Data Science for Beginners: Data Acquisition and Data Science Life Cycle

Data Science for Beginners: Data Acquisition and Data Science Life Cycle

As any data scientist will tell you, when working with Big Data, it is always better to follow a well-defined data science workflow. This is because, having a standard data science workflow, as explained over at runrex.com, will ensure that the various teams within the organization you are working with are in sync, avoiding any delays as far as the project is concerned. As the gurus over at guttulus.com are quick to point out, there is no one-size-fits-all workflow process for data science projects, which means that you as a data scientist will have to determine which workflow best fits the business requirement of the organization where you are working. Having said that, there is a standard workflow for data science projects, known as the data science lifecycle, and this article will look to explain what it is all about, including what data acquisition is also about.

The data science lifecycle entails 7 stages as covered below.

• Data Acquisition

For any data science project, you need data. This is why the first stage in the data science lifecycle is data acquisition, which entails identifying the person who knows what data to acquire and when to do so based on the question that is to be answered. Here, as explained over at runrex.com, you start by identifying various data sources, which could include web servers, social media data, data from online repositories such as the US census datasets, data streamed from online sources like APIs, among many other sources. Therefore, data acquisition involves the acquiring of data from all the identified internal and external sources that can help answer the business question at hand as far as the project is concerned.

• Data preparation

This phase is also known as the data cleaning or data wrangling phase, and for many data scientists, this is probably the most boring and time-consuming phase of them all. It involves the identification of various data quality issues, as data acquired in the first phase of the data science project is usually not in a usable format that will allow one to run the required analysis. Such data may contain missing entries, inconsistencies, and semantic errors, and therefore has to be cleaned ad reformatted by manually editing it in the spreadsheet, or by writing code. This stage of the data science lifecycle doesn’t produce any meaningful insights. After reformatting the data, it can now be converted to JSON, CSV, or any other format that makes it easy to load into one of the data science tools.

• Hypothesis and Modelling

According to the subject matter experts over at guttulus.com, this is the core activity of a data science project, requiring writing, running, and refining the programs to analyze and derive meaningful business insights from data. These programs are often written in languages like Python, R, MATLAB, or Perl. Here, diverse machine learning techniques are applied to the data to identify the machine learning model that fits best the business needs as far as the project is concerned.

• Evaluation and Interpretation

Different evaluation metrics exist for different performance metrics. For example, if the machine learning model is meant to predict the daily stock, then the root mean squared error, RMSE, will have to be considered for evaluation, among other examples given over at runrex.com. Here, machine learning model performances should be measured and then compared using validation test sets to identify the best model accuracy and over-fitting.

• Deployment

The fifth phase of the data science lifecycle is the deployment stage. To start with, machine learning models might have to be recorded before deployment since data scientists usually favor Python programming language, although the production environment supports Java as explained over at guttulus.com. Once this is done, the machine learning models are first deployed in a pre-production or test environment before they are deployed into production.

• Operations or Maintenance

This phase involves developing a plan for the monitoring and maintaining of the data science project in the long run. The model performance will be monitored, with performance downgrade being monitored in this phase as covered over at runrex.com. As a data scientist, you can achieve your learnings from a specific data science project for shared learning as well as to speed up similar data science projects in the near future.

• Optimization

The final phase of the data science lifecycle is known as the optimization phase. It is the final phase of any data science project that involves the retraining of the machine learning model in production whenever new data sources are coming in or taking the necessary steps to keep up with the performance of the machine learning model, as is explained over at guttulus.com.

The above discussion captures what the data science life cycle entails, although it is important to note that the above process is not definitive and can be altered accordingly to improve the efficiency of a specific data science project as pertains to your business requirements. You can uncover more information on this and other related topics by visiting the excellent runrex.com and guttulus.com.