Data Science Project Management Methodologies
DATA SCIENCE
Big data is need of time, the requirement for its storage has likewise developed. It was the primary concern and a big challenge for business enterprises until 2010. The fundamental center of attention was on building structure and responses for storing information. Presently when Hadoop and different frameworks have effectively tackled the issue of capacity, the focus has moved to the management of this data.
The chief economist at Google, Hal Varian, suggested that the ability to take data to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it, that’s going to be a hugely important skill in the next decades, and this is what Data Science is all about. Data Science is the future of AI. In this manner, it is critical to comprehend what is Data Science and how might it increase the worth of your business.
Generally, the information that we had was mostly organized and limited in size, which could be interpreted by utilizing the basic Business Intelligence or BI tools. But unlike the data in the conventional frameworks which was for the most part organized and structured, today the vast majority of the data is unstructured or semi-organized.
In the current world, traditional BI devices are not equipped for arranging this tremendous volume and assortment of data. This is the reason we need increasingly perplexing and progressed diagnostic apparatuses and calculations for handling, analyzing, and drawing significant fractions of knowledge out of it.
NEED TO MANAGE DATA SCIENCE PROJECTS
By equipping yourself with a broader understanding of different project management approaches and how they can be connected particularly for information science, you are more likely to discover or create a technique that efficiently works.
There is an abundance of project management tools that are applied to maintain and report on project progression. You can use these tools not only to keep senior management notified, but also help you and stakeholders write assumptions and project dependencies.
The agile declaration says we should always prioritize people and communications over processes and tools. Likewise, for a data science project, you should prioritize answering the business question over external and unnecessary buzzes. Moreover, while one can make the thought that project management tools are related, you would be amazed at how these tools can help you plan and think through difficulty in an organized way.
Furthermore, the agile platform commands that the application of these tools should be in proportion to the project size. Therefore you do not need to create a fancy Gantt chart if you are just working on a freelance data science project for your portfolio. But a Risks, Assumptions, Issues, and Dependencies log or RAID log will be beneficial to keep your assumptions top of the mind as you progress the modeling of a Data Science project. Even after the project finishes, it is a great relief to have to continue to advise you of your assumptions months or years afterward.
METHODS
1. KDD
Knowledge Discovery in Databases or KDD is a technique for how administrators can separate examples and additionally required data from information. It comprises of five phases that are as follows:
Selection: Designing a target data set, or concentrating on a subset of variables or data units that require further research.
Pre-processing: Target data pre-treatment to obtain uniform data.
Transformation: Data transformation using dimensionality decrease or alteration techniques.
Data Mining: Hunting for patterns of concern in a distinct representational order that depends on the Data Mining goal.
Interpretation/Evaluation: Description and examination of the mined patterns.
2. SEMMA
SEMMA or Sample, Explore, Modify, Model, and Access is another method for project management in Data Science projects. It contains the following steps:
Sample:
A segment of an immense informational collection is taken that is sufficiently large to separate huge data and sufficiently little to control rapidly.
Explore:
Information exploration can help in increasing comprehension and thoughts just as refining the disclosure procedure via scanning for patterns and abnormalities.
Modify:
Information modification stage centers around making, choosing, and change of factors to center model determination process. This stage may likewise search for exceptions and lessening the number of factors.
Model:
There are distinctive demonstrating methods present and each sort of model has its qualities and is proper for a particular objective for information mining.
Access:
This last stage centers around the assessment of the dependability and handiness of discoveries and assessments of the accomplishment
3. CRIPS DM
CRISP-DM has been consistently the most regularly used methodology for analytics, data mining, and data science projects. Despite its notoriety, CRISP-DM has not been altered since its origin. The following are steps included in this methodology.
Business Understanding: Decide business targets; evaluate circumstances; decide data mining objectives; produce venture plans.
Data Understanding: Gather introductory information; depict information; investigate information; confirm information quality.
Data Preparation: Select information; clean information; build information; incorporate information; position information
Modeling: Select demonstrating method; produce test configuration; construct model; survey model.
Evaluation: Assess results; audit process; decide subsequent stages
Deployment: Plan deployment; plan to observe and support; produce last report; survey venture.
CRISP-DM effectively works for Data Science. It is useful for what it is; a characteristic portrayal of the work process in data mining projects. As it is the initial move towards characterizing a data science approach, CRISP-DM has had a noteworthy effect to carry some order to information science. In any case, as other KDD approaches, CRISP-DM gives an assignment centered methodology and neglects to address group and correspondence issues.
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