Step One – Select Relevant Projects
So, the first step in building my data science portfolio was to “Select Relevant Projects”, at least according to ChatGPT. After thinking about this for some time, I decided I was wasting my time trying to think of project, so back to the ChatGPT I went and provided the following prompt:
Can you suggest some relevant data science projects to start with?
The next set of answers really started to provide me with some direction. If you are not an industry expert in a particular field, it can be difficult to see the possibilities in all the data that is available out there in the world. At work, I know what data I have available to me, and I can visualize in my mind’s eye the questions my clients haven’t even thought to ask about. This way, I can proactively dig into my data and tease out details. Presenting the answers to the client before they even know to ask the question tends to make me look like a rockstar. However, coming up with practice projects and problems without being a subject matter expert tends to give me a bit of “writer’s block” so to speak. Asking for help from ChatGPT helped to refine my focus. From the list provided below, I can now take each topic and develop them into individual project to showcase my skills. These are the suggestions and explanations it provided.:
- Exploratory Data Analysis (EDA) on a Dataset:
Conduct a thorough analysis of a dataset, including data cleaning, visualization, and deriving meaningful insights. You can choose a dataset related to a topic of interest, such as finance, healthcare, or sports. - Predictive Modeling with a Classic Dataset:
Work with well-known datasets like the Iris dataset, Boston Housing dataset, or Titanic dataset. Build predictive models using techniques like linear regression, decision trees, or logistic regression. - Natural Language Processing (NLP) Project:
Analyze and extract insights from text data. You can start with sentiment analysis on Twitter data or build a simple chatbot using NLP libraries like NLTK or spaCy. - Image Classification:
Implement an image classification project using deep learning. The MNIST dataset for digit recognition or the CIFAR-10 dataset for object recognition are good starting points. - Time Series Analysis:
Work with time-series data to forecast trends or anomalies. You can use financial market data or weather data to build forecasting models. - Customer Churn Prediction:
Analyze customer data to predict customer churn for a business. This is a common problem in industries like telecommunications and subscription-based services. - Recommendation System:
Build a recommendation system, like the ones used by e-commerce platforms or streaming services. Collaborative filtering and content-based methods are popular approaches. - Healthcare Data Analysis:
Analyze healthcare data to identify trends, patient outcomes, or factors affecting disease prevalence. You can use datasets related to COVID-19 or electronic health records. - A/B Testing:
Design and analyze an A/B test for a hypothetical or real-world scenario. Determine the impact of changes in a website or app on user behavior. - Fraud Detection:
Build a fraud detection model using transaction data. This is valuable in the finance and e-commerce sectors. - Social Media Analysis:
Analyze social media data to gain insights into user behavior, sentiment, or trends. Use APIs to collect data from platforms like Twitter or Reddit. - Geospatial Data Analysis:
Work with geospatial data to solve location-based problems. You can analyze transportation data, plot maps, or predict real estate prices. - Market Basket Analysis:
Analyze transaction data to discover associations between products and make product recommendations. - Text Generation with GANs:
Experiment with Generative Adversarial Networks (GANs) to generate realistic text data, such as generating paragraphs in the style of famous authors. - Time Series Anomaly Detection:
Implement anomaly detection techniques on time series data to identify unusual patterns or outliers.
