Once you’ve concluded that, yes, you want to dive into the fascinating world of data and AI, it isn’t easy to know where to begin. It can be dizzying to look at all the innovations you have to learn and the instruments you are meant to master. What measures in data science training do you take first?
Fortunately for you, designing your first project plan for data analytics is not as complicated as it sounds. Yes, beginning with a tool designed to inspire people of all backgrounds and ability levels, such as Dataiku, supports, but first, you need to understand the process of data science itself. First and foremost, being data-powered is about understanding the basic steps and phases of a project in data analytics and pursuing them from raw data preparation to creating a machine learning model and, finally, to operationalization.
These seven steps in data science will help ensure that you understand business value from each specific project and mitigate the risk of error.
Blog Contents
- Understand the Company
- Get Your Data
- Explore your data and clean it
- Enrich Your Collection of Data
- Build helpful visualizations
- Get Predictive
- Iterating, Iterating, Iterating
- Conclusion
- Understand the Company
To ensure its effectiveness and the first stage of any sound data analytics project, it is important to understand the organization or operation that your data project is part of. Your project must respond to a strong organizational need to inspire the multiple actors needed to bring your project from concept to production. Go out and speak to AI developers in your company whose processes or whose business you are trying to develop with data before you even think about the data. Sit back, then to identify a timeline and basic primary performance metrics. Planning and procedures sound dull, I know, but ultimately, they’re a vital first step to kickstart your data initiative!
- Get Your Data
It’s time to start searching for your results, the second step of a data analytics project, once you’ve had your objective figured out. What makes a data project amazing is combining and integrating data from as many data sources as possible, so look as far as possible.
Here are a few ways to get some usable data for yourself:
Connect to a database: Ask data science experts, and IT teams about the information available or open your private database and start digging into it to understand what data has been gathered by your organization.
Use APIs: Think of the APIs for all the resources your organization has been using and the knowledge these people have gathered. You have to focus on setting up all of these to use the open and click stats of those emails, the information the sales team put in Pipedrive or Salesforce, the help ticket someone submitted, etc.
Look for open data: To enrich what you have with additional knowledge, the Internet is full of datasets.
- Explore your data and clean it
The next stage in data science is the dreaded data preparation method that usually takes up to 80% of the time allocated to a data project.
It’s time to get to operate on it in the third project process of data analytics once you’ve had your data. To accomplish your original aim, start exploring what you have and how you can connect it. Start taking notes on your first assessments and ask business people, the IT team, or other organizations questions to understand all the factors.
- Enrich Your Collection of Data
It’s time to exploit it now that you have clean info to get the most value out of it. To narrow your data down to the critical features, you should start the project’s data enrichment process by joining all your various sources and group logs. One example of this is to enrich the knowledge by creating time-based characteristics such as:
Extracting components of a date (month, hour, day of the week, week of the year, etc.)
Differences calculation between date columns
National Holidays Flagging
- Build helpful visualizations
Now you have a nice dataset (or maybe several), so this is a good time to start constructing graphs to explore it. Visualization is the most reliable way to explore and communicate your results as you deal with vast amounts of data and is the next step of your project for data analytics.
The complex part here is to be able at any moment to dig through your graphs and answer any question anyone may have about a specific insight. This is where data preparation comes in convenient: you’re the guy or gal who did all the nasty work, so you know the data like your hand’s palm!
- Get Predictive
Phase six of the data project, the next data science stage, is where the real fun begins. Machine learning algorithms will assist you in getting insights and anticipating future patterns a step further.
You can create models to discover patterns in the data that are not distinguishable in graphs and statistics by working with clustering algorithms (aka unsupervised). These build groups of related events (or clusters) and convey more or less clear what is the deciding feature of these outcomes.
- Iterating, Iterating, Iterating
In every business endeavor, the main objective is to show its success as quickly as possible to justify, well, your work. For data ventures, the same goes. You will easily go to the end of the project and get your initial results by obtaining data cleaning and enrichment time. This is the final stage in achieving your project in data analytics and one that is vital to the entire life cycle of data.
Conclusion
Ironically, you need to realize that your model will never be completely “complete” to complete your first data project effectively. You need to continually reevaluate, retrain it, and develop new functionality to stay useful and accurate. If there is something you take away from these basic steps in analytics and data science, the role of a data science expert is never re-evaluated.
