Data Science- An overview of its capabilities

Data science solutions are steadily becoming more relevant for businesses across multiple verticals. Businesses thrive on insights and recommendations that are gathered from mining data on previous performance of the organization, current circumstances in the market and desired results. These insights backed by data science are helpful for businesses when it comes to making their future blueprints.

Here are some of the things that Data Science can help you with:

  •  Highly advanced data science solutions and services can fuse analytical technologies with real-time deployment
  • This helps companies spot trends and predict future trends at the earliest
  • Companies are helped with leveraging this data for taking pro-active steps and initiatives
  • These analytical solutions cover a wide variety of business sectors and industries, helping mitigate risks in decision making and creating business strategies
  • Data science helps in boosting profitability of a company
  • Companies are helped in boosting customer satisfaction
  • Revenues can grow along with companies being helped to be first-movers in particular categories with these solutions
  • Models like forecasting, segmentation, market mix, multi-touch attribution and propensity can be addressed
  • Simulators can be built for helping users gain outcomes from analytics in a manner which is more user friendly and boosts decision making
  • Pre-built data models and connectors help with information modeling and optimization

The right data science solution provider can help you with suitable cutting edge technological approaches which enable gathering all the required answers to queries and doubts, particularly in functional zones such as lifecycle management of customers and optimization of resources along with the right pricing, customer experience and related issues and even marketing.
Organizations can leverage data science solutions for using data which may or may not be structured in diverse formats and sources. The right solutions offered by companies come with AI (artificial intelligence) and NLP (Natural language processing) abilities while several analytical programs such as IBM SPSS, Python,R and SAS are also used in this regard.
Businesses can thus stay ahead of the competition with the right data science technologies and solutions. These analytical techniques are tailored to cover all major focus areas for businesses and look to optimize business performance while helping business models to be scaled up and deployed seamlessly.

Machine Learning

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Deep Learning

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Image Analytics

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Cognitive Programming

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Machine Learning

Machine Learning- Helping businesses across various operational areas

Machine Learning has innumerable benefits for businesses in the form of a service. Multiple machine learning tools have relevance across key operational areas for companies.

Customers can thus get benefits from machine learning and its bevy of advancements without having to waste a lot of time and money and take major risks while setting up an in-house machine learning based framework.

From an organizational perspective, here’s what machine learning companies can do for you:

  • Business model evaluation
  • Data pre-processing
  • Model training
  • Predictive analytics
  • Deep learning tools
  • Data visualization
  • NLP and APIs
  • Data Centers for computation needs (of the service provider)
  • Facial recognition
  • Machine learning algorithms to obtaining new data patterns for developing new models and platforms that have predictive abilities

The solutions are generic and ready to be adapted across diverse business sectors and operations. These encompass facial recognition, visualization and many other tasks. The AI or artificial intelligence platform ensures smooth consolidation of several systems right from mobile applications and enterprise information to industrial automation and control.

Here’s what you also get:

  • Probability based reasoning
  • Data pattern recognition
  • Flexible solutions with customized workflow creation
  • DNN (deep neural network) and CNN (convolutional neural network) support
  • Probability based models and RBM

Some areas where machine learning comes in handy:

  • Risk analysis
  • Fraud detection
  • Supply chain optimization
  • Marketing
  • Network analytics
  • Inventory management
  • Manufacturing
  • Predictive maintenance
  • Advertising

Major industries where machine learning solutions can play a vital role include financial services, banking, telecom, retail, healthcare, manufacturing, insurance and transportation and logistics among others.

Machine Learning Platforms for Tackling Business Issues

Companies usually strive to increase the level of visualization across their operations nowadays. With specialized machine learning platforms companies can easily harness several benefits. The platform will ensure greater consistency in terms of data and workflows which help in boosting productivity. Here are some key aspects of these platforms offered by machine learning companies:

  • Feature Engineering- This helps in enhancing predictive abilities of learning based algorithms. The ML platform helps in the identification of business values which are needed for intelligence creation.
  • Data Validation- The platform helps users ascertain data quality along with other consistency problems. This also helps in checking whether Sorted data  has been addressed suitably.
  • Data Collection- The platform enables data importing into the system from various data sources. The data types applicable include CSV files, spreadsheets and more.
  • Classification- This helps in identifying categories for data. The platform offers classification which covers the definition and addition for observations in various stacks.
  • Data Cleansing- This helps in eliminating data which may lead to analysis distortion. The platform enables swift removal of data which is unwanted while the format can be standardized.
  • Predictive Analytics- This helps in seeing insights and forecasting parameters.
  • Regression- The platform has cutting edge algorithms for easy identification of relationships between multiple variables.

These platforms come in handy for sectors like BFSI, healthcare, retail and telecom. There is seamless data management, model deployment, evaluation and training of business models and gauging predictions.

Deep Learning

Deep Learning- Why it is useful

Deep learning is a specialized machine learning type which trains computers for carrying out activities like identification of images, recognition of speech and prediction making. Deep learning trains Machines  using basic data parameters, particularly in terms of pattern recognition with multiple processing layers. This enables swifter decision making.

Why is deep learning vital?

  • Deep learning is one of the AI (artificial intelligence) foundations
  • Backbone of systems like Cortana and Siri
  • Deep learning methods are enhanced by improvements in algorithms
  • Accuracy boost through new machine learning innovations
  • New neural network classes for classifying images and translating text
  • Graphics processing and distributed cloud computing for enhanced power
  • Dynamic, continually adapting to changes and improving based on analytics
  • More personalization of client analytics and better performance
  • Streamlines analytical operations and enhances efficiency
  • Speech Recognition for diverse voice patterns
  • NLP for pattern discovery in reports, notes from physicians and complaint
  • Usage in law enforcement and self-driving cars among other sectors
  • Recommendation based systems based on earlier behavior
  • Predictive systems that are easily adaptable and can be improved with new data

Image Analytics

Image Analytics for better operations

Image Analytics is the automatic extraction of algorithms and logical information analysis for images with the use of digital techniques for image processing. Image based data comprises of a whopping 80% of all big data which is unstructured. Image analytics examples include QR and bar codes which are interpretations of images. Other applications include facial recognition and medical imaging diagnosis along with recognition of objects in case of self-driving technologies. Image analytics also covers UGC images, analysis of brand logos, social media monitoring and rich media formats.

Some of the key benefits include-

  • Ecommerce and retail sector applications
  • Analysis of medical images
  • Conversion of videos and images into variable sets or data which is analytically prepared
  • Image segmentation into structured components for extraction
  • Relationship detection between time, variables and features
  • Variable extraction with values which are time-stamped
  • Greater value and creation of rich time series sets

Cognitive Programming

About Cognitive Computing in a nutshell

Cognitive Computing encompasses systems for self-learning that make use of techniques such as recognition of patterns, data mining and NLP (natural language processing) for mimicking the methods in which the human brain usually functions. Cognitive Computing keeps searching for data patterns and can forecast/anticipate new issues and offer solutions accordingly.

Here’s what it can do for you:

  • Healthcare sector- information collation, evidence-based treatment, identification of potential risk elements
  • Detection of safety & other problems with products, product performance assessment
  • Search & analysis of customer data sources, 360-degree customer viewing
  • Enhanced business processes and decision making based on new pattern identification
  • Finding issues in real-time and cashing in on emerging opportunities
  • Better customer engagement

Cognitive Computing comes with multifarious advantages and helps organizations personalize and experiment more en route towards garnering higher business gains.