Here's how you can master data analysis and visualization as a software engineer.

As a software engineer, mastering data analysis and visualization opens doors to endless possibilities. Dive into learning platforms, leverage online resources, and enroll in relevant courses. Practice with real-world datasets, experiment with different tools and techniques, and seek mentorship. By honing your skills in data analysis and visualization, you enhance your ability to extract insights, make informed decisions, and create impactful software solutions.

Transitioning from a Data Analyst to a Software Engineer is possible and can be a rewarding career move. However, it requires a commitment to learning new skills and may come with challenges. 1. Establish the goal of your visualization 2. Clean up and understand your dataset 3. Know your audience 4. Choose a type of chart 5. Don’t try to pack too much into one chart 6. Map the data to visual variables 7. Text is “totally underrated.” Use It 8. Include the source of the data and link to the original dataset, if possible 9. Know the rules — so you know when to break them

1.Start by understanding different data types, data visualization, exploratory data analysis (EDA), and data cleaning processes. 2.Select tools like Microsoft Excel, Tableau, Python (with Pandas and Matplotlib), R (with ggplot2 and dplyr), SQL, and Jupyter Notebook. 3.Ensure data accuracy and reliability by handling missing data, removing duplicates, standardizing formats, and dealing with outliers. 4.Use algorithms and statistical techniques to uncover patterns and structures within datasets. 5.Create visual representations using tools like Tableau, Power BI, Python, and Excel. 6.Pay attention to design, labels, scales, and provide context to effectively communicate findings to stakeholders.

Stats aren't just for textbooks! Think about how a simple "average" can reveal customer spending habits. A spike in "variance" could warn of quality control issues. Don't memorize formulas, imagine them in action. This makes real-world data analysis less intimidating, more of a treasure hunt

Begin by understanding fundamental concepts such as data types, data structures, and statistical analysis methods. Dive into learning programming languages like Python or R, which are widely used for data analysis tasks.

Across Wales' tidal lagoons, a problem swirled. Local communities, passionate about clean energy, voiced concerns in public meetings. But deciphering their sentiment from lengthy transcripts was a chore. Enter NLP. Researchers trained a system to analyze the language. It identified not just opposition, but specific worries about visual impact or potential harm to marine life. With this knowledge, developers could tailor communication, addressing concerns directly and fostering a more collaborative approach. This NLP win-win helped smooth the path for Wales' burgeoning tidal energy sector.

While the statistics part is important, I am certain that a software engineer would do better if data analysis is learned from the perspective of machine learning and AI at large.

Understand the fundamental concepts of data analysis, including statistical measures like mean, median, mode, and standard deviation. Get familiar with different data visualization techniques like bar charts, line charts, scatter plots, and pie charts. You can practice creating these charts using spreadsheet software like Microsoft Excel or Google Sheets.

I used R for Data cleaning, Mining and Analyzing through Statical Representation like Regression, correlation, Time series, hypothesis testing Etc. For Visualizations, I used Tableau its easy to learn. in visualization also we can represent through statistically by making Pareto charts, Moving Average charts and Forecasting.

Picking the right tools is important for analyzing data. Python and R are great choices with useful libraries. Python has Pandas and SciPy, which are easy to use. For making visuals, tools like Matplotlib and ggplot2 help turn data into clear pictures. It's about finding tools that fit what you need and can work with well. The right tools make analyzing data easier and more successful.

Don't get lost in the tool jungle! Start with the giants: Python (especially with those Pandas superpowers) is a great all-rounder. Need serious stats? R is your beast. Don't be afraid to geek out on tutorials, the basics are surprisingly intuitive. For stunning visuals, Matplotlib (with Python) or ggplot2 (with R) will turn those numbers into a work of art.

Select appropriate tools and libraries based on your project requirements and goals. Popular tools include Pandas, Matplotlib, and Seaborn for data manipulation and visualization in Python.

In the case described, the correct tool used for analyzing the public meeting transcripts is Natural Language Processing (NLP).

I use Python mostly because I can leverage data manipulation with Pandas or Polars. Plus, it integrates smoothly with visualization tools like Plotly Dash and Vega-Altair for clear communication of insights. On top of that, Python allows for rapid prototyping and deployment, working seamlessly with Tableau and other existing data platforms.

For data analysis, there are numerous tools to choose from depending on the purpose. It is noteworthy to point out that it is wrong, although not entirely wrong, to recommend the use of ggplot2 and matlotlib for visualizations because, from experience, these tools are preliminary ones and can only be used in development environments (i.e Jupyter notebook, Visual Studio etc.) of which purpose is not meant for presentation of data for non technical professionals. They cannot be used for building dynamic dashboard and presentations because they lack interactivity and cannot display dynamic data at the click of a button. Tools like Power BI, Tableau, Microsoft Excel, Synapse Analytics among others are developed to handle such sophistication.

To add to the above, I would choose high level plotting libraries for a start. In Python, I can personally recommend using seaborn or plotly express for quick charts. Once you are familiar with all the moving parts (ie. API, loading in data, saving plots) - then move on to low level libraries for more customizability. Again, I still use a mix of tools to cater to what needs to be done. Quick viz or detailed, scientific viz.

Start with a general-purpose programming language like Python or R. Both languages have extensive libraries for data analysis and visualization, such as Pandas, NumPy, and Matplotlib for Python, and ggplot2 for R. Online tutorials and courses can help you learn the basics of these languages and libraries.

Not all data holds equal value; some may be duplicated or outdated. Therefore, implementing an effective data cleansing strategy is crucial to ensuring that only relevant and valuable data is collected for analysis. It's essential to keep the ultimate goal of visualization in mind, focusing on what insights you aim to derive from the data to achieve successful conclusions.

Pandas offers several built-in functions for data cleaning. However, when dealing with textual data and automation tasks, a good knowledge of regular expressions can minimize the extensive use of cleaning functions and make the script much more readable.

Think of data cleaning like washing your vegetables – you wouldn't eat them covered in dirt! Messy data leads to disastrous insights. Missing numbers, typos...they're like rotten spots ruining your whole analysis. Get ruthless about cleaning, it's the foundation for everything that comes after.

Prioritize data cleaning to ensure accuracy and reliability in your analysis. Address missing values, outliers, and inconsistencies using techniques like imputation and data normalization.

Cleaning data is like tidying up a messy room. Fixing mistakes and filling in missing info makes your analysis accurate. Tools like Pandas' dropna() or fillna() help do this quickly. Good data means better results. Cleaning upfront saves time and makes sure your analysis is reliable.

While data cleaning is an important part of data analysis, it is crucial to note that not all dirty data looks dirty. The first part of data cleaning is filtering out or correcting visibly messy data Other steps of data cleaning entails; 1. Classification: filtering out or taking care of outliers which are not necessary a mess but just looking like they do not conform to the objective of the data project i.e abnormally high value, abnormally high frequency of infinitesimal values (although infinitesimal values are sometimes what are being looked for) etc. 2. Normalization: converting abnormally high or low value to the usable format such that their effects can be seen and observed.

Be prepared to spend a significant amount of time cleaning data. This may involve identifying and correcting missing values, outliers, and inconsistencies in the data. Spreadsheets can be helpful for cleaning small datasets, but for larger datasets, you’ll need to use programming languages like Python or R.

Once cleaning is done with the standard python libraries like pandas, ast, re. I would strongly recommend validation using pydantic. It ensures no dirty data and immediately makes the whole pipeline consistent

Analyzing patterns is both an art and a science, requiring a strategic approach to uncover meaningful insights. These insights can lead to the development of new products or services targeted at specific demographics or inform decisions regarding ad targeting. Patterns offer unbiased data, allowing for informed decisions regardless of factors like age or gender. Developing a comprehensive strategy for pattern analysis, including data collection, preprocessing, and interpretation, is essential for extracting valuable insights and driving successful outcomes.

Once your data is tidy, it's time to spot patterns, like finding shapes in clouds. Look for trends or groups of similar data. Using tools like regression or clustering helps. Understanding these patterns helps make predictions or decisions. For instance, in user engagement, seeing when users are active or what they like helps improve products or services.

When doing exploratory data analysis, plotting correlations with Seaborn and Matplotlib is a way to go. I usually like to make a heatmap to compare features and drop those that have extreme correlations. However, this method is not always reliable, and the features should be encoded beforehand.

Start by exploring the data to get a general understanding of its distribution and key characteristics. You can use basic statistical methods and data visualization techniques to identify trends and patterns. Use more advanced statistical techniques, such as hypothesis testing and regression analysis, to draw meaningful conclusions from your data. Be able to interpret the results of your analysis and communicate them effectively to stakeholders, even if they don’t have a technical background.

Some ways to do this - observe the central tendencies with distributions - causality with heatmaps - box blots to understand the spread - analyzing patterns depends a lot on the end problem statement. One doesn't need to do everything

Storytelling is one of the most underrated but hardest skill to master in data visualization. I recommend looking into the works of Cole Nussbaum Knaflic, she explains what storytelling is. I used to think it is about bringing the audience on a journey and it was too abstract for me. It goes beyond the initial 1-2 seconds of when you audience interacts with the visualization. It is also not just actionable items from the data but can encompass a whole suite of narratives that include trends, comparison and focus.

Create basic data visualizations using the tools you learned. Focus on clarity and ensure your visualizations accurately represent the data. Experiment with different data visualization techniques to find the most effective way to communicate your insights. Consider the audience for your visualizations and tailor them accordingly. Learn about design principles for data visualization, such as color theory and chart choice. There are many online resources available to learn about data visualization best practices.

Data work is like polishing a painting. After analyzing and visualizing, ask others for advice to make it better. Maybe a different chart or more info could help. Making changes based on feedback makes your work clearer. It's about telling a good story with your data. Making it better each time makes your work more useful and understandable.

My favourite part of data visualization work. The work is never truly done! There is always a tweak, adjustment, spacing, alignment, color profiling, etc that can be done. Exploring various ways of showing the data is the best approach, explore different types of charts and positioning. Don't forget, most of the time - less is better. Less visual clutter helps the audience zoom into the key things. So, remove unrequired axis lines, grids, ticks as necessary. Basically test and find inspiration from your favourite visuals (ie. the Economist, BBC, etc).

Stay close to stakeholders who are interested in your work. Iteration is always a step but what counts is the frequency - so if understood the problem well enough helps one reduce this count.

Building Data Pipeline. Often times, data analysis projects need be maintained to reflect current data due to changing data. If data pipeline is not factored into the development from the beginning, it will be cumbersome to update dashboards or build visualizations. Data Pipeline entails automation of all the processes taken in a data analysis project with the objective of easing data flow and updates so that the processes defined above will automatically be run or triggered without human interference. Data gathering e.g pulling data from database etc., data cleaning, wrangling, analysis, visualization etc are automated such that the processes are built only once while eradicating the cumbersomeness of repetitive tasks.

Of late, I have been considering deployment of data viz work. An accessible dashboard that can be viewed across various form factors (ie. mobile responsive) can potentially allow your work to reach more people.

Be sure to use the high level libraries so as not to re-invent the wheel. Use proper variable names so as to avoid unnecessary comments. Pydantic like already recommended or maybe typer for cli commands