What tools and skills do you need for effective data analysis and visualization?

SQL continues to be one of the most powerful tools for data analysis. The logic driving SQL queries is foundational to many other tools and is valuable not only for large datasets, but also for creating a systematic approach to an analysis. While the SQL ecosystem is vast, a strong foundation in a few basic verbs will get you a long way for most analyses.

R and Python have some of the best and largest collection of Data visualization packages. Excel also has data visualization features for general users. There are other software too such as SPSS, JASP, Medcalc, SAS and others. It is my recommendation to know a combination of programming languages such as R or Python and few other software for effective data visualization spectrum.

There are many software tools that can help you to perform data analysis, from simple spreadsheet applications to advanced statistical packages. Depending on your research question, data type, and analysis method, you may need to use different tools to manipulate, clean, explore, and model your data. Some of the most popular data analysis tools are R, Python, SPSS, SAS, Excel, and Stata. Each tool has its own advantages and disadvantages, so you should choose the one that suits your needs and preferences best.

Understanding of algorithms and exceptional programming skills would be an asset Also background in mathematics/statistics Unfortunately a lot of analysts know how to run packages on different statistical languages but do not know the science behind them

Depending on the size of the dataset, (to deal with small to large datasets) I highly recommend using MS Excel, R, and Python.

Here are some examples of how I have used data visualization tools in my work: I created a Python Dash dashboard to visualize the distribution of air pollutants in a major city. The dashboard allowed users to explore the data by location, time period, and pollutant type. I used Plotly to create an interactive map that showed the changes in land use over time in a rural area. The map allowed users to zoom in and out of different regions and to compare different time periods. I used QGIS to create a 3D map of a forest that showed the distribution of different tree species. The map allowed users to rotate and zoom in on the forest to get a better understanding of the spatial distribution of the trees.

I actually love Looker Studio. It seamlessly integrates with all of your Google data sources and makes for an easy beginner data visualization tool. Like Tableau, it’s customizable to match your branding if necessary. It’s also very user friendly for those stakeholders who are not very savvy at interpreting data visualizations just yet.

There are many different ways to build data analysis skillsets. Here are a few tips: Take online courses or bootcamps: There are many online courses and bootcamps available that can teach you the basics of data analysis. Read books and articles: There are many great books and articles available on data analysis. Contribute to open source projects: Open source projects are a great way to gain experience with data analysis tools and techniques. Participate in hackathons: Hackathons are a fun and challenging way to learn new skills and build projects with other data enthusiasts. Network with other data analysts: Networking with other data analysts is a great way to learn from their experiences and find job opportunities.

Some of the most important data analysis skills are skills in understanding the data and statistical methods. One must have skills in specific domains to be able to apply specific methods and interpret results.

SAS (SAS/SQL) and Stata can do all. SAS can handle data of unlimited size but needs an institutional license. Stata is more affordable for individuals but the data size has a max. Do write programs to get the efficiency.

SAS and Stata can do it well, too. Master the codes to make amazing graphs and tables. They have abundant online tutorials of data visualization.

My favorite (free) data visualization resource to recommend to friends and colleagues is the “Fundamentals of Data Visualization” by Claus O. Wilke. While many data viz resources break down visualizations based on the type of data you have (e.g., one vs. two continuous variables), this book splits the chapters by the type of story you are trying to tell (e.g., visualizing multiple distributions, visualizing uncertainty). This segmentation emphasizes the storytelling aspect of data - the ultimate goal of data visualization. This book is universal to all languages as it emphasizes more general data viz rules, but it also includes all the R/ggplot2 code used to generate the examples. I would highly recommend this book.

Visuals Impact Messaging! Here are a few chart-types and the best use: Line- trends or evolution over time Bar- compare quantities; changes over time (few categories); sub-categories as stacked Pie- parts/percent of whole (few categories) Area- quantities through colors; trends over time; cumulative or stacked data Radar/Spider- multivariate data; multiple axes, performance metrics Waterfall- sequential cumulative , +ve/-ve values; financial data Gantt- tasks/events vs. time; project management Scatter Plot- relationships between pair of features; Buble chart for a 3rd dimension Histogram- spread of single continuous feature Heat Map- correlation matrices; intensity as colors Box/Whisker- range, quartiles; outliers & anomalies

In addition to technical tools and skills, fostering a data-driven culture within an organization is vital. Collaboration, clear objectives, and ongoing learning are essential. Data analysts should continuously seek to improve their storytelling abilities, making data insights accessible to non-technical stakeholders. Additionally, staying updated on data privacy regulations and ethical practices is crucial in today's data-driven world.

Hey Doc... awesome you got that ... how would you go about HUMANIZING data so the public can better absorb and play with it? I ask because for my public facilitation work I need to make data more accessible and help people integrate it into their real world experience.