Introduction to Data Science - Unit : 1 - Topic 4 : DATA SCIENCE PROCESS IN BRIEF

 

DATA SCIENCE PROCESS IN BRIEF

Data Science workflows tend to happen in a wide range of domains and areas of expertise such as biology, geography, finance or business, among others. This means that Data Science projects can take on very different challenges and focuses resulting in very different methods and data sets being used. A Data Science project will have to go through five key stages: defining a problem, data processing, modelling, evaluation and deployment.

1. Problem Definition

• Objective: Define the problem clearly and understand the goals of the project.
• Tasks: Communicate with stakeholders, define success metrics, and identify the key questions the project aims to answer.

2. Data Processing

          2.1 Data Collection

·        Objective: Gather the necessary data required to solve the problem.

·        Tasks:

o   Collect data from multiple sources (databases, APIs, web scraping, sensors, etc.).

o   Ensure data quality, relevance, and completeness.

          Data Cleaning and Preprocessing

·        Objective: Prepare the data for analysis by removing inconsistencies and handling missing values.

·        Tasks:

o   Handle missing data (imputation, removal).

o   Remove duplicates and outliers.

o   Normalize/standardize data.

o   Feature engineering (creating new variables, converting categorical to numerical data, etc.).

          Exploratory Data Analysis (EDA)

·        Objective: Explore the data to understand its characteristics and patterns.

·        Tasks:

o   Visualize data distributions (histograms, scatter plots).

o   Calculate summary statistics (mean, median, standard deviation).

o   Identify correlations between variables.

o   Check for skewness or any potential issues in the data.

3. Modelling

• Objective: Select and apply the appropriate machine learning or statistical models to analyze the data.
• Tasks:
• Split data into training and testing sets.
• Select appropriate algorithms (e.g., regression, classification, clustering).
• Train models on the training set.
• Tune hyperparameters using cross-validation.

3.1 Model Evaluation

• Objective: Evaluate the performance of the trained models.
• Tasks:
• Use appropriate evaluation metrics (accuracy, precision, recall, F1 score, RMSE, etc.).
• Compare different models based on their performance.
• Check for overfitting or underfitting.

4.     Evaluation - Interpretation and Insights

4.1 Evaluation

·       Objective: Quantitatively assessing the model’s performance.

·       Tasks:

o   Use metrics like accuracy, precision, recall, F1 score, RMSE, etc., depending on the type of model.

o   Compare different models to select the best one.

o   Check for overfitting or underfitting by analyzing performance on both training and test data.

 

   4.2 Interpretation and Insights

• Objective: Interpret the model results and derive actionable insights.
• Tasks:
• Explain the model's findings in a way that is understandable for non-technical stakeholders.
• Identify trends, patterns, or predictions that answer the initial problem.
• Generate reports or visualizations to communicate results effectively.

5.    Deployment

• Objective: Deploy the model into a production environment for real-time or batch predictions.
• Tasks:
• Develop a user interface (UI) or API for accessing the model.
• Integrate the model with existing systems or databases.
• Monitor the model's performance in production.

   5.1 Monitoring and Maintenance

• Objective: Ensure the model continues to perform well over time.
• Tasks:
• Monitor the model's performance with new data.
• Retrain the model periodically with fresh data.
• Address model drift (when the model's predictions degrade over time).