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Reimagining Startup and Enterprise Innovation

A Data Science primer for startups 

Vikas Raina
Principal Cloud Architect, Oracle for Startups

The science of predicting the future

Data Science is an amalgam of various streams of technology, statistics, and analysis to extract insights and meaningful patterns from huge data volumes. It is a loosely coupled hub with many spokes like domain, math, advanced computing, analytics, and data engineering. Insights gleaned via data science help brands understand their markets and their customers and help drive informed decision-making with predictive analysis of repetitive patterns.

The market famously recognizes data as “the new oil,” because over the years, organizations have collected huge amounts of data through various channels, but few have optimized using this data beyond storing it in archives and databases. Using data effectively will be the next big wave in the business and technology ocean.

Organizations that recognize the value of their data may work with startups, external partners, and internal teams to slice and dice their data to find meaning and insight. Use cases may extend from gathering data from distributed machinery on oil rigs to prevent a breakdown to a retail chain collecting behavior patterns from their customers to predict purchase intent.  

Data science is the key to unlocking the potential of this stored data, and it requires two primary components: data and an intelligent mechanism to analyze it, both of which in turn require infrastructure and tools. 

Oracle Cloud offers a Data Science platform to handle data science challenges for enterprise organizations and startups alike. 

Learning the language of data

One of the keys to successful data science efforts lies in identifying the problem to be solved, and the important data points to address the problem. Data science is less like finding a needle in a haystack, and more like learning a new language; understanding words, their context, exceptions, and finally making sense out of the complete statement. Like learning any new language, data science models should start simple and small, so data scientists can validate and evaluate results before adding complexity to the model.

Real-world empirical data is often complex, with more exceptions than rules. It can have more holes than a slice of Swiss cheese. That’s why reviewing and cleansing datasets is a key step when it comes to training new data models. 

Model training involves continuously iterating, training, evaluating, and improving algorithms using Data Science. Machine learning models are trained against one dataset and then tested against another to test its effectiveness. 

The whole process of extracting information out of data involves data management, data model building, training the model to achieve a high percentage of accuracy by running it repeatedly with newer data and evaluating it, and eventually deploying this model by operationalizing it. Over time, as factors change, models’ performance may drop. Continuous monitoring and retraining maintain the model’s health and efficacy.  

The keys to understanding any data science modeling project:

  1. Data extraction: Data needs to be extracted from various sources, which can exist in various formats. This means raw data collected from source(s) which may not yet be formatted for efficient use.
  2. Data cleansing: This is most important step in building effective algorithms. Detecting incorrect, Incomplete, duplicate, or missing data and replacing or purging such data. This also includes outlier datasets.
  3. Data transformation and labeling: Data needs to be converted to common format using data integrators, data warehouses, and application integrators. This also includes annotation and data tagging.
  4. Model building:  Defining an algorithm to build a predictive process for the problem being solved from the datasets
  5. Training and evaluation: Running the model against different datasets to achieve better performance, while continuing to evaluate the performance and accuracy of predictions. 
  6. Deployment and monitoring: A trained model must be deployed to face real-world scenarios. Operationalizing a machine learning model is a tedious task, hence the need for a stable and well-assisted platform. 

Deploying is not the end of the journey. The model’s health should be continuously monitored to ensure the accuracy, with data scientists standing by to retrain the model if needed. 

What Oracle offers

The Oracle Data Science Platform offers a fully managed and serverless platform with Automated machine learning, which offers more accuracy in less time. AML tunes the model and explains the model’s results faster and with more accuracy. 

Oracle’s serverless platform helps control costs while compute machines are available on-demand. It is a fully scalable and highly available platform, capable of handling huge volumes of data. It offers a variety of open-source libraries for building machine learning models. Model evaluation tools help recognize when a model is ready for a production environment. One can also create docker images of the model and push them into Oracle’s registry to be invoked by Oracle function.

The platform seamlessly Integrates with the rest of the Oracle Cloud Infrastructure stack, including Functions, Data Flow, Autonomous Data Warehouse, and Object Storage. Users can access Data Science using the Console, REST API, SDKs, or CLI.

The great thing about Oracle’s Data science platform is that it is data-source agnostic. Oracle data science platform can work with any dataset regardless of its location, be it object storage, a database, or a file system. The Oracle data science platform offers open-source notebooks, which are web applications for writing and running code, visualizing data, and seeing the results—all in the same environment. Popular ones include Jupyter, RStudio, and Zeppelin.

Oracle’s is a collaborative platform with access and version controls which allow data scientists to design and train their models. It allows users to share assets and models with team members with no pressing need to understand the underlying infra related operations. 

Opportunities for data-centric startups 

Data science is a comparatively new space, and both use cases and functionality are increasing quickly. Data scientists looking for tools to help them build, train, and deploy highly accurate models with a large amount of data faster should consider giving Oracle Cloud a try.

Startups can experiment with $500 in free cloud credits, access to the Oracle Cloud free tier, and a 70% discount on Oracle Cloud for 2 years. 

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