Once you’ve decided that you want to dive into the fascinating world of data and artificial intelligence, it’s not easy to know where to start. It can be dizzying looking at all the innovations you have to learn and the tools you must master. What are the first steps you take in data science training?
Fortunately for you, designing your first data analytics project plan is not as complicated as it sounds. Yes, starting with a tool designed to inspire people from all backgrounds and skill levels, as Dataiku supports, you first need to understand the data science process itself. Being data-driven is first and foremost about understanding a data analytics project’s basic steps and phases and following them from raw data preparation to machine learning model creation and operationalization.
These seven data science steps will help you understand each specific project’s business value and reduce the risk of error.
The seven steps are broken down as follows:
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Understand the company
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Get your data
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Explore your data and clean it up
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Enrich your data collection
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Create useful visualizations
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Get predictive
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Iterating, Iterating, Iterating
1. Understand The Company
To ensure its effectiveness and the first phase of any healthy Data Analysis project, it is essential to understand the organization or operation that your data project is a part of. Your project must respond to a strong organizational need to inspire the many actors needed to take your project from concept to production. Go out and talk to the AI developers in your company whose processes or business you’re trying to develop with data before you even think about data. Sit back and identify the timeline and critical primary performance metrics. I know planning and procedures sound tedious, but in the end, they are a necessary first step to jump-starting your data initiative!
2. Get Your Data
It’s time to start looking for your results, which is the second step of a data analysis project, once you’ve identified your goal. What makes a data project awesome is combining and integrating data from as many data sources as possible, so look as far as you can.
Here are several methods for getting information that you can use:
Database Connections:
To learn more about the data your company has collected, consult data science professionals and IT teams, or start by exploring your private database.
Use APIs:
Think about APIs for all the resources your organization uses and the knowledge those people have gathered. You need to focus on setting all of these up so that you can use those emails’ open and click statistics, the information that the sales team put into Pipedrive or Salesforce, the aid ticket someone submitted, etc.
Search open data:
The internet is full of datasets to add more knowledge to what you have.
Our Learners Also Read: Types of Sampling Techniques in Data Analytics You Should Know
3. Explore Your Data and Clean it up
The next phase in data science is the dreaded data preparation method, which typically takes up to 80% of the time allocated to a data project.
Once you have your data, it’s time to start working with it in the third project data analysis process. To achieve your original goal, begin by examining what you have and how you can connect it. Start taking notes from your first assessments and ask questions of the marketers, IT team, or other organizations to understand all the factors.
4. Enrich Your Data Collection
Now is the time to use it when you have clean information to get the most out of it. To narrow your data to critical functions, you should begin the project data enrichment process by bringing together all the different sources and group logs. One example is knowledge enrichment by creating time-based characteristics such as:
Extracting parts of a date (month, hour, day of the week, week of the year, etc.)
Calculating the differences between columns of data
Designation of public holidays
5. Create Useful Visualizations
A graphic showing dashboard build reports and charts in a data project.
Now that you have an excellent data set (or maybe several), it’s a good time to explore it by creating graphs. The next stage of any data analysis project should involve visualization because it is the greatest approach to examine and present your findings when working with vast amounts of data. The challenging element is being able to access your charts at any moment and respond to any inquiries concerning that insight. Since you completed all the grunt work and know the data like the back of your hand, that is when data preparation comes in helpful.
Arnold Schwarzenegger’s robotic hand GIF shows the palm shifting position.
Use APIs and plugins to distribute these statistics to where your end consumers want them if this is the last stage of your project. Graphs are another way to enrich your dataset and develop more exciting features. For example, by placing data points on a map, you may notice that specific geographic zones are more expressive than particular countries or cities.
6. Get Predictive
graphic of a geometric building symbolizing construction observations and graphs in a data project
The next step in data science, the sixth phase of a data project, is when the real fun begins. You may get more information and forecast future trends by using machine learning techniques. You can develop models to find patterns in data that cannot be seen in graphs or statistics by using (or unsupervised) clustering methods. Their aggregate related events into clusters and more or less explicitly state the feature that determines these outcomes.
More sophisticated data scientists can go even further and predict future trends using guided algorithms. They discover features that have influenced previous trends by examining historical data, and they employ these features to predict future trends. More than just gaining knowledge, this last step can create entirely new products and processes.
Even if you’re not quite there yet on your personal data journey or your organization’s journey, it’s essential to understand the process so that all parties involved can understand the end result.
Finally, for your project to gain real value, your predictive model must not sit on the shelf; you need to make it work. A Machine Learning model is operationalized (o16n) when it is implemented throughout an enterprise. For your business to fully profit from your data science efforts, operationalization is essential.
7. Iterating, Iterating, Iterating
Any company project’s major objective is to swiftly establish its effectiveness in order to support your task. For data initiatives, the same holds true. You can quickly complete the project and obtain preliminary results by taking the time to clean and enrich the data. This is the last step in finishing your data analysis project, and it’s an important one for the whole data life cycle. One of the biggest errors people make with machine learning, according to O’Reilly, is believing that once a model is developed and functioning, it will remain operational indefinitely. On the contrary, models deteriorate over time if they are not constantly improved and fed with new data.
Ironically, to successfully complete your first data project, you must realize that your model will never be ultimately complete. To remain valuable and accurate, you must constantly reevaluate, retrain, and develop new features. If there’s one takeaway from these basic steps in analytics and data science, it’s that the work of data scientists is never really done, but that’s what makes working with data so fascinating!
Conclusion
In this blog, we covered the main steps of the data analysis process. These basic steps can be modified, rearranged, and reused as you see fit, but they are fundamental to the work of any data analyst:
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Understand the company
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Get your data
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Explore your data and clean it up
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Enrich your data collection
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Create useful visualizations
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Get predictive
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Iterating, Iterating, Iterating
