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Our Offerings

We configure customizable data analytics solutions to transform your enterprise data into intelligence and steer you to get great business outcomes.


Data And Analytics Strategy

Our data experts can map your analytics initiatives into quantifiable business outcomes with a data-driven approach.


Data Discovery & Augmentation

We deliver the 360-degree customer prospects by improving your assets with third-party data and predictive analytics.


Data Management & Beyond

On the extra edge of data integration and analytics, we advance with governance, monetization, and compliance.


Data Democratization

Our solutions allow employees to use custom, user-friendly tools that can embrace data and take you to the edge of AI.

The Step-By-Step Data Analytics Process

Data Cleansing

This step recognizes and improves the unstructured records into a dataset. The data may include unfinished, unreliable. incorrect data that requires to be reviewed and put in actual format with the support of data cleansing techniques.

Data Exploration

The data exploration process manages model preparation, evalution, estimation, and tuning, which provides robust data insights that allow our clients to make successful business decisions.

Feature Engineering

This process helps to convert raw data into features that show a better solution to the underlying problem. it allows us to give flexibility, better results in the data analytics process.

Check Out Our Successful Data Analytics Solutions And How We Delighted Our Customers

Select Your Suitable Data Analytics Option With Us

Data Analytics Implementation

As a leading data analytics service provider, we are able to offer a time-effective solution that is sufficiently compliant according to your business objectives. Our experts can implement a data analytics solution to advance your business or entirely revamping the existing one.

Data Analytics As A Service

We offer analytics solutions that help our clients to receive first meaningful insights from the raw data within days. Our top-notch analytics solution reduces the burden of cost and time for developing and maintaining a considerable analytical solution.

Data Analytics Support & Evolution

Mindbowser can advance your existing analytical solution with an effective time frame by studying the current analytical setting, determining the stumbled blocks, and fixing problems that limit you from using data analytics.

We Produce 100+ Analytic Roadmaps Every Year Saving Costs For Our Clients With The Help Of Our Expert Developers, Agile Methodology, And More


Our experts put statistical and mathematical methods and tools to invent solutions that help organizations automate processes, optimize in-and-out operation processes, and extract valuable data insights for your business.

App Strategy & Consulting
Predictive Analytics
App Strategy & Consulting
NoSQL Databases
App Strategy & Consulting
Knowledge Discovery Tools
App Strategy & Consulting
Stream Analytics
App Strategy & Consulting
In-memory Data Fabric
App Strategy & Consulting
Distributed Storage
App Strategy & Consulting
Data Virtualization
App Strategy & Consulting
Data Integration
App Strategy & Consulting
Data Preprocessing
App Strategy & Consulting
Data Quality

Why Mindbowser For Data Analytics Consulting Services?

Our lean and agile team of full-stack data scientists, engineers, and application developers accelerate innovation and implementation of custom machine learning and AI products. We bring extensive cross-industry expertise backed by scientific rigor and deep knowledge of state-of-the-art techniques to design, build, and deploy bespoke AI solutions.

Frequently Asked Questions

What is Exploratory Data Analytics?

Exploratory data analytics is a statistical approach that is used to analyze and produce descriptive graphical summaries. The most significant advantage of using EDA instead of a statistical model is that, with EDA, analysts are able to foresee what the data can reveal beyond the formal modeling.

Which tools you use in the data analytics process?

Tools are primarily decided as per requirement. Major tools that we use are

  • Tableau Public
  • Qlikview
  • R Programming
  • Python
  • SAS
  • Microsoft Excel
  • RapidMiner
  • OpenRefine
  • Apache Spark
Do you sign a NDA?

Yes, we do. Our developers, too, are covered under NDAs and confidentiality clauses.

How do you guarantee code quality?

All our code goes through a quality audit and review by
The reports are available in an easy to understand format as part of the sprint.

What kind of performance guarantees are in place?

We have SLAs defined for projects. This includes performance parameters for individuals as well as key goals for the overall project. We keep revisiting this SLA at regular intervals to ensure that we are in line with the plan. Any deviation is immediately fixed.

Who will be my point of contact throughout the project?

We provide you a technical project manager based out of the US or India to work directly alongside you. During the initial plan, our CTO, VP of Engineering, and CEO are involved too. Once the project journey is planned, the project manager is your main point of contact with a well-established escalation procedure.
The project manager keeps you updated as per plan on all the development information and acts as your primary quality analyst.

How to select the right tools for your needs?

There is no one size fits all. We pick a tool based on the task. Usually, there would be a stack of different tools within an engagement.

What are the tools that you use for data science?

In data science work, we usually used the following tools.

  • Libraries: Pandas, Numpy, Beautiful Soup, JSON, SKLearn, Keras, Plotly, MatplotLib, Tableau, etc.
  • Tools: Pycharm, Jupyter Notebook
What type/size of data is the team working with?

We deal with structural and unstructured data like SQL, NoSQL, CSV, JSON, Flat Files, Images, etc.

The data size depends on the case to case basis, but generally, we handle data records in thousands to millions. We have worked with data in 100s of GBs.

What is the typical background of team members?

The team comes from different backgrounds. We primarily focus on analytical thinking a proactive approach in Data Science. Our team members generally have a strong background in mathematics.

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