With the exponential growth of AI, businesses are willing to employ skilful Data Scientists or data analytics to help them grow.
Aside from obtaining a Data Science Certification, it’s often beneficial to have a few Data Science Projects on your CV as formal education is never sufficient.
Implementation of data science into businesses is increasing day by day but since the technology is new for a lot of people, managing data science projects and implementations become confusing for them.
This is where the 5Ps of data science projects come into play.
These 5Ps make the whole process of managing the data science project easier and more efficient. Scroll down and equip yourself with all the necessary information about the 5Ps of the project.
The 5Ps Of Data Science Projects
purpose
A goal or purpose must always be defined, just as it is in the typical project management approach.
The following can be the purpose of building a data science project:
- Improved business insights
- prediction
- Fraud detection and prevention
- Problems related to maximization.
A specific purpose or goal is required for any project in the profession of Big Data or Data Science. You should never work on a project thoughtlessly just because everyone else is. This will not benefit you or your company.
People
A data science project requires a diverse range of people with specific skill sets. Developers, testers, data scientists, and domain experts are required for successful data work.
Data projects also involve stakeholders/project sponsors and a project manager/product owner. In this regard, the former group of individuals must be informed about the project’s progress, whereas the latter must mediate between interested parties and the engineering team.
For instance, if you wish to code in python languageyou need to hire an individual or development team which is well-versed in the concepts of Python.
Data scientists are frequently viewed as individuals with skills in a variety of areas. These include scientific knowledge or business domain expertise; machine learning, analysis using statistics, and mathematical concepts; programming, data management and computing. This is typically a group of scientists made up of individuals with similar skills.
processes
There are two types of processes that you need to perform in a data science project: organizational processes and technical processes. You must take into account two distinct types of processes while working on the project.
On the one hand, the organizational processes and topics include:
- Agile vs classic
- Project Marketing
- Change processes
On the other hand, the technical processes include questions and topics like:
- The type of data process you are trying to support.
- Processes related to data integration
- Data science and data analysis are being used.
While managing the project, you need to be sure of the processes you are going to carry out and the tool you will be using.
For instance, if you wish to perform data for certain processes like data analysis or sales forecasting, make sure that you have all the relevant tools and skills for the same.
The data science process faces several challenges, including:
- How to seamlessly integrates all tasks required to build such a process
- How to determine the best computational power and effectively schedule procedure executions to the assets based on process description, parameters, and preferences of the users.
At this point, you are required to find an answer to these questions to ensure that you are going in the right direction and making the right decisions.
platforms
Along with the factors mentioned above, fundamental and strategic questions such as technologies individuals will use for their data analysis and products are also crucial for properly implementing a data science project.
Below are some examples of possible questions:
- What does my information systems management require?
- Which (IT) strategies am I going to pursue?
- Previously used (IT) architecture?
- Will I use IT at one or two speeds?
- What are the requirements for my compliance/security?
Answering these questions will lead to other questions including;
- What type of cloud ought to be used? (AWS versus Google versus Azure, as well as public versus private versus hybrid)
- What SLAs are better suited to my requirements? (a contract between a service provider and a customer)
- What else are my technical specifications?
- How should data integration be carried out? (either manually through Java or through tools such as Talend, Dataflow, and so on.)
For instance, to manage a project, you may need panel for analyzing and storing the data whereas, for some other projects, a simple excel is enough. Answering questions related to platforms will help you ensure that you have enough resources to manage the project and if not, what all alternatives do you have?
Always make sure to consider scalability as the main criterion of choosing a platform that you are going to use in your data science projects.
programmability
The last and most important “P” for managing the data science project is programmability. Finally, consider which equipment and coding languages you intend to employ. This point is, of course, impacted and motivated by IT governance and strategy, as well as the answers to the previous questions.
Here are a few examples of tools and programming languages:
- PowerBI tools: Qlik, Tableau, Google Data Studio
- Big data tools: Google’s Cloud Storage & Big Query, Hadoop, AWS Redshift and S3
- streaming software: Talend, Spark, Kafka
Analysis all the available tools and choose the one that best fits your needs.
final words
While managing a data science project, you need to consider multiple factors.
However, the 5Ps that we have mentioned are the most important among them for the successful implementation of the project that you are working on. These 5Ps will help you ask questions that you must ask yourself while working on the project.
Those who are intrigued to know about data science can enrol for a Data Science Course to learn in detail.
It will give detailed information about managing and designing a data science project and beginning their career in this field.
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