Data Science Life Cycle | Life Cycle Of A Data Science Project | Data Science Tutorial | Simplilearn

Data Science Project Life Cycle

The video explains a six-stage process for taking a data science problem from initial understanding through practical use. The stages can be iterative: if a model performs poorly during testing, the data or model may need to be revised.

1. Concept study

• Understand the business problem and the organization’s goals.
• Consult stakeholders to clarify requirements, specifications, budget, and the desired outcome.
• Assess what data is available and whether the problem is feasible to solve.
• Example: Predict the price of a 1.35-carat diamond.

2. Data gathering and preparation

Explore raw data and make it usable for analysis or modeling. This work is also called data munging or data manipulation.

Check whether the data’s format and structure are suitable, especially when combining data from different sources. Common preparation tasks include:

• Integration: Combine data from multiple sources.
• Transformation: Align data formats or structures.
• Reduction: Reduce data volume while preserving useful information.
• Cleaning: Correct errors and handle missing, null, or invalid values.
• Deduplication: Resolve redundant records, such as duplicate customer entries.

Choose an appropriate approach for missing values. A small number of incomplete rows may be removed, while larger gaps may be filled with a meaningful statistic, such as the column mean or median.

For machine-learning work, split the data into training and test sets. The video gives examples of 50/50, two-thirds/one-third, and 80/20 splits. Keep the test set separate from the training data so that evaluation is meaningful.

3. Model planning and exploratory data analysis

Explore the data to understand its types, quality, ranges, and relationships between variables. Use descriptive summaries and visualizations—such as histograms, box plots, and scatter plots—to identify patterns, trends, and data issues.

Select a model suited to the problem. For example, use a regression algorithm for a regression task or a classification algorithm such as logistic regression, a decision tree, or SVM for classification.

Tools mentioned include R and RStudio, Python, MATLAB, and SAS.

4. Model building and testing

Train the chosen model using the training data, then evaluate its performance on the held-out test data.

Model training and testing are iterative. If accuracy is insufficient, the data scientist may retrain the model, use more data, or try a different model or algorithm.

In the diamond example, linear regression estimates the relationship between an independent variable and a dependent variable. It finds a line of best fit—illustrated as y = mx + c—and uses the fitted parameters to make predictions. The estimated price for a 1.35-carat diamond is $10,000.

Once the model passes testing, it can be deployed.

5. Communicating results

Present findings to the stakeholders who raised the problem, using a presentation, dashboard, or another suitable format. Explain the analysis and recommend actions that could help solve the original problem. Completing the analysis alone is not the final step.

6. Operationalizing

After stakeholders accept the results, put the recommendations or model into practice so they can help address the original business problem.

Speakers and sources featured

• Mohan: The presenter, who introduces and explains the data science project life cycle.
• Simplilearn outro voice: An unidentified voice delivers the closing channel subscription and related-video message.