Big Data Science Solutions
Data science lies at the intersection between statistics, programming and hacking.
By researching and analysing your data we can easily reveal the patterns behind your business’s operations and convert them into effective actions.
We can either use your existing data lakes, databases or gather new data to create predictive machine learning models for improving your future business decisions.
Applying Data Science To Your Business
Data science is at its most effective when we understand your business. We will work with you to establish your goals and ambitions, suggesting where and when data can intervene to enhance your strategy. Maybe you want to penetrate new markets or customer bases, or re-engage with past customers, or perhaps you’re looking to optimise your business’s purchasing strategies and timing. We will establish your goals and illustrate how data can empower you.
You might be looking to leverage predictive analytics to discover when to buy into the trends that keep you ahead of your competitors, or maybe you want to fine-tune operations in your brick-and-mortar stores. We work with a huge array of clients across almost every sector and believe passionately that data can always make a positive difference.
Data Science From the Ground Up
We provide a full spectrum of data services that range from data engineering, fundamental data collection and extraction systems (e.g. web crawling) to data analysis and machine learning modelling.
We can use your data to help you answer queries relating to your industry or customer base, enhancing the ways in which you use data to connect to your audience and shape business strategy to increase revenue, yield and ROI.
We ensure data extends its value throughout your entire business.
We’ll Help Find The Right Stories in Your Data
Data science is about storytelling, its effervescent, rising above the surface of raw data into the world of action and strategy. We understand that visualising data is key to understanding data, and that visual communication has become pivotal to the data industry.
Using state of the art random forest and neural network models, we can solve your classification problems such as custom lead scoring models or fraud detection.
Regression with machine learning is an effective way to predict a single continuous variable. Whether you’re looking to predict house price, stock price or anything else, we’ll help you to find the right features and use then to tune an effective machine learning model.
Simulation modelling is a branch of data science that attempts to simulate simple and complex systems to shed light on various outcomes.
We are proficient in Simpy, a process-based simulation environment and framework using Python that model active components like people, devices or other variables both in real-time or in steps.
Monte Carlo modelling, a branch of simulation modelling, is the process of modelling complex systems that are influenced by high volumes of random variables. Used to assess and understand variance, risk and uncertainty in large-scale systems across fields such as finance, engineering, meteorology, science and astronomy.
Custom A/B experiments
A/B or split testing has become an integral process of designing high-performing CTAs, landing pages and other content. By organising audiences and testing different types of content for each, you can gain valuable data insights on what makes who tick and implement the results.
Clustering is a subsection of machine learning whereby algorithms classify variable data into clusters, or categories, that bring associated information closer together. It can be employed via Doc2vec to numerically represent documents and text by grouping syntactical elements together into related categories. This can aid in spam detection, when clustered variables are found to repeat, and in search functionality/retrieval.
Community detection, another area of clustering, can be used in contexts ranging from sociology to microbiology and assesses the related and disparate variables that exist in networked systems.
Survival analysis is a branch of data science that deals with the probabilities of system failure and is employed in many fields ranging from engineering to healthcare. Also called time-to-event analysis, it can help predict the progression of a system from a healthy state to decline and death.
Need Help With Your Data Science?
We’d love to help make your next machine learning / data science project a success.
Python is the apex programming language of the data world, though fundamentally, it is also an extremely flexible and dynamic programming language.
We can either collaborate with your current developers or provide development services for deploying your machine learning models.
Every model can be deployed as a platform or a simple REST API.
Our Data Science Progress
1. Data Transformation/Munging
Depending on how the data is collected, it will be cleaned, wrangled and then transformed to support future upstream processes.
2. Data and Graph Analysis
Data exploration and graph analysis is foundational in data science. Gaining a comprehensive understanding of your data can shed information on your queries and shape your future business strategies. We will employ data exploration techniques (EDA) to explore your data for suggesting the necessary next steps.
We are adept at graph analysis and can help you to extract and explain the data points that are relevant for your project.
3. Machine Learning Modelling
Following data collection, transformation and analysis, we’ll begin to design machine learning programs that can be directly deployed into your systems and products.
We will setup automated data pipelines so that the model will automatically execute according to your desired time period.
4. Hyperparameter Tuning
Machine learning algorithms are highly sensitive to the values that are set prior to the learning process, named hyperparameters. These effectively control the capacity and flexibility of the model and will then be tuned to influence the predictive accuracy of your machine learning model.
5. Model Deployment
On the back of data exploration, analysis and optimisation, we will start to deploy models into your products as a micro-service via AWS / Google Cloud Platform.
Data Science Service Frequently Asked Questions
What is the Difference Between Data Science and Analysis?
Data science works to establish questions and provide paths towards the answers that form understandings from data. Data science is fundamental in both engineering and analysis. In practical terms, though, data scientists are more likely to work with data inducted through a system created through data engineering. They can then ask questions about the data and help refine systems to ensure they are purposeful in the context they exist to serve, e.g. sales, health, science, etc.
Data scientists will assist in the creation of models that use data for certain purposes. Data analysts are instead focussed on interpreting and understanding what data means. They lie at the intersection between the architecture, data, and its endpoints. Without analysis, data is merely a list of numbers and letters, after all!
What is Data Transformation?
Simply put, data transformation turns, or converts, data from one format into another format. In the process, the data could restructured or cleaned. Data transformation pipelines flow from the source where data is determined and its structure is mapped. The data is then structured in a way which is interpretable by other programs downstream, and for storage in data warehouses (DWHs) or data lakes. It unifies and flattens data so that it’s easier to work with.
What is Machine Learning?
Machine learning is a type of AI that learns automatically through experience. Machine learning programs can access data and use it themselves without being explicitly programmed to do so. They still have to be designed in the first instance, but then, within certain parameters, they can evolve and change.
They learn from stimulus in a more organic way, evolving to learn, adapt and change based on incoming data. Machine learning can be very simple, for example, a program which learns from customer waiting times to automatically call in new staff to a shop floor. They can also be extremely complex, for example, a program that computes and learns from complex sensory data like sound, light and touch.