How to Succeed in Data Science Thanks to AI

1. What is Data Science?
2. How AI supports you in Data Science
3. What you should consider when implementing AI in Data Science
4. This software helps you use AI in Data Science
5. Conclusion: The best data you'll catch with etracker

Do you run a website and want to delve deeper into the world of analytics? Are you collecting data like Hansel and Gretel their breadcrumbs in the forest? We'll show you that you don't need to be a computer scientist to get started in data science. The secret weapon is AI. What exactly this looks like and why you shouldn't miss out on it, you'll find out in this article.

What is Data Science?

Data stands for data and science for science. So it's all about data science - so far so good. But what exactly is behind it? Data Science stands for the extraction of new information from large amounts of data (so-called Big Data). The path there leads over mathematics, statistics and computer science. Sounds totally boring? Don't worry. If this is the case, we can reassure you: fortunately, there is a way to benefit from data science without this dry background: with artificial intelligence in the field of data science.

How AI supports you in Data Science

In classical web analysis, you as a website operator track the data for your most important KPIs (Key Performance Indicators). These can be metrics such as unique visitors, bounce rates and time-on-site.

But as soon as you want to go deeper, you have to have the technical equipment (e.g., statistical software) and really know your stuff: You should master the programming languages and bring a basic understanding of clustering & co., and of course bring the time and nerves to sift through huge amounts of data.

AI in Data Science can transform the concentrated complexity not only into a simple and user-friendly interface for you, but it can also add a boost at the limit of your human analytical abilities. It can check all the combinations that involve all your channels, websites, campaigns and devices. With its help, you identify patterns, trends, anomalies and correlations in a short time.

To avoid the complexity of data science, you can carry out your deep Advanced Analytics with Large Language Models (LLMs) like ChatGPT. You give the AI an instruction by voice command (prompt), and it delivers quick results.

What you should consider when implementing AI in Data Science

Is it really that simple in practice? Let's take a look at the example of ChatGPT. With this command we ask ChatGPT for an analysis showing trends and anomalies:

And there comes a response that we can work with:

This is really feasible for everyone. A classic cluster analysis requires (including briefing, data extraction, -preparation and -analysis as well as the preparation of results) about a time effort of eleven hours. Regardless of whether you perform it yourself or commission external parties, a considerable sum can accumulate. In doing so, the smooth flow of data must always be ensured. At first glance, ChatGPT seems to be the more efficient choice. However, you should keep a few points in mind when using AI in Data Science.

• Data quality: If the input is junk, the output can't be better. High data quality is therefore the crucial point for your analysis.
• Completeness: The biggest challenge is to avoid data losses and distortions. These are caused by consent and tracking protection measures such as blocking.
• Accuracy: AI now delivers relatively good and structured results. The illusion of high data quality can be deceiving. Unfortunately, the contents are often trivial and superficial - not to mention the absence of company-specific peculiarities.
• Capacities: You can't perform unlimited uploads. This limits your data volume and thus the meaningfulness of your evaluations. Currently, the upload capacity is 50 MB with Advanced Analytics.
• Data sovereignty and data protection: The handling of sensitive data is regulated in particular by the GDPR (General Data Protection Regulation) and the TTDSG (Telecommunication Telemedia Data Protection Act). When you use AI tools, the protection of data must be ensured.
• Technical competence: An artificial intelligence can only support people who are competent enough to verify the results.

This software helps you use AI in Data Science

There is an alternative that wonderfully avoids the challenges mentioned above in terms of data security, upload limitation and data quality. For this, you either use another software in addition to the AI tool or you work directly in a tool with integrated AI functions.

You can simply have an analysis script created by an AI (e.g., ChatGPT). Then, you use it to automatically transfer the raw data (repeating daily) into a two-dimensional data structure like pandas.DataFrame. Your prompt in this case would be: I need a Python script that ... This method is suitable for visualization, probability calculation, and time series analysis, among other things.

For meaningful numbers, you need a lot of data. This data should be cookie- and consent-independent. You get there best without questionable tricks when designing your cookies. More and more analytics and BI solutions (Business Intelligence) contain AI functions (e.g., Tableau) that provide you with a data basis. Some AI apps have been specifically developed for analytics (e.g., Rows).

With all-in-one solutions like etracker, you avoid the need for consent and tracking blocking. This way, you can legally capture all the data of your users and rely on the best possible data quality. Even the new consent modes (Basic and Advanced) from Google Analytics require consent. What massive impact this has on your results is shown by this example:

What do specific queries look like?

With software, you can perform both simple and complex queries. A standard query could concern your sessions with orders:

But also complex relationships like the reasons for your followers' interaction on your social media channels can be determined with software.

What should a software for AI in Data Science be able to do?

Your software should answer these four crucial questions:

1. How do you secure your data base sustainably despite ad and tracking blocker without consent and cookies?
2. How do you get from your raw data to a 360-degree view?
3. Where do you get these valuable data automatically into your existing system landscape (e.g., for conducting personalized campaigns)?
4. How should prompts look to deliver good results?

The biggest advantages with etracker as AI tool in Data Science

• All-In-One: One tool for managing all your web, app, and marketing activities
• Interactive Dashboard: Retrieve the most important information on your PC, tablet, or smartphone
• Understandable Reports with Data Visualizations: Basic Reports (characteristics of website visitors and content success), Marketing Reports (campaign success), E-Commerce Reports (optimization potential), and Email Reportings (automated reports)
• Precision: Detail analysis of your website
• UX analysis: Detailed information about the dwell time of your visitors
• Tag and Consent Management: integrated tag and consent manager, additional connection possibility to all common consent managers (CMPs)
• Privacy First App Analytics: Consent-free and GDPR-compliant tracking
• Raw Data Export: (automated) raw data exports from BI systems
• Connectivity: anonymized and server-side forwarding to your marketing platforms
• Dynamic Segmentation: with drill-down function and segment comparisons
• Control: Client and user management

If you would like to get more background information about the analysis tool, feel free to watch the following YouTube video, where you can see a live demonstration of etracker.

Conclusion: The best data you'll catch with etracker

Data Science is a challenging topic. Nevertheless, etracker, if you are a little tech-savvy, allows you to perfect your data science with artificial intelligence. With the tool for privacy-friendly, client- and server-side tracking, you get to the highest quality and most comprehensive data. All you need now is a basic understanding of data management, advanced analytics, and the formulation of prompts.