C Ahead provides Data Science services and solutions which best suit the client’s ETL Process needs and Machine Learning Model requirements to gain insights from data that grows continuously across various industries.
With digital transformations powering today’s hyper-connected world, a vast amount of data needs to be continually managed across four distinct dimensions: volume, variety, velocity, and veracity. We provide Data Science Consulting which helps enterprises to extract value from these massive amounts of data to drive business growth and efficiency.
Today, enterprises are applying data science to unlock the value of Big Data with actionable insights to allow for data-driven decisions for products and services that reduce customer churn, improve customer satisfaction, optimize operations, re-define business strategies and increase revenue.
Our certified Data Science experts have extensive hands-on experience with various Data Science tools and technologies such as Apache Spark, Apache Hadoop, Tableau, R studio, QlikView, Google TensorFlow™ and more, to implement multi-step ETL processing, data visualization, and machine learning solutions.
Data Science Expertise
Data Engineering
- Data Migration & Integration
- ETL Processing
- Data Pipeline
- Continuous Integration & Deployment
- Data Modeling & Consultation
- Support and Maintenance
Machine Learning
- Feature Selection
- Feature Transformation
- Feature Extraction
- Model Building
- Model Evaluation and Optimization
- Multi-model Validation
Data Science Capabilities
Our Data Science services help customers meet the demands of today, plan for tomorrow and quickly realize tangible business benefits through data integrity and actionable insights.
Data Collection
- Structured and Unstructured
- Semi-structured
- RDBMS & Big Data
- Distributed File System (HDFS)
- Flat file (text, csv, json, logs)
- Emails, Websites & Web APIs
Data Processing
- Data Cleansing
- Data Profiling
- Normalization, Text Mining
- Data Extractor
- Data Transformation
- Load Data to Data Warehouse
Feature Engineering
- Locality Sensitive Hashing (LSH)
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- Text Transformation (word2vect, TF-IDF)
- Vectorization, Indexer
- Feature Scaling
Optimization & Evaluation
- Cross Validation
- Hyper parameter Tuning
- Gradient Descent, SGD
- Ensemble & Boosting
- RSS, RSME, MSE
- Log-loss, F-measure, Precision-Recall
Machine Learning
- Regression Algorithms
- Classification Algorithms
- Support Vector Machine (SVM)
- KD-Tree, Decision tree, Random Forest
- K Nearest Neighbors (KNN)
- K-means, Latent Drichlet Allocation
Deployment
- Model Deployment
- Model Serving
- Model Pipeline
- Managed Deployment
- Monitoring
- Evaluation