What are the best data research practices for accurate and reliable results?
Data research is a process of collecting, analyzing, and presenting data to answer questions, solve problems, or generate insights. However, data research is not just about crunching numbers and making charts. It also requires following some best practices to ensure the accuracy and reliability of the results. In this article, you will learn about six data research practices that can help you produce high-quality data products and communicate them effectively.
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Hema Kalyan MurapakaTop Data Science Voice | Machine Learning & Deep Learning Explorer | Artificial Intelligence | MongoDB | Technical…
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Jaskaran Singh WaliaResearcher at MIT - Massachusetts & Cambridge | CEO @Travellio | 5M+ Impressions & 22k | Top Voice | Lead at Google…
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Akshay BahadurRec + ML @ Yahoo! | CMU '22 | x²-x+41
Before you dive into data collection and analysis, you need to have a clear and specific research question that guides your data research. A good research question should be relevant, feasible, and answerable with data. It should also be aligned with your research objectives, audience, and context. To formulate your research question, you can use frameworks such as SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) or PICOT (Population, Intervention, Comparison, Outcome, and Time).
Depending on your research question, you may need to use different data sources and methods to collect and analyze the data. For example, you may use primary data (collected by yourself) or secondary data (obtained from existing sources), or a combination of both. You may also use quantitative data (numerical) or qualitative data (textual or visual), or a mix of both. You should choose the data sources and methods that are suitable for your research question, objectives, and resources. You should also consider the quality, validity, and reliability of the data sources and methods.
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Hema Kalyan Murapaka
Top Data Science Voice | Machine Learning & Deep Learning Explorer | Artificial Intelligence | MongoDB | Technical Content Writer
When developing a research strategy, we carefully choose the methods and data sources, making adjustments based on the particular issue. The choice between primary and secondary data depends on availability and significance, balancing the advantages of primary data collecting against the resources already in place. Also selecting between quantitative and qualitative data would be careful. A careful blending of the two provides a more thorough understanding most of the time. Crucially, in order to maintain the integrity of the research project, data quality, validity, and reliability must be given top priority.
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Hammad Zaki
Data Scientist | Deep Learning & Machine Learning Specialist | NLP | Proficient in Python, SQL, and Data Visualization Tools
Once we've defined our research questions to understand what we're seeking, we can begin the collection process. It's crucial not to rely on just one source when gathering information. Instead, we should utilize diverse, dependable sources that match our study and research questions. These sources may be primary, secondary, or tertiary, but they must align with what we're investigating.
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Parth Patel, PhD, MS
Executive Director | Global Head AI | Cloud-based Population Genomics | Real-World Data (EHR Systems) | Data Science | Life Sciences & Healthcare | 2x LinkedIn TopVoice
Tailoring data approaches is key: Select sources/methods aligned with research goals, ensuring robustness and relevance. Blend quantitative and qualitative insights judiciously, mindful of data quality and reliability. Strategic choices amplify research impact.
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Nwaamaka Iduwe
Data Science | Machine Learning | Digital Currencies & Finance
To obtain quality data, start by verifying its accuracy and completeness. Scrutinize the data collection methods and consider using multiple sources to cross-verify information. Clean and preprocess the data to eliminate inconsistencies or errors. Incorporate diverse data types, ensuring a comprehensive representation of the issue. Collaborating with subject matter experts or utilizing established datasets can also enhance data quality. Rigorous quality checks and validation processes further ensure the data's reliability, fostering confidence in the subsequent analyses and insights derived from it.
Data research involves working with sensitive and personal information that may affect the rights and interests of the data subjects, stakeholders, and society. Therefore, you should follow ethical principles and standards to protect the privacy, confidentiality, and consent of the data subjects, as well as the integrity, transparency, and accountability of the data research. You should also comply with the relevant laws, regulations, and policies that govern data research in your domain and context.
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Jaskaran Singh Walia
Researcher at MIT - Massachusetts & Cambridge | CEO @Travellio | 5M+ Impressions & 22k | Top Voice | Lead at Google DSC | Ex Assurant-USA | Ex IIT | Ex NUS-Singapore | Ex HPE-Singapore
Following ethical standards and norms can be done by taking the following steps: Privacy Protection: Safeguard privacy by ensuring the responsible handling of sensitive and personal information. Confidentiality Assurance: Uphold confidentiality standards to maintain the trust of data subjects and stakeholders. Consent and Transparency: Obtain informed consent from data subjects and maintain transparency throughout the research process. Integrity Maintenance: Ensure the integrity of data by employing rigorous methods and avoiding manipulation or bias. Accountability Practices: Establish mechanisms to track and address ethical considerations in data research. Legal Compliance: Adhere to relevant laws, regulations & policies
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Hijab Waheed
Research | Development | Data
While there could be multiple ways to ensure the quality of research, in my opinion having an accurate and thorough tool translates into addressing the research question effectively along with gathering useful findings.
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Jayanth MK
Data Scientist | Phd Scholar | Research & Development | ExSiemens | IBM/Google Certified Data Analyst | Freelance Trainer | Instructor | Mentor | Data Science | Machine Learning | AI | NLP/CV |
Adhering to ethical principles in data research is paramount for maintaining trust and responsibility. Respecting the privacy and consent of individuals, upholding transparency, and complying with legal standards are essential. From my experience, ethical conduct not only safeguards against potential risks but also contributes to the credibility and long-term success of data research initiatives. It's a foundation for building ethical and responsible data practices.
Data analysis is the core of data research, where you use various techniques to explore, process, and interpret the data. However, data analysis is not a straightforward or simple task. It requires applying rigorous data analysis techniques that are appropriate for your data type, research question, and objectives. You should also use data quality checks, data cleaning, data transformation, data visualization, and data modeling to ensure the accuracy and reliability of your data analysis. You should also document and justify your data analysis steps and choices.
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Akshay Bahadur
Rec + ML @ Yahoo! | CMU '22 | x²-x+41
Adding my perspective: - Data Exploration: Conduct a comprehensive dataset exploration, distributions, and basic statistics. - Quality Checks: For instance, identifying and addressing missing values, outliers, or anomalies that could impact the analysis. - Cleaning and Transformation of data - Visualization: Utilize graphs, charts, and other visual tools to represent patterns and trends in the data. - Modeling: Apply statistical or machine learning models based on the research question. - Documentation: Comprehensive documentation ensures transparency and allows for analysis replication. - Justification of Choices: Provide clear justifications for the chosen analysis techniques to enhance the credibility of the research.
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Parth Patel, PhD, MS
Executive Director | Global Head AI | Cloud-based Population Genomics | Real-World Data (EHR Systems) | Data Science | Life Sciences & Healthcare | 2x LinkedIn TopVoice
Adding my perspective: elevate data insights through precision: Rigorous analysis tailored to data type and research objectives is paramount. Employ thorough quality checks, meticulous cleaning, transformation, and visualization to enhance accuracy. Documentation and justification of analytical steps ensure transparency and trust in findings.
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Ifitumi Audu, Ph.D, MPH, CHES
Health Education Specialist
Having a clear hypotheses: both null and alternate hypotheses would help guide your research. This would pave the way to having results that are measurable and applicable.
Data findings are the results and insights that you derive from your data analysis. However, data findings are not useful if they are not communicated clearly and effectively to your target audience. Therefore, you should use various data communication tools and strategies to present your data findings in a way that is understandable, engaging, and actionable. You should also use data storytelling, data visualization, and data interpretation techniques to convey your data findings in a compelling and convincing manner.
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Raghavendra Siddi Reddy
Looking for Analytical roles | Head of Analytics | Driving Data-Driven Growth | Strategy | Applied Statistics | AI & ML Practitioner | Analytical Solutions | Data Architecture | Luxury Branding
Good data communication is more than just numbers; it's about telling a compelling story. Use data storytelling to make complex insights understandable and relevant. Visualizations like dashboards or infographics are key, turning data into engaging visuals. But don't just show; explain. Back up your findings with solid analysis to address questions. This combination of storytelling, impactful visuals, and thorough interpretation not only informs but also captivates your audience, highlighting the true value of your data.
Data research is not a one-time or static process. It is a dynamic and iterative process that requires constant evaluation and improvement. Therefore, you should use various data evaluation methods and metrics to assess the quality, impact, and value of your data research process and products. You should also use data feedback, data review, and data revision techniques to identify and address the gaps, errors, and limitations of your data research process and products. You should also document and share your data research process and products for future reference and learning.
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Raghavendra Siddi Reddy
Looking for Analytical roles | Head of Analytics | Driving Data-Driven Growth | Strategy | Applied Statistics | AI & ML Practitioner | Analytical Solutions | Data Architecture | Luxury Branding
Continuously evaluating and upgrading your data research process is key to maintaining its quality and relevance. Use specific methods and metrics to assess and refine your work. Incorporate feedback and make revisions to fix any issues. Learn from each project and apply those lessons to the next. Documenting and sharing your methods and results not only boosts your own learning but also enriches the wider data science community. This cycle of assessment, improvement, and sharing is essential for keeping data research robust, relevant, and valuable.
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N.A. Mahalakshmi Srinivasan
NAS Bioventures, Datum Culminations (Microbiologist, Bioinformatician, Data Science),
On ground how well did the report of your analysis do, how much did it benefit an organisation? Data Science journey involves numerous disciplines, algorithms and parameters. After being perfect at all these, how well did it help address the question in hand is vital to gauge how well the analysis was done.
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Rafee Al Ahsan
Doctoral Researcher - Ph.D., Canada 🇨🇦 I Alberta Innovates Scholar | Electrical and Software Engineer | 6G Telecom. & AI Enthusiast | Sessional Instructor | Python & MATLAB Programmer | Invited IEEE Reviewer
Best data research practices for accurate and reliable results include: Clear Objectives: Define research goals and questions clearly to guide your data collection and analysis. Robust Design: Develop a strong research design with appropriate methodologies, ensuring it aligns with your objectives. Quality Data Collection: Gather data meticulously, ensuring accuracy, consistency, and relevance. Validate and clean data to remove errors. Transparent Documentation: Document your data collection and analysis processes comprehensively, making it easy for others to replicate your study. Ethical Considerations: Adhere to ethical standards in data collection, ensuring participant privacy and informed consent.
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