Here is a table summarizing the main differences between the two activities:
Posted: Sat Jan 04, 2025 7:18 am
While it is important to distinguish between Data Analysis and Data Science, it is equally important not to confuse Data Science and Business Intelligence. We are clearly talking about two different things:
BI is the activity of analyzing existing company data to generate insights to help decision-makers in their decision-making. BI uses external and internal data, prepares it, executes queries on this data and creates dashboards for managers or decision-makers. BI can also be used to conduct impact studies.
Data Science is a much more prediction-oriented approach. It is essentially about exploring and analyzing past and present data to predict future outcomes. Data Science allows you to answer open questions (“How…?”, “What is…?”).
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Here is a table summarizing the main differences between the two activities:
Business Intelligence (BI) Data Science
Data sources
Structured Data (SQL, Data Warehouse) Structured and unstructured data (logs, cloud data, SQL, NoSQL, text)
Approach
Statistics and visualization Statistics, Machine paraguay whatsapp list Learning, Graph analysis, Neuro-linguistic programming (NPL)
Focus
Past and present Present and future (predictive)
Tools
Pentaho, Microsoft BI, QlikView, R RapidMiner, BigML, Weka, R
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The life cycle of a Data Science project
In a Data Science project, a fairly common mistake is to rush headlong into data collection and analysis, without having first taken enough time to define the business needs and issues. To succeed in a Data Science project, it is necessary to follow all the steps carefully. This diagram summarizes the different stages of the life cycle of a Data Science project.
definition data science life cycle
These steps can be described one by one.
Step #1 Discovery
Before starting a Data Science project, it is important to understand the various specifications, needs, priorities... without forgetting the budgetary issue. You must start by asking yourself the right questions. For example, ask yourself if you have the necessary resources to carry out the project, whether from a human, technical or data point of view. In this initial phase, you must also frame the business problem and formulate the hypotheses to be tested.
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Step #2 Data preparation
Data preparation is an essential step in analysis. You need to load all your data into an analysis tool (such as Dataiku for example), which will serve as your "sandbox" throughout the project. You need to preprocess and condition the data before starting the modeling work. You will need to follow an "ETLT" (Extract, Transform, Load and Transform) procedure to integrate your data into the sandbox.
BI is the activity of analyzing existing company data to generate insights to help decision-makers in their decision-making. BI uses external and internal data, prepares it, executes queries on this data and creates dashboards for managers or decision-makers. BI can also be used to conduct impact studies.
Data Science is a much more prediction-oriented approach. It is essentially about exploring and analyzing past and present data to predict future outcomes. Data Science allows you to answer open questions (“How…?”, “What is…?”).
Find out where to find and how to select a good web consultant .
Here is a table summarizing the main differences between the two activities:
Business Intelligence (BI) Data Science
Data sources
Structured Data (SQL, Data Warehouse) Structured and unstructured data (logs, cloud data, SQL, NoSQL, text)
Approach
Statistics and visualization Statistics, Machine paraguay whatsapp list Learning, Graph analysis, Neuro-linguistic programming (NPL)
Focus
Past and present Present and future (predictive)
Tools
Pentaho, Microsoft BI, QlikView, R RapidMiner, BigML, Weka, R
seo-agency
Discover the best rated web agencies according to their field
Showcase sites
E-commerce sites
Mobile applications
SEO
Online advertising
Social networks
I discover the best Web Agencies
The life cycle of a Data Science project
In a Data Science project, a fairly common mistake is to rush headlong into data collection and analysis, without having first taken enough time to define the business needs and issues. To succeed in a Data Science project, it is necessary to follow all the steps carefully. This diagram summarizes the different stages of the life cycle of a Data Science project.
definition data science life cycle
These steps can be described one by one.
Step #1 Discovery
Before starting a Data Science project, it is important to understand the various specifications, needs, priorities... without forgetting the budgetary issue. You must start by asking yourself the right questions. For example, ask yourself if you have the necessary resources to carry out the project, whether from a human, technical or data point of view. In this initial phase, you must also frame the business problem and formulate the hypotheses to be tested.
Discover our complete guide to scrum and “agile” project management .
Step #2 Data preparation
Data preparation is an essential step in analysis. You need to load all your data into an analysis tool (such as Dataiku for example), which will serve as your "sandbox" throughout the project. You need to preprocess and condition the data before starting the modeling work. You will need to follow an "ETLT" (Extract, Transform, Load and Transform) procedure to integrate your data into the sandbox.