top of page

Data Science & Analytics

Today, big data and data analysis is as important as ever for:


-Understanding your customer

-Understanding your competition

-And much more...


HelpT has all kinds of expertise needed to analyze data, test it, and more.  Popular areas of expertise include:

-Data Extraction/ETL

-AI & Machine Learning

-Business Intelligence (BI)

-Data Mining & Management

-And much more...

Machine Learning

It is no longer a pipe dream to imagine machines performing difficult, repetitive tasks that were previously only accomplished by humans. Thanks to advancements in machine learning, we can now digest data at previously unimaginable speeds and gain previously undiscovered knowledge about our surroundings without the need for our busy human brains. The rate of digital disruption shows that the tech sector is currently in the golden mean; businesses are embracing machine learning quickly, but there isn't much competition yet.

The machine learning specialists in HelpT’s network will assist you in implementing cutting-edge procedures and differentiating your good or service from that of your competitors. We can create end-to-end machine learning solutions for your specific business needs by utilizing sophisticated statistical techniques and knowledge of a variety of ML algorithms and models, including Deep Learning.

AI Solutions

Clients are using all areas AI adoption, which is raising customer expectations and boosting competition. The pressure to digitize increases as you prosper and take up more of the market. Check with us if you think AI is not for you. We'll clear up the most frequent myths and respond to all your inquiries. We're sure that the information and our experience will make it simple for you to change your mind and determine that AI belongs in your near future.

Big Data

ML largely relies on data. It is the most important factor that enables algorithm training and explains why machine learning has gained so much popularity recently. However, no matter how many actual terabytes of data you have or how skilled you are at data science, if you can't make sense of data records, a computer will be essentially worthless or even dangerous.

All datasets contain errors. That is why the first phase in the machine learning process, data preparation, is so crucial. In a word, data preparation is a series of steps that aid in improving the suitability of your dataset for machine learning. In a larger sense, the data preparation also involves choosing the best method for collecting data. And the majority of the time spent on machine learning is spent on these processes. Who is better to do those processes is the question.  We believe freelancers offer a fantastic solution to the mundane tasks of cleaning and preparing data. Especially if they are well trained in the area Big Data, data cleansing.

Business intelligence tools have traditionally been used by businesses and IT professionals to create thorough analyses of a company's operations and state. This feature is insufficient today. Modern business intelligence solutions support the important decisions that the people operating your organization make every day. They not only help make informed decisions, educated estimates, and establish strategies, but also require a system that supports your current workflow and provides the appropriate solutions to the appropriate individuals at the appropriate times in order to fully incorporate this data insight into your operations.

Our network of experts will map the specifications for future BI and advanced analytics solutions only after examining your IT and business infrastructures. This includes the necessary integrations, KPIs, alarms, and reports that user groups and their unique use cases require. In less complicated situations, we'll recommend a pre-made solution from a third-party vendor and assist you in integrating it into your workflow. If you want to construct a totally customized BI system or modernize your current one, we can start by offering data mining services. For the purpose of creating a data warehouse (DWH) architecture, we will prepare, purge, and model the data. We will test, implement, and embed the product's capabilities into other systems once the DWH is ready. In the end, the product, equipped with both historical and current data,

bottom of page