TUGAS
MATA KULIAH DASAR PERENCANAAN PROYEK SAINS DATA KELAS A
‘’ MENYUSUN RINGKASAN KONSEP DASAR PERENCANAAN PROYEK SAINS DATA ‘’
DISUSUN OLEH:
Aisyah Kirana Putri Isyanto (21083010065)
DOSEN PENGAMPU:
Kartika Maulida Hindrayani S.Kom, M.kom .
PROGRAM STUDI SAINS DATA FAKULTAS ILMU KOMPUTER
UNIVERSITAS PEMBANGUNAN NASIONAL “VETERAN” JAWA TIMUR
2023
Task 01 :
Compose a summary in the form of about:
a. Basic concepts of data science project planning
Data science is an interesting field that can influence almost all industries and all aspects of daily life. Unfortunately, data science projects often involve multiple stakeholders across multiple teams, and must juggle business and technical goals. To overcome this, it is critical to implement the right project approach that can cater to the unique aspects of data science.
To produce useful data analysis results, data science project planning is essential to implement. Good data science project management can reduce the risk of failure and increase efficiency. A project management framework can provide guidance to help plan, allocate resources, and execute tasks to achieve goals on time and within budget. The main goal is to ensure that data science projects have a clear direction and can add significant value.
b. Identify the planning stages of a data science project
Following are the steps to follow to start a data science project:
- Define project purposes: Clearly identify the purposes. Research begins with understanding what, why, and how research is conducted. Does the organization want to solve a specific problem, make predictions, optimize processes, or discover hidden patterns in data? Define goals specifically and measurably.
- Data capture: Identify data sources relevant to organizational goals. Data can come from various sources, such as internal databases, public data, or APIs.
Make sure the data is high quality, accurate, and includes variables relevant to the project.
- Data exploration: Conduct initial exploration of the data that has been collected. Examine the data structure, check for missing or invalid values, and identify initial patterns. Visualize data using graphs or plots to understand its characteristics better.
- Clean data: This step involves cleaning the data from missing values, outliers, or other errors. Correct inappropriate data formats and perform normalization if necessary. Make sure the data is ready for further analysis.
- Data analysis: Use analysis techniques appropriate to the project objectives.
This may include descriptive statistics, regression, classification, clustering, or other methods depending on the research question being asked. Use relevant algorithms and data science techniques to derive insights from data.
- Interpretation and modeling: Interpret analysis results and identify important findings. Create models that can help in decision making or predictions in the future. Ensure the model is valid by performing cross validation or other appropriate methods.
- Communicate results: Present results and findings in a way that is clear and understandable to the intended audience. Use data visualizations, reports, or presentations to communicate findings effectively. Provide relevant context and recommendations based on project results.
- Evaluation and iteration: Evaluate the project and identify areas that can be improved. Learn from the process that has been carried out and think about
how you can improve the analysis in the future. Data science is an iterative process, so feel free to repeat these steps to gain better insights.
c. Analyze and evaluate the effectiveness of data science projects
Evaluation of the effectiveness of a data science project is an important step to ensure the added value of the project. This involves:
- Performance Monitoring: Regularly monitor model performance and project results to ensure that business objectives are achieved.
- Success Measurement: Determine relevant success indicators and measure the extent to which the project has achieved them.
- Feedback and Improvement: Receive input from stakeholders and use the results of the analysis for continuous improvement.
- Compliance with Initial Objectives: Evaluate whether the project has met the initial business objectives and whether there are any lessons learned for future projects.
By understanding the basic concepts of data science project planning, identifying relevant planning stages, and conducting careful evaluations, a data science project can be executed effectively and provide significant results for the organization.
BIBLIOGRAPHY
https://projeksainsdata.com/mulai-belajar-proyek-sains-data/
https://medium.com/data-science-at-microsoft/managing-a-data-science-project-87945ff7948 3
https://www.kdnuggets.com/2023/07/guide-data-science-project-management-methodologies.
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https://www.datacamp.com/blog/data-science-project-checklist
