Must-Have Checklist for Beginners: The 8-Steps to a Perfect Data Science Project
Must-Have Checklist for Beginners: The 8-Steps to a Perfect Data Science Project
There are a lot of buzzwords like "data science" floating around on the Internet these days. Some of you may have been swayed by the "sexiest job of the 21st century" label and decided to pursue a career in data science.

There are a lot of buzzwords like "data science" floating around on the Internet these days. Some of you may have been swayed by the "sexiest job of the 21st century" label and decided to pursue a career in data science.


On the other hand, you're probably new to Data Science, and you're about to embark on your first project and want to ensure that you're on the right track so you don't fall into bad habits. Your main focus is to build a project to showcase your skills and abilities to interviewers, plus that you have a record of doing what you're capable of doing. 


Deciding to work on data science projects can feel overwhelming and complicated. But, it doesn't have to be. Using the right strategy and steps, you'll feel more in control of your next data science project. You may even consider taking data science courses and developing your data science projects without any hassle. 


Before you embark on a data science project, there are some factors to consider and even a checklist to help you get started.


  1. Hypothesis generation

  2. Problem statement

  3. Data collection 

  4. Data cleaning

  5. Exploratory Data analysis

  6. Feature engineering

  7. Modeling

  8. Communication 


Let's dive deeper into each of these processes and address them with helpful ideas and tricks.

  1. Hypothesis generation


This is the most important phase in every data science project. And yet almost all newbies to data science are unprepared for it. A hypothesis-driven approach is significantly more effective. You'll begin by formulating a hypothesis or an assumption and then compiling a list of potential variables for the study. These factors are not always available. Following this activity, you'll go over the data and select the necessary variables.

  1.  Understanding the problem and stating them 


Be it a personal project or a business problem, a well-defined and clear problem saves a lot of time and effort. The purpose of the problem statement is to express the issue concisely you're attempting to resolve. If done correctly, you easily define and explain the problem. 

Hence, try to understand the problem and state them concisely for better results. 

  1. Collecting the right set of data


Data collection is the process of obtaining and analyzing data on specific variables of interest in an organized system, which enables you to find the relevant answers to the problem. Example: Kaggle, Company dataset server, or self-collected data (must be authorized and verified from reliable resources). 

Keep in mind that your data is both validated and relevant. No matter how good your model is, if your data isn't appropriate for the problem you're trying to solve, your results will be useless. Remember, QUALITY IS IMPORTANT! 

  1. Cleaning data is a very important step!


Data cleaning will take almost 80% of your time. It must not be ignored since it is a very crucial step. It is a task that can "make or break" your whole analysis process. The most common cleaning activities include outlier treatment, categorical encoding features, etc.

Cleaning the data results in good quality information and ultimately leads to better conclusive and accurate decisions. 


  1. EDA (Exploratory data analysis)


EDA is another important stage in any data science project. This is the phase where you show your creative side. EDA helps in a deeper understanding of data. As a part of the EDA process, we look into the dataset in search of patterns, outliers, and hypotheses based on what we've learned about it.

Exploratory data analysis aims to find underlying patterns within the data, detect outliers and test assumptions to find a model that fits the data well.


  1. Feature Engineering


Feature engineering is a crucial aspect of a data science project. It is a technique that leverages data to create new variables that aren't in the training set. To make data transformations faster and more accurate, it can generate newly supervised and unsupervised learning features. Bear in mind that you will likely return to this stage numerous times throughout the procedure, and it's NORMAL! 


  1. Modeling is the key! 


One of the first things every data scientist has to do is figure out how to get their model out into the world (aka Model deployment). If you want to build a model successfully, you need to learn to program. Many beginners tend to ignore coding as it is hectic for them. 

This is a critical obstacle that every data scientist encounters during their first project, as no one needs to deploy their model as a beginner. Hence, you must develop some fundamental coding and computer science abilities. Acquire as much knowledge as possible about version control, how to write neat code, and how to use GitHub. All of this is connected to your data science capabilities.


Data modeling is critical to the success of any data science project. This stage involves implementing, executing, and improving programs that will be used to evaluate and collect critical business information from the data. Modeling can be accomplished using a variety of open-source tools. 


After making a few machine learning models, the models need to be trained by changing the hyperparameters to make them work better. 

For example, you can use R, Python, and SAS to create a statistical model. 


  1. Communicating the results! 


Lastly, work on your communication skills. Data science is all about effectively explaining your results. Do it passionately using a storytelling technique and show your audience or client why your finding is intriguing. 

You can do it via presentation, a formal report, or even a blog post. What matters is that the world recognizes what an incredible job you performed. Before beginning a data science project, ensure that you understand your audience and that the information flows efficiently.




To conclude, good data science projects are built on solid foundations. By keeping a checklist before you start, you can ensure that your work is the best it can be. Eventually, creating good-quality projects will gain the respect of your peers and boost your reputation as a data scientist. 


I hope this article has given you an idea of what things can go right or wrong with your data science projects. When planning your data science projects, keep them in mind to avoid these pitfalls. If you want to learn more about data science and gain confidence in developing data science-related projects, check out the data science course in Chennai, where experts will walk you through various domain-specific data science projects. 


This will certainly help you strengthen your portfolio and land your desired data science job in one go.