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 ACCURATE

PREDICTIONS 

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ENHANCED PERFORMANCE

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NEW 

INSIGHTS

Data science services include data science consulting, development, and support companies to run experiments on their geoscientific data in search of new business insights, improved performance, and accurate predictions.

Machine learning models, facies prediction, GeoData Science and Visualization

DATA SCIENCE & ANALYTICS

 

​We will map your analytics initiatives to quantifiable business opportunity  with data-driven innovative solutions

  • Data understanding and problem elaboration

  • Data management with clean and efficient SQL

  • Designing scalable data processing pipelines

  • Building Python application for data visualization and analysis (custom build)

  • Iterate models over and over to enhance predictions

  • Deploying applications into cloud-based platforms (e.g., AWS), GUI production and dashboards

  • Result-oriented data manipulation for analytical purposes 

  • Probability and statistical analysis, data distribution 

MACHINE LEARNING

 

  • Designing efficient and innovative machine learning solutions from pipelines to products

  • Statistical modeling and hypothesis testing

  • Building, training, and validating results from various machine learning algorithms

  • Optimizing hyperparameters, model constructing with these parameters, evaluating model performances using different metrics

  • Building deep learning networks using modern methods, such as PyTorch or TensorFlow 

  • Evaluating models iteratively to avoid overfitting and under-fitting

Deep Learning, Neural Networks, Ensemble Tree Models, SVM

GeoData Science Process​

Step 1: Define Prediction to Make

Define a hypothesis to test or parameter to make a prediction about (problem understanding)

Fault?                Velocity?        Lithology?      Inversion?      

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Repeat

Step 2: Gather Data

Gather data with various sources and formats

 (Data Lake: SQL)

Step 3: Clear Data

Handle missing values, statistical analysis

(feature engineering)

Step 4: Visualize Data & Explore

Plot numerical & categorical data 

(matplotlib and seaborn in python)

Step 5: Build Predictive Models

Input-target error minimization

(Scikit-Learn, TensorFlow)

Step 6: Model evaluation & Production

Use evaluation metrics for 'goodness of fit'

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