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Data Science Services

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AI-Driven Data Solutions

Conquerors, one of the top Data Science companies in India, is a pioneer in analytical applications for any consumer-facing businesses. The specialty areas include Retail Analytics, Consumer Goods Analytics, Customer Analytics, Big Data Analytics, Advanced Analytics Solutions, Cloud Analytics, Retailer Supplier Collaboration, Predictive Analytics, eCommerce Analytics, and Analytics-driven and location-based marketing.



We offer consulting services (customer strategy, data management, marketing, digital campaigns management, analytics, and social media), data management platforms,  insights platforms, customer analytics, and marketing optimization platform.


Machine Learning and its Applications

Practically all together achievements mentioned so far come from machine learning, a subset of AI that brings the vast majority of the accomplishments in the field in recent years. When people talk about Artificial Intelligence, they are generally talking about machine learning also. 

in simple terms, machine learning is where a computer system process continuously learns how to perform a task rather than being programmed on even how to do so.

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Streams of Data science
Data Science lifecycle
Machine Learning Applications

Deep learning

Deep learning is a machine learning technique that teaches computers to do more of what comes naturally to humans. Deep learning is a key technology behind each of them like driverless cars, enabling them to recognize a stop sign or even to distinguish a pedestrian from a light post. It is the key to voice control in consumer devices just as phones, tablets, hands-free speakers, and TVs. Deep learning is getting lots of attention lately and for most good reason. It’s achieving results that were not obviously possible before.

In deep learning, a computer model learns better to perform classification tasks directly from images, sound, and text. Deep learning models can achieve always state-of-the-art accuracy, sometimes exceeding the level of human performance. Models are trained by using a large set of labeled data and neural network architectures that suitably contains many layers.

Python and its major implementation

Conquerors have expert Python developers for scalable and even reliable application development for your business. We provide Python development services with Django, Tornado, and Flask for the best results. With our truly skilled, resourceful, and agile Python development practices, our only motto is to provide the most effective and efficient working solutions for your business. We have experienced Python developers who can develop scalable, interactive solutions, powerful to make your business processes easier. Get end-to-end Python development services right from conceptualization to support and even maintenance, with Conquerors. We have experts who have great knowledge of data science, machine learning, data analytics, and other modern technologies.

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Components of AI Workflow

Prerequisites for Data Science

Here are some of the technical concepts you should know about before starting to learn what is data science.

  • Machine Learning

Machine learning is the backbone of data science implementation. Data Scientists need to have solid grasping techniques of Machine Learning in addition to basic knowledge of statistics.

  • Modeling

Mathematical models enable you to make quick calculations and most of the predictions are based on what you already know about the data. Modeling is also a part of Machine Learning and this involves identifying which algorithm is the most suitable to solve any given problem and how to train all these models.

  • Statistics

Statistics are at the heart of data science. A well-built handle on statistics can help you extract more intelligence and even obtain more meaningful results.

  • Programming

Some level of programming is definitely required to execute a successful data science project. The most common programming languages are as follows Python, and R. Python is especially popular because it’s easy to learn and implement, and it supports multiple libraries for data science and machine learning.

  • Databases

A capable data scientist needs to understand most of the processes of how databases work, even how to manage them, and mostly how to extract data from them.

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