Data Science, Business Intelligence and Analytics – Analyzing differences and similarities with Python – Update: Map added

Within the last years, the data world was confronted with a new reality. The availability of data and computing power has increased dramatically and so have the job roles. The most prominent among this new role is the Data Scientist. The definition is as follows:

„Someone who studies or works in data science (= the use of scientific methods to obtain useful information from computer data)“ (Cambridge University Press 2014)

So how does it fit into the other roles related to data: Business Intelligence and Analyst?

This article has the aim to shed more light into these fields by first describing the roles and then analyze the job market in Germany with the help of Python.

Data Science, Business Intelligence and Analytics – is it all the same?

Looking at this question – my answer would be: No it is not the same, but there are similarities.

Data Science uses structured (tables) and unstructured (text documents, pictures, videos) data to extract insights and patterns. Data Science focuses on future information while Business Intelligence focuses on past data. Both share Data Virtualization and Data Management. This picture summarizes it very well:

(Pugsley 2017)

Analytics on the other hand is: “A process in which a computer examines information using mathematical methods in order to find useful patterns” (Cambridge University Press 2014).

This short overview gave us an insight about the three fields and their differences as well as similarities. Let us have a look on the job market in Germany. The analysis of this market can show us where and how these fields are emerging.

Job Market for Data Science, Business Intelligence and Analytics in Germany:

In an analysis from September 2018 it was claimed that there are 900 jobs under the keyword “data science” in Germany (Piatetsky 2018). Can we do better nowadays?

For this analysis, I got data from three job searches on LinkedIn: “Data Scientist”, “Analyst” and “Business Intelligence” (BI). There are roundabout 975 items for each search term, totaling 2,923 jobs.

There are more jobs on LinkedIn for these areas with most of the jobs under the BI term. Analyst is second and Data Science last. Data Science job offers increased by already 2,784 jobs, which is an increase of more than 400% to last year!

The full amount of jobs from this search is still only 5% of the IT function job market in Germany.

Job Title:

A look on the Top 10 mentioned Job titles reveals that Data Scientist, Data Analyst and Business Intelligence Analyst are mentioned mostly. 

There seems to be some overlap in the data so I counted the occurrence of the titles in the data. Interestingly, mostly the word “Analyst” was mentioned. The analyst term is used in BI and Data Science jobs quite frequently. The fourth largest group shows jobs with none of the search terms. Which are they?

They are mostly coming from the BI search but the most mentioned job was the Data Engineer which is as important as the three other jobs.


Most of the jobs have been posted by Campusjäger which is a recruitment agency from Germany focused on students and young professionals.


You will probably have guessed it already and you are correct:

Most of the jobs are placed in Berlin, followed by Hamburg and Munich. The map confirms our observation – most of the jobs are located in these cities and but also in other areas such as the Ruhrgebiet. They have in common that they are urban areas.

Lets look on the distribution with the seaborn heatmap. For this chart I applied a standard scaler which means that we are comparing which jobs are more frequent per city (darker color). It means for example that there are more Analysts in Berlin than jobs from the BI and Data Science term. Let us focus on Frankfurt for example: More Analysts than BI and Data Science. This could be caused by the financial industry which is relying on Analysts. Hannover on the other hand has more Data Scientists – caused by the major insurance companies situated here and which are starting to explore this domain.

Time posted:

Most of the jobs have been posted within the last 1 week up to 4 months.

Seniority level:

Good news for young applicants, most of the jobs can be found in the Entry Level area. Standing out here is the Data Science domain, which is relatively new and there are not so many Executive and Director positions compared to the established BI area.

Job Function – first mention:

The top three job function areas are Business Development, IT and Engineering.

While Analysts and BI experts are dominating the Business Development area, the IT function uses more Data Scientists. Data Scientists dominate the Engineering area.

As being outlined before, Finance is heavily dominated by Analysts.

Industry – first mention:

Almost 50% of the jobs are situated in the IT industry.

The IT industry is dominated by BI experts and Data Scientists while Marketing, Internet and Chemicals rely on more Analysts in their industry.

Word Cloud:

A good way to analyse the unstructured information from the job offer texts are word clouds or word maps. They show the most frequent words and the word size describes the frequency.

A look on the Word Map for Analyst:

Data Science:

Business Intelligence:

All three rely on the word “team” and “data” heavily. The Analyst word cloud shows that the word customer is quite important. On the Data Science map, the word “Machine Learning” is very important. Business Intelligence and Data Science share the importance on “Big Data”. Business Intelligence then focuses of course on “Data Warehouse”.

Coming to the end of this article, it can be stated that Analytics, Business Intelligence and Data Science share some similarities but do also have differences. These differences and similarities were underlined by the analysis of the German job market for these three areas.

The three areas share the passion for data, which is in my opinion the most important similarity.

You can have a look on the full code on nbviewer here or on GitHub.



Cambridge University Press. 2014. Analytics.

Cambridge University Press. 2014. „Data Scientist.“

Piatetsky, Gregory. 2018. How many data scientists are there and is there a shortage? 09.

Pugsley, Stan. 2017. Understanding the Differences Between Data Science and BI. 5.12.

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