Saman Khan’s Post

DATACAMP - Understanding Data Visualization- Understanding Data Topics track 2h training Continuous vs categorical variables Histograms : binwidth, modality, skewness, kurtosis Box plots : the box and the whiskers Scatter plots : correlation, trend lines, logarithmic scales, line plots, Bar plots : sorting by count, stacking bars, Dot plots : log scales, sorting rows Higher dimensions : color, size, transparency, shape, lots of panels, thickness, line type (solid, dashes, dots) Polar coordinates : pie plots, rose plots Dual axes Chartjunk Multiple plots #datacamp

💯 Really enjoyed this one: quick, straight-no-chaser, very interesting course to kickstart the Why, When, and How regarding the effective use of various plot types such as histograms, scatter plots, box plots, and line plots, depending on the type of variables we're analyzing (continuous or categorical). It also goes further into a bit more complex visualizations like pair plots and correlation heatmaps, which are super useful for analyzing multiple variables simultaneously. It finishes off with a nice "call to arms" on minimizing chartjunk and avoiding some (sometimes rather scandalous) bad practices. 🏆 Nice one, DataCamp. 😎📈 #DataVisualization #Analytics #DataScience #ContinuousLearning #ProfessionalDevelopment #ContinuousImprovement #DataInsights #VisualizationTechniques #SkillBuilding #DataCamp

Day 24 of #100daysoflearningwithI4G I finished this course and moved on to Data Communication concepts. This course was a refresher on statistics but added other visualizations such as bar plots, rose plots and pie plots. I learnt about interpreting each visualization and its best use case. I also learnt about visualizing data using color with the chroma-luminance hue and transparency theory and also how to reduce chartjunk which are outliers that make the visualization a bit hard to interpret to the reader. The reader should be able to extract insights from the visualization at a glance without having to strain so much. Overall it was a good course and I learnt a lot. #Datacamp

Just earned my Data Visualization Certification from DataCamp! Excited to share that I’ve mastered the art of turning complex data into compelling stories. This course taught me to choose the right visualizations for my datasets and interpret common plots like histograms, scatter plots, line plots, and bar plots1. I’ve gained hands-on experience with over 20 datasets, exploring topics from global life expectancies to the greatest hip-hop songs2. Learned best practices for using colors and shapes, and how to sidestep common visualization pitfalls3. Can’t wait to apply these skills to make data-driven decisions and create impactful visuals that speak volumes! #DataVisualization #DataStorytelling #ChartsAndGraphs #LifelongLearning #DataAnalysis

🚀 Completed the 'Understanding Data Visualization' course! 📊 Learning how to transform complex data into impactful visual stories has been an eye-opener. From choosing the right visualization to mastering histograms, scatter plots, and more, this course provided hands-on experience with diverse datasets—from global life expectancies to the greatest hip-hop songs of all time. Excited to apply these insights to create more meaningful and engaging data visualizations! 🌍📈 #DataVisualization #LearningJourney #DataScience #ContinuousLearning #DataCamp

Understanding Data Visualization.........

A quick summary of what I learnt on Understanding Data visualizations: *Chapter 1:* - *Histograms:* Best for showing the distribution of a continuous variable. - *Box Plots:* Compactly show distributions of multiple continuous variables. *Chapter 2:* - *Scatter Plots:* Display the relationship between two continuous variables. - *Line Plots:* Ideal for showing trends over time. - *Bar Plots:* Show counts or proportions by categories. - *Dot Plots:* Similar to bar plots but support logarithmic scales and multiple metrics. *Chapter 3:* - *Adding a Third Dimension:* Use colors or multiple panels instead of 3D plots for better interpretability. - *Color Scales:* Three types - qualitative, sequential, and diverging. - *Analyzing Many Variables:*  - *Pair Plots:* Show relationships between pairs of variables.  - *Correlation Heat Maps:* Highlight related variables.  - *Parallel Coordinates Plots:* Reveal patterns across multiple variables. *Chapter 4:* - *Polar Coordinates:* Generally not recommended, except for cyclical data like time of day. - *Dual Axes:* Usually misleading, best avoided. - *Minimalism:* Remove elements that distract from the plot's interpretation.

Understanding data visualization is a beautiful course. The main thing I picked from this course is that less is more. Your data presentation should be simple, precise and easy to derive insight from. There is one more course to go to before taking my certification exam. Do stick with me as I journey to the end✨ . #I4GDataCampScholarship #DataCamp

🎓 Just completed the Data Analysis and Data Visualization course in R! 🚀 Gained invaluable skills in creating graphs, histograms, plots, and knowledge about analysation and visualization and more. Ready to apply these new insights to my work! #DataScience #RProgramming #DataVisualization #ContinuousLearning

Looking for the most enjoyable tool to make data visualization? Take a look at this network displaying what tools dataviz people are enjoying with: https://lnkd.in/dFahucbR Not very surprising though Svelte + D3.js are popular among journalists and RAWGraphs is closely connected with Illustrator. Nevertheless, it was interesting to dig into the dataset and get a general map of tool combinations people prefer over others. This work was made as a contribution to the State of the Data Visualization Industry 2023 Challenge. #dataviz #datavisualization #network #graph