One of the most trending field of studies is the Data Science. Usually every beginner in this field has a question on how to deal with the project. Therefore, a standard workflow has been put by the Data Scientists. This is to ensure that every project synchronizes in well and can be communicated easily. The product of a Data Science project will always be effective results that can be interpreted. To reach the product, Data Scientists have formulated a systematic step-by-step workflow process. We should understand that not all Data Science projects follow the same workflow process, but most of them do. There is no single workflow process that can be defined for all Data Science projects. However, there is one standard procedure put up by the data scientists.
Here are the steps to obtain effective results from the Data Science Projects:
- Data Collection / Data Acquisition:
It is the first step of the data science project in which we obtain the data from the available sources. The data source can be from online repositories, data from social media, online data, online web servers or web scraping. These are the ways to acquire the data. The acquired data must help the data scientists to solve the problem. The data must be collected from genuine sources and ensure the data is up-to-data to obtain better model and results.
- Data Cleaning / Data Preparation:
The next step is Data Cleaning or Data preparation. The data obtained from various sources is in raw format, i.e. the data contains errors, missing values, outliers etc. The raw data should be processed in order to build the model using the data. The data is cleaned using some code or using the spreadsheet. The splitting and merging of the columns are done depending upon requirement. It one of the most important steps that needs to be performed in Data Science Project.
- Exploratory Data Analysis (EDA):
EDA is one of the interesting steps in Data Science project. In this step, we examine the data. To understand the complete data we perform the exploratory data analysis. The properties, trends, patterns and relationship among the data can be found in this step. It helps us to visualize the data and helps us in better understanding of it. The different types of data require different methods of solving. We have categorical data, continuous/ numerical data, ordinal and nominal types of data, which are solved in different ways.
Thus, EDA is a very important step helps in understanding various features of the data.
- Build the Model:
Before we build the Data Science Project model, we need to perform a small step that is the splitting of data into training data and testing data. This is important to see the performance of the data. The training data is used for training the model where it learns from the data. The testing data is used to evaluate the performance of model.
We often chose the model depending upon the kind of data we have obtained. Various algorithms are used to build the model. We have to perform operations on the data because not all the features and values are essential for the model. Selecting the relevant variables help in better results.
- Evaluate the model:
After the model is built, the model needs to be evaluated for its performance. The model is fed with testing data. Here, the model has not seen the testing data. But the results for the testing data are known to us. The model predicts the output for the testing data. Using various performance metrics, we compare the known and predicted output. If better results are obtained, we retain this model; else, we make some minor or major changes to optimize the model. Again, the model is tested for performance and then deployed, if the results are found to be good. The model accuracy is an important factor in determining its deployment.
- Model Deployment :
The best model is selected based on various performance parameters and is deployed in the Data Science project. The predictions of this model are accurate to a certain extent. The predictions results can be used for further interpreting the results.
This is the final step of the Data Science project. In this step, we draw conclusions based on the results that we obtained. The predictive power of such a model depends on the ability to generalize the unseen data. Interpreting the results means presenting the results to a common-man. The model solves or gives answers to most of the questions that are raised in the project.
In addition, the results must be visualized for better understanding. The visualizations must be in accordance with the Data Science Project. Better understanding and communication skills come handy while interpreting the results of the Data Science Project.