What data analysis and visualization techniques do you want to master?

Tableau, Power BI are the technology that are trending for 📉📈 for visualisation and most for any data analyst to learn which wil be helpful in shaping number and visualisation

Mastering data analysis and visualization techniques is crucial in today's data-driven world. While the "best" techniques can depend on your specific field and the nature of your data, some universally valuable skills. These techniques are not only valuable in terms of technical ability but also in terms of the insights and business intelligence they can provide. It's also important to continuously update these skills as new tools and methodologies emerge in the field of data science and analytics.

I aspire to master various data analysis and visualization techniques within Power BI, including advanced data modeling, DAX calculations, interactive dashboards, and storytelling with data. My goal is to harness the full power of Power BI to transform complex data into actionable insights that drive informed decisions.

Your major time will be used for data cleaning so ask the right questions, ask your colleagues and manager, "Is there any standard practices we are using for data cleaning?" There is also a straightforward methodology you can use, not necessarily in the same way but it will be helpful. 1. Make sure your data is separated in appropriate columns (you can use split, merge and a few other functions) 2. Rename the column names and make sure to follow standardized name such as first letter capital of each word and remove space between words 3. Check for the missing values 4. Check for the anomalies such wrong data or conflicting data 5. Check the statistical summary and boxplot for each column to find true outliers 6. Fill the missing values

There are a lot of useful tools such as Tableau or the Excel 3D tool, or many GIS programs from Maptitude to ESRI, but graphing skills are also useful including PIVOT tables and PIVOT graphs in EXCEL. My advice is make fonts big, legends clear, sources noted, and use 2 Y axis whenever using two data series over time.

It's always important to explore your data after it's clean and ready for analysis. Understanding your data helps you know exactly what to do next with regards to inferential statistics. One of the important things to do is to know the shape of the data, it's distribution, if there are any outliers, it's mean and variance. This will enable you transform the data to meet the assumptions of the test before performing it.

What I find more valuable about data is to master the art of finding insights from data, because that is where everything starts. Using day to find oportunidades is the most valuable skills we can master because if we don’t have insights, and opportunities to improve all the process stop there and there is no value from the previous process( data cleaning, dat processing) and further process data visualization and modeling…. At the end what really matters is the value we can provide and that start by finding opportunities and insights

Descriptive statistics summarize and describe data using measures like mean, median, and standard deviation. Inferential statistics make predictions or inferences about populations based on sample data. Linear regression models relationships between variables with a linear equation. Logistic regression predicts binary outcomes using logistic function. Decision trees use a tree-like graph to make decisions based on data. Clustering methods group data points based on similarity, e.g., K-Means. Principal Component Analysis (PCA) reduces data dimensions while preserving variance. Hypothesis testing assesses the significance of observed differences. Common tools include Python, R, and libraries like matplotlib and scikit-learn.

There is a strong relationship between identifying patterns from data and making decisions. This is where data exploration comes. It opens you up to more structured data that you can see with your physical eyes and understand. With unstructured data, you will look but you cannot see. With explored data and statistics, you will look, see, process and understand.

Mastering DAX is essential for me because it allows me to create dynamic calculated measures, columns, and tables. This proficiency enhances data exploration, provides root-cause insights, facilitates drill-through capabilities, enables hierarchies, and offers many other useful manipulations.

For me, one of the data visualization skills I want to master is able to arrange the chart/graph in a way that tells a story, choose colors and background that resonates with companies colors, design dashboards that are simple, easy to understand by stakeholders and add buttons that will make it very interactive. These I want to really master because the way you communicate your insight and recommendations to stakeholders is very important. Is either they listen to you or not.

The thing I think we will all always be chasing is learning to tell a very simple, clean, straightforward, and digestable story with data. Especially very complex data. So many people think about mastering the most difficult and technical techniques, but when it comes to visualization, most of your stakeholders wont benefit from anything more complex than the basics. But they will benefit from the basics done well.

- Always look for your target audience or stakeholders. Don't try to overcomplicate things, make it simple. - Infographics can be helpful when your clients belong to the same industry and understand them correctly, when you have a large group of stakeholders don't try to be fancy. - Always highlight your plots (where you want your audience to be focused while presenting) - Start with the most important part of the business problem and dive deep on individual visualization Always remember, power point, powerbi, tableau or any other popular software are just a tools but you are the weapon who will accomplish the job of finding right insights and communicate with stakeholders.

Always think about the Fastest way to communicate actionable insights to relevant stakeholders! ⌛️ Here are some suggestions to think about, before you get started (6WH) 💬 - WHY invest in building anything? (impactful?) - WHO are the key users? (HigherMgmt/Operational) - HOW will they use it? (strategic/ tactical) - WHAT will be their next course of action, after seeing the viz? - WHICH form of output works best for them? (emailalerts/excel/pdf/interactive dashboard) - WHEN will a change in underlying data be more meaningful to them? (adhoc/realtime/hourly/monthly?) - WHERE does the use case flow begin and end? (know the process) Be Impactful - Your users should not spend more than 10-15 secs to realise the story 🏆 Hope this helps! 😇

Data visualization and story telling is very critical and important. To tell a good story, you first need to know your audience, are they executive, senior managers, nontechnical or clients. Know the purpose of the project. Continue by using clear and concise languages, reduce jargons or eliminate them if possible. Structure your presentation especially if it’s a ppt. Use the right charts to convey the right story. Lastly, prepare to answer some questions.

Fundamentals of Machine Learning Algorithms: Understand the core machine learning algorithms, including regression, clustering, classification, and reinforcement learning, to effectively model and analyze various types of data. Feature Engineering and Selection: Learn how to preprocess and engineer features, as well as select the most relevant ones for building accurate machine learning models, improving predictive performance. Model Evaluation and Hyperparameter Tuning: Master techniques to evaluate model performance using metrics like accuracy, precision, recall, and F1 score. Also, grasp the process of fine-tuning model parameters to optimize for the best results.

Machine learning propels data analysis into the predictive domain. Instead of just understanding past trends, businesses can now forecast future outcomes, optimize processes, and even automate decision-making. Python, with its rich ecosystem of libraries like scikit-learn, TensorFlow, and Keras, has become a go-to language for machine learning. R, with its suite of statistical tools and packages, offers an alternative for those steeped in statistical analysis. But, platforms like Azure Machine Learning, AWS SageMaker, and Google AI Platform are making advanced analytics accessible even to those without a deep programming background. These platforms provide user-friendly interfaces and pre-built models, reducing the barrier to entry.

Deciding on what ML algorithm to choose for a given problem comes from understanding the specific functionality of each models. Should be open to try and see which one improves the predictive performance. Any model is only good as the data is so the data modelling is very key and basic requirement for any analytics team to effectively model and analyze data to provide viable solutions.

Think of SQL as the go-to tool for organizing and keeping track of data. It’s like a super-smart librarian who knows exactly where everything should go and how to find it quickly. SQL helps set up the data library, making sure all the information is sorted and connected in a way that makes sense. This way, when you need to find specific data or want to learn something new from it, SQL has already laid the groundwork, making it easier to get those insights and build smart data-driven tools. It’s all about keeping data neat, tidy, and ready for action!

Always document your analysis steps and if a data dictionary and entity-relationship diagram don’t exist, create them. This will save you and others a lot of time on the long run.

Data Privacy: Safeguarding sensitive information, ensuring compliance with regulations like GDPR. Transparency: Transparent data practices, ensuring stakeholders understand data sources and methodologies. Bias Mitigation: Actively addressing bias in data analysis, using techniques to promote fairness. Accountability: Importance of clear accountability in data handling, minimizing risks and ensuring responsible data use. Continuous Monitoring: ongoing monitoring and audits to maintain data integrity and ethical standards. Education: personally i emphasize educating teams on data ethics, fostering a culture of responsible data use. Ethical AI: exploring ethical AI practices to ensure algorithms make unbiased decisions.

Lesson one - governance must not be an afterthought. With less monolithic architectures having evolved in the last decades, one of the microservices drawbacks is growing complexity of services comms. This applies to the security of data stack as well. Combine this with the fact that in era of AI the sourcing of quality data is becoming more demanded, and we arrive in the very "wild west" situation for the data governance.

Data ethics demand privacy, fairness, and accountability. Obtain consent, ensure unbiased algorithms, and prioritize security. Accountability is crucial for responsible data handling. Upholding these principles builds trust and safeguards societal well-being. Regular evaluation is vital in adapting to evolving data challenges.

There are several key players who have crucial roles in a data governance plan. These include data executives, who provide direction and final decision making in data-related issues and data custodians, who manage the security and storage of the organization’s data. Identifying who should hold these key roles is an important step as you build your plan and your governance team.

There is no one size fits all these days! So you should be open and flexible according to your organisations needs. Anyway they are all very similar and mastering one of them will help to escalate the learning with others quickly according to the need

Mastering one data visualization tool can indeed provide a solid foundation for working with others. While each tool may have its own unique features and interface, the fundamental concepts of data visualization remain consistent across different platforms. Understanding principles such as data preparation, selecting appropriate chart types, designing effective layouts, and storytelling through data are essential skills that can be applied universally. Tableau, Power BI, Looker, and other similar tools offer powerful capabilities for creating insightful visualizations and reports. They often provide user-friendly interfaces, drag-and-drop functionality, and a wide range of visualization options to suit different data analysis needs.

So many visualization tools out there ! Adapt to the market, be open-minded and willing to learn ! Once you understand how one works, the others are much easier to understand. Looker, Tableau, Power BI are the main and most important tools and are easy to use and practice with.

As a business analyst, it's essential to become proficient in data analysis and visualization techniques. There are many tools and methods to explore based on your preferences, we MUST keep learning as much as we can to evolve.

Experience comes by practice and lesson learned. Every client or customer can hand over a complex or simple data to you. You need to have hands on and analytical skill to examine it and perform the data analysis. For Visualization techniques, there are several sites or books which can guide you to choose be best chart for different data types. But ultimately the developer should be skilled enough to think out of the box and make sure the dashboard aligns with the story. FYI, I had used a bar chart to present trend analysis for different metrics. The client loved it as it was identifying the patterns and giving insights to them.

I completely agree with the previous comments, Successful analytics starts by truly understanding what the business needs. That means talking to the people involved and using tools like interviews or surveys. Clear documentation keeps everyone on the same page. Remember, "It's not just about numbers; it's about solving the right problems and making a real impact!" Like @reshma was saying,

I have learned data science the quickest through existing problems that needed data insights in order to be solved. Almost anything you start on in the field of data science has been solved before. There are data communities that publish the steps they took in detail to solve the problem. This is how Netflix got great at recommending movies. They put out a prize for anyone online who could create an appropriate algorithm using data science. The challenge was undertaken and the steps implemented by the winners of the prize are available for public consumption. So make use of platforms like kaggle.com

At the core of any analytics project is understanding what the business truly needs. This involves actively engaging with stakeholders to gather, understand, and document their requirements. The elicitation process can involve techniques such as interviews, workshops, surveys, or even shadowing end-users. Once gathered, these requirements should be documented clearly, often using tools like user stories, use-case diagrams, or process flowcharts. It's not just about crunching numbers; it's about ensuring every number crunched has purpose and impact. Be the analyst who solves the RIGHT problem.

Adhering to the KISS principle, I often keep in mind the simplicity of a pedestrian traffic light. Its brilliance lies in its universal understanding – a green background with a walking person, and a red background with a hand symbol, transcending language and age barriers. While striving for simplicity in every visualization is vital, there is room to go in for some complex visuals based on the audience's sophistication.

When it comes to data visualization, I would like to keep developing my knowledge of building an interactive dashboard with clarity. Having more in-depth knowledge of Tableau and Power BI is crucial. Learning more about storytelling is essential as well to present your outputs. Understanding the concept of design is an advantage as well.