250 gigabytes of data per hour from smart cameras and sensors. 

Big data is also used to find out how people wanted their cars to be improved. Nowadays, Ford listens carefully to what their customers are saying online, on social networks or in the blogosphere, and performs sentiment analysis on all sort of content online and uses Google Trends to predict future sales.

Internally, Ford uses big data to optimize its supply chain and to increase its operational efficiency. From the parts before they reach the Ford factory, to the car waiting in the dealer for a customer, big data has infiltrated every part of the supply chain, creating large amounts of data. With so many different parts coming from so many different suppliers, it is vital for Ford to get a complete and detailed overview of all parts within the supply chain at any moment in time. To read more visit: http://www.bigdata-startups.com/BigData-startup/ford-drives-direction-big-data/

Online social networks give vital information. If someone posts something on Twitter, Facebook, they generate a digital footprint. If someone reads it, or watches a YouTube video, they add to the data trail. We can analyze it to take business decisions. The information that we look for depends on business goals. Some companies use social media to increase sales, some concentrate on brand consciousness, others are focused on brand trust, or increasing customer satisfaction. An analyst records keywords which customers are discussing, use them into status updates or tweets to be more relevant to customers. They check which messages attract readers most so that they can reach new customers. There are different social media analytics tools. Some are Hoot Suite; span multiple channels, Twitter etc. Some tools include workflow; they allow social media managers to connect information to others. Customer feedback raises sentiment analysis. Read more at: 

http://www.theguardian.com/technology/2013/jun/10/effective-social-media-analytics