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Data Analytics Project steps

What Are the 8 Essential Steps for Successfully Executing a Data Analytics Project

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Data Analytics allows researchers to optimise the process of collecting processing and analysing data. Ensuring that you have collected quality data from reputable sources improves the data resources you have at hand when you are developing conclusions based on the data you have collected. If you want inspiration you should look at completed data analytics projects that were conducted by other people to give you a sense of what you should be aiming for.  

Find An Interesting Topic

If you want to produce your best work and remain motivated whilst doing so, it helps if you centre your project around something that interests you. This increases the likelihood that you will perform more effectively in the research stage of your project because, instead of feeling like you have to learn more for the sake of your project, you may feel compelled to learn more about the topic you have chosen.

Plus, when it comes to collecting your own data, you are more likely to perform this task to a higher standard because you understand more about the topic that you are researching and can draw better-educated conclusions from the data sets you have been presented with. Research and data collection are both extremely critical parts of every data analytics project, ensuring that you do a good job when executing tasks within both of these subsets will likely improve your data analytics project overall.

Obtain And Understand The Data

There are multiple ways that you can collect data for your data analytics project, these include:


Data can be collected on forms where recipients can write long-form qualitative responses to questions presented to them. This enables those working on a data analytics project to have access to detailed accounts of what others think about certain topics.

In the past two decades, an increasing amount of data has been collected electronically rather than through more traditional hard copy methods. This can help speed up the process of collecting data and ensure that data is not lost on the way to the researcher. Researchers can collect forms from websites, vendor forms, client or customer intake forms, and human resources applications.


Surveys allow you to ask as many questions as needed to your survey recipients, they allow for both qualitative and quantitative responses from survey recipients. However, the quantitative responses gained from surveys often have a reduced word count than what researchers would gain from forms. However, the ability to obtain a higher amount of information about quantitative matters can help researchers draw conclusions more easily, as the data they have been presented with is less subjective and more defined into a specific number.

For example, a survey could ask the likelihood of people visiting a certain website and ask recipients to mark their answer between 1 as the least likely and 10 as the most likely. Researchers may have a clearer understanding of the likelihood that people will visit the website using a quantitative method. If they wanted to, they could also ask survey recipients to explain their answers in a quantitative way to provide a higher level of understanding to researchers.

Additionally, the structured nature of quantitative responses from survey recipients minimizes subjectivity and ambiguity, facilitating clearer conclusions. While forms may yield more detailed qualitative insights, the concise and defined numerical data from surveys allow researchers to make statistically significant inferences. Combining these quantitative insights with optional qualitative explanations enhances the depth of understanding, as researchers can explore the reasons behind the numerical ratings. This dual approach not only enriches the data collected but also strengthens the reliability and validity of the research findings. 

Interviews And Focus Groups

Interviewing individuals individually or in focus groups can be an effective way to obtain information. You can ask participants their opinions on topics relevant to your project and collect their responses to use in your project.

Online Data ​​Repositories

You can also obtain information from sources such as Google Cloud Public Datasets, Kaggle and You can also obtain data from academic papers that include datasets, you can find academic papers using Google Scholar. If you are affiliated with a university you may also have access to academic papers that would otherwise be behind a paywall. Other data sources include social media sites such as Facebook and X (formally Twitter) as they allow users to connect to their web servers and access their data. When you are searching for data online it is important to ensure you are not giving sensitive information away about yourself as this could open you up to internet scams.

Ensure That The Data You Have Collected Is Stored Safely And Securely

Making sure that any data you have collected is stored in a secure place and that there are backup versions or copies in different, secure locations minimises the chances of your data being lost. If you are storing your data on your computer hard drive you should also upload your data to a cloud storage provider such as Google Drive, this not only backs up your data but means that you can access your data on any internet-connected device for added convenience.

Data Preparation

Within the next step, you need to perform a task called ‘data cleaning’ or ‘data wrangling’. This is essentially taking out and disposing of data that isn’t useful for your project and keeping data that is relevant. Within this step, you will also need to confirm that you have all the data that you need for your project as it is easier to ensure that you have all the data necessary before you continue to the next steps of your data analytics project. You may start writing your conclusions but then realise that you don’t have an important piece of information; it may take you longer to go back and obtain this piece of research, and when you do it may cause your conclusion of your findings to be different. This may mean that you would have to scrap your current research and start again, making sure that you have all of your research before you start the next steps in your research can save you time overall.

Once you have ensured that you have enough high-quality research you will then want to start processing your data so that you can easily store it and understand it in the future. Make sure that you have written down all of your research techniques and your thoughts and conclusions whilst you collect your data, this can help you when you are writing your conclusions later on in the process.

Data Modelling

Data modelling is the process of creating visual representations of data objects and explaining their significance to other data objects. Within data modelling, text and symbols are used to represent the way data will flow. Data modelling can be used as a blueprint for creating a new piece of software or changing or adding functionality to existing software or applications.

Data Visualisation

Data visualisation is the process of creating visual graphical representations of information and data. Charts, bar graphs and maps can be used to plot data in an easily understandable visual way. It can be easier for other people to understand your research and understand why you have come to the conclusions you have based on trends, patterns and anomalies in data.


This is where you come up with a conclusion for your findings. By combing through the data and using the graphical representations of data to help you further understand the data you have collected, you can start to investigate the reasoning why the data you have collected suggests a possible outcome. Once you have concluded your findings you can start to theorise what the implications may be.

Explanation Of Your Research Methods

Explaining your decision-making process within the research and data collection part of your data analytics project can help the recipients of your data analytics project understand if there is anything that may have skewed your data. You can provide this information yourself if you think that you have made any mistakes within your research findings, also including how this could have affected your findings. You could also include what you have learnt from conducting the data analytics project and what you would have done differently so that you can show that you have learnt from any mistakes you may have made.


Developing a quality data analytics project can be a complicated process, but ensuring that you have collected quality data is one of the most pivotal aspects of the process. Once this has been completed you can follow a step-by-step process to achieve your final conclusion.

Wanna become a data scientist within 3 months, and get a job? Then you need to check this out !