Data Science Lifecycle: How to build a Data science project from End-to-End?

Note: The following article is also available on my medium account: https://mariamkilibechir.medium.com/data-science-lifecycle-how-to-collect-clean-analyze-and-visualize-data-41eb0fdb092e

Data science is a process of using scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. Data Science Life Cycle is an iterative set of steps that data scientists take to deliver a project or analysis. The life cycle is different for every data science project and team, but most data science projects tend to flow through the same general life cycle of data science steps. The following are the 8 steps of the general data science life cycle:

1- Problem understanding

This step involves understanding the business problem or question that you are trying to solve with data science. It also involves collaboration with domain experts to ensure alignment between data analysis and the real-world problem. To understand the definition of the problem, you should ask these questions: What are the specific goals of the project? What data is available? What are the constraints?

2- Data collection

Once the problem has been defined, the next step is to collect and clean the data. The data can come from a variety of sources, such as internal databases, external databases, APIs, spreadsheets, web scraping, sensors, surveys and more. It is important to collect data that is relevant to the problem or question that you are trying to solve. The following steps need to be followed if you want to clearly collect your data:

1.Define Objectives: Begin by clearly defining the objectives of your data science project. What questions do you want to answer? What problems are you trying to solve?

2. Identify Data Sources: Determine where your data will come from. It could be internal databases, external APIs, a combination of sources, or any others source from where data can be collected.

3. Start collecting data: Gather the data using appropriate methods and tools. Ensure you have the necessary permissions and consider data privacy and ethics.

4. Data Storage: Organize and store the collected data securely. Common options include relational databases, data warehouses, or cloud-based storage solutions.

3- Data Cleaning and Preprocessing

Once you have collected data, you need to clean it. Data cleaning is the process of identifying and correcting errors and inconsistencies in the data. This may involve removing duplicate records, filling in missing values, correcting formatting errors, identifying and deal with outliers that can skew your analysis, Performing transformations such as normalization, standardization, or encoding categorical variables.

4- Exploratory Data Analysis (EDA)

Once the data is clean, you can start to analyze it. EDA is the process of using statistical and machine learning techniques to extract knowledge and insights from the data. This may involve identifying patterns and trends in the data, building predictive models, and testing hypotheses. The following steps are helpful to use when you are analysing your data:

1.Data visualization: Visualize the data and generate summary statistics to understand its distribution, relationships, and patterns. This may involve creating charts, graphs, and dashboards. Data visualization can help you to identify patterns and trends in the data, communicate your findings to others, and make informed decisions.

2. Feature Engineering: Create new features or modify existing ones to improve the performance of predictive models.

3. Statistical Analysis: Apply statistical tests and methods to test hypotheses and validate findings.

5- Model Building

In this phase, data scientists design and build predictive models, classifiers, or regressors, depending on the project’s objectives. Machine learning algorithms, statistical models or deep learning models are employed to extract patterns and make predictions.

6- Model Evaluation and Validation

Once you have built a model, you need to evaluate its performance on a held-out test set. This will help you to assess how well the model will generalize to new data. Common metrics and techniques are used to measure the model’s performance, such as accuracy, precision, recall, and cross-validation.

7- Deployment and Integration

Successful models are deployed into production systems, where they can make real-time predictions or assist in decision-making. Deployment involve integrating the model into a software application or making it available as a web service.

8- Monitoring and Maintenance (Datascience Ops)

This phase involves monitoring and maintaining the deployed model. It includes monitoring its performance, retraining it with new data, and updating it as necessary

The Data Science Life Cycle is an essential process for any data science project. It ensures that all aspects of a project are considered and that all stakeholders are aligned with the project’s goals. By following this process, data scientists can ensure that their projects are successful.