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Understand your data
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Define your goals
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Choose your methods
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Choose your tools
5
Evaluate and iterate
6
Here’s what else to consider
Data analysis is the process of collecting, organizing, exploring, and interpreting data to answer questions, solve problems, or generate insights. Data analysis methods and tools are the techniques and technologies that help you perform data analysis effectively and efficiently. Choosing the right data analysis methods and tools can make a big difference in the quality, speed, and impact of your data analysis projects. In this article, you will learn some of the best practices for choosing data analysis methods and tools in the context of data analytics critical thinking and problem-solving.
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- Dr. Priyanka Singh Ph.D. Engineering Manager - AI @ Universal AI 🧠 Linkedin Top Voice 🎙️ Generative AI Author 📖 Technical Reviewer @Packt… 4
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1 Understand your data
Before you choose any data analysis method or tool, you need to understand your data. What type of data do you have? Is it structured or unstructured, quantitative or qualitative, discrete or continuous, static or dynamic? How much data do you have? Is it enough to answer your questions or solve your problems? What are the sources, formats, and quality of your data? How reliable, accurate, and consistent is your data? Understanding your data will help you narrow down your options and select the most appropriate data analysis methods and tools for your data.
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- Dr. Priyanka Singh Ph.D. Engineering Manager - AI @ Universal AI 🧠 Linkedin Top Voice 🎙️ Generative AI Author 📖 Technical Reviewer @Packt 🤖 Building Better AI for Tomorrow 🌈
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First, deeply understand your data's type, quantity, source, and quality. This helps you establish if your dataset is robust enough to meet your objectives. Second, define your project goals clearly. Know the questions you aim to answer and the metrics to gauge success. Third, select the appropriate data analysis methods based on your data and goals. Options range from descriptive statistics to machine learning. Finally, validate your results rigorously to ensure they're reliable and unbiased. A transparent validation process bolsters the integrity of your findings. By following this roadmap, you'll be better positioned to choose tools and methods that yield accurate, valuable insights.
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2 Define your goals
Another important step in choosing data analysis methods and tools is to define your goals. What are you trying to achieve with your data analysis? What are the questions you want to answer or the problems you want to solve? What are the assumptions, hypotheses, or expectations you have about your data? What are the criteria or metrics you will use to measure your success or failure? Defining your goals will help you align your data analysis methods and tools with your objectives and outcomes.
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Don't limit the analysis strictly to what were requested. Understand who needs it, why, and how it will be used. The most relevant insights may hide just beneath what was demanded. Be open-minded when doing the exploratory analysis. When comparing months' results with the same month of last year, you may find a tendency in the performance of the previous months or the key influencers. During the analysis of what products have the most deviation, you may uncover insights about whether there is a pattern of product categories, production sequences, or time of the day/week/year when the deviations occured. Sharing this information besides the original demand can deeply enhance the impact of the analysis.
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3 Choose your methods
Data analysis methods are the techniques that help you process, manipulate, visualize, and model your data. There are many data analysis methods available, such as descriptive statistics, inferential statistics, exploratory data analysis, confirmatory data analysis, data mining, machine learning, and more. Depending on your data type, goal, and domain, you will need to choose the most suitable data analysis methods for your project. Some factors to consider when choosing data analysis methods are the level of complexity, the level of uncertainty, the level of scalability, and the level of interpretability of the methods.
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4 Choose your tools
Data analysis tools are the technologies that help you implement, automate, and optimize your data analysis methods. There are many data analysis tools available, such as spreadsheets, databases, programming languages, frameworks, libraries, software, platforms, and more. Depending on your data size, method, and skill level, you will need to choose the most appropriate data analysis tools for your project. Some factors to consider when choosing data analysis tools are the functionality, usability, compatibility, security, and cost of the tools.
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- Mike M. MBA Candidate at WashU in St. Louis - Olin Business School | Entrepreneurship Fellow
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First determine whether you will be performing an ad-hoc analysis or building a robust and reusable analytical tool. While Excel excels at one-off tasks, a more robust system is crucial for ensuring data integrity, automating repetitive tasks, and facilitating scalability, thereby allowing for future growth and complexity.
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5 Evaluate and iterate
Finally, after choosing your data analysis methods and tools, you need to evaluate and iterate your data analysis process. You need to check if your data analysis methods and tools are working as expected, producing accurate and meaningful results, and meeting your goals. You also need to identify any errors, limitations, or gaps in your data analysis methods and tools, and make adjustments or improvements as needed. Evaluating and iterating your data analysis methods and tools will help you ensure the validity, reliability, and efficiency of your data analysis projects.
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One of the most common mistakes is taking days or weeks to analyse data, put everything into something presentable format and then go back to a stakeholder and explain it all. Instead, adopt a more lean approach and consider short, iterative cycles: - Analyse- Digest- Report> then repeat.Analyse: do the analysis work based on the methods you identified, exploring one question you had.Digest: distill your findings into a condensed format.Report: speak with your stakeholder(s) and decide on the next course of analysis.
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6 Here’s what else to consider
This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?
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- Kaushal Gianchandani Oceanographer | Climatologist
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It is a good practice to benchmark your data analysis methods using standard use-cases before deploying it on actual data. This can help you with ruling out mistakes in your technique rather quickly. For instance, if you have written a python program to carry out regression analysis on a given dataset, use the program to fit two sets of 1000 randomly generated numbers between -1 and 1. You should obtain a slope of ~1 and an intercept of ~0. If the slope and intercept you obtain in this trivial exercise is substantially different from 1 and 0, there is a mistake in your program.
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