Data Science

Tecxed helps in galvanizing useful insights based on the roles, processes and modules of application. We work with our clients on the requirements and walk through

• Define requirements
• Data gathering & polishing
• Data Analysis
• Create Analytical Model
• Data Interpretation & gathering insights
• Communication

1. Defining the requirements

In order to initiate a data science project, it is vital for the company to understand what they are trying to discover. What is the problem presented to the company or what kind of objectives does the company seek to achieve? How much time can the company allocate to working on this project? How should success be measured?

For example, Netflix uses advanced data analysis techniques to discover viewing patterns from their clients, in order to make more adequate decisions regarding what shows to offer next; meanwhile, Google uses data science algorithms to optimise the placement and demonstration of banners on display, whether for advertisement or re-targeting.

2. Data gathering & Polishing

After defining the topic of interest, the focus shifts to the collection of fundamental data to elaborate the project, sourced from available databases. There are innumerable data sources, and while the most common are relational databases, there are also various semi-structured sources of data. Another way to collect the necessary data revolves around establishing adequate connections to web APIs or collecting data directly from relevant websites with the potential for future analysis (web scrapping).

The next step – and the one that comes across as more natural – because after extracting the data from their original sources, we need to filter it. This process is absolutely essential, as the analysis of data without any reference can lead to distorted results.

In some cases, the modification of data and columns will be necessary in order to confirm that no variables are missing. Therefore, one of the most important steps to consider is the combination of information originating from various sources, establishing an adequate foundation to work on, and creating an efficient workflow.

It is also extremely convenient for data scientists to possess experience and know-how in certain tools, such as Python or R, which allow them to “polish” data much more efficiently.

3. Data Analysis

When the extracted data is ready and “polished”, we can proceed with its analysis. Each data source has different characteristics, implying equally different treatments. At this point, it is crucial to create descriptive statistics and test several hypotheses – significant variables.

After testing some variables, the next step will be to transfer the obtained data into data visualisation software, in order to unveil any pattern or tendency. It is at this stage that we can include the implementation of artificial intelligence and machine learning.

4. Creating analytical models

This is where the collected data is modelled, treated and analysed. It is the ideal moment to create models in order to, for example, predict future results. Basically, it is during this stage that data scientists use regression formulas and algorithms to generate predictive models and foresee values and future patterns, in order to generalise occurrences and improve the efficiency of decisions.

6. Interpreting data & gathering insights

We are nearly entering the last level for implementing a data science project. In this phase, it is necessary to interpret the defined models and discover important business insights – finding generalisations to apply to future data – and respond to or address all the questions asked at the beginning of the project.

Specifically, the purpose of a project like this is to find patterns that can help companies in their decision-making processes: whether to avoid a certain detrimental outcome or repeat actions that have reproduced manifestly positive results in the past.

7. Communication

Presentation is also extremely important, as project results should be clearly outlined for the convenience of stakeholders (who, in the vast majority of instances, are without technical knowledge). The data scientist has to possess the “gift” of storytelling so that the entire process makes sense, meeting the necessary requirements to solve the company’s problem.

Data Science with AI services:

Extract Hidden Insights from Data with AI

In today's data-driven world, extracting valuable knowledge from vast information reservoirs can provide a competitive edge. At TECXED, we utilize the latest data science and artificial intelligence techniques to help clients make optimal use of their data assets.

Our expert team of data engineers, mathematicians, programmers and domain specialists work closely with your team to define valuable data mining objectives. Advanced analytical models are developed using machine learning algorithms and deep neural networks tailored to your specific use cases and data environment.

Whether classifying images, predicting failures, detecting fraud patterns or recommending hyper-personalized offers, we architect end-to-end optimized solutions. Robust data pipelines are created to feed terabytes of structured and unstructured information into intelligent platforms that evolve and scale on demand.

TECXED also provides AI-as-a-Service capabilities through our secure cloud environments. This removes the need for heavy upfront investments while giving control over model development, retraining and operationalization.

Our data science consultants help in strategic planning, model governance and compliance matters to fully realize the business value of AI initiatives over the long run.

Leverage TECXED's applied AI and data science skills to gain the critical advantages of automating discovery, decision-making and foresight from your information ecosystem.