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Data Science

Solutions related to data science are now becoming more important for businesses in multiple domains. Businesses usually flourish due to recommendations and insights based on data mining. These insights help businesses create their strategies for the future.

vElement has specialized data science solutions that can help you with the following tasks:

  • Advanced solutions for deploying in real-time and for analysis.
  • Tapping available data for shaping company strategies and predicting future trends.
  • Mitigating decision-making risks and venturing into new segments.
  • Revenue growth, customer satisfaction, profitability and being first-movers in a particular category.
  • Building forecasting, market mix, segmentation and propensity models.
  • Building simulators for building outcomes from predictive analytics. This enables superior decision making.
  • Pre-built data connectors and models for information optimization and modeling.
  • Gaining the right insights for tackling doubts and queries pertaining to customer lifecycle management, resource optimization, future product/service launches, pricing and customer experience.

The key components of data science and analytics that we offer at vElement

machine training

1. Machine Learning

Machine learning helps save time and money while avoiding major risks for companies. From evaluating business models and pre-processing of data to model training, predictive analytics, visualizing data, deep learning and facial recognition, there’s nothing that it cannot do!

Machine learning also covers aspects like APIs and NLP along with data centers for computation requirements. Machine learning is based on algorithms for garnering new patterns of data. Backed by AI, they ensure seamless system consolidation covering enterprise data, mobile applications and industrial control and automation. Probability based reasoning, recognizing data patterns, customized creation of workflows, DNN and CNN and RBM are also covered. ML helps in areas like detecting fraud, analyzing risk, marketing, supply chain, network analytics, inventory management, predictive maintenance and manufacturing along with advertising.

Core Functions:

  • Feature Engineering (boosts predictive learning through algorithms and identification of business values)
  • Data Validation (obtain data quality and consistency while checking sorted data)
  • Data Collection (Data imports into system across spreadsheets and CSV files)
  • Classification (Identifying data categories)
  • Data Cleansing (Elimination of data that may distort analysis)
  • Regression (Algorithms for easy relationship identification between variables)
  • Predictive Analytics (Insights and forecasting)

2. Deep Learning

Deep Learning is a highly specialized type of machine learning and enables training of machines for identifying images, recognizing speech and making predictions. This trains machines on the basis of data parameters for recognizing patterns across multiple layers.

deep learning

Here is why Deep Learning is so useful:

  • It is one of the core pillars for AI and back-bone of systems for communication and problem-solving like Siri and Cortana.
  • Methods are boosted by algorithm upgrades.
  • Enhances accuracy and classifies images or translates text.
  • Processes graphics while covering distributed cloud computing.
  • Personalizes client analytics for enhanced performance while streamlining analytical operations.
  • Covers speech recognition for multiple voice patterns.
  • NLP (Natural Language Processing) is used for discovering patterns in text and complaints by customers.
  • Can be used for law enforcement and self-driving vehicles along with systems that thrive on recommendations.
  • Predictive systems can be established with more adaptability to improve with new data additions.
image analytic

3. Image Analytics

Image Analytics refers to automatically extracting algorithms and logical data analysis for images with digital techniques for processing. Data based on images usually accounts for 80% of all big data which is largely unstructured. Some examples may include bar codes or even QR codes that we are so accustomed to using and seeing today. Facial recognition is another application along with medical imaging, diagnosis and recognition of objects along with self-driving techniques. This covers brand logo analysis, UGC images, social media tracking and other rich media formats.

There are applications in the retail and e-commerce sectors along with converting images/videos into variable data or sets along with image segmentation. Relationships are also easily discovered between variables, time and key features. Time-stamped values for variable extraction are also made possible along with the creation of rich time series sets.

4. Cognitive Programming

Cognitive Programming has systems for self-learning that are based on techniques including data mining, pattern recognition and natural language processing (NLP). These help in imitating methods by which the brain operates. Cognitive programming can help in collating information in the healthcare sector while covering aspects like identifying potential elements for risk and treatment on the basis of evidence. It helps with detecting safety and other issues with products and assessing performance.

cognitive programming

It can also help you analyze and search for sources of customer data along with covering customer viewing on a 360 degree basis. Business processes are also enhanced greatly by cognitive programming while it also improves decision making once new patterns are identified. In fact, businesses can use this technology to find out actual issues on a real-time basis while also tapping new prospects or opportunities. Thus, more personalization is possible with customers which helps in ensuring superior engagement with customers too.

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