How do you tackle big, messy data sets?

Absolutely! Having a clear goal is crucial when dealing with big, messy data. It's like having a compass to navigate through the complexities. Without a defined purpose and questions to answer, it's easy to get lost in the data wilderness. Setting your course with a clear goal is the first step towards successful data analysis.

I agree with this assertion but I will like to add that: ~I also check for the validity of my data and how it was collected. ~Also most importantly I compare and contrast my data with other sources and methods of analysis to see how my data differs from or agree with existing knowledge or expectations to ascertain the advantages and limitations of my approach.

Having a specific research question can definitely pinpoint the approach of data utilization. Once you have defined what you need to solve, it'll be easier to collect the right data and cleanse it for analysis purposes.

One more thing I have found helpful is the choice of tool and software I decide to use for my analysis and visualization because I would have to put into account the type, size and quality of my data, the complexity and scope of my analysis and level of automation and customisation I need, the budget and time constraints I have, etc. I most importantly consider my skills and preferences and that of my audience when choosing a tool or software, be it spreadsheets, databases, programming languages, statistical software, visualisation tools, etc.

Understanding the pattern and how the data is built, are the first main step to understanding the best approach to deal with the data, then we have to build the methodology to collect and clean the data if it’s from multiple sources.

Clearly outline your objectives and what you want to achieve with the data analysis. Are you trying to uncover insights, make predictions, or solve a specific problem? Formulate specific research questions that your analysis should address. These questions will guide your entire data analysis process.

This is absolutely correct and crucial because cleaning and transforming your data will ensure that your data is consistent, complete and compatible for analysis.

Cleaning is an important step this will make sure to remove unwanted data normally outlier. But one should understand the domain whether to clean or let it be. Duplicate values can be removed only when condition is not suited.

I totally agree and they is nothing more to add to this other than to point out that the choice of tools and software to use depends on one's analytical techniques and modeling methods.

Common tasks that I do in data visualization and communication are: 1. Data Selection: I select the most relevant and important data for my visualization and communication such as key metrics, indicators, variables etc. 2. Data presentation: I present my data using different types of charts, graphs, dashboards or reports, such as bar charts, line charts, pie charts, scatter plots, histogram, box plots, heat maps etc. 3. Data Explanation: I explain my data using clear and concise language, labels and annotations, such as titles, subtitles, captions, legends, axes etc. 4. Data Interpretation: I interpret my data using findings, conclusions and recommendations based on my data analysis and modeling results.

Tackling big, messy data requires a structured approach, patience, and continuous learning. 1. Define Your Objectives:Begin by clearly defining your objectives and what you want to achieve with the data analysis. Understand the problem or questions you aim to address with the data. 2. Data Collection:Gather raw data from various sources, ensuring it is well-documented and properly structured. Data sources can include databases, APIs, spreadsheets, logs, or external data providers. 3. Data Cleaning:Start by cleaning the data. This includes dealing with missing values, outliers, and inconsistencies. It's a crucial step to ensure the quality and reliability of your analysis.

Visualization plays a major role. It makes the audience understand your results. Story telling to visualization makes the work more easily understandable to the stockholders.

Imagine you're diving into this massive sea of data. First, figure out what you're looking for, your goal in this data sea. Define Objectives: Clearly outline goals for analysis. Data Exploration: Understand data through stats and visuals. Data Cleaning: Address missing values, outliers, and inconsistencies for quality. Feature Engineering: Enhance dataset relevance with new or modified features. Scaling and Normalization: Standardize numerical features to a common scale. Advanced Analytics: Use ML, statistical modeling, or clustering for insights. Data Visualization: Communicate findings visually to understand patterns. Documentation: Document all steps for transparency and reproducibility.