Definties
In Distinguishing Analytics, Business Intelligence, Data Science, schreef dataversity het volgende.
- Business Intelligence: “A set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery. Research coverage includes executive dashboards as well as query and reporting tools.”
- Data Science: “Data Science combines the allure of Big Data, the fascination of Unstructured Data, the precision of advanced mathematics and statistics, the innovation of social media, the creativity of storytelling, the investigation and inquiry of forensics, and the ability to use all of those skills together while still being able to demonstrate the results to non-technical audiences.”
Vrijwel iedereen is het er over eens dat BI zich richt op het 'wat' en Data-Science zich richt het waarom, in Data Science? Business Intelligence? What's the difference? uitgelegd als: Business Intelligence usually asked the question “what happened?” Data Science often focuses on developing models that can answer “what will happen if we do X?”
In de tabel hieronder is het vertaalt naar een vergelijking op onderwerp
In een forumdiscussie over het onderwerp gaf een data-scientest het volgende aan: "Data scientists are scientists. BI analysts are analysts. And no, that doesn't mean one wears a white lab coat and the other wears a suit. :) It's a mindset issue.
Scientists want to understand why. Analysts want to get the "right answer". Scientists are inherently curious. BI analysts are focused on getting actionable info in the hands of the client ASAP and could care less about the nuances of the underlying methods, unless they are going to screw up the answer.
Both need to be focused on the business problem at hand. A data scientist who gets so caught up in the mathematical and computer science details that they lose track of the application is of little use.
Put more simply: BI analysts see data cleaning/wrangling/munging as a necessary evil before getting to the problem. Data scientists realize that data munging IS the problem."
De rollen
In de vacatures wordt een duidelijk onderscheidt gemaakt tussen tussen de verschillende posities.Business Intelligence
- Business Intelligence engineer
- Business Intelligence analyst
- Data engineer
- Data Scientist
Business Intelligence
Maar welke vaardigheden heb je nodig? In Business Intelligence Skills somt het onderzoeksbureau Forrester een aantal competenties op. Hieronder een aantal van de genoemde vaardigheden.
"Since the term BI is often used to also include data management processes and technologies, let’s assume that in your case you are only looking for expertise required to build reports and dashboards and it does not include
( Business Intelligence engineer)
- Data integration (ETL, etc) expertise
- Data governance (master data management, data quality, etc) expertise
- Data modelling (relational and multidimensional) expertise
With above caveat (lees: waarschuwing) here is a short list of REQUIRED skills
(Business Intelligence analyst)
- Training/certification/experienced in your specific BI platform
- Working knowledge of relational DBMS and SQL query language
- Working knowledge of multidimensional DBMS and MDX query language (if using OLAP data sources, like Microsoft SQLServer Analysis Services or Oracle Essbase)
In het vaardigheden-overzicht wordt een goed onderscheidt gemaakt tussen de vaardigheden van een data-engineer en front-end-specialist.
Data Science
Ook in de wereld van Big Data wordt het onderscheidt gemaakt tussen de Data-Scientist en de data- -engineer. In het artikel Do You Need a Data Scientist or a Data Engineer? wordt het uitgelegd.Data Scientists almost always have a background in scientific or mathematical research. They usually have some amount of postgraduate education and often have a PhD. This position is concerned with exploration and analysis of information to extract new insight and use it to produce new value. This generally takes the form of an improved algorithm that can drive a company’s automated systems in a new and better way.
Data Engineering is a specialized form of software engineering concerned with building the systems to store and process information. Data Engineers often work on systems for "big data," defined (by Mike Loukides) as “when the size of the data itself becomes part of the problem." Some of their skills overlap those of the Data Scientist they work with Machine Learning algorithms, they clean data,
and they use specialized tools for handling high information volume.
A Data Engineer usually begins as a software engineer with some mathematical background. To specialize:
- The engineer takes additional training in Machine Learning - Linear Algebra and Calculus are helpful here - and in storing and retrieving information.
- These skills include mastering SQL and NoSQL databases and sometimes data visualization.
- For handling Big Data, engineers specialize further in technologies such as Hadoop, Apache Spark/Storm/Flink, and cloud technologies.
Machine learning
Belangrijk is dat machine learning valt bij de data-engineer valt en niet bij de data-scientist. Het zijn geen twee afgescheiden muren, want machine learning valt wel onder de grote paraplu van data-science. Maar zoals aangegeven in 5 Skills You Need to Become a Machine Learning Engineer: "To begin, there are two very important things that you should understand if you’re considering a career as a Machine Learning engineer. First, it’s not a “pure” academic role. You don’t necessarily have to have a research or academic background. Second, it’s not enough to have either software engineering or data science experience. You ideally need both."Belangrijke is programmeervaardigheid en kennis van o.a.: "data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.) "
Rollen en competenties
De BI-engineer- Data-integratie (ETL-tools als SQL Server Integration Services (SSIS), Informatica PowerCenter, Oracle Data Integrator (ODI), etc)
- Data governance (master data-management, data-quality, etc)
- Data-modellering (relationeel, multidimensional en Data Vault)
- Databasetalen (SQL, T-SQL, PL-SQL)
- Databases: Oracle, Microsoft, Teradata, etc.
- Cloud (Amazon Web Services (AWS), Microsoft Azure, IBM Bluemix)
- Reporting (met tools als SQL Server Reporting Services (SSRS), Power BI, Tableau, Cognos, SAS Visual Analytics, QlikView, BusinessObjects, etc )
- SQL en/of MDX
- Databases (Oracle, Microsoft, Teradata, etc.)
- Cubes (SQL Analysis Services (SSAS), Oracle Essbase)
- Cloud (Amazon Web Services (AWS), Microsoft Azure, IBM Bluemix)
- Programmervaardigheid (Java, C++, PHP and Python)
- Machine Learning (packages en software libraries, zoals scikit-learn, Theano, Spark MLlib, H2O, TensorFlow, etc.)
- Kennis van Lineare algebra en algoritmes (ANN, Bayesian network, SVM, Probabilistic Graphical Modeling, HMM, MCMC)
- SQL and NoSQL databases (Teradata, MySQL, MongoDB, Casandra, etc.)
- Data-visualisatietools (Tableau)
- Platformen (Hadoop, Apache Spark/Storm/Flink, Kafka, Hbase en Hive, etc.)
- Cloud (Amazon Web Services (AWS), Microsoft Azure, IBM Bluemix)
- Programmervaardigheid (Python, R, Scala, Phyton, SAS Enterprise guide/visual analytic, etc.)
- Databases (Teradata, MySQL, MongoDB, Casandra, CouchDB, HDFS-Hadoop)
- Data mining packages (Weka, SAS, SPSS, Rapidminer, etc. )
- Visualisatie (D3.js, Tableau, etc.)
- Statistics: Chi-Squared test, p-values, F-test, Standard error, AROC, ROC curve, Hypothesis testing
- Useful algorithms: ANN, Bayesian network, SVM, Probabilistic Graphical Modeling, HMM, MCMC
- Cloud (Amazon Web Services (AWS), Microsoft Azure, IBM Bluemix)
Bronnen
BI Analyst vs BI Developer - what's a better career?What is the difference between a data scientist and a business intelligence analyst?
5 Skills You Need to Become a Machine Learning Engineer
Data Scientist: What Skills Does It Require?
Do You Need a Data Scientist or a Data Engineer?
Business Intelligence Skills
Distinguishing Analytics, Business Intelligence, Data Science
Data Scientist: The Sexiest Job of the 21st Century
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