How can you work with large datasets as a beginner?

Consider adopting a modular approach when setting goals. This involves breaking down the overall objective into smaller, manageable modules, each with a specific mini goal. This strategy facilitates a step-by-step progression, making it easier to manage and assess progress with large datasets.

Essa etapa se resumirá em três perguntas: - Qual contexto da empresa/setor? - Qual problema preciso resolver? - Para quem será essa solução? Gaste tempo respondendo essas três perguntas, elas serão cruciais para o desenvolvimento do seu projeto!

Define your aim and objectives to narrow down your focus. This reduces the burden of working on a large dataset significantly.

Two key points would be: 1. Start by thoroughly understanding the structure and content of your dataset. Know the types of variables, data distributions, and any missing values. 2.Choose appropriate data types for your variables to minimize memory usage. For example, use integers instead of floating-point numbers if possible.

To handle big data, we need to: Define the objective of the data analysis. Explore the data and understand its structure and patterns. Select the appropriate tool to process and transform the data. Present the results and answer the questions based on the data model.

When choosing your tool, think about your skill set, your resources, your goals and the time you have for the analysis. Do you have the computational resources to analyse a huge dataset with excel? Do you have the skills to use R? Do you have the time to learn how to use a new tool? Do you want to produce complex visualisations? I sometimes use different tools to analyse different parts of my datasets; for example I prefer cleaning with excel, analysing text with python and analysing numeric data with R. At the and of the day, the tool is only as good as its user so go for for what you can use to get the job done within the allotted time.

Focus on the practice of tool comparison trials. Before settling on a specific tool, conduct short trials with multiple tools to assess their performance with sample data relevant to the project. This hands-on approach allows for a practical evaluation of each tool's effectiveness in managing and analysing the dataset in question.

Sometimes the perfect tool is the tool you already know how to use. Ask yourself, can the tool I already use do what I need, even if with potentially some extra steps? Chances are the time you save with a new tool will be spent trying to make the old and the new work together. As I like to say “the shortest road is the one you already know”

To work with large datasets as a beginner ,first you must question your wants as important and as additional information. Rather your question should focus on your search in dataset . Do not meander,if you focus,you wants from the large datasets will be minimised to work with you get appropriate answers,else you will deviate from the answers

Embracing scalable data pipeline tools is crucial when working with large datasets, especially considering diverse skill levels within a team and the ability to be nimble. Tools like Alteryx stand out for their ability to facilitate efficient knowledge transfer and streamline reusable workflows, accommodating both novice and expert users. This adaptability not only boosts individual productivity but also enhances team collaboration, making it an invaluable asset in our analytical toolkit.

Para melhorar a performance da sua solução, mantenha apenas as informações necessárias que serão utilizadas no seu projeto! Um erro comum de quem está começando é achar que precisamos de tudo que foi disponibilizado. Seu papel como bom analista de dados é saber priorizar informações e escolher o que é mais relevante!

Develop a data cleaning checklist. This checklist should include common data issues pertinent to large datasets, such as inconsistent formatting or erroneous entries. Regularly referring to this checklist ensure a thorough and systematic approach, enhancing data accuracy.

Garbage In Garbage Out is so true when using large datasets. One realises this often only at the time of analysis unfortunately. So cleaning up data becomes a bigger task than actual analytical work. It makes sense then to design a validated tool or spreadsheet with conditional formating, drop down lists or even just formating cells to contain numerical, text data etc. Work well begun is half done. Its cost effective too, as data gathering processes use a lot of resources in terms of man hours, instruments of delivery, training, and travel and other logistics. No use crying over spilt milk later.

Cleaning of data set is very important and should be ensured throughout the analysis process. It is especially important to start cleaning your data right from the goal definition stage.

The available data cannot be used as is blindly as it could have missing information or duplicate information in some columns. We need to get this missing information on the basis on mean/median/mode of the column. We need to remove duplicate values so that the data gets correctly represented. Sometimes the values could be way off and could be an outlier while checking for distribution. This outlier could make us take wrong conclusion. So the outlier has to be removed. Some columns might have to be merged to reach at some values. The most sought after data manipulation tool is Pandas and Numpy libraries of Python.

Understanding and exploring your data is crucial and often overlooked in many analytical tasks involving large datasets. Familiarize yourself with the data's structure and the type of information it contains. Employ statistical methods and diagrams for a good overview before deep diving into specific areas. Exploring your data is the key to be able to analyze them correctly and thoroughly. - From a security analyst who spent far too much time dealing with a huge amount of data during security incidents and learned it the hard way 😅

Once the data is cleaned and all the necessary data is available, inferences can be drawn. Data can be analyzed to see trends and patterns. These trends, patterns and relationships between data points can be visualized in the forms of tables, charts and graphs using visualization tools like Matplotlib, Seaborn, Plotly libraries of Python or PowerBI.

Start with descriptive statistics to grasp central tendencies. Use data visualization tools like histograms and scatter plots to uncover patterns and outliers visually. Conduct correlation analysis to understand relationships between variables. Examine feature distributions for data quality assessment. Detect and analyze outliers, deciding whether to retain or transform them. Group and aggregate data to analyze patterns at different levels. Perform temporal analysis for time-dependent datasets and use cross-tabulations to explore associations. Leverage interactive tools for dynamic exploration. Systematic data exploration sets the stage for informed decision-making and guides subsequent analysis steps.

La actualización de los datos es clave para tomar una decisión correcta y más en una etapa en la que lo más significativo es el equilibrio inestable

La visualización de datos es toda una especialidad. Recomiendo un curso en LinkedIn Learning llamado "Data Visualization: A Lesson and Listen Series" para aprender mucho acerca de lo que dicen los expertos en este tema.

Once we have the trends, patterns and relationships between the data points, we can make conclusions based on certain statistical hypothesis testing. We could also make a machine learning model and evaluate it for performance.

Begin with data preprocessing, addressing missing values and encoding variables. Explore feature engineering to enhance model performance. Choose a suitable model, considering options like linear regression or decision trees. Define evaluation metrics aligned with your analysis goals and implement cross-validation for robust performance assessment. Embrace an iterative approach, refining parameters and models based on results. Visualize findings through graphs and charts for effective communication. Document the analysis process and interpretations for collaboration and future reference. Continuous learning and experimentation are key to developing analytical skills over time.

Afortunadamente ya existen herramientas para todo este análisis. SPSS es una de mis predilectas pero no la única. Sin embargo, lo más importante es saber el objetivo real de tu estudio. Esto determinará los recursos, el equipo y el tiempo que se podrá dedicar.

Analyzing data means applying gathered knowledge for deeper understanding. Fresh perspectives often reveal new insights, enhancing learning. This process, blending expertise and open minded analysis, uncovers overlooked aspects, enriching conclusions and guiding effective decision making.

Present your findings in a way that is easy to understand. If the audience is top management then it should be a summary form rather than details.

Identify your audience and define what they need to know, what decisions (if any) will be made with the information you will present or how the information will be used. I have a strong preference for always starting with a helicopter-view and then digging into the details, as needed; for this apply the pyramid principle (Barbara Minto) to explain the major idea and how this is supported by minor ideas/arguments.

Proper communication comes after thorough exploration and understanding. To communicate effectively you must first of all have a clear focus, and secondly knowing which data you have at your disposal. Keep it simple and coincise.

Avec de grands ensembles de données, il est souvent préférable de présenter les résultats en utilisant des exemples marquant, des éléments qui rendront tangibles les conclusions. Cette approche pédagogique permet de réduire la complexité du sujet quand il s'agit de présenter les conclusions a une personne n'ayant pas participé à l'analyse.

Depois de concluir um projeto, sempre faça um cronograma de acompanhamento e ajustes: - Avalie as métricas de acesso do seu Dashboard - Marque 1 reunião a cada 15 dias para realizar algum ajuste e coletar feedbacks Não se esqueça que quem determina o sucesso do seu projeto é o seu cliente e não você!

Start by building relationships with the stakeholders. Then, do the following: - Understand the objectives. If there are more than one, rank them and start from the most impactful. - Establish clear timeline of delivery. But stay open for adjustments. - Break your process down to small steps. - Collect - clean - validate your data. Document the structure and transformations as you go along. - Select the simplest tools to visualise. - Do regular backups. - Learn from others.

I have 21 years in data science and this is what I advice my students. Navigating large datasets as a beginner involves starting with smaller datasets to grasp foundational concepts. Joining communities like Kaggle provides a supportive environment for learning and collaboration. Engage in forums, participate in competitions, and leverage shared resources. Incrementally tackle larger datasets as skills develop. This progressive approach, combining hands-on experience with community support, cultivates a solid foundation for working effectively with large datasets in the dynamic field of data science. #DataScienceBeginner #Tips #LargeDatasets #DataSkills #KaggleCommunity #HandsOnLearning #DataScienceJourney #DataAnalysisTips

We made a rush and symple analysis of "Customers purchase intentions into spesific engineering products, involving 4P/4A/4C (marketing mix factors) thru intermediate variable Customer's Brand Loyalty". The result was Brand Loyalty has a good impact to the success of new product launched. And it was successfuly to captured a high market share, closed to expected revenue, and distribution, spread. Tools ; SmartPLS, SPSS, and others.