A Blueprint for Successful Data Science Projects Development

Earlier business decisions were made based on lengthy and laborious market research processes. But now with the availability of a huge amount of data at their fingertips, certified Data Scientists in every industry can make accurate and informed decisions. The problem with developing data science projects is the humongous amount of data, the unavailability of experienced data science professionals, and the unavailability of the right information.

In this article, we shall discuss the key steps required to develop a successful data science project, the common problems that occur while developing the project, and how to get rid of them. So, if you are starting your data science career, then this blog is a must-read.

What is a data science project?

A data science project involves collecting and cleaning data, performing exploratory data analysis, developing, and training machine learning models, and deploying the model to solve the problem or answer the question. Developing a successful data science project requires data science professionals to have strong domain knowledge, good communication skills, and technical skills such as machine learning and data analysis.

Key steps to develop a successful data science project

Let us begin with the important phases of developing a successful data science project:

• Defining the problem and goals

Before developing a data science project, the foremost thing to consider is the problem and scope of the project. This could be done by defining the problem statement and mentioning research questions that need to be answered. The data science professionals will be able to work on specific data sets and not all the unnecessary data the company accumulates over time.

• Collecting and cleaning data

After the problem and scope of the project are defined, the certified Data Scientists get a clear understanding of what sort of data they need. Thereafter, the data are collected from all sources like databases, APIs, web scraping, etc. Then data filtration is carried out based on the project requirements by removing duplicate contents, eliminating missing values, errors, etc. Towards the end, the data scientist is left with complete, clean, and accurate data.

• Performing exploratory data analysis

This being a crucial step in project development, the certified Data Scientist will work with various tools, work with visualization techniques, summarize the data, and gain data insights. This helps to identify the pattern and relationships in the data.

• Development and training of machine learning models

The students who aspire to establish themselves in a data science career need to be efficient with programming skills as developing a successful data science project requires the development of an efficient machine learning model. This involves selecting the appropriate machine learning algorithms, tuning the parameters, and evaluating their performance. You’ll need to split the data into training, validation, and testing sets to ensure that the model performs well on unseen data.

• Evaluation and deployment of the model

Once the model is developed, it needs to be continuously evaluated and tested. The model should be able to work on unseen data sets without compromising its performance. When the model produces satisfactory results with the testing set and new data sets, it can be deployed and integrated with the current business operations. This will ensure the data science project is efficient. Also, the model can be scaled with business over time.

Common problems in data science project development

Although it sounds simple, developing a successful data science project can be a very frustrating task. Often, developers come across common problems that delay the development and deployment of the project. The most common problems include:

• Knowledge gap– Technology is evolving rapidly and therefore the existing knowledge, be it technical skill or domain expertise, falls short. This can be corrected by doing an online course, necessary data science certifications, and researching online.
• Data quality – Often businesses must deal with inaccurate, irrelevant, and incomplete data. Sometimes there is a lack of required data. In such cases, the analysis and ultimately the modeling is affected.
• Communication gap – Right from the leaders to engineers, everyone needs to be in sync. They need to be aware of the problem and the solution they are working on. Proper communication ensures that data science professionals collectively work towards a single goal.

Conclusion

We learned about the key steps in developing a successful data science project and understood the common problems that might occur during the process. If you are an aspiring candidate for a flourishing career in data science, then earning a credible data science certification and experience on building a real-time data science project can help you advance your career.