Data science is an interdisciplinary field that involves the use of statistical, computational, and machine-learning techniques to extract insights from data. Solving data science problems requires a systematic approach that involves understanding the problem, identifying the data source, cleaning the data, extracting insight using exploratory data analysis and preparing data for model building, evaluating the performance of the model and then using a recursive approach to improve the quality and performance of the model. In this blog, we will discuss a step-by-step approach along with tools and techniques used for a data science problem.
Step 1. Understanding the problem
To solve a problem effectively, the first and most crucial step is to understand it thoroughly. Whether the problem pertains to various fields, having background knowledge is necessary to make informed decisions during the problem-solving process. Therefore, conducting extensive research around the problem statement is essential to ensure sound decision-making. Additionally, analyzing the business goals and target audience is equally important.
Tools: Google, AI tools, research paper, article and opinion of a subject expert.
Techniques: Reading and researching.
Step 2. Collecting the data
Data is essential to data science. Defining the data sources is the very next step. We may need to extract data from web pages or read data from a database or through an API. It is important to ensure that the data is relevant, accurate, and representative of the problem domain.
Tools: Databases, API's and web pages.
Techniques: Querying and web scrapping.
Step 3. Cleaning and analyzing data.
Most of the time, the data extracted from external sources is not perfect, necessitating the need for cleaning. This involves identifying and correcting errors, filling in missing values, removing duplicates, and transforming the data into a format suitable for analysis. As the data is clean now, analysis is performed to derive insight and test hypotheses around the problem. This involves visualizing data with the help of charts and graphs. Analysis of data gives a new insight that we are not aware of thus giving us better intuition to move forward to build our model.
Tools: Numpy, Pandas, Matplotlibs and Seaborn.
Techniques: Analysis and visualization.
Step 4. Building the Model.
After cleaning the data and gaining a better understanding of it, the following step is constructing the model. This involves selecting the most appropriate statistical or machine learning method based on the problem statement.
Here are some more key steps used in building the model.
Encoding: Since algorithms only understand numerical data, encoding is employed to convert categorical data into numerical data.
Feature scaling: Features with larger magnitudes have a greater impact on the model's outcome than those with smaller magnitudes. To address this, feature scaling is implemented to standardize all feature magnitudes.
Train-test split: The available data is divided into two parts: the train and test sets. As the name implies, the train set is used to train the model, while the test set is used to evaluate its performance.
Constructing a model is an iterative process since several techniques can be utilized to address the same issue. Thus, we must iterate over possible techniques and select the most effective one.
Tools: Sci-kit Learn, Tensorflow, PyTorch
Techniques: Encoding, Feature selection and scaling
Step 5. Evaluating and Improving the Model.
After constructing the model, we assess its performance using test data. This is accomplished by measuring various metrics such as error, variance, bias, accuracy, precision, recall, and so on. To enhance model performance, we may conduct hyperparameter tuning, which involves iteratively selecting the optimal parameters for our model.
Tools: Sci-kit Learn, Tensorflow
Techniques: RMSE, MSE, Accuracy, Precision, Recall, F1 Score, Hyperparameter tuning
Step 6. Deployment
Our final task is to make our model accessible to end-users by deploying it. To achieve this, we must create an interface that allows users to input the necessary data or gather data through user interaction. Once the data is available, our model will process it and generate the desired outcome for the user.
Tools: Flask, AWS, Azure
Technique: Cloud computing, Web development
Conclusion
In conclusion, approaching data science problems requires a systematic and rigorous approach that involves understanding the problem, collecting data and building, improving and deploying the model. By following these steps, data scientists can ensure that their analyses are relevant, accurate, and aligned with the business goals.