When I was a researcher at University, I had to come up with creative ideas at a rapid pace. Here’s a straightforward approach that may help you generate innovative concepts for your job, business, or research goals.

My particular research focus was graph partitioning, i.e., dividing a massive computer science graph structure into smaller chunks that can be distributed to multiple machines for parallel computing.

To find research ideas, I tried multiple things. One thing I tried was this simple Python script that helped me uncover interesting research gaps in my area of expertise.

You can easily modify it to help you generate unique ideas in your field!

This code is writing the combination of concepts and properties to a file. The file is opened for writing, and for each concept, the properties are written with the concept appended to each line. After the last property for the concept, a new line is written to the file. This process is repeated for each concept before the file is closed.

concepts = ["GAS", "Edge-cut", "Vertex-cut", "BSP"]

properties = ["elastic", "geo-distributed", "GPU-based", "disk-based", "asynchronous"] outfile = "combinatoricalIdeas.dat"

with open(outfile, "w") as out: for c in concepts: for p in properties: out.write(p + " " + c + "\n") out.write("\n") 

Here’s the output of my idea recombination framework for enhanced creativity :

elastic GAS
geo-distributed GAS
GPU-based GAS
disk-based GAS
asynchronous GAS elastic Edge-cut
geo-distributed Edge-cut
GPU-based Edge-cut
disk-based Edge-cut
asynchronous Edge-cut elastic Vertex-cut
geo-distributed Vertex-cut
GPU-based Vertex-cut
disk-based Vertex-cut
asynchronous Vertex-cut elastic BSP
geo-distributed BSP
GPU-based BSP
disk-based BSP
asynchronous BSP

The code simple combines nouns with adjectives or specifiers that are relevant in my particular domain. You don’t need to understand terms such as “BSP” or “Vertex-cut” to see that these are potentially interesting combinations of ideas.

Many of them have never been researched by anybody! See the Google Scholar results of “Geo-Distributed Vertex-Cut”:

It is a 100% solid, perfect “research gap” that is open for the taking. The first paper filling it will surely contribute to the scientific community — even though it may not be super relevant.

However, if you’re not interested in rocket science, this approach to finding “research gaps” through simple recombination of concepts in your area of expertise will surely give you some interesting food for thoughts!

Here’s this same idea applied to find a great niche business:

app = ["decentralized app", "social network", "dating app"]

target = ["gay people", "todlers", "rich people", "newly-weds"] for a in app: for t in target: print(a, 'for', t) print()

The output:

decentralized app for gay people
decentralized app for todlers
decentralized app for rich people
decentralized app for newly-weds social network for gay people
social network for todlers
social network for rich people
social network for newly-weds dating app for gay people
dating app for todlers
dating app for rich people
dating app for newly-weds

There are some really promising and fun ideas among those. Don’t underestimate the power of a simple idea recombination Python script!

Action Step: Whatever your current area of expertise or interest. Create your own version of this simple yet powerful Python recombination script for inspiration!

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I wish you a new year of health, prosperity, and joy!

Over the past couple of weeks, I’ve been reflecting on the future of Finxter and the positive impact we can make in 2023.

Last November, we achieved a remarkable milestone – educating one million coders each month! That’s more people than can fit in 20 filled soccer stadiums!

I’m incredibly humbled by the trust these one million people have placed in us, investing their time and attention in our mission of improving collective intelligence.

Thank you for being here!

Even if you bought nothing from us, you’re already giving us something far more valuable than money: a small chunk of your limited time on this planet.

I’m truly grateful and want to give you lots of value back. To make this happen, I developed this new strategy for the Finxter newsletter you’re currently reading:

Suggestion: I’ll send you 100 practical programming projects in 2023 – for free in this email newsletter!

Why practical programming projects? For six reasons:

1. Because solving practical coding projects is a great way to gain valuable experience and build your portfolio!
2. You’ll learn real-world skills that are useful outside the “ivory towers” that are academia and school.
3. You can apply the theory and concepts you have learned hands-on and quickly develop a working coding knowledge.
4. You’ll have the opportunity to collaborate with others, expand your problem-solving skills, and stay up to date with the latest technologies.
5. You don’t just learn – but you create real value and positively impact the world.
6. You can also earn money — possibly a lot — by implementing a real project. Myriads of million-dollar coding startups were started with a fun, practical project.

Working on real-world problems can give you the edge to land a job, a freelancing gig, or take your career to the next level.

Consequently, you’ll be far more motivated as a result, and your brain will absorb all the learnings effortlessly.

That’s my thinking anyways.

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Would you like to get 100 big and small practical coding projects via email in 2023 for inspiration and learning?

If yes, subscribe to the Finxter email newsletter, and read our 100% free Finxter blog tutorials!

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Idea 1: Web Scraping Tool

Challenge: Create a tool that can scrape data from websites, such as product prices, stock prices, news articles, etc.

This tool could be built using Python libraries such as BeautifulSoup, Selenium, and Requests. It could extract data from web pages and store it in a structured format, such as a CSV file.

This data could be used for various purposes, such as data analysis, machine learning, and data visualization.

Recommended Tutorial: Convert HTML Table to CSV in Python

Idea 2: Image Recognition System

Challenge: Create a system that can recognize objects in images and videos, such as faces, license plates, and products.

This system could be built using Python libraries such as OpenCV and Scikit-image.

It could be used to detect and recognize objects in images and videos, such as faces, license plates, and products. This system could be used for practical purposes, such as security and surveillance, facial recognition, and product identification.

Recommended Tutorial: OpenCV Course Part 1: Working with Images

Idea 3: Machine Learning Model

Challenge: Create a machine learning model for predicting stock prices or other values.

If you’re looking for a Python project with real value, why not create a machine-learning model for predicting stock prices or other values?

With the right tools and libraries, you can build a powerful model that can accurately predict stock prices or other values.

Recommended Tutorial: TensorFlow vs PyTorch — Who’s Ahead in the New Year?

Popular Python libraries such as scikit-learn and TensorFlow can be used to build the model, while data analysis libraries such as pandas and NumPy can make the analysis of the data easier.

Once the model is built, it can be deployed to a cloud platform such as Google Cloud Platform or Amazon Web Services for easy access.

Idea 4: Chatbot

Challenge: Create a chatbot that can answer questions and provide customer service.

Creating a chatbot is a great Python project with real value. From customer service to marketing and sales, chatbots can be used to answer customer queries, provide product information, and even assist with transactions!

To create a chatbot, you can use tools such as Dialogflow, Chatfuel, and Botpress. Even GPT-3 is a great choice!

These tools make it easy to create a chatbot and integrate it with existing back-end systems. With a great chatbot, you can provide a better customer experience and save time and resources.

Idea 5: Voice Recognition System

Challenge: Create a system that can recognize and respond to voice commands.

Voice recognition systems can automate tasks, such as customer service, that would otherwise require manual input.

This system can be created using Python and tools such as NLTK, OpenCV, and SpeechRecognition.

The system could understand and respond to voice commands, such as ordering products, making payments, and answering questions. It would also be able to detect accents and language variations, making it accessible to a wider audience.

This system could be used in a wide range of applications, such as customer service, home automation, and even medical diagnosis.

Idea 6: Natural Language Processing System

Challenge: Create a system that can understand written or spoken language.

Creating a natural language processing system is a great way to automate tasks such as customer service, document analysis, and text summarization.

To do so, there are a variety of tools available, such as spaCy, NLTK, OpenNLP, Gensim, and deep learning frameworks like TensorFlow and PyTorch.

With these tools, you can tokenize, lemmatize, and parse text, as well as create machine learning models for natural language processing tasks.

Getting started with natural language processing is easier than ever, so why not give it a try?

Recommended Tutorial: Deep Learning Engineer — Income and Opportunity

Idea 7: Automated Testing Tool

Challenge: Create a tool that can test software automatically.

Save time and money with automated testing! Automated testing tools are becoming a must-have for businesses and organizations looking to reduce costs and increase efficiency.

Popular tools such as Selenium WebDriver, Robot Framework, and Cucumber make it easy to create automated tests that can be quickly and easily run across multiple browsers and operating systems.

Automated testing helps teams identify and fix bugs quickly, resulting in a higher-quality product.

Idea 8: Data Visualization Tool

Challenge: Create a tool that can visualize data in graphical formats, such as charts and graphs.

Data Visualization is an exciting project for those looking to gain experience in data analysis and Python programming.

With libraries such as Matplotlib, Seaborn, and Plotly, you can create stunning visualizations of data – from line graphs to bar charts to scatter plots and more. Analyze financial data, customer data, or anything else that needs to be understood.

Create interactive visualizations that let users explore and interact with the data. This project is a great way to gain experience in data analysis and create something that provides valuable insights to users.

Recommended Tutorial: Plotly Dash — Your First Dashboard App in 10 Minute

Idea 9: Recommendation Engine

Challenge: Create a system that can recommend products or services based on user data.

Developers can use various Python tools, such as scikit-learn, TensorFlow, NumPy, and Pandas, to create a powerful recommendation engine. This engine can provide personalized product or service recommendations to users based on their individual data by analyzing user data and making predictions about what they may be interested in.

By utilizing the right combination of these tools, a recommendation engine can be built that is capable of making highly accurate predictions and delivering personalized recommendations to users.

Idea 10: Financial Analysis Tool

Challenge: Create a tool to analyze financial data and provide insights.

Financial analysis tools can be a powerful asset for businesses and investors, giving them valuable insights into the financial health of a company or industry.

By analyzing data such as income statements, balance sheets, and cash flow statements, these tools can provide invaluable information on the performance of investments and companies.

Python libraries such as NumPy, pandas, and scikit-learn can be used to create a financial analysis tool, allowing users to analyze data, create visualizations, and generate predictive models.

With the right tools and knowledge, Python can create a powerful financial analysis tool that can be a real asset for businesses and investors.

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