Explore our Data Science expertise

Data Science as a Service (DSaaS) helps businesses to build complex analytical algorithms, which supports competitive intelligence. We collect, analyze and process data, helping businesses and service providers to thrive.

CodeCoda helps businesses turn their raw data into strategic business insights. We offer Data Science as a Service and create complex analytics systems for technology businesses and service providers. Our Data Scientists harness the power of data. We create custom data analysis tools, which processes big amounts of data, turning it into meaningful and understandable information for your business.

We transform structured and unstructured data into clear visual metrics that can drive business decisions. By visualizing the results, our Data Scientists make findings understandable for non-technical users and business stakeholders. Our nearshore development teams automate data processing and integrate big data analytics into your organizations existing workflow.

The goal is to turn
data
into
information,
and information into
insight.

- Gordon Gekko

1

Tech Stack

Our Data Scientists utilize languages like Scala, C/C++, Python, R, GoLang, Java, SQL. They use tools like Apache Spark, Pig, Hive frameworks, Apache Hadoop, Cassandra, SAS, and many others. Additionally, due to our expertise in AI and Machine Learning, the horizon has even broadened. Every Data Science project has it's own proven tech stack, including several technologies in order to achieve business goals.

2

Predictive Modeling

Predictive modeling is the process used by CodeCoda's Data Scientists to harvest data, and to help forecast potential outcomes. Each model is developed for specific predictors, variables that are likely to influence future results. Once data has been collected, a reliable statistical model is formulated. We create both classification and regression models to support virtually any part of your business: from business strategy to marketing to distribution or operations.

3

Data Science and Machine Learning

The power of Machine learning, when it digests huge sets of structured or unstructured data, is inconceivable. Our Data Scientists, together with Machine Learning experts, create custom Deep Learning Models to match your data sets. They integrate cutting edge AI frameworks like Keras, TensorFlow, MatLib, CNTK, Torch and many others. Our Data Scientists also use other AI Methods, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), to drill down into the raw data producing structured data sets.

Added Value of Data Science

CodeCoda strives to add value to our clients operative business. Our custom developed Data Science solutions can help your business, taking data driven decisions.

Our Data Scientists, with nearly 10 years of experience in a variety of industries, deliver insightful data, fully visualized to senior executives and business stakeholders. We extract, combine and build around all sorts of Data by applying cutting edge tools. CodeCoda's experts love data and above all, compiling this data into meaningful and valuable business assets. CodeCoda provides solutions end-to-end.

Predictive modeling and analysis

Machine learning libraries

Data Science as a Service (DSaaS)

Price optimization solutions

Competitive intelligence

Market basket analysis

Big data solutions for data science

Sentiment analytics

Behavior analysis

Delivery Process

How Data Science projects are delivered

Analyzing Data helps businesses identify important signals.

CodeCoda delivers end-to-end data science projects by following a detailed process. Scoping helps define KPIs, and sets clear expectations on the overall outcome. Data Exploration helps to get an in-depth understanding of the projects aims and sets the technical aspects. Once data exploration is done, with a suggestion for a possible solution, Data Engineers, Developers and Data Scientist predict the form and complexity of the solution in production.
Now models are developed and experimental frameworks are set-up. With the required infrastructure in place, actual model development can begin in earnest. While developing the model, different versions of it (and the data processing pipeline accompanying it) will be continuously tested against the predetermined hard metric(s).
Once Models are set-up in production environments, data pipelines in place, monitoring is added to the pipeline to check on performance.

1

Ideation

Data Scientists analyze the impact of data volumes, the variety of data and the velocity on businesses business impact.

2

Scoping

We help enterprises define effective data management strategies and identify important signals by scoping the data flow.

3

Research

We analyze data, scope and technical feasibility. We create custom data processing models.

4

Development

Our experts develop self-learning algorithms and solutions which process and analyze data in real time.

5

Deployment

We set-up Solution Production Environments and Monitoring. We make sure your solution provides scalable data ingestion and processing across the entire predicted lifecycle.

6

Monitoring

Our Experts Monitor and maintain your Data Science project continuously even after final deployment.

Process Overview of Data Science Project delivery

01

Ideation / Scoping

Analysis, Scoping, KPIs

Ideation / Scoping

Data Scientists analyze the impact of data volumes, the variety of data and the velocity on businesses business impact. We help enterprises define effective data management strategies and identify important signals by scoping the data flow. This way our systems are capable of tracking critical variables.

02

Research / Development

Research, Data Modeling, Algorithms

Research / Development

We create custom data processing models. Our experts develop self-learning algorithms and solutions which process and analyze data in real time. Additionally, we can integrate analytics tools into a reporting and visualization system, or we can offer solutions based on your primary platform.

03

Opportunities

Insights, Competitive advantages, Operations, Cost

Opportunities

Transformation of collected data into actionable insights, graphs, and visuals that are easy to interpret by business stakeholders and non-technical staff is the main target. We ensure that these insights provide competitive advantages, make data-driven decisions possible, improve operations, cut costs, and increase revenues.

Data Science Tech Stack

Tools and applications we use to build Data Science solutions.

Keras Tensorflow Matlib CNTK Torch / PyTorch Apache Spark Hadoop Apache Hive Apache Pig Cassandra Python GoLang C R C++ Java SAS SPSS
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