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Data Science

Our Data Scientists explores data to find patterns, extract actionable insights, and answer important questions

Person Analyzing Data
What is a Data Scientist?

Part Analyst & Part Artist

Data Mining & Cleaning

They cleanse existing raw data and build models to predict future data.

Creates Data Driven Solutions

They go beyond merely collecting and reporting data to look at data from multiple angles, and give meaning to it

Takes a Scientific Approach

They go beyond merely collecting and reporting data to look at data from multiple angles, and give meaning to it


The Data Science Life Cycle

Operational Understanding

CrestPoint will ask relevant 

questions and define Objectives for the problem that  needs to be tackled.

Data Mining & Cleaning

Gather , Clean, and aggregate data 

From disparate sources. CresPoint will  

Organize and fix the inconsistencies

and handle the missing values.

Dashboard & Visualization

Communicate the findings with key

Stakeholders using plots and

Interactive visualization.

Evaluation Against Metrics

CrestPoint will evaluate the data  performance for effectiveness 

against set parameters and if suitable  to make predictions.

Data Exploration

CrestPoint will analyze the data sets 

and identify trends. Will work together

to draw meaningful conclusions for

strategic operational decisions


Model Tuning

Select important features and

Construct more meaningful ones 

Using the raw data that  is available.

Data Science process.png

Data Aggregation

Data aggregation is the foundational step in data science where data is collected from various sources. This process involves gathering, compiling, and presenting data in a summarized format. The goal here is to amass data from different datasets, possibly across different systems or formats, to create a comprehensive pool of information. This aggregated data can then be used for more effective analysis. It’s crucial to ensure the data is relevant, accurate, and covers all necessary facets of the problem at hand.

Secure The Data

Securing the data involves ensuring that the data is stored, processed, and used in a manner that maintains its confidentiality, integrity, and availability. This step is crucial, especially when dealing with sensitive or personal information. Measures include implementing robust data encryption, access controls, and compliance with data protection regulations. Data security also involves maintaining data quality, preventing unauthorized access, and safeguarding against data breaches.

Define The Problem

Defining the problem is a critical step where the goals and objectives of the data science project are outlined. This phase involves understanding the business or research question that needs to be answered. Clear problem definition helps in determining the scope of the project, the type of data needed, and the analytical approaches to be used. This step sets the direction for the project and ensures that the team remains focused on addressing the key issues.

Use Algorithms

Using algorithms is where the actual data processing and analysis take place. This step involves selecting and applying appropriate algorithms to the aggregated data to extract meaningful patterns, insights, or predictions. The choice of algorithm depends on the nature of the problem, the type of data, and the desired outcome. This step can involve machine learning, statistical methods, or other data analysis techniques to process the data and achieve the project's objectives.


Analysis is the final step where the results of the algorithms are interpreted and conclusions are drawn. This stage involves translating the output of the data processing into actionable insights. Analysis can reveal trends, patterns, or correlations that address the defined problem. The key here is to present the findings in a clear, understandable manner, often using visualizations, to inform decision-making or further research. This step may also involve evaluating the model's performance and making adjustments as necessary.

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