universalprogramming

[universalprogramming]

Don’t knock the BASICS in Cybersecurity: • Buy a BASIC laptop - 4 core 8GB RAM • Get 2 BASIC certs - Network+ - Security+ • Learn some BASIC skills - Linux - Python - Traffic analysis • Make a BASIC portfolio • Apply everywhere BASIC done well gets you $80k+/y

[universalprogramming]

Codecademy and similar have really good beginner courses that would take you a full weekend, and then you'd know the basics of code structure, logic. Then what you do is you use that knowledge to tinker around with somebody else's code in online sandboxes, make small edits, get different results. Figure out how to make your own functions then, little machines that do calculations, and run those. Give it different inputs and get different outputs. Then find out how to make it wait for a prompt, and create a short chat bot or mock up of a menu system. Then expand this mockup by abstracting bits of your code to other functions. Keep them in other documents and learn how to import for use in your machine. Use this knowledge to make a little choose your own adventure game. Just a short thing. Then introduce combat, simple dice rolls against the cpu. Then expand that by making different monsters with stat blocks, and a random chance for one or the other to show. Just take a simple idea and expand on it. After a few weeks you will have a fun little 5 minute game, and you'll feel good, you'll feel comfortable with basic code. Most frontend developers i know are incapable of most of this btw. They lack the faustian spirit.

[universalprogramming]

grats its not too difficult, but still you gotta study. i suggest going to udemy and buying dion's practice exams at a discount. once you consistently get at least 80% on all the practice exams, you are ready. also use anki if you want to study on the go with the phone, but i suggest sticking with the udemy dion practice tests and downloading messers a+ cheat sheets somewhere. good luck. p.s. don't take shit from users but remain professional

[universalprogramming]

https://www.youtube.com/watch?v=3u7MQz1EyPY

https://www.youtube.com/watch?v=Tfk3ae0qz3A

https://www.youtube.com/watch?v=AGrl-H87pRU

https://www.youtube.com/watch?v=I6ZNt8fszLU

https://www.youtube.com/watch?v=O6nIE1fdTEI

[universalprogramming]

[universalprogramming]

https://www.coursera.org/learn/google-data-analytics-capstone/discussions/weeks/2/threads/f5ss3CSxEey6BA7JXK5-Mw

https://www.holistics.io/blog/startup-data-analyst-interview-case-studies/

https://dataelixir.com/

https://excel-practice-online.com/

https://github.com/Mishkanian/Google_Data_Analytics_Capstone

https://github.com/akorez/Google-Data-Analytics-CapStone-Project/blob/main/Capstone_project_report.pdf

https://divvy-tripdata.s3.amazonaws.com/index.html

https://www.kaggle.com/code/tendobosa/cyclistic-in-what-ways-can-bike-sharing-improve/notebook

https://www.kellyjadams.com/post/google-capstone-project

https://campus.datacamp.com/courses/introduction-to-sql/selecting-columns?ex=2

https://www.kaggle.com/code/diaodiallo/md-s-bellabeat-case-study-with-r/notebook

https://www.linkedin.com/pulse/google-data-analytics-course-capstone-project-case-study-akmyradov/

https://www.linkedin.com/pulse/google-data-analytics-course-capstone-project-wen-hao-tan/

https://rpubs.com/AugustoMas/capstone_project_1

[universalprogramming]

‘Sophisticated, Clear, and Polished’: Divvy and Data Visualization (Case Study)

This fictional case involving Chicago-area bike ride share program Divvy teaches students how to use publicly available data and tools to create data visualizations that reflect the three rules of the evaluation framework for the visual form: sophisticated use of contrast, clear meaning, and refined execution.

Summary

Lindsay Silk-Kremenak, Director of Marketing for the Chicago area’s Divvy bike-share program, understood the company’s future success depended on a focused approach to marketing that maximized the number of annual memberships. She was sure that data analysis could unlock the insights needed to design marketing strategies that would help her achieve that objective. But what Lindsay knew most certainly was that Divvy’s executive team would only support her team’s recommendations if members communicated their insights through expertly crafted data visualizations.

Key characters

Divvy: A Chicago-based bike-share program that features nearly 6,000 bicycles and more than 600 docking stations.

Lindsay Silk-Kremenak: Director of Marketing for Divvy, responsible for the development of campaigns and initiatives to promote the Divvy program using email, organic social, paid media, Out Of Home (OOH), and other media channels.

Divvy marketing analyst team: Team of data analysts that reports to Lindsay Silk-Kremenak and is responsible for the collection, analysis, and reporting of data that help guides Divvy marketing and other initiatives.

Divvy executive team: Notoriously detail-oriented executive team that provides approval for marketing campaigns, initiatives, and programs Silk-Kremenak recommends.

About Divvy

Divvy, a Chicago Department of Transportation program, is a bike-share system in Chicago and Evanston. Divvy is a convenient, fun, and affordable transportation option for commuting to work, getting around town, and exploring Chicago. Like other bike-share systems, Divvy consists of a fleet of specially designed, geotracked, and durable bikes that are locked into a network of docking stations throughout the region. The bikes can be unlocked from one station and returned to any other station in the system 24/7.

Consumers can buy access to Divvy bikes using these options: (1) Single-ride passes for $3 per 30-minute trip; (2) Full-day passes for $15 per day for unlimited three-hour rides in a 24-hour period; and (3) Annual memberships for $99 per year for unlimited 45-minute rides. Small charges (15 cents per minute) are incurred when single rides exceed the maximum time allowance to dissuade consumers from checking out bikes and not returning them on time.

How can Divvy drive more annual memberships?

Lindsay Silk-Kremenak had a clear ambition.

As Director of Marketing for the Divvy bike-share program, Silk-Kremenak and her small but effective marketing team had overseen a successful launch of the program in the Chicago area, with 750 bikes at 75 stations, in 2013. Since that launch, Silk-Kremenak and her team were the engine behind the program’s growth. By 2019, Divvy had expanded to feature 5,800 bicycles and 608 stations, covering almost all of the city of Chicago and two nearby suburbs.

She was also instrumental in brokering a deal for the management of Divvy’s bikes with the ride-share company, Lyft, in April 2019. The deal made perfect sense for Divvy, allowing the company to focus on its core competency of sales and marketing, while handing over fleet management to a company with deep expertise. Lyft manages all of the largest bike-share systems in the U.S. and many of the largest systems in the world, including Bay Wheels (California Bay area), Bluebikes (Boston, Massachusetts), Citi Bike (New York and Jersey City), Divvy (Chicago), CoGo Bike Share (Columbus, Ohio), Capital Bikeshare (Washington, D.C.), Nice Ride (Minneapolis, Minnesota), and BIKETOWN (Portland, Oregon).

The program was a tremendous success by nearly every measure. But Silk-Kremenak knew the program’s future success required a more focused marketing strategy. To date, Divvy marketing relied on building general awareness and a well-defined set of value propositions that appealed to broad consumer segments. This approach led to great market awareness for the bike-share program and diverse marketing tactics that promoted its many benefits.

One such benefit was the flexibility of its pricing plans. Consumers could use a bike for short trips by buying a single-ride pass or for an entire day through a full-day pass (internally, Divvy referred to these consumers as “casual riders”). Alternatively, consumers could become annual members to gain unlimited access to Divvy bikes for the entire year (customers Divvy called “members”). Silk-Kremenak’s finance analysts concluded that members were much more profitable than casual riders (both single ride and full day). Although the pricing flexibility helped Divvy attract more customers, it was clear to Silk-Kremenak that her primary objective was maximizing the number of annual members.

Silk-Kremenak knew the easiest path to annual memberships was to convince Chicago-area casual riders to become members. These customers were already aware of the Divvy program, which made messaging easier, as there was no need to cover the basics of the Divvy experience. Furthermore, casual riders had chosen Divvy for their mobility needs, demonstrating consideration for the program and making them easier to guide toward becoming members than consumers who might want to first try the bike-share program before committing to the annual plan.

Data privacy introduced one important complication. The city of Chicago’s recent regulatory changes and data privacy commitments prohibited Silk-Kremenak and her team from working with casual riders’ personally identifiable information. In other words, Divvy was precluded from connecting pass purchases to individuals. Linking credit card numbers to passes could help the Divvy marketing team segment casual riders by their home addresses (i.e., separating those who live in the Divvy service area from tourists), consumers who bought multiple or repeat passes, and other important views into the data. Silk-Kremenak would need to rely on general insights to design her marketing approach, along with the marketing intuition she had earned from years spent in her role.  

Silk-Kremenak turned to her team of data analysts for help. With her objective of converting casual riders to members, she worked with her team to design three questions which, once answered, could provide guidance for the marketing program she needed:

What ways do members and casual riders use Divvy bikes differently?

Why would Casual Riders want use Divvy more?

How can Divvy influence Casual Riders to become Members?

Silk-Kremenak was sure that answers to her questions could be found in the Divvy trip data. What she was less convinced of was that those insights would sway her executive team. Divvy’s executives were notoriously detail-oriented. That attention to detail contributed directly to Divvy’s success by producing a program that thought through and met every consumer need. It also earned them a reputation of being hard on their teams and partners, especially when it came to data analysis.

The Divvy executive team would require data-rooted answers to the questions Silk-Kremenak handed her analytics team. Securing buy-in, however, meant the analytics team would need to present these insights through data visualizations that featured a sophisticated use of contrast, expressed clear meaning, and exhibited refined execution.

“To convince our executive team,” Lindsay advised her analysts, “your visuals will need to be sophisticated, clear, and polished.

https://artscience.blog/home/divvy-dataviz-case-study

[universalprogramming]

q2_2019 <- read_csv("Divvy_Trips_2019_Q2.csv") q3_2019 <- read_csv("Divvy_Trips_2019_Q3.csv") q4_2019 <- read_csv("Divvy_Trips_2019_Q4.csv") q1_2020 <- read_csv("Divvy_Trips_2020_Q1.csv")

colnames(q3_2019) colnames(q2_2019) colnames(q1_2020)

q4_2019 <- rename(q4_2019                  ,ride_id = trip_id                  ,rideable_type = bikeid                  ,started_at = start_time                    ,ended_at = end_time                    ,start_station_name = from_station_name                  ,start_station_id = from_station_id                  ,end_station_name = to_station_name                  ,end_station_id = to_station_id                  ,member_casual = usertype) q3_2019 <- rename(q3_2019                  ,ride_id = trip_id                  ,rideable_type = bikeid                  ,started_at = start_time                    ,ended_at = end_time                    ,start_station_name = from_station_name                  ,start_station_id = from_station_id                  ,end_station_name = to_station_name                  ,end_station_id = to_station_id                  ,member_casual = usertype) q2_2019 <- rename(q2_2019                  ,ride_id = "01 - Rental Details Rental ID"                  ,rideable_type = "01 - Rental Details Bike ID"                  ,started_at = "01 - Rental Details Local Start Time"                    ,ended_at = "01 - Rental Details Local End Time"                    ,start_station_name = "03 - Rental Start Station Name"                  ,start_station_id = "03 - Rental Start Station ID"                  ,end_station_name = "02 - Rental End Station Name"                  ,end_station_id = "02 - Rental End Station ID"                  ,member_casual = "User Type") str(q1_2020) str(q4_2019) str(q3_2019) str(q2_2019)

q4_2019 <-  mutate(q4_2019, ride_id = as.character(ride_id)                   ,rideable_type = as.character(rideable_type)) q3_2019 <-  mutate(q3_2019, ride_id = as.character(ride_id)                   ,rideable_type = as.character(rideable_type)) q2_2019 <-  mutate(q2_2019, ride_id = as.character(ride_id)                   ,rideable_type = as.character(rideable_type))

all_trips <- bind_rows(q2_2019, q3_2019, q4_2019, q1_2020)

all_trips <- all_trips %>%    select(-c(start_lat, start_lng, end_lat, end_lng, birthyear, gender, "01 - Rental Details Duration In Seconds Uncapped", "05 - Member Details Member Birthday Year", "Member Gender", "tripduration"))

colnames(all_trips)

nrow(all_trips)

dim(all_trips)

head(all_trips)

str(all_trips)

summary(all_trips)

all_trips <-  all_trips %>%  mutate(member_casual = recode(member_casual                                ,"Subscriber" = "member"                                ,"Customer" = "casual"))

table(all_trips$member_casual)

all_trips$date <- as.Date(all_trips$started_at) all_trips$month <- format(as.Date(all_trips$date), "%m") all_trips$day <- format(as.Date(all_trips$date), "%d") all_trips$year <- format(as.Date(all_trips$date), "%Y") all_trips$day_of_week <- format(as.Date(all_trips$date), "%A")

all_trips$ride_length <- difftime(all_trips$ended_at,all_trips$started_at)

str(all_trips)

is.factor(all_trips$ride_length)

all_trips$ride_length <- as.numeric(as.character(all_trips$ride_length)) is.numeric(all_trips$ride_length)

all_trips_v2 <- all_trips[!(all_trips$start_station_name == "HQ QR" | all_trips$ride_length<0),]

mean(all_trips_v2$ride_length) #straight average (total ride length / rides)

median(all_trips_v2$ride_length)

max(all_trips_v2$ride_length)

min(all_trips_v2$ride_length)

summary(all_trips_v2$ride_length)

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = mean)

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = median)

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = max)

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = min)

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)

all_trips_v2$day_of_week <- ordered(all_trips_v2$day_of_week, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)

all_trips_v2 %>%  mutate(weekday = wday(started_at, label = TRUE)) %>%  group_by(member_casual, weekday) %>%  groups(number_of_rides = n(), average_duration = mean(ride_length)) %>%      # calculates the average duration  arrange(member_casual, weekday)

all_trips_v2 %>%  mutate(weekday = wday(started_at, label = TRUE)) %>%  group_by(member_casual, weekday) %>%  summarise(number_of_rides = n()            ,average_duration = mean(ride_length)) %>%  arrange(member_casual, weekday)  %>%  ggplot(aes(x = weekday, y = number_of_rides, fill = member_casual)) +  geom_col(position = "dodge") +  labs(    title = "Number of rides by rider type",    subtitle = "Sorted by weekday"  )

all_trips_v2 %>%  mutate(weekday = wday(started_at, label = TRUE)) %>%  group_by(member_casual, weekday) %>%  summarise(number_of_rides = n()            ,average_duration = mean(ride_length)) %>%  arrange(member_casual, weekday)  %>%  ggplot(aes(x = weekday, y = average_duration, fill = member_casual)) +  geom_col(position = "dodge") +  labs(title = "Average duration by rider type",       subtitle = "Sorted by weekday")

all_trips_v2 %>%  mutate(weekday = wday(started_at, label = TRUE)) %>%  group_by(member_casual, month) %>%  summarise(number_of_rides = n()            ,average_duration = mean(ride_length)) %>%  arrange(member_casual, month)  %>%  ggplot(aes(x = month, y = average_duration, fill = member_casual)) +  geom_col(position = "dodge") +  labs(title = "Average duration by rider type",       subtitle = "Sorted by month")

[universalprogramming]

Stay relatively open, be curious and try a bunch of stuff to see where you fit... it’s too early to tell if you’re learning and asking, better to poke around and see what fits. Learn core skills while you can, debugging and problem solving are unique to each person and making sure you’ve got those skills is important. Sites like advent of code and programming challenge websites are great for language agnostic tasks that help build problem solving skills. They also help build a portfolio, even if it’s just a GitHub repo you pump challenge answers into it goes a long way. Communication is also key, develop your social and presentation skills. The stereotype is that devs are nerdy introverts that you need a translator (read PM) to talk to. If you don’t fit that stereotype you’re ahead of another potential candidate with similar tech skills & experience. Front end web has done me well, I’m always in demand. Skills that put me above are actually being able to design something. But, that might not end up being your thing. Specific languages are a pain to nail down without geography, look at the job boards around you and what the overall language trend is. Last 5 years around me the majority is either C# .Net stack or full stack JS. But I know people in different locations that are php and python heavy. Make your CV relative to your field, I’m full stack web, my CV is a website, the printed copy is the psd mock-up. That shit sells me before they’ve even read a word. For interviews, learn to be interesting. If you’re given fizzbuzz throw in a code golf version, if you’re asked about personal projects have something that you’ve developed just because you wanted to. Interviews are boring as sin to conduct, if you have the basic skills required for a position you’re okay... if you can stop your interviewees from being bored whilst finding this out you’ll win.

[universalprogramming]

I studied Data Anlytics, theres a small amount of Math so get ready for that, mostly linear algebra/ set theory/ limits etc. Job prospects are fucking limitless in this field, every company is fucking fiending for analysts to make them more money and manipulate the public into buying shit. You can also use analytics in the biological sceinces (genomics etc.) which is my end goal. In terms of languages, you will pick them up as you need them, but I currently know (and in order of importance to me right now); - Python - SAS - R (learn shiny R too) - SQL - Java - Web app shit (ASP, PHP, C#, JS, JQuery, D3 etc.) - Powershell (not really a PL but important sometimes) - MATLAB Start with python and then go to Java or C++ and you can pretty much learn anything. I really like SQL so I would tell you to learn that but its not really that important. Scala seems to be gaining traction too.

[universalprogramming]

CTCI and introduction to algorithms (bless that anon for reminding me of that book) are good books to get into. Even if you're doing webdev, it's good to know how to do these things. Otherwise it'll be like learning to draw by copying anime characters and not knowing the proper fundamentals. Do leetcode, hackerrank, anything that can acquaint you with the more theoretical side of things. Take on a non-trivial project too, it'll help stretch your limits and provide a good portfolio item. If you're concentrating on webdev, maybe something like a fully-working storefront with a complete, SQL-backed backend and a responsive frontend with desktop and mobile layouts would be good. >future of Web Development I think things will be fine. It's not like investing where we as software engineers pick a certain set of technologies or languages to specialize in and then never use anything else. Plan on constantly reinventing yourself tech-wise and learning new things if you want to keep up with the industry. Know what's currently in vogue and use that to land jobs, tinker with new/experimental stuff you find interesting, but I wouldn't try to be ahead of the curve outside that. Not saying you need to prepare for the possibility of waking up one day and being an embedded developer, just saying that the current year stack is just that and will be replaced given enough time.