Data Driven Strategy
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Data science is an evolution of the Analytics. Compared to the analytics, it includes more of the business knowledge and understanding, and technically it goes more into the predictive analytics and machine learning. It is usually more flexible and agile, and it has an ability to answer to less traditional questions.
Machine learning represents algorithms that can learn from the data. They allow computers to find hidden insights and patterns in datasets, without being explicitly programmed where to look.
Analytics is the discovery, interpretation, and communication of meaningful patterns in data. Analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance and interpretation of the results to the decision makers.
Big Data represents data that is coming at high speed and is in quantities that exceed the capabilities of traditional software for storing, processing and data management.
Small data is a dataset that contains very specific attributes. Small data is used to determine current states and conditions or may be generated by analyzing larger data sets.
Data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered as a core component of business intelligence environment. DWs are central repositories of integrated data from one or more disparate sources.
24 Apr 2020
We live in the era of information technology, when we generate more data in 10 minutes than the amount of total data generated from prehistoric times by the year of [...]Read moreFriday talks: Process Mining – where process science meets data science
31 May 2019
Retail industry. In the glory of Data Science, it’s all about the data and tailor-made targeting. If you want to brag about it, you would say – I’ve got the [...]Read moreFRIDAY TALKS: FRIENDS OR FOES? Propensity to purchase vs. Survival analysis
29 Mar 2019
Pedro Domingos is a professor of computer science and engineering at the University of Washington. He is a winner of the SIGKDD Innovation Award, which FYI is the [...]Read moreFriday talks: The Holy Grail of Machine Learning
25 Jan 2019
When dealing with anomalies in the data, there are lots of challenges that should be solved. We’ve already had a couple of articles regarding this topic, and if you [...]Read moreFriday talks: The dark horse of Isolation Forest
11 Dec 2018
The fact that there is a deep connection between the telecommunications and Big Data is very clear – the main task for telecommunications is in exchanging [...]Read moreHow the Big Data Won the Hearts in Telecommunications
16 Nov 2018
Why time series anomaly detection? Let’s say you are tracking a large number of business-related or technical KPIs (that may have seasonality and noise). It [...]Read moreTime series Anomaly Detection using a Variational Autoencoder (VAE)
9 Nov 2018
The client’s path, from conceiving an idea to making it a project in the bank, used to be clear, but unpredictable. It involved a potentially noticed ad or the [...]Read moreRecommender Systems and Banks: Precious Recommendation
5 Nov 2018
Students of the first Data Science Academy spent three months on a journey through the world of data science, big data and analytics. Although most of them are [...]Read moreData Science Academy Finals: „New Knowledge Is Always Welcome”