Data Analytics
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What is Data Analytics?
Is the process and methodology through which data scientists draw useful insights by manipulating data. They gather, organize, analyze, and turn the raw data into intelligibly visualized information to assist and augment our decision-making abilities, which is not possible without an efficient record-keeping or data-generating system.
When the data is huge that exceeds our comprehension capacity in terms of structure and power, that is where modern computing and analytical constructs come into the frame. That is Data Science.
What is the importance of Data Analytics?
The world of data analytics
Data analytics is crucial for improving business performance and efficiency. In the banking and financial sectors, it predicts market trends, assesses risks, determines credit scores, and detects fraud.
In healthcare, data analytics predicts patient outcomes, enhances diagnostic techniques, and allocates funding efficiently.
In scientific research, advanced analytic techniques help scientists identify trends in complex systems. Data analysts specialize in interpreting these trends.
Our Data Services
Data Science
Data Analytics
Data Visualization
Data Managment
Big Data Services
Data report and more!
Types of Data Analytics
Diagnostic Analytics
Diagnostic analytics involves studying the data to determine the causes of correlations and trends between variables. It is the next step after descriptive analytics is done manually. This type is used to examine market trends, explain customer behavior, identify technical issues, and improve company culture. It helps analysts know why something has happened at this time.
Descriptive Analytics
The process of analyzing and interpreting the historical data to understand various changes that occurred in business over this period. This process helps analysts draw comparisons by using a range of historic data.
Predictive Analytics
Predictive analytics uses modeling techniques and statistics to make future predictions about the outcomes and performance of the business. It uses advanced analytics techniques like data mining, modeling, statistics, machine learning, and artificial intelligence to look at the historical and current data patterns and determine if they are likely to emerge again.
Prescriptive Analytics
Prescriptive analytics help analysts prescribe what move to make next to eliminate the future risks or take full advantage of a promising trend that is most likely to come in the future. It is used for making important investment decisions, lead scoring, content curation through algorithms recommendations, banking sector for fraud detection, product management, and marketing sectors for email automation.
Our Data Analytics Services
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Consulting Services
Basic Data Analytics Service
Data Analytics + Implementation
Consulting services involve providing expert advice, guidance, and support to organizations seeking to leverage data analytics effectively.
Involves fundamental processes aimed at understanding and extracting insights from data without delving into advanced analytical techniques.
Is a comprehensive service that not only involves analyzing data but also implementing actionable insights derived from the analysis to drive business improvements.
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Our Data Service Includes
Defining goals & requirements
All configurations work as a drag and drop so your experiment is easy to configure, change, and collect data easily
Datification
All configurations work as a drag and drop so your experiment is easy to configure, change, and collect data easily
Data Modelling & Computation
All configurations work as a drag and drop so your experiment is easy to configure, change, and collect data easily
Modeling Results & Data Analysis
All configurations work as a drag and drop so your experiment is easy to configure, change, and collect data easily
Data Results & Reports
All configurations work as a drag and drop so your experiment is easy to configure, change, and collect data easily
Customer Feedback & Final Delivery
All configurations work as a drag and drop so your experiment is easy to configure, change, and collect data easily
How We do it
Our Work Process?
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Identify The Problem/ Searching a research goal
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Data Exploration / Data Organization
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Collect the Pertinent Data / Gather the required Data
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Data Modeling / Data Analysis
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Communicate Results / Data Visualization
Our Methods
Deep Learning
Object recognition and localization, style transfer.
Image synthesis/translation using GANs and VAEs
Image visual similarity detection, transfer learning
Creating Language models
Training custom word/sentence embeddings
Semantic question answering over a large corpus
Speech to Text, Speech Language Identification, Speaker identification
Improving performance: Distributed network training (across CPUs/GPUs)
Neural Networks (CNN, RNN, ANN, DNN)
Wireless Sensor Network
Iris Recognition
Object Detection
Emotion detection
Video Surveillance
Prediction Models
Image impainting
Recommendation systems
Computer Vision
Image Classification
Image Recognition & Processing
Disease Prediction
Number Plate Detection
Blur Detection
SVM Classifier
Machine Learning
Computer Vision
OpenCV
OCR
Keras
Scikit-image
Gesture Recognition
Car Number Plate Extraction
Matplotlib
Image Processing & Classification
Image Recognition
Face Recognition
Facial Expression Recognition
Object detection
Spicy
CNN
Gensim for text analysis and processing
Machine translation
Speech to text conversion
Text mining
NLTK
Textblob
Speech recognition
Text to speech conversion
Language modelling
Text classification & summarisation
NLP
Data Resources
What is Data Management?
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Demystifying Data Science
Have you ever pondered the question, “what exactly is Data Science”? Why is it becoming increasingly popular? Or what even advanced analytics, or data mining for that matter, mean?
That’s exactly what we aim to explain in the following article.
Have questions? Ask anything!