Continuing with the delve into Climate Change, looked into understanding how people feel, positive/neutral/negative, about the subject on Twitter. Using TextBlob, with Tweepy, Pandas, and Plotly Dash…I built initial revision of a data visualization dashboard, Climate Mood, that analyzes tweets over a sample of the last 2,000 collected hourly. The idea is to provide a simple, sentiment-focused look across multiple analyses. I’ve particularly enjoyed building and re-applying some of the methodologies and statistical analyses I’ve learned recently.
As mentioned, the dashboard pulls from Twitter over an hourly basis and deposits into a PostgreSQL database. The top chart provides a linear regression trend line (unevenly for now) across tweets collected that can be toggled. Also, tweets can be seen on the top-right tweet-box on hover. Other simple sentiment analyses include average verbosity of tweet length, popularity of tweets, and average sentiment-based amplitude of tweets. Feel free to check it out!
Looking forward to applying more advanced statistical analyses including a live Monte Carlo Simulation calculation across these auto-updated samples…among others. If you have any feedback or suggestions, please submit an issue on the GitHub repo or contact me.
Been thinking about climate a bunch lately and started looking into trying out the latest data tools and trends out there on the Internets. I’ve particularly enjoyed R and Pandas/Python, especially via Plotly Dash, and re-applying some of the methodologies and statistical analyses I’ve learned while on the job. The latest curiosity is in global temperature change since 1910 and various other societal factors. Lo and behold, the first iteration and not-so-polished, Climate Trends.
The dashboard has a drop-down menu of various analyses around global temperature change and linear fits across birth rates, fertility rates, death rates, life expectancy at birth, and rural population growth rate as well as a nominal years passed chart. Feel free to dabble!
If you have any feedback or suggestions, please submit an issue on the GitHub repo, respond on Dribbble, or contact me.
I was talking to family and friends about various investments made (and some missed out on) over the years. Have gotten decent at picking winners (using a Value-Investment framework inspired by this book that mixes intuition, selection, and analysis). Here’s the base-level rubric for what I look into before I investment into any company. Some analyses go further. Generally, 9 months to 1 year is taken before any action…essentially driving the associations of the entity into muscle memory and relying on intuition, alongside any other company/product/market, to make the call.
Read on 3-6 months of articles on stock and target company
Read on 3-6 months of analyst coverage on stock
Find Market Cap potential of target company
Find Market Leader
Identify Competitor Set
Identify Lead Competitor(s)
Find Growth Darkhorse(s)
Identify any market regulation
Determine CEO capabilities and habits
Determine CEO’s style of communications (past 2 years)
Identify Product Roadmap
Read last 5 product announcements
Linkedin Lookup on Corporate/Employee Composition (specifically product/marketing/engineering)
Read past 10 10-Q, 2 10-K
Identify, if possible, Sales & Marketing + R&D spend
Identify, if possible, Growth of S & M + R & D spend
Would love to hear your thoughts! Feel free to DM via LinkedIn.
I’ve written about Tesla’s growing community of advocates back in 2013…well, it’s now becoming real. Though is it happening because of an explicit push or something happening naturally due to increased sales?
My hypothesis is that I think Elon is being Elon and going on a major PR push in 2018.
Been tracking since February and it keeps on confirming along the way. With the Starman launch (February), Grimes (May), and “Pedo” (sigh) (July), I think there’s something going on in terms of media coverage on various issues in the US/Worldwide, and Elon Musk is either trying to take advantage of it or doing his own thing. Feel free to add the search term “pedo” to this Google Trends chart…I won’t.
Maybe this is Elon “taking control of the narrative”?
I haven’t done a multivariate analysis with Tesla’s share price, but it might be worthwhile now. Once the shorts/media started their campaign last year, it became clear that Tesla’s share price was going to be affected in a hedge-fund kind of way. I think this company should be valued, at least, at 2-2.5x higher than the current by end of 2018, if the market is accurate and corrects itself.
SJ Charts is available now fully open-sourced here! Plot.ly has been fun and the documentation, as well as the community forums, have been great to work with. Wish there was more in-grained analytics support via Google Analytics and Mixpanel, but that seems to be a work-in-progress. Here’s what it looks like now:
The charts now have time-based charts for Housing Prices, Unemployment rate, and Jobs by Sector for the City of San Jose. The idea behind this app is to show various analyses via a top-level tabbed navigation for the City of San Jose residents. Basically, to build out useful analyses for the open data available from the City of San Jose.
So, I took a few weeks to get on-ramped with Pandas 0.23.0 and Python and am still learning the ropes. Though, I wanted to share some output. Namely, using the San Jose Open Data Portal, there were some good insights to be had from looking at economic data pushed out by the city for 2006-2016.
What did I do?
Looked at unemployment rates across various housing prices (condos/townhomes & single-family homes) using ordinary-least-squares regression.
Looked at, over time, labor force and housing price changes.
SJ Economic Changes, 2006-2016
Condo/Townhome prices vs. SJ Metro Unemployment
Single Family Homes prices vs. SJ Unemployment
What did I find? Namely, housing prices are increasing rapidly compared to labor change (i.e. job growth) in San Jose over the course of 2006-2016. Also, there’s a strong correlative relationship between something that we all take for granted and as obvious: increased housing prices in San Jose runs with lowered unemployment rates.
If you’d like to look at the data analysis done, here’s the GitHub repo. If you have any questions/comments/suggestions on more analysis, feel free to contact me via Twitter, GitHub, or LinkedIn.