Leveraging Programming Skills: 3 Easy Ways to Generate Income
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Chapter 1 Discovering Opportunities
Many individuals seek a less intense way to earn money through their programming abilities.
I initially ventured into programming with dreams of striking it rich. At 25, I was eager and somewhat naïve. Data Science was all the rage, and I believed that securing a role as a data scientist with a salary exceeding $150,000 would free me from mundane work. I learned to code, but I soon realized the demands of the profession were overwhelming.
How could I balance a family while managing the long hours typically expected of a programmer? The answer was simple: I couldn’t.
So, what could I do with my programming knowledge? I had no interest in committing to a tech job, freelancing, tutoring, or becoming a hobbyist app developer. Instead, I opted for the following methods to monetize my skills.
Section 1.1 Writing Programming Guides
If you find yourself explaining technical tasks to colleagues regularly, it makes sense to start creating programming guides. Personally, my guides haven’t gained much traction; I’ve only penned two tutorials, which have garnered a mere 16 views collectively.
Admittedly, I enjoy crafting engaging stories about life and financial success more than focusing solely on programming. These tutorials were specifically about data science, but I still count my earnings of $2.05 as income from programming content.
The key takeaway is that there is a market willing to pay for your programming tutorials — you just need to identify your target audience.
If you’re uncertain how to structure a tutorial, here’s a sample paragraph you might consider using:
One of the biggest challenges for data analysts working with transactional data is managing multiple row IDs. These can complicate matters as tidy data is often required for machine learning: one ID per row. Research Group ZZZ found that approximately 50% of business data consists of multiple row IDs. Analysts need effective tools to tackle this problem! Here are two methods in Pandas:
import pandas as pd
df[::2] # This retrieves every second row, starting from index 0.
df.groupby(['ID'], ['Program']).count() # Counts occurrences of ID and program combinations.
Subsection 1.1.1 Automating Financial Calculations
I prefer to analyze annual reports over using stock screeners, as I find them less trustworthy. I suspect it’s because the numbers aren’t always backed by auditor confirmations or perhaps I’m just overly cautious.
What I do is read a company's financial statements and input the data into a Google Form, which channels the information into a Google Sheet that’s then processed by a Python script. The results are visualized through Google Data Studio.
The programming aspect here is the Python script, which isn't complex. It calculates ratios like debt-to-equity. You might wonder why I don’t just rely on Google Sheets or Excel functions, and that’s a valid question. Once you handle a large volume of data and numerous functions, spreadsheets can become sluggish. Additionally, business intelligence tools like Data Studio can interpret function outputs as strings, complicating visualization.
Here’s a sample Python script that pulls data from Google Sheets, processes it, and then exports the results back:
import pandas as pd
import numpy as np
from google.colab import auth
auth.authenticate_user()
import gspread
from google.auth import default
creds, _ = default()
gc = gspread.authorize(creds)
ws = gc.open('Stock Screener (Responses)').sheet1
df = pd.DataFrame(ws.get_all_records())
# Ratios
df['ROE'] = df['EBIT'] / df['Total Equity']
df['ROIC'] = df['EBIT'] / (df['Total Equity'] + df['Borrowings'])
df['DE'] = df['Borrowings'] / df['Total Equity']
df['Operating PM'] = df['EBIT'] / df['Revenue']
df['ROA'] = df['EBIT'] / df['Total Assets']
# Export to Google Sheets
ws1 = gc.open('Calculations').sheet1
df.fillna(0, inplace=True)
df.replace(np.inf, 0, inplace=True)
ws1.update([df.columns.values.tolist()] + df.values.tolist())
Section 1.2 Gleaning Insights with Machine Learning
At times, I prefer utilizing machine learning to extract insights. It’s worth clarifying that I don’t rely on AI to compose my articles; instead, I use it to discover insights that serve as the foundation for my writing.
The rationale behind this is straightforward: AI identifies statistical relationships between words and phrases without grasping their actual meaning.
Essentially, I input a dataset or text corpus into a suitable machine learning model and extract insights using Shapley values. These insights inform my writing. Here’s an example:
import pandas as pd
import shap
import sklearn
X = pd.read_csv("train_data.csv")
y = pd.read_csv("train_labels.csv")
model = sklearn.linear_model.LinearRegression()
model.fit(X, y)
X100 = shap.utils.sample(X, 100)
shap.partial_dependence_plot(
"Feature", model.predict, X100, ice=False,
model_expected_value=True, feature_expected_value=True)
shap.plots.waterfall(shap_values[5], max_display=14)
Essentially, you create a machine learning model and use Shapley values to explain the importance of different factors.
Chapter 2 Conclusions: Has Programming Altered My Life?
Yes, programming has fundamentally changed my life. I’ve gained a valuable skill set and automated tasks that would otherwise consume considerable time.
Am I becoming wealthy from these relaxed programming approaches? Not at all. However, they have enriched my writing topics and enabled me to automate tedious tasks. As previously mentioned, the cumulative gains from my programming tutorials have increased my income.
One day, I hope that the earnings from programming will go beyond just covering coffee and extend to enjoying ramen as well!
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Chapter 3 Video Insights
The first video title is 12 Ways to Make Money with CODING - YouTube. This video explores various strategies to leverage coding skills for income generation.
The second video title is How to Make F**k-You Money with Coding - YouTube. This video discusses unconventional methods to achieve substantial earnings through coding.