A data scientist has an ever-evolving role that requires precision and efficiency in every step of the process. Besides, deep analytical skills are also crucial for a data scientist. Previously, it was easier for me to handle operations like cleaning datasets or fine-tuning models due to their smaller sizes. Nowadays, data volume has increased notably, and fine-tuning complex models has become way more challenging. However, GenAI, or Generative AI is a game-changer for me these days. It can generate human-like texts, automate code writing, assist in data gathering and cleaning, and do many more.
Due to the help of GenAI, I can now focus more on high-level problems that require strategic thinking rather than being stuck with repetitive tasks. In this article, I will break down the key use cases of GenAI for data scientists. I will also talk about some essential tools.
Whether you are an aspiring data scientist, experienced AI/ML practitioner, business analyst, or AI engineer, learn more about how GenAI transformed my work as a data scientist.
What Is GenAI?
GenAI or Generative AI refers to AI models that can generate new content based on its training data. For example, think of an AI model that is trained with tons of geopolitical data. When you ask the model to write you a paragraph or essay that isn’t present in the training data, the model can write entirely new things based on what it has learned from the training data. Such models are called generative AI or GenAI.
These AI models usually have transformer-based architectures. Some of the most effective GenAI models are GPT-4, BERT, T5, etc. Check the following chart to learn how GenAI is different from traditional AI.
| Feature | Traditional AI | Generative AI |
| Primary Purpose | Predictive analytics, classification, clustering | Content generation, synthetic data creation, automation |
| Process | Takes structured data and generates a prediction | Takes input from users and generates completely new data |
| Use Cases in Data Science | Feature selection, model training | Dataset augmentation, automating code, synthesizing data |
Why Is GenAI Important for Data Scientists?
Data scientists need to perform an array of complex and time-consuming tasks. GenAI can assist in many of these tasks in the following ways.
GenAI Automates Repetitive Tasks
Preprocessing data takes up to 80% of a data scientist’s time. Previously, I had to process raw data manually to make the data suitable for model training. But, now I can use GenAI tools like OpenAI Codex, Pandas AI, etc., for automated preprocessing.
With these tools, I don’t need to do these repetitive tasks anymore and can save a lot of time that I use on other complex tasks.
It Enhances Data Quality and Augmentation
If I have to work with an imbalanced dataset, I can use GenAI to generate synthetic data. The data generated by AI simulates real-world distributions, so I can train the model with that synthetic data. It reduces the need for additional real-world data samples.
Code Generation and Debugging Gets Faster
Writing basic codes for AI models is another repetitive task that GenAI can now take over. I use GenAI tools like GitHub Copilot to generate code snippets. These tools can also be used for debugging and code improvements.
AI Does Better Model Tuning and Optimization
Fine-tuning hyperparameters is a complex job. Using GenAI tools helps me select the best possible configuration for ML models.
Easy to Get Insights and Reports
Generative AI can create detailed reports, brief summaries, etc., to provide the necessary insights in simple language. As a result, I can present the development of the process to all shareholders much easier than before.
What GenAI Can Do for a Data Scientist?
GenAI is now involved in the following areas of my workflow.
Data Processing and Augmentation
- It cleans up and normalizes raw data for me.
- I can fill in missing values of datasets using AI-powered imputation
- Data classes can be balanced by generating synthetic datasets
Feature Engineering and Selection
- Extracting important features from raw data has become more convenient
- It transformed unstructured data into structured formats automatically
- GenAI can recommend strategies for selecting model features
Code Generation and Debugging
- I can write Python, SQL, and other codes by just entering natural language prompts
- GenAI can debug my written code and suggest optimizations for a better structure
- Machine learning pipelines can be generated automatically
Model Optimization
- GenAI finds the best hyperparameter configurations for me
- Designing deep learning architectures is less time-consuming
- Training models become faster with GenAI
How GenAI Transformed My Data Science Workflow?
I have already mentioned areas where GenAI has been most helpful. Now, I want to give you a detailed breakdown of how GenAI transformed my work as a data scientist.
Task 1: Data Preprocessing and Cleaning
Traditional Workflow
Previously, I had to handle missing values, remove outliers, normalize data, and encode variables manually. For each task, I would need to write separate scripts or complete the tasks separately. It would take hours, or even days for large datasets. So, developing a model would be tougher.
GenAI Workflow
Now I can use natural language prompts, such as ‘fill missing values in my dataset’ to handle missing values. I can also generate preprocessing scripts quickly and fix data quality issues.
Task 2: Data Augmentation and Synthetic Data Generation
Traditional Workflow
Imagine I need to make a fraud detection model. Previously, I had to collect a huge amount of data on fraudulent transactions. But, collecting such data can be tedious and time-consuming. It also involves a lot of permissions and approvals from authorities as this data is highly sensitive.
GenAI Workflow
With GenAI tools, I can now generate realistic synthetic data for this situation. For example, generated data on fraudulent transactions will mimic actual distributions. I can also create variations of existing datasets and balance datasets without collecting real-world data through costly and tedious processes.
Task 3: Feature Engineering and Selection
Traditional Workflow
Extracting features from raw data is one of the most tedious tasks in the workflow. It requires a high level of domain expertise, as well as a lot of time and experimentation. So, I had to invest a notable amount of time and effort in feature engineering and selection earlier.
GenAI Workflow
Now I have automated tools to generate meaningful features from raw data. I can also use AI-powered selection techniques to identify the most impactful features. It helps me reduce dimensionality without losing important information. For example, I can extract time-series features for a predictive maintenance tool using Featuretools.
Task 4: Code Generation and Debugging
Traditional Workflow
Before generative AI, I had to write all my codes manually. The process involves writing codes for machine learning models, SQL queries, Python scripts, and more. These would take up a lot of my time. Moreover, writing code manually leads to a lot of unwanted errors. As a result, debugging would be much more difficult and time-consuming.
GenAI Workflow
Now I have multiple tools to use for code generation and debugging. Instead of writing the code manually, I simply input a prompt, such as ‘write a SQL query to find the top 5 customers by revenue’. The tool gives me the necessary code without any error.
If I need to modify any part of the code, these tools help me with auto-complete features. I can also find errors in codes much easier than before.
Task 5: Model Optimization and Tuning
Traditional Workflow
The success of a model greatly depends on fine-tuning its hyperparameters. Earlier, I had to tune the model manually to find the best hyperparameters. But, the process was slow and inefficient. Grid Search and Random Search would take a long time. So, the development lifecycle was much longer.
GenAI Workflow
I don’t have to manually tune the model now because GenAI tools can optimize it much faster. These tools find the best hyperparameters automatically and efficiently search for the best model configurations. They also visualize results instantly to identify patterns in model performance.
Task 6: Extracting Insights and Reports
Traditional Workflow
Be it model performance or any other technical data, I would face a lot of challenges in communicating data with non-technical stakeholders. For them, I had to make reports manually. It would consume a notable share of my workflow.
GenAI Workflow
Now I can generate data insights and reports in just a few clicks with almost no manual labor. I can generate automated summaries of data trends and patterns, easy-to-digest reports, etc., in just minutes. It saves a lot of my time that I can use in the complex tasks of my workflow.
Essential GenAI Tools for Data Scientists
Many specialized tools have now come to the market to streamline the workflow of a data scientist. I use the following tools frequently and want to give you a quick overview of their use cases. Check it out.
Data Preprocessing and Cleaning Tools
- Pandas AI: With AI-based automation, it is commonly used for data wrangling and transformation.
- Trifacta: This is a GenAI tool for data cleaning, preparation, and anomaly detection.
- Dataprep: I use this tool to understand data rapidly through exploratory data analysis.
- DataRobot AI: It is used for end-to-end machine learning automation.
Data Augmentation and Synthetic Data Generation Tools
- Gretel.ai: This AI-powered tool generates synthetic datasets for augmentation.
- Mostly AI: It is also used for synthetic data generation and balancing datasheets.
- YData Synthetic: This is the best tool for time-series generation.
- Microsoft Presidio: It is used for data anonymization and augmentation.
Feature Engineering and Selection Tools
- FeatureTools: It generates time-series and structured data.
- TSFresh: It extracts features from time-series data.
- AutoFeat: It selects the most impactful features from high-dimensional datasets.
Code Generation and Debugging Tools
- GitHub Copilot: It helps complete code for Python, SQL, and ML scripts.
- OpenAI Codex: It is used for general-purpose coding.
- Tabnine: The best predictive code generation tool I use.
Model Optimization Tools
- Optuna: I use it for tuning hyperparameters.
- Weights & Biases: It is used for experiment tracking and tuning.
- SigOpt: It is used for parameter tuning.
Data Visualization Tools
- Tableau AI: It can generate interactive dashboards.
- DataRobotAI: Automated predictive analysis is its most powerful feature.
- Narrative Science: It generates automated reports.
Challenges of Using GenAI as a Data Scientist
While GenAI transforms the workflow of a data scientist, it comes with its own challenges and limitations. Here are some of the most common challenges of using GenAI as a data scientist and how to overcome them.
- GenAI models, especially large language models generate outputs based on probabilistic predictions. As a result, they can hallucinate, lack verifiability, and struggle with numerical precision. Cross-checking outputs with trusted sources and having human experts review the outputs can help overcome this challenge.
- Due to a lack of explainability, GenAI models may generate biased outputs. This is why data scientists must perform bias audits continuously. Also, you should use ethically sourced datasets.
- Blindly trusting GenAI tools can result in flawed outputs. Besides, data scientists can gradually lose human intuition, creativity, and domain expertise if they continue to rely on GenAI tools for even the smallest of tasks. To overcome this, data scientists must use GenAI tools as an assistant, not a decision-maker.
- With a higher dependency on tools, data scientists may tend to perform tasks they don’t excel in. This can set a bad example for aspiring data scientists, especially for those who think someone can become a data scientist just by using tools.
Conclusion
Data scientists usually have a complex workflow that involves preprocessing data, extracting features, transforming raw data into structured data, and many more. They would do most of these tasks manually before GenAI emerged. But, now they commonly use an array of GenAI tools that have made the workflow much more efficient.
I talked about how GenAI transformed my work as a data scientist in this guide and explained what tools I use to boost my efficiency. However, you must remain careful so that GenAI tools don’t get dominant over yourself. Use tools to assist you but continue putting your creativity and human intuition into the process.
