Data Science Support

ThinkCode provides comprehensive support for data science and machine learning workflows, offering specialized tools, intelligent code assistance, and powerful features designed to enhance productivity throughout the entire lifecycle of data science projects.

Getting Started

Setup and Configuration

ThinkCode automatically detects data science projects. For optimal experience:

Install Data Science Extension:
- ThinkCode will prompt to install the Data Science extension when you open relevant files
- Alternatively, open the Extensions view (Ctrl+Shift+X / Cmd+Shift+X) and search for "ThinkCode Data Science"
Install Required Tools:
- Ensure Python, R, or Julia is installed on your system
- ThinkCode will detect these installations automatically
- Configure versions in settings if needed
Project Configuration:
- ThinkCode supports standard data science project structures
- Automatically recognizes requirements.txt, environment.yml, and other dependency files
- Configures the environment variables appropriately
Create a New Project:
- Command Palette (Ctrl+Shift+P / Cmd+Shift+P)
- Type "ThinkCode: Create New Project"
- Select Data Science from template categories
- Choose from templates:
  - Data Analysis Project
  - Machine Learning Project
  - Deep Learning Project
  - Research Notebook Collection
  - Data Visualization Project

Language Support

Python for Data Science

ThinkCode provides exceptional support for Python data science libraries:

Core Libraries:
- NumPy
- pandas
- Matplotlib/Seaborn
- SciPy
Machine Learning:
- scikit-learn
- TensorFlow/Keras
- PyTorch
- XGBoost
Data Visualization:
- Plotly
- Bokeh
- Altair
- Dash

Example of Python data science code with intelligent assistance:

# ThinkCode provides intelligent assistance for data science
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
 
# ThinkCode offers autocompletion for data loading methods
data = pd.read_csv('customer_data.csv')
 
# ThinkCode provides insights on data preview and exploration
print(data.head())
print(data.info())
print(data.describe())
 
# ThinkCode suggests data cleaning operations
# Handling missing values
data.dropna(subset=['income'], inplace=True)
data['age'].fillna(data['age'].median(), inplace=True)
 
# ThinkCode proposes feature engineering methods
# Creating new features
data['income_per_family_member'] = data['income'] / data['family_size']
data['is_high_value'] = data['purchase_amount'] > 1000
 
# One-hot encoding categorical variables
data = pd.get_dummies(data, columns=['category', 'location'])
 
# ThinkCode assists with visualization code
plt.figure(figsize=(10, 6))
sns.histplot(data=data, x='age', hue='is_high_value', bins=20, multiple='stack')
plt.title('Age Distribution by Customer Value')
plt.xlabel('Age')
plt.ylabel('Count')
plt.show()
 
# ThinkCode provides smart suggestions for model preparation
# Preparing data for modeling
X = data.drop(['customer_id', 'is_high_value'], axis=1)
y = data['is_high_value']
 
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)
 
# ThinkCode understands ML model APIs
# Training a model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
 
# ThinkCode assists with evaluation code
# Evaluating the model
y_pred = model.predict(X_test)
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
 
# ThinkCode provides feature importance visualization suggestions
# Visualizing feature importance
feature_importance = pd.DataFrame({
    'feature': X.columns,
    'importance': model.feature_importances_
}).sort_values('importance', ascending=False)
 
plt.figure(figsize=(12, 8))
sns.barplot(x='importance', y='feature', data=feature_importance.head(10))
plt.title('Top 10 Feature Importance')
plt.tight_layout()
plt.show()

R Language Support

Comprehensive support for R data science workflows:

Core Packages:
- tidyverse (dplyr, ggplot2, tidyr, etc.)
- data.table
- caret
- mlr3
Machine Learning:
- randomForest
- xgboost
- e1071
- neuralnet
Data Visualization:
- ggplot2
- plotly
- shiny
- leaflet

Example of R data science code with intelligent assistance:

# ThinkCode provides intelligent assistance for R
library(tidyverse)
library(caret)
library(randomForest)
 
# ThinkCode offers autocompletion for data loading
customer_data <- read_csv("customer_data.csv")
 
# ThinkCode provides insights on data exploration
glimpse(customer_data)
summary(customer_data)
 
# ThinkCode suggests data cleaning operations
# Handling missing values
customer_data <- customer_data %>%
  filter(!is.na(income)) %>%
  mutate(age = if_else(is.na(age), median(age, na.rm = TRUE), age))
 
# ThinkCode proposes feature engineering methods
# Creating new features
customer_data <- customer_data %>%
  mutate(
    income_per_family_member = income / family_size,
    is_high_value = purchase_amount > 1000
  )
 
# ThinkCode assists with visualization code
# Visualizing age distribution by customer value
ggplot(customer_data, aes(x = age, fill = is_high_value)) +
  geom_histogram(bins = 20, position = "stack") +
  labs(
    title = "Age Distribution by Customer Value",
    x = "Age",
    y = "Count"
  ) +
  theme_minimal()
 
# ThinkCode provides smart suggestions for model preparation
# Preparing data for modeling
customer_data <- customer_data %>%
  select(-customer_id) %>%
  mutate_if(is.character, as.factor)
 
# Creating training and test sets
set.seed(42)
train_indices <- createDataPartition(
  customer_data$is_high_value, 
  p = 0.8, 
  list = FALSE
)
train_data <- customer_data[train_indices, ]
test_data <- customer_data[-train_indices, ]
 
# ThinkCode understands ML model APIs
# Training a random forest model
model <- randomForest(
  is_high_value ~ ., 
  data = train_data,
  ntree = 100,
  importance = TRUE
)
 
# ThinkCode assists with evaluation code
# Evaluating the model
predictions <- predict(model, test_data)
conf_matrix <- confusionMatrix(predictions, test_data$is_high_value)
print(conf_matrix)
 
# ThinkCode provides feature importance visualization suggestions
# Visualizing feature importance
importance_df <- as.data.frame(importance(model)) %>%
  rownames_to_column("feature") %>%
  arrange(desc(MeanDecreaseGini))
 
ggplot(importance_df[1:10, ], aes(x = reorder(feature, MeanDecreaseGini), y = MeanDecreaseGini)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Top 10 Feature Importance",
    x = "Feature",
    y = "Importance (Mean Decrease in Gini)"
  ) +
  theme_minimal()

Julia Support

Support for the Julia language for scientific computing:

Core Packages:
- DataFrames.jl
- Plots.jl
- Statistics.jl
- MLJ.jl
Machine Learning:
- Flux.jl
- ScikitLearn.jl
- DecisionTrees.jl

Interactive Notebooks

Jupyter Notebook Integration

Seamless Jupyter notebook experience:

Notebook Editor: Rich editing experience for .ipynb files
Code Execution: Run cells directly in ThinkCode
Output Visualization: Rich output display (plots, tables, etc.)
Variable Explorer: Inspect variables and their values
Kernel Management: Switch between different kernels

Example notebook features:

Syntax highlighting for code cells
Markdown preview for text cells
Interactive widgets support
Export to various formats (HTML, PDF, etc.)

Polyglot Notebook Support

Work with multiple languages in a single notebook:

Multiple Languages: Python, R, SQL, and more in the same notebook
Shared Memory: Exchange data between cells of different languages
Rich Output: Consistent visualization across languages
Magic Commands: Special commands for notebook-specific operations

Data Management and Visualization

Data Explorer

Visual exploration of datasets:

Data Preview: View datasets in tabular format
Filter and Sort: Interactively explore data
Column Statistics: View quick statistics for each column
Custom Queries: Run SQL or code snippets on datasets

Access Data Explorer:

Right-click on a CSV, Excel, or other data file
Select "Open with Data Explorer"
Interact with the dataset visually

Visualization Preview

Interactive visualization capabilities:

Plot Preview: See plots directly in the editor or notebook
Interactive Plots: Zoom, pan, and hover for details
Export Options: Save visualizations in various formats
Theme Customization: Apply custom styles to visualizations

AI-Powered Data Science Features

Smart Code Generation

Generate data science code with natural language prompts:

Analysis Code Generation:

Add a comment describing the analysis goal
Press Alt+I / Option+I for AI implementation

Example:

# Generate code to analyze the correlation between customer demographics and purchase behavior
# Press Alt+I here and ThinkCode generates the implementation
 
# Load and prepare the data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
 
# Load the customer data
data = pd.read_csv('customer_data.csv')
 
# Basic data exploration
print("Data shape:", data.shape)
print("\nData types:")
print(data.dtypes)
print("\nMissing values:")
print(data.isnull().sum())
 
# Calculate correlation between numeric features
numeric_cols = data.select_dtypes(include=[np.number]).columns
correlation = data[numeric_cols].corr()
 
# Visualize correlation matrix
plt.figure(figsize=(12, 10))
mask = np.triu(correlation)
sns.heatmap(correlation, annot=True, mask=mask, cmap='coolwarm', linewidths=0.5)
plt.title('Correlation Between Customer Features')
plt.tight_layout()
plt.show()
 
# Analyze relationship between demographics and purchase amount
plt.figure(figsize=(16, 12))
 
# Age vs Purchase Amount
plt.subplot(2, 2, 1)
sns.scatterplot(data=data, x='age', y='purchase_amount', hue='gender')
plt.title('Age vs Purchase Amount by Gender')
 
# Income vs Purchase Amount
plt.subplot(2, 2, 2)
sns.scatterplot(data=data, x='income', y='purchase_amount', hue='gender')
plt.title('Income vs Purchase Amount by Gender')
 
# Purchase Amount by Region
plt.subplot(2, 2, 3)
sns.boxplot(data=data, x='region', y='purchase_amount')
plt.title('Purchase Amount by Region')
plt.xticks(rotation=45)
 
# Purchase Amount by Customer Segment
plt.subplot(2, 2, 4)
sns.barplot(data=data, x='customer_segment', y='purchase_amount')
plt.title('Average Purchase Amount by Customer Segment')
plt.xticks(rotation=45)
 
plt.tight_layout()
plt.show()
 
# Calculate and show key statistics grouped by demographic factors
demographic_analysis = data.groupby(['gender', 'customer_segment', 'region'])[['purchase_amount', 'purchase_frequency']].agg(['mean', 'median', 'std'])
print("Demographic Analysis:")
print(demographic_analysis)

Model Building:
- Describe model requirements in a comment
- ThinkCode generates model building and evaluation code
Data Visualization:
- Specify visualization needs
- ThinkCode generates tailored visualization code

Data Analysis Assistant

AI-powered assistance for data analysis tasks:

Exploratory Analysis: Get suggestions for exploring your dataset
Feature Engineering: Receive recommendations for creating new features
Model Selection: Get guidance on appropriate models for your task
Results Interpretation: AI-assisted interpretation of model results

Access Data Analysis Assistant:

Command Palette
Type "ThinkCode: Data Analysis Assistant"
Enter your analysis question or goal

Example assistant interactions:

"Suggest ways to handle missing values in my dataset"
"Recommend feature engineering for customer churn prediction"
"Help me interpret these model coefficients"
"Suggest visualizations for exploring the relationship between variables X and Y"

Code Improvement Suggestions

Get intelligent suggestions for improving data science code:

Performance Optimization: Identify and fix slow code
Best Practices: Suggestions for following data science best practices
Vectorization: Convert loop-based code to vectorized operations
Memory Usage: Tips for reducing memory consumption

Project Management for Data Science

Experiment Tracking

Track and manage machine learning experiments:

Experiment Logging: Record parameters, metrics, and artifacts
Comparison View: Compare different experiment runs
Visualization Tools: Plot metrics across experiments
Integration Options: Connect with MLflow, Weights & Biases, etc.

Data Version Control

Manage datasets and models with Git-like versioning:

Dataset Versioning: Track changes to datasets
Model Registry: Version and catalog models
Artifact Storage: Store and retrieve large files efficiently
Integration with DVC: Full support for Data Version Control

Debugging and Profiling

Data Science Debugging

Specialized debugging for data science workflows:

Array Visualization: Debug NumPy arrays and pandas DataFrames
Value History: Track how variable values change
Conditional Breakpoints: Break when data conditions are met
Tensor Inspection: Visualize and inspect deep learning tensors

Performance Profiling

Identify and resolve performance issues:

Code Profiling: Find bottlenecks in data processing code
Memory Profiling: Track memory usage and detect leaks
GPU Monitoring: Monitor GPU utilization and memory
Optimization Suggestions: Get actionable advice for improvements

Example profiling and optimization:

# ThinkCode provides memory and performance profiling
from thinkcode.profiling import profile_memory, profile_time
 
# Profile memory usage of a pandas operation
@profile_memory
def preprocess_data(data):
    # Various preprocessing steps
    data = data.copy()
    data['new_feature'] = data['A'] * data['B']
    data = pd.get_dummies(data, columns=['category'])
    data = data.groupby('group').transform('mean')
    return data
 
# Profile execution time
@profile_time
def train_model(X, y):
    model = RandomForestClassifier(n_estimators=100)
    model.fit(X, y)
    return model
 
# ThinkCode shows memory and time usage, and provides suggestions
# for improving performance and reducing memory usage

Machine Learning Model Development

Model Building Workflow

Comprehensive support for the ML development lifecycle:

Data Preparation: Tools for cleaning, transforming, and splitting data
Feature Engineering: Assistance for creating and selecting features
Model Training: Support for various ML libraries and frameworks
Hyperparameter Tuning: Tools for optimizing model parameters
Evaluation: Comprehensive model evaluation capabilities

Deep Learning Support

Specialized tools for deep learning development:

Architecture Visualization: Visualize neural network architectures
Training Monitoring: Track and visualize training progress
GPU Utilization: Monitor and optimize GPU usage
TensorBoard Integration: Visualize TensorFlow logs directly

Example TensorFlow code with intelligent assistance:

# ThinkCode provides intelligent assistance for TensorFlow
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, optimizers
import numpy as np
import matplotlib.pyplot as plt
 
# ThinkCode understands TensorFlow APIs
# Build a CNN model for image classification
def create_model(input_shape, num_classes):
    model = keras.Sequential([
        layers.Input(shape=input_shape),
        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dropout(0.5),
        layers.Dense(num_classes, activation="softmax")
    ])
    
    # ThinkCode suggests appropriate optimizers and loss functions
    model.compile(
        optimizer=optimizers.Adam(learning_rate=0.001),
        loss="categorical_crossentropy",
        metrics=["accuracy"]
    )
    
    return model
 
# ThinkCode provides data preparation assistance
# Load and prepare dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
 
# Normalize pixel values
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0
 
# One-hot encode the labels
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
 
# Create and train the model
model = create_model((32, 32, 3), 10)
 
# ThinkCode understands callbacks and training configuration
# Set up callbacks for monitoring training
callbacks = [
    keras.callbacks.EarlyStopping(patience=5, restore_best_weights=True),
    keras.callbacks.ReduceLROnPlateau(factor=0.2, patience=3),
    keras.callbacks.TensorBoard(log_dir="./logs")
]
 
# Train the model
history = model.fit(
    x_train, y_train,
    batch_size=64,
    epochs=20,
    validation_split=0.2,
    callbacks=callbacks
)
 
# ThinkCode assists with evaluation and visualization
# Evaluate the model
test_loss, test_acc = model.evaluate(x_test, y_test)
print(f"Test accuracy: {test_acc:.4f}")
 
# Plot training history
plt.figure(figsize=(12, 4))
 
plt.subplot(1, 2, 1)
plt.plot(history.history["accuracy"], label="Train")
plt.plot(history.history["val_accuracy"], label="Validation")
plt.title("Accuracy")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
 
plt.subplot(1, 2, 2)
plt.plot(history.history["loss"], label="Train")
plt.plot(history.history["val_loss"], label="Validation")
plt.title("Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
 
plt.tight_layout()
plt.show()

Deployment and Productionization

Model Deployment

Tools for deploying ML models to production:

Export Formats: Save models in various formats (ONNX, TensorRT, etc.)
Containerization: Package models with Docker
API Generation: Create REST APIs for models
Serverless Deployment: Deploy to serverless environments

Example model deployment code:

# ThinkCode provides model deployment assistance
from thinkcode.deployment import prepare_model_for_deploy, create_api
 
# Save the trained model for deployment
model_path = prepare_model_for_deploy(model, format='onnx', quantize=True)
 
# Create a REST API for the model
api_code = create_api(
    model_path,
    framework='fastapi',
    input_example=x_test[0:1],
    requirements=['numpy', 'pandas', 'onnxruntime']
)
 
# ThinkCode can generate Docker and Kubernetes configurations
# for deploying the model API

Monitoring and Maintenance

Support for monitoring models in production:

Performance Tracking: Monitor model metrics over time
Data Drift Detection: Identify shifts in input data distributions
Automated Retraining: Tools for updating models with new data
A/B Testing: Compare different model versions

Integration with External Services

Cloud Services Integration

Connect with popular cloud ML platforms:

AWS SageMaker: Develop, train, and deploy on SageMaker
Azure ML: Integration with Azure Machine Learning
Google AI Platform: Connect with Google's AI services
Databricks: Work with Databricks environments

Dataset Repositories

Access and publish datasets:

Public Datasets: Browse and load from Kaggle, UCI, etc.
Dataset Search: Find relevant datasets for your task
Version Control: Track changes to datasets
Publishing Tools: Share datasets with the community

Customization

Extension Points

Extend ThinkCode's data science capabilities:

Custom Visualizations: Create specialized visualization tools
Analysis Templates: Define reusable analysis templates
Model Interpreters: Build custom model interpretation tools
Integration Plugins: Connect with additional services

Configuration Options

Comprehensive configuration for data science workflows:

{
  "thinkcode.dataScience": {
    "python": {
      "condaPath": null,
      "pythonPath": null,
      "defaultInterpreter": "conda",
      "virtualEnvPath": "${workspaceFolder}/.venv",
      "pipenvPath": null
    },
    "r": {
      "rPath": null,
      "termsOfUse": true,
      "sessionWatcher": true
    },
    "jupyter": {
      "enableAutoMoveToNextCell": true,
      "allowKernelInterop": true,
      "themeMatching": true,
      "maxOutputSize": 10000,
      "sendSelectToInteractiveWindow": true
    },
    "plotting": {
      "enablePlotViewer": true,
      "plotTheme": "auto", // Options: "auto", "light", "dark"
      "defaultPlotPackage": "matplotlib", // Options: "matplotlib", "plotly", "bokeh"
      "savePlotPath": "${workspaceFolder}/plots"
    },
    "experiment": {
      "trackingEnabled": true,
      "trackingProvider": "mlflow", // Options: "mlflow", "wandb", "tensorboard", "custom"
      "trackingUri": null,
      "artifactLocation": "${workspaceFolder}/mlruns"
    },
    "gpu": {
      "monitoring": true,
      "preferredBackend": "auto" // Options: "auto", "tensorflow", "pytorch", "mxnet"
    }
  }
}