Are you struggling with the time-consuming and tedious task of creating algorithms for data science challenges? If yes, then using ChatGPT with these Data Science, Data Analyst, and Data Engineering Prompts can help you save a lot of time and effort.
You simply need to enter the specific details about the subject you need assistance with in square brackets “[]”. You can mention the model’s name and the task that needs to be done within these square brackets to get specific answers.
You can generate code, troubleshoot, debug, collect data, clean data, analyze data, train, and develop efficient machine learning models for deployment in production environments. Working with different libraries and translating code becomes an easy task after mastering these prompts. The prompts assist in streamlining the process to deliver accurate results.
The prompts listed below cater to the needs of data scientists, data analysts, and data engineers, which will help them to boost productivity and make their work easier. Using ChatGPT, you can optimize models, discover new insights, create innovative solutions and complete time-consuming tasks in a single click.
Table Of Contents 👉
- Best ChatGPT Prompts For Data Analyst, Data Science, And Data Engineering
- 1. Prompts for training a Classification Model
- 2. Prompts for Automatic Machine Learning with TPOT
- 3. Prompts for Data Exploration
- 4. Prompts for developing clustering models
- 5. Prompts for developing Recommendation Systems
- 6. Prompts for text summarization models
- 7. Prompts for Natural Language Processing
- 8. Prompts for Dimensionality Reduction
- 9. Prompts to Get Feature Importance
- 10. Prompts to Tune Hyperparameters
- 11. Prompts for generating data
- 12. Prompts for Data cleaning and preprocessing
- 13. Prompts for writing Regex
- 14. Prompts for Working with Time Series Data
- 15. Prompts for Anomaly Detection in Time Series Data
- 16. Prompts for Time Series Decomposition
- 17. Prompts for Addressing Imbalance Data
- 18. Prompts for explaining code
- 19. Prompts for optimizing code
- 20. Prompts for formatting code
- 21. Prompts for translating code
- 22. Prompts for Data visualization
- 23. Prompts for working with LIME and SHAP
- 24. Prompts for writing Multithreaded Functions
- 25. Prompts for Comparing Function Speed
- 26. Prompts for creating NumPy Array
- 27. Prompts for validating columns
- 28. Prompts for Data Wrangling
- 29. Prompts for Data Ethics and Bias
- 30. Prompts for Data Science Career and Education
- 31. Prompts for other Data Science Tools
- 32. Prompts for writing Unit Test
- 33. Prompts for Python
- 34. More prompts for explaining concepts
- 35. Prompts for getting idea suggestions:
- 36. Prompts for Code Debugging and Troubleshooting
- 37. Prompts for working in SQL
- 38. Prompts for writing other codes
- 39. Prompts for Deep Learning and Neural Networks
- 40. Prompts for Big Data and Distributed Computing
- 41. Prompts for conceptual knowledge in Machine Learning
- 42. Prompts for conceptual knowledge of Data Exploration
- 43. Prompts for conceptual knowledge on Web scraping
- 44. Prompts for conceptual knowledge of Data Visualisation
Best ChatGPT Prompts For Data Analyst, Data Science, And Data Engineering
1. Prompts for training a Classification Model
To develop a prediction model: “I want you to act as a data scientist and code for me. I have a dataset of [describe dataset]. Please build a machine learning model that predicts [target variable].”
To train a classification model based on features: “I want you to act as a data scientist and train a classification model to predict [target variable] based on [features] dataset.”
To develop a model that can classify based on features: “I want you to act as a machine learning engineer and build a classification model that can classify [label] based on [features] features.”
To train a CNN using image data: “I want you to act as a deep learning specialist and train a convolutional neural network to classify [object] using [image format] images.”
2. Prompts for Automatic Machine Learning with TPOT
To generate Python code for finding the best classification model: “I want you to act as an automatic machine learning (AutoML) bot using TPOT for me. I am working on a model that predicts […]. Please write Python code to find the best classification model with the highest AUC score on the test set.”
To generate Python code for the machine learning pipeline: “I want you to act as an AutoML system and generate Python code to build a machine learning pipeline that optimizes [metric] on [dataset].”
To develop an AutoML script to tune hyperparameters: “I want you to act as an ML engineer and create an AutoML script that tunes [hyperparameters] to achieve the best performance on [dataset].”
To develop a model using Auto-sklearn: “I want you to act as a data scientist and use Auto-sklearn to automatically build a classification model that predicts [target variable] based on [features] features.”
3. Prompts for Data Exploration
To generate a program for data visualization and exploration: “I want you to act as a data scientist and code for me. I have a dataset of [describe dataset]. Write code for data visualization and exploration.”
To visualize the distribution of a feature: “I want you to act as a data analyst and generate a visualization that shows the distribution of [feature] in [dataset].”
To generate summary statistics: “I want you to act as a data scientist and generate summary statistics of [feature] in [dataset].”
To clean a dataset: “I want you to act as a data explorer and clean [dataset] by removing missing values, duplicates, and outliers.”
4. Prompts for developing clustering models
To cluster customers based on purchase history: “I want you to act as a data scientist and cluster the [customers] in [dataset] into [n] groups based on their [purchase history].”
To cluster documents based on their content: “I want you to act as a machine learning expert and develop a [clustering model] that groups the [documents] in [dataset] based on their [content].”
To visualize clusters using dimensionality reduction: “I want you to act as a data analyst and visualize the [clusters] in [dataset] using [dimensionality reduction] techniques.”
5. Prompts for developing Recommendation Systems
To develop a recommender system that suggests articles: “I want you to act as a data scientist and develop a [content-based recommender system] that suggests [articles] based on [user interests].”
To develop a model that recommends using purchase history: “I want you to act as a machine learning expert and build a [collaborative filtering model] that recommends [products] to [customers] based on their [purchase history].”
To analyze the accuracy of the system: “I want you to act as a data analyst and evaluate the [accuracy] of the [recommendations] generated by the [recommender system] in [dataset].”
To develop a collaborative filtering model using Surprise: “I want you to act as a recommender systems expert. I have a dataset of user-item ratings. Please help me build a collaborative filtering model using the Surprise library.”
To develop a content-based recommender: “I want you to act as a recommender systems expert. I have a dataset of items with metadata [describe dataset]. Please help me build a content-based recommender.”
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6. Prompts for text summarization models
To summarize a book: “I want you to act as a technical book summarizer. Can you please summarize the book [name] with 5 main points?”
To build a text summarization model: “I want you to act as a natural language processing expert. I have a large text dataset [describe dataset]. Please help me build a model for text summarization.”
To summarize a paper: “I want you to act as an academic. Please summarise the paper […] in simple terms in one paragraph.”
7. Prompts for Natural Language Processing
To develop a positive or negative customer feedback model: “I want you to act as a machine learning expert and build a [text classification model] that classifies [customer feedback] in [dataset] as positive or negative.”
To analyze the sentiment of reviews: “I want you to act as a data scientist and analyze the [sentiment] of the [reviews] in [dataset] using [natural language processing] techniques.”
To develop a model that can generate data according to training data: “I want you to act as a language model researcher and develop a [language model] that can generate [text data] similar to the [training data].”
To develop a text classification model with BERT: “I want you to act as a natural language processing expert. I have a text dataset [describe dataset]. Please help me build a text classification model using BERT.”
To extract named entities with SpaCy: “I want you to act as a natural language processing expert. I have a text dataset [describe dataset]. Please help me extract named entities using SpaCy.”
8. Prompts for Dimensionality Reduction
To reduce the dimensionality of image data: “I want you to act as a data scientist and reduce the [dimensionality] of the [image data] in [dataset] using [principal component analysis] technique.”
Steps to perform t-SNE on the dataset: “I want you to act as a data scientist and provide a step-by-step guide on how to perform [t-SNE] for my dataset.”
To reduce dimensionality using PCA and LDA: “I want you to act as a data scientist and explain the difference between [PCA] and [LDA] and how they can be used for [dimensionality reduction] in my dataset.”
9. Prompts to Get Feature Importance
To get important features of a decision tree model: “I want you to act as a data scientist and explain the model’s results. I have trained a decision tree model and I would like to find the most important features. Please write the code.”
To build a model that identifies the most important features: “I want you to act as a machine learning expert and train a [model] on [dataset] to identify the top [number] most important features for [target variable].”
Using permutation feature importance technique: “I want you to act as a data analyst and use the permutation feature importance technique to assess the importance of [features] for predicting [target variable] in [dataset].”
Using feature selection algorithm: “I want you to act as a data scientist and use [feature selection algorithm] to calculate the feature importance of [dataset] for [target variable].”
10. Prompts to Tune Hyperparameters
To code for a particular model: “I want you to act as a data scientist and code for me. I have trained a [model name]. Please write the code to tune the hyper parameters.”
To optimize the hyperparameter of a particular algorithm: “I want you to act as a hyperparameter tuner and optimize the [hyperparameter] of an [algorithm] algorithm to achieve the highest [metric] on [dataset].”
To optimize using Optuna: “I want you to act as a machine learning expert and use Optuna to perform a Bayesian optimization of [hyperparameters] for a [model] on [dataset].”
To perform a random search of hyperparameters for an algorithm: “I want you to act as a data scientist and perform a random search of [hyperparameters] for a [algorithm] algorithm to achieve the best [metric] on [dataset].”
11. Prompts for generating data
To generate data in table format: “I want you to act as a fake data generator. I need a dataset that has x rows and y columns: [insert column names]”
To generate data with features and instances: “I want you to act as a data generator and create a synthetic dataset with [number of features] features and [number of instances] instances.”
To generate time series data: “I want you to act as a data scientist and generate a time series dataset with [seasonality] seasonality and [trend] trend.”
To generate a dataset that simulates with parameters: “I want you to act as a data simulation expert and generate a dataset that simulates [process] with [parameters] parameters.”
12. Prompts for Data cleaning and preprocessing
To preprocess raw data by removing duplicate and missing values: “I want you to act as a data analyst and preprocess the [raw data] in [dataset] by removing [duplicate records] and [missing values].”
To preprocess time-series data by resampling: “I want you to act as a data engineer and preprocess the [time-series data] in [dataset] by resampling it to a [lower or higher frequency].”
To preprocess text data by tokenizing: “I want you to act as a data scientist and preprocess the [text data] in [dataset] by [tokenizing] it and removing [stop words] and [punctuation marks].”
13. Prompts for writing Regex
Regex in python: “I want you to act as a coder. Please write me a regex in Python that [describe regex]”
Regex to match pattern in the text: “I want you to act as a regex writer and write a regular expression that matches [pattern] in [text].”
Regex to extract data from log file: “I want you to act as a data engineer and use regex to extract [data] from [log file].”
Regex to match pattern in HTML source: “I want you to act as a web scraper and write a regex that matches [pattern] in [HTML source].”
14. Prompts for Working with Time Series Data
To develop a machine learning model: “I want you to act as a data scientist and code for me. I have a time series dataset [describe dataset]. Please build a machine learning model that predicts [target variable]. Please use [time range] as train and [time range] as validation.”
To develop a recurrent neural network: “I want you to act as a time series expert and build a recurrent neural network that predicts [target variable] based on [time series data].”
To train a seasonal ARIMA model: “I want you to act as a data scientist and train a seasonal ARIMA model to forecast [variable] in [time series data] using [forecast horizon] forecast periods.”
To train a long short-term memory network: “I want you to act as a machine learning engineer and train a long short-term memory network that detects [event] in [sensor data].”
15. Prompts for Anomaly Detection in Time Series Data
To detect anomalies in the time series dataset: “I want you to act as a data scientist and code for me. I have a time series dataset of [describe dataset]. Please help me identify anomalies in the data.”
To detect anomalies in network traffic: “I want you to act as a data scientist and detect [anomalies] in the [network traffic] of [organization] using [machine learning] algorithms.”
To identify intrusions in the security system: “I want you to act as a security analyst and identify [intrusions] in the [system logs] of [server] using [anomaly detection] techniques.”
To detect fraudulent transactions: “I want you to act as a fraud analyst and detect [fraudulent transactions] in the [financial data] of [company] using [statistical analysis] methods.”
16. Prompts for Time Series Decomposition
To plot the components: “I want you to act as a data scientist and code for me. I have a time series dataset of [describe dataset]. Please perform a time series decomposition and plot the components.”
To forecast using ARIMA model: “I want you to act as a data scientist and code for me. I have a time series dataset of [describe dataset]. Please help me build an ARIMA model to forecast the data.”
To forecast sales using time series forecasting: “I want you to act as a data scientist and forecast the [sales] of [product] for the next [n months] using [time series forecasting] techniques.”
To develop a neural network model to predict stock prices: “I want you to act as a machine learning expert and develop a [neural network model] that predicts the [stock prices] of [company] based on [historical data].”
To analyze trends and patterns in weather-related data: “I want you to act as a time series analyst and analyze the [trends and patterns] in the [weather data] of [city] using [time series decomposition] techniques.”
17. Prompts for Addressing Imbalance Data
To oversample or undersample in Python: “I want you to act as a coder. I have trained a machine learning model on an imbalanced dataset. The predictor variable is the column [Insert column name]. In python, how do I oversample and/or undersample my data?”
To oversample the minority class for classification using SMOTE: “I want you to act as a data scientist and use SMOTE to oversample the minority class of [imbalanced dataset] for classification tasks.”
To balance the distribution using stratified sampling: “I want you to act as a machine learning expert and use stratified sampling to balance the distribution of [target variable] in [dataset].”
To train a model using random undersampling: “I want you to act as a data engineer and apply random undersampling to address the class imbalance in [imbalanced dataset] for training a model.”
18. Prompts for explaining code
To explain Python code: “I want you to act as a code explainer. What is this code doing? [Insert code]”
To explain SQL code: “I want you to act as a data science instructor. Can you please explain to me what this SQL code is doing? [Insert SQL code]”
To explain Google Sheets Formula: “I want you to act as a Google Sheets formula explainer. Explain the following Google Sheets command. [Insert formula]”
19. Prompts for optimizing code
To increase code speed: “I want you to act as a software developer. Please help me improve the time complexity of the code below. [Insert code]”
To optimize pandas: “I want you to act as a code optimizer. Can you point out what’s wrong with the following pandas code and optimize it? [Insert code here]”
To optimize Python code: “I want you to act as a code optimizer. The code is poorly written. How do I correct it? [Insert code here]”
To optimize SQL code: “I want you to act as a SQL code optimizer. The following code is slow. Can you help me speed it up? [Insert SQL]”
To simplify a code: “I want you to act as a code simplifier. Can you simplify the following code?”
20. Prompts for formatting code
To write documentation of function: “I want you to act as a software developer. Please provide documentation for func1 below. [Insert function]”
To improve readability and maintainability: “I want you to act as a code analyzer. Can you improve the following code for readability and maintainability? [Insert code]”
To format SQL code: “I want you to act as a SQL formatter. Please format the following SQL code. Please convert all reserved keywords to uppercase [Insert requirements]. [Insert Code]”
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21. Prompts for translating code
To translate from Python to R: “I want you to act as a code translator. Can you please convert the following code from Python to R? [Insert code]”
To translate code between DBMS: “I want you to act as a coder and write SQL code for MySQL. What is the equivalent of PostgreSQL’s DATE_TRUNC for MySQL?”
To translate from R to Python: ” I want you to act as a code translator. Can you please convert the following code from R to Python? [Insert code]”
22. Prompts for Data visualization
To visualize data with Matplotlib: “I want you to act as a coder in Python. I have a dataset [name] with columns [name]. [Describe graph requirements]”
To create a plot that shows the relationship between two variables: “I want you to act as a data visualization expert and create a [type of plot] that shows the relationship between [variable1] and [variable2] in [dataset].”
To create a plot that displays the distribution of a variable in data: “I want you to act as a data scientist and create a [type of plot] that displays the distribution of [variable] in [dataset] and compare it across different [categorical variables].”
To create a plot that shows the trend of a variable over time: “I want you to act as a data analyst and create a [type of plot] that shows the trend of [variable] over time in [dataset].”
To visualize Image Grid Matplotlib: “I want you to act as a coder. I have a folder of images. [Describe how files are organised in directory] [Describe how you want images to be printed]”
23. Prompts for working with LIME and SHAP
To explain models results using Shap: “I want you to act as a data scientist and explain the model’s results. I have trained a scikit-learn XGBoost model and I would like to explain the output using a series of plots with Shap. Please write the code.”
To make predictions for an instance: “I want you to act as a machine learning specialist and use Lime to explain how a [model] made a prediction for a specific instance in [dataset].”
To identify important features for prediction: “I want you to act as a data scientist and use Lime to identify the important features that contributed to the prediction of [target variable] for [model] on [dataset].”
To explain how a model handles interaction between features: “I want you to act as a model explainer and use Lime to explain how a [model] handles the interaction between [features] in [dataset].”
To explain models results: “I want you to act as a data scientist and explain the model’s results. I have trained a [library name] model and I would like to explain the output using LIME. Please write the code.”
24. Prompts for writing Multithreaded Functions
To parallelize a code across threads in Python: “I want you to act as a coder. Can you help me parallelize this code across threads in python?”
To perform a task on input using threads: “I want you to act as a Python developer and write a multithreaded function that can perform [task] on [input] using [number of threads] threads.”
To optimize a multithreaded function: “I want you to act as a performance optimizer and write a multithreaded function that can parallelize the [bottleneck task] in [code section] of [Python script].”
To write a multithreaded function that asynchronously processes tasks: “I want you to act as a concurrency expert and write a multithreaded function that can asynchronously process [list of tasks] with the help of a thread pool.”
25. Prompts for Comparing Function Speed
To compare the efficiency of two algorithms: “I want you to act as a software developer. I would like to compare the efficiency of two algorithms that perform the same thing in Python. Please write code that helps me run an experiment that can be repeated 5 times. Please output the runtime and other summary statistics of the experiment. [Insert functions]”
To compare the processing speed of two functions in Python: “I want you to act as a performance tester and compare the speed of [function1] and [function2] when processing [input data] in [Python script].”
To compare the speed of two machine learning algorithms: “I want you to act as a data scientist and compare the speed of different [machine learning algorithms] on [dataset] using the [timeit] module.”
To compare the speed of two Python libraries: “I want you to act as a speed optimizer and compare the speed of different [Python libraries] for [task] in [code snippet].”
26. Prompts for creating NumPy Array
To create a NumPy Array of a given shape: “I want you to act as a data scientist. I need to create a numpy array. This numpy array should have the shape of (x,y,z). Please initialize the numpy array with random values.”
To create a 1D NumPy Array: “I want you to act as a data scientist and create a 1D NumPy array of [length] that contains [values].”
To create a 2D NumPy Array: “I want you to act as a Python developer and create a 2D NumPy array of shape [row, column] that represents the [matrix] in [dataset].”
To create a 3D NumPy Array: “I want you to act as a machine learning expert and create a random 3D NumPy array of shape [batch_size, height, width] that simulates [image data].”
27. Prompts for validating columns
To validate column that contains valid data type: “I want you to act as a data analyst and validate the [column] in [dataset] to ensure that it contains only [valid data type].”
To validate a column that contains only acceptable range of values: “I want you to act as a data quality analyst and validate the [column] in [dataset] to ensure that it contains only [acceptable range of values].”
To validate a column that is not affected by missing values and outliers: “I want you to act as a data scientist and validate the [column] in [dataset] to ensure that it is not affected by [missing values] and [outliers].”
28. Prompts for Data Wrangling
To clean and preprocess text data: “I want you to act as a data scientist and code for me. I have a dataset of text data [describe dataset]. Please help me clean and preprocess the data for further analysis.”
To combine multiple datasets: “II want you to act as a data scientist and code for me. I have several datasets with different structures [describe datasets]. Please help me combine them into a single dataset for analysis.”
29. Prompts for Data Ethics and Bias
To identify and mitigate bias in algorithms: “I want you to act as a data ethics expert. How can we identify and mitigate biases in AI algorithms?”
To get some privacy-preserving techniques: “I want you to act as a data privacy expert. What are some privacy-preserving techniques we can use in data science projects?”
30. Prompts for Data Science Career and Education
For advice to aspiring data scientists: “I want you to act as a data science career coach. What advice would you give to aspiring data scientists?”
For data science course suggestions: “I want you to act as a data science education expert. What are the best courses and resources for learning data science?”
31. Prompts for other Data Science Tools
To format tables in Google Docs: “I want you to act as a document formatter. Please format the following into a nice table for me to place in Google Docs? [insert text table here]”
To perform geospatial analysis with Python: “I want you to act as a geospatial expert. I have a dataset with geospatial information [describe dataset]. Please help me perform geospatial analysis using Python libraries.”
To perform an A/B Test: “I want you to act as a data scientist and code for me. I have a dataset of user behavior [describe dataset]. Please help me design and analyze an A/B test to optimize a specific metric.”
To create interactive visualizations with Plotly: “I want you to act as a data visualization expert. I have a dataset [describe dataset]. Please help me create interactive visualizations using Plotly.”
32. Prompts for writing Unit Test
To write unit tests for function: “I want you to act as a software developer. Please write unit tests for the function [Insert function]. The test cases are: [Insert test cases]”
To write unit tests for function in Python: “I want you to act as a Python developer and write a unit test for the [function] in [Python script] to verify that it returns the expected output when provided with [input].”
To write unit tests for handling error conditions: “I want you to act as a software engineer and write a unit test to ensure that the [web service] handles [error condition] correctly.”
To write unit tests for verifying correct updation of GUI component: “I want you to act as a test automation engineer and write a unit test to verify that the [GUI component] updates the [UI element] correctly when the [user action] is performed.”
33. Prompts for Python
To explain code to a Five-Year-Old: “I want you to act as a data science instructor. Explain [concept] to a five-year-old.”
To generate a python function to do a task: “I want you to act as a Python code generator and create a function that will do [task].”
To generate a python program that can scrape data from a website: “I want you to act as a Python script writer and write a program that will scrape [data source] data from a website.”
To generate a python module that can calculate metric using data: “I want you to act as a Python developer and write a module that will calculate [metric] using [dataset].”
To test pandas dataframe: “I want you to act as a data scientist. Please write code to test if that my pandas Dataframe [insert requirements here]”
34. More prompts for explaining concepts
To explain code to an undergraduate: “I want you to act as a data science instructor. Explain [concept] to an undergraduate.”
To explain code to a professor: “I want you to act as a data science instructor. Explain [concept] to a professor.”
To explain code to a Business Stakeholder: “I want you to act as a data science instructor. Explain [concept] to a business stakeholder.”
To explain code like Stackoverflow: “I want you to act as an answerer on StackOverflow. You can provide code snippets, sample tables and outputs to support your answer. [Insert technical question]”
35. Prompts for getting idea suggestions:
To get edge cases suggestions: “I want you to act as a software developer. Please help me catch edge cases for this function [insert function]”
To get dataset suggestions: “I want you to act as a data science career coach. I want to build a predictive model for […]. At the same time, I would like to showcase my knowledge in […]. Can you please suggest the five most relevant datasets for my use case?”
To get portfolio ideas suggestions: “I want you to act as a data science coach. My background is in […] and I would like to [career goal]. I need to build a portfolio of data science projects that will help me land a role in […] as a […]. Can you suggest five specific portfolio projects that will showcase my expertise in […] and are of relevance to [company]?”
To get resources suggestions: “I want you to act as a data science coach. I would like to learn about [topic]. Please suggest 3 best specific resources. You can include [specify resource type]”
To get time complexity suggestions: “I want you to act as a software developer. Please compare the time complexity of the two algorithms below. [Insert two functions]”
To get features suggestions: “I want you to act as a data scientist and perform feature engineering. I am working on a model that predicts [insert feature name]. There are columns: [Describe columns]. Can you suggest features that we can engineer for this machine learning problem?”
To get A/B test suggestions: “I want you to act as a statistician. [Describe context] Please design an A/B test for this purpose. Please include the concrete steps on which statistical test I should run.”
To get career suggestions: “I want you to act as a career advisor. I am looking for a role as a [role name]. My background is […]. How do I land the role and with what resources exactly in 6 months?”
36. Prompts for Code Debugging and Troubleshooting
To debug python code: “I want you to act as a software developer. This code is supposed to [expected function]. Please help me debug this Python code that cannot be run. [Insert function]”
Troubleshooting Problems: To troubleshoot PowerBI Model: “I want you to act as a Power BI modeler. Here are the details of my current project. [Insert details]. Do you see any problems with the table?”
To correct own ChatGPT code: “Your above code is wrong. [Point out what is wrong]. Can you try again?”
To correct SQL Code: ”I want you to act as a SQL code corrector. This code does not run in [your DBMS, e.g. PostgreSQL]. Can you correct it for me? [SQL code here]”
37. Prompts for working in SQL
To calculate running average: “I want you to act as a data scientist and write SQL code for me. I have a table with two columns [Insert column names]. I would like to calculate a running average for [which value]. What is the SQL code that works for PostgreSQL 14?”
To solve leetcode question: “Assume you are given the tables… with the columns… Output the following… [Question from Data Lemur)”
38. Prompts for writing other codes
To write Google Sheets Formula: “I want you to act as a bot that generates Google Sheets formula. Please generate a formula that [describe requirements]”
To write R script: “I want you to act as a data scientist using R. Can you write an R script that [Insert requirement here]”
To write in Shell: “I want you to act as a Linux terminal expert. Please write the code to [describe requirements]”
To write VBA: “I want you to act as an Excel VBA developer. Can you write a VBA that [Insert function here]?”
To write SQL code: “I want you to act as a data scientist and write SQL code for me. I have a table with two columns [Insert column names]. I would like to calculate a running average for [which value].”
39. Prompts for Deep Learning and Neural Networks
To develop a Neural Network: “I want you to act as a deep learning expert. Please write code to create a simple neural network with TensorFlow for [describe task].”
To develop code for transfer learning with pretrained models: “I want you to act as a deep learning expert. I have a dataset [describe dataset]. Please write code to perform transfer learning using a pretrained model from TensorFlow Hub.”
40. Prompts for Big Data and Distributed Computing
To analyze big data with Dask: “I want you to act as a big data expert. I have a large dataset [describe dataset]. Please help me analyze it using Dask.”
For distributed machine learning using Apache Spark: “I want you to act as a big data expert. I have a dataset [describe dataset]. Please help me build a machine learning model using Apache Spark.”
41. Prompts for conceptual knowledge in Machine Learning
“Can you write a code that will apply 6 different classification algorithms at once, and evaluate them by using precision-recall and f1 score and append the results to the data frame called pred_df?”
“What are some popular Python libraries for machine learning, and how are they used?”
“Can you provide an example of a basic machine learning script using Python?”
“How can you perform regression and classification tasks using Scikit-Learn?”
“How can you perform clustering and dimensionality reduction tasks using Scikit-Learn?”
“How can you evaluate the performance of an unsupervised learning model using different metrics?”
“What is a model selection and how can you choose the right algorithm for a machine learning problem?”
“How can you compare the performance of different machine learning models using different metrics?”
“Can you provide an example of a machine learning script that performs model selection using Scikit-Learn?”
“What are some best practices for deploying machine learning models in production?”
42. Prompts for conceptual knowledge of Data Exploration
“What is data exploration and how is it useful in data science?”
“What are some popular Python libraries for data exploration and how are they used?”
“Can you provide an example of a basic data exploration script using Python?”
“How can you perform dimensionality reduction using PCA to explore relationships between variables?”
“Can you provide an example of a data exploration script that uses t-SNE, PCA, and clustering to explore relationships between variables?”
“How can you identify patterns and trends in time series data using Pandas and Matplotlib?”
“Can you provide an example of a data exploration script that identifies patterns and trends in data using Pandas and Seaborn?”
“What are some common techniques for exploring relationships between variables using Pandas and Matplotlib?”
“How can you generate scatter plots and line charts to explore relationships between variables using Pandas and Matplotlib?”
“How can you perform dimensionality reduction using PCA to explore relationships between variables?”
43. Prompts for conceptual knowledge on Web scraping
“What are some popular Python libraries for web scraping and how are they used?”
“What is web scraping and how is it useful in data science?”
“How can you install and import a Python library for web scraping?”
“Can you provide an example of a basic web scraping script using Python?”
“What is HTML and how can you extract data from an HTML page using BeautifulSoup?”
“How can you extract data from an XML page using BeautifulSoup?”
“How can you extract data from dynamic websites using Selenium and WebDriver?”
“Can you provide an example of a web scraping script that extracts data from a specific website using BeautifulSoup?”
“What is web crawling and how can you implement it using Scrapy?”
“What is dynamic content and how can you scrape it using Selenium and WebDriver?”
44. Prompts for conceptual knowledge of Data Visualisation
“What are some popular Python libraries for data visualization and how are they used?”
“Can you provide an example of a basic data visualization script using Python?”
“How can you choose the right chart or graph for different types of data?”
“How can you ensure that your visualizations are accessible and readable?”
“Can you provide an example of a data visualization that adheres to best practices for effective visualization design?”
“What are some common techniques for creating static visualizations using Matplotlib and Seaborn?”
“How can you create line charts, bar charts, scatterplots, and other visualizations using Matplotlib and Seaborn?”
“How can you perform correlation analysis and heat mapping using Pandas and Matplotlib?”
“How can you create line charts, bar charts, scatterplots, and other visualizations using Matplotlib and Seaborn?”
“Can you give me an example of creating interactive graphs with Plotly?”
You can master these data science chatgpt prompts and use them efficiently to explore, collect, clean, visualize and analyze data. They can be leveraged to build powerful machine-learning models that can be used economically. You can also change the prompts version to suit your preferences and requirements. You can also prompt differently to get more accurate results.
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