There are many steps in the data science project. Capturing the data science process translates directly to life and death for any project. Let's take a look at the key steps of a data science project lifecycle.
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Data Science Project Lifecycle
Data science is rapidly developing into one of the most demanding fields in the technology industry. With rapid advances in computational growth now enabling analysis of massive data sets, we are able to uncover patterns and insights into user behavior and global trends to an unprecedented extent.
Often when we talk about a data science project, it seems very vague as to how the entire process takes place, from data collection to data analysis and data production.
The data science project life cycle begins with the business question through which the client raises a need, either specific to their own company or more general, common to companies in the same sector.
In general, most data science projects follow a very similar structure, standardized by academic books and the community. This structure includes the steps needed to find the best mathematical model and work with quality data. However, the best mathematical model does not always have to be the one that brings the most benefits to the company.
In this article, we'll break down the entire data science framework, taking you through each step of the data science project lifecycle.
What are the Key Steps of a Data Science Project?
There are many steps in the data science project. Capturing the data science process translates directly to life and death for any project.
Here is a very helpful framework that covers every step of the data science project lifecycle to understand what data scientists do, and break down any data science problem.
The key steps of a data science project lifecycle can be summarized in the following ways:
Understanding the problem and the business: Understanding the business or activity of which a data project is a part is the first stage of any sound data analysis project to ensure its success.
It is imperative that the details of the problem are clear before you dive into the actual implementation part. It is important to find out what is right to get the right data and get the right solution. Once the problem is understood, it is mandatory to get the right data to perform the operation.
Gathering Data: The first step in any data science project is to collect and get the data you need and gather information from the data sources available. If you don't have any data at all, you won't be able to process anything. There are many resources to obtain data. The most convenient way to obtain data is straight from the files.
Data Cleaning: The next step is data cleaning, referring to data scrubbing and filtering. Therefore, the team needs to identify the data required to solve the underlying problem. This procedure requires converting the data to a different format.
The data cleaning process includes detecting and removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within the recordset, table, or database.
It is said that 90% of the work done by a data scientist is related to data analysis.
The term "data analysis" refers to the cleaning and pre-processing of data before the construction of a statistical model. This step includes outliers, duplicate data, null values , and many other anomalies, which do not fall within the data agreement required for business purposes.
Depending on the business domain, the metric that will determine the completeness of a model should be selected.
Exploring Data: Once your data is ready for use, and before moving directly into AI and machine learning, you will have to explore the data.
Your manager will throw you a bunch of data, and it's up to you to understand it, figure out the business questions, and turn them into a data science project.
You will have to examine the data and its properties, calculate descriptive statistics to extract features, and test important variables.
Visualizing Data: Data visualization is a general term that refers to a graphical representation of information and data using visual elements such as graphs, charts, and maps.
Data visualization tools help people understand the importance of data and provide an accessible way to see the trends, patterns, and relationships in data.
Once data has been cleaned and processed in advance, it is necessary to visualize the data to determine the correct features or columns to use in the statistical model.
Data Modeling: Data modeling is the process of creating a data model to explore data-oriented structures, determining how data is exposed to the end-user and how data is stored in a database. The selection of the correct model is necessary for a particular problem statement because each model may not fit perfectly to each data set.
Hierarchical Encoding: This step of the data science process is applicable for instances where input attributes are explicit and need to be converted into numbers used in the model because the machine cannot function properly with some ranges.
Communication: Businessmen, salesmen or shareholders, usually do not understand the technical knowledge of data science, and therefore it is necessary for their business to communicate the findings, products, and services to their customers in simple terms, which can then come up with measures to alleviate any potential risk.
Model Deployment: Sometimes, the word "implementation" is used to mean the same thing. Once the statistical model is built, and the business domain is satisfied with the findings and results, test data science models before actually deploying them into production. This model can be deployed and implemented to build analytical tools and improve business efficiency.
Read Also:
1. The Importance of Data Science in the Modern World
2. What Data Scientists Do and How to Become a Data Scientist?
3. What is Data Analysis? Data Analytics Trends and Objectives
4. Applications of Data Science in Finance and Business Analytics
5. Big Data Types and Characteristics: Applications of Big Data in Real Life
6. Data Warehouse (DW)- Characteristics, Architecture, and Principles