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 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.
Analyze
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.