Data Science is the amalgamation of two fields – Data and Science. Data is any real or imaginary thing, and science is nothing but the systematic study of the world, both physical and natural. So Data Science is nothing but a systematic study of data and derivation of knowledge using testable methods to do predictions about the Universe. In simple words, it's applying science to data that may be of any size and from any source. Data has become a new oil that is driving businesses today. That’s why understanding the data science project life cycle is crucial. As a Data Scientist or Machine Learning Engineer or as a Project Manager, you must be aware of the important steps. A Data Science course will help you get a clear understanding of the entire data science lifecycle.
What is a Data Science Life Cycle?
A data science lifecycle indicates the iterative steps taken to build, deliver and maintain any data science product. All data science projects are not built the same, so their life cycle varies as well. Still, we can picture a general lifecycle that includes some of the most common data science steps. A general data science lifecycle process includes the use of machine learning algorithms and statistical practices that result in better prediction models. Some of the most common data science steps involved in the entire process are data extraction, preparation, cleansing, modeling, evaluation etc. The world of data science refers to this general process as the “Cross Industry Standard Process for Data Mining”.
We will go through these steps individually in the subsequent sections and understand how businesses execute these steps throughout data science projects. But before that, let us take a look at the data science professionals involved in any data science project.
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Who Are Involved in The Projects?
The data science projects are applied in different domains or industries of real life like Banking, Healthcare, Petroleum industry etc. A domain expert is a person who has experience working in a particular domain and knows in and out about the domain.
A business analyst is required to understand the business needs in the domain identified. The person can guide in devising the right solution and timeline for the same.
A data scientist is an expert in data science projects and has experience working with data and can work out the solution as to what data is needed to produce the required solution.
Machine Learning Engineer:
A machine learning engineer can advise on which model to be applied to get the desired output and devise a solution to produce the correct and required output.
Data Engineer and Architect:
Data architects and Data engineers are the experts in the modeling of data. Visualization of data for better understanding, as well as storage and efficient retrieval of data, are looked after by them.
The Lifecycle of Data Science
The major steps in the life cycle of a Data Science project are as follows:
1. Problem identification
This is the crucial step in any Data Science project. The first thing is understanding in what way Data Science is useful in the domain under consideration and identification of appropriate tasks which are useful for the same. Domain experts and Data Scientists are the key persons in the problem identification of problem. Domain expert has in depth knowledge of the application domain and exactly what the problem is to be solved. Data Scientist understands the domain and help in identification of problem and possible solutions to the problems.
2. Business Understanding
Understanding what customer exactly wants from the business perspective is nothing but Business Understanding. Whether customer wish to do predictions or want to improve sales or minimize the loss or optimize any particular process etc forms the business goals. During business understanding, two important steps are followed:
For any data science project, key performance indicators define the performance or success of the project. There is a need to be an agreement between the customer and data science project team on Business related indicators and related data science project goals. Depending on the business need the business indicators are devised and then accordingly the data science project team decides the goals and indicators. To better understand this let us see an example. Suppose the business need is to optimise the overall spendings of the company, then the data science goal will be to use the existing resources to manage double the clients. Defining the Key performance Indicators is very crucial for any data science projects as the cost of the solutions will be different for different goals.
Once the performance indicators are set then finalizing the service level agreement is important. As per the business goals the service level agreement terms are decided. For example, for any airline reservation system simultaneous processing of say 1000 users is required. Then the product must satisfy this service requirement is the part of service level agreement.
Once the performance indicators are agreed and service level agreement is completed then the project proceeds to the next important step.
3. Collecting Data
Data Collection is the important step as it forms the important base to achieve targeted business goals. There are various ways the data will flow into the system as shown in figure 2.
The basic data collection can be done using the surveys. Generally, the data collected through surveys provide important insights. Much of the data is collected from the various processes followed in the enterprise. At various steps the data is recorded in various software systems used in the enterprise which is important to understand the process followed from the product development to deployment and delivery. The historical data available through archives is also important to better understand the business. Transactional data also plays a vital role as it is collected on a daily basis. Many atistical methods are applied to the data to extract the important information related to business. In data science project the major role is played by data and so proper data collection methods are important.
4. Pre-processing data
Large data is collected from archives, daily transactions and intermediate records. The data is available in various formats and in various forms. Some data may be available in hard copy formats also. The data is scattered at various places on various servers. All these data are extracted and converted into single format and then processed. Typically, as data warehouse is constructed where the Extract, Transform and Loading (ETL) process or operations are carried out. In the data science project this ETL operation is vital and important. A data architect role is important in this stage who decides the structure of data warehouse and perform the steps of ETL operations.
5. Analyzing data
Now that the data is available and ready in the format required then next important step is to understand the data in depth. This understanding comes from analysis of data using various statistical tools available. A data engineer plays a vital role in analysis of data. This step is also called as Exploratory Data Analysis (EDA). Here the data is examined by formulating the various statistical functions and dependent and independent variables or features are identified. Careful analysis of data revels which data or features are important and what is the spread of data. Various plots are utilized to visualize the data for better understanding. The tools like Tableau, PowerBI etc are famous for performing Exploratory Data Analysis and Visualization. Knowledge of Data Science with Python and R is important for performing EDA on any type of data.
6. Data Modelling
Data modelling is the important next step once the data is analysed and visualized. The important components are retained in the dataset and thus data is further refined. Now the important is to decide how to model the data? What tasks are suitable for modelling? The tasks, like classification or regression, which is suitable is dependent upon what business value is required. In these tasks also many ways of modelling are available. The Machine Learning engineer applies various algorithms to the data and generates the output. While modelling the data many a times the models are first tested on dummy data similar to actual data.
7. Model Evaluation/ Monitoring
As there are various ways to model the data so it is important to decide which one is effective. For that model evaluation and monitoring phase is very crucial and important. The model is now tested with actual data. The data may be very few and in that case the output is monitored for improvement. There may be changes in data while model is being evaluated or tested and the output will drastically change depending on changes in data. So, while evaluating the model following two phases are important:
Changes in input data is called as data drift. Data drift is common phenomenon in data science as depending on the situation there will be changes in data. Analysis of this change is called Data Drift Analysis. The accuracy of the model depends on how well it handles this data drift. The changes in data are majorly because of change in statistical properties of data.
To discover the data drift machine learning techniques can be used. Also, more sophisticated methods like Adaptive Windowing, Page Hinkley etc. are available for use. Modelling Drift Analysis is important as we all know change is constant. Incremental learning also can be used effectively where the model is exposed to new data incrementally.
8. Model Training
Once the task and the model are finalised and data drift analysis modelling is finalized then the important step is to train the model. The training can be done is phases where the important parameters can be further fine tuned to get the required accurate output. The model is exposed to the actual data in production phase and output is monitored.
9. Model Deployment
Once the model is trained with the actual data and parameters are fine tuned then model is deployed. Now the model is exposed to real time data flowing into the system and output is generated. The model can be deployed as web service or as an embedded application in edge or mobile application. This is very important step as now model is exposed to real world.
10. Driving insights and generating BI reports
After model deployment in real world, the next step is to find out how the model is behaving in real-world scenario. The model is used to get insights that aid in strategic decisions related to business. The business goals are bound to these insights. Various reports are generated to see how business is driving. These reports help in finding out if key process indicators are achieved or not.
11. Taking a decision based on insight
For data science to do wonders, every step indicated above has to be done very carefully and accurately. When the steps are followed properly, then the reports generated in the above step help in making key decisions for the organization. The insights generated help in taking strategic decisions for example, the organization can predict that there will be a need for raw materials in advance. Data science can be of great help in making many important decisions related to business growth and better revenue generation.
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Data Science is the buzzword now because of its success in many applications. Right from the Petroleum industry to the retail business, everyone is ripping benefits from data science. A careful understanding of the data science life cycle and proper implementation of the steps indicated above helps in business growth. There are many tools available to extract insights from the data and can then be used to improve business. A Knowledgehut's data science with Python can be a pioneer in better-understanding data science and the data science life cycle in a better way.