Eight easy steps to begin your first data science project

Key takeaways:

• Data science is an interdisciplinary field that helps develop strategies to analyze, prepare, explore, visualize, and build data through programming languages.
• Data science uses modern tools and techniques to find complex patterns in volumes of data.
• Data science projects include data collection, analysis, machine learning, and programming skills. 

Global business organizations leverage data science to solve multiple problems. Every digital product or experience demands data science application to ensure efficient customer experience and personalization. That is why data science professionals get opportunities to showcase their talent and help worldwide business organizations to resolve data-related issues.

However, early career professionals must know the steps for data science projects and have a theoretical foundation to land a job with organizations. Hiring managers want data scientists with hands-on experience delivering projects to solve real-world problems. 

If you aspire to be a data scientist, you must know how to find data science projects and demonstrate your abilities.

Why should you start a data science project?

Data science enables organizations to understand data from multiple sources and derive meaningful insights for value-driven business decisions. It applies to industry domains like healthcare, marketing, finance, policy work, etc. 

Here is a breakdown of why you should start a data science project.

• Clarity with data science basics

Data science projects give you an idea of real-world problems in business organizations and the techniques and strategies to solve them. You can also learn about the tools used in data creation and analysis, such as machine learning, Tableau, spreadsheets, statistics, etc. You can use the projects to advance your career and become an experienced professional in the field.

• Hands-on experience with tools and techniques

You can put your theoretical knowledge into practice if you learn how to build a data science project from scratch. The process also allows you to experience modern tools and techniques that help create, visualize, and analyze data. It will help you build your technical skills in data science and get employment opportunities in the same field.

• Team collaboration

You always get to work with team members whenever you participate in a data science project. The project motivates you to work in a team environment with constraints, real-world deadlines, and opinion clashes regarding tasks and activities. The experience makes you ready to face the professional world efficiently and develop your skills in the process.

• Professional portfolio

You must have hands-on experience in how to deploy data science projects to build a professional portfolio. You can work on multiple projects to build a strong resume and show your expertise in handling workload to potential employers.

Steps to follow in a data science project

A data science project is a systematic approach to solving data-related problems in organizations. The project provides a structured framework to articulate problems as questions, ways to solve them and present a solution to the stakeholders.

You may have multiple data science project ideas, but they will be irrelevant if you do not know the steps to conduct them.

Here is a breakdown of steps to follow in a data science project.

1. Find a project topic

The first step in a data science project is to find a relevant topic. You also need to know what field you want to work in as a data science professional. Try to choose the area you know the best. Healthcare, finance, marketing, social media, and IT are some of the most popular data science areas. You can choose a domain that suits you the best and start with the project.

Once you have found the project topic, research it thoroughly, and use the best resources to work on it.

2. Choose a dataset

Choose a relevant dataset after you choose a topic for your data science project. You can find datasets from multiple sources, such as academic databases and government websites. You can also ask the organization to provide you with databases for the project. You can try searching datasets from online platforms like Reddit or Twitter.

If you have hands-on experience working on similar projects, you can target a complex dataset from a particular domain and start your new project.

3. Load dataset to your IDE

An integrated development environment (IDE) helps build applications to combine developer tools into a single graphical user interface (GUI). You can choose an IDE that suits your project and load your data set in it. You will find multiple IDEs online, such as Sublime, Atom, Visual Studio Code, PyCharm, NetBeans, etc., to help you with your data science project.

You can think of the IDE as your text editor with additional functionalities like autocomplete and syntax highlighting. You need to open your IDE and click on open or file to start the process. Navigate where the dataset gets saved on your computer, select it, and click open to get the relevant information.

4. Data processing and cleaning

You can start collecting and processing the data after you load your dataset into the relevant IDE. You can seek help from tools like Python, R, Excel, or SPSS to process the data into usable forms.  

However, there is a chance that the collected data may be unstructured, unfiltered, or irrelevant. Such data may lead to inefficient results. Consider cleaning the data to eliminate null or duplicate values, corrupt data, missing data, invalid entries, and improper formatting. It is a time-intensive process and helps you know how long a data project takes to complete.

5. Data analysis

Data analysis is one of the most significant steps for data science projects. Once you have processed and cleaned the relevant data, you can begin conducting exploratory data analysis (EDA). The process helps to convert high-quality and organized data into valuable and meaningful insights that will come in handy in the upcoming phase of a data science project.

6. Data visualization

The visualization process helps you know how to deploy data science projects. You must transform the analyzed data into charts, graphs, columns, or spreadsheets to give it uniformity ahead of implementation into organizational systems. The data visualization step deals with the graphical representation of data and information that is easy to use and understandable for the audience.

7. Prepare a summary

You must prepare a summary at the beginning of the project to have clarity on the tasks and activities that must happen during the course. The summary must include all relevant information on the project, its goal, scope, and objectives. It should also include a brief description of your approach to solving the problem, including any assumptions you made along the way. 

8. Present or share your project

You’ve spent a lot of time and effort on your data science project and want to ensure everyone sees it. But how to share your data science project ideas? Whether you’re presenting in person or sharing via digital mediums like social media or email, having a clear and concise presentation can help your audience understand the purpose of your work.

Tips to complete a data science project

As the demand for data scientists continues to grow, it is important to hone your skills and bolster your resume with projects that showcase your talent.

Here are the tips for completing a data science project successfully.

• Define the problem and the goal

The first step in any data science project is to define the problem and determine the project’s goal. This is often done through collaboration with stakeholders but can also be done independently.

If you’re working on your own, make sure you clearly understand what it is that you are trying to achieve before starting work on your project. Make sure that everyone involved in the project understands this goal as well—this will help ensure everyone’s efforts are aligned and working toward the same objective.

• Understand the data

Once you have defined your problem and goal, get familiar with what data is required for the project. It may include the type of numerical or textual data, its collection procedure, and whether there is any missing information in the dataset, and what kind of relationship exists between any two pieces of information in your dataset.

• Get familiar with the environment

Before starting your data science project, you should know what environment it will run in. Sometimes, your data science project will be used for production—meaning that it is the real solution that the company is using to operate. Other times, you’ll be building a prototype or something that’s meant to be used as part of a larger system. You can use a staging environment if you don’t have access to a production environment.

• Conduct error analysis

Error analysis is systematically searching for errors in your code and fixing them so they don’t affect the results.

• Documentation

Data science projects take two weeks to six weeks, and the last step to completing the project is documentation. Documentation is one of the best ways to prepare a plan to complete your project within the stipulated timeline. Try to make a calendar of your daily tasks and activities and document them on a spreadsheet. It will help you track the completed and pending tasks under a unified platform. 

Final thoughts

You must learn how to collect, visualize, and transform data into valuable insights as a data science professional. It will help you complete your projects on time without any errors or deadline extensions. However, you must learn how to build a data science project from scratch to gain expertise in the field. You can undertake training courses from Online Mahipal to master the concepts in data science. 

The Manipal Academy of Higher Education (MAHE) with Online Manipal offers the following courses.

• Postgraduate program (MSc) in data science
• Online MBA in Data Science

Choose a course that suits your needs and execute your knowledge and experience in data science projects successfully. Enroll today!