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Data-driven businesses are continually looking for ways to update and improve their data stack to give them better insights and a competitive edge. In the data science field, these technologies and tools are evolving at a rapid pace. AI tools and machine learning have exploded in popularity in recent years, and these tools are becoming more affordable and accessible than ever. In fact, AI tools are helping many businesses build a better data science stack built for the future. In this blog, we’ll take a look at how you can incorporate these tools into your data stack.
The Future of AI and Data Science
Artificial intelligence has the potential to revolutionize data science, along with numerous other industries. AI has only become more accurate and efficient, and it will continue to evolve in the coming years rapidly. Harnessing the power of AI will be key for data scientists as the ever-increasing demand for data-driven insights continues to grow.
Utilizing AI tools, organizations can improve their data analysis capabilities, improve decision-making and democratize data access across an organization. In short, it can give organizations a competitive edge.
For some, it may seem like the future of data science is already here and readily available. This is evidenced by the fact many companies are already utilizing advanced AI tools to help automate tasks and streamline workflows. Some of these powerful tools include:
- ChatGPT - ChatGPT is an advanced AI tool known for its text generation capabilities. However, it is also an effective tool in the data science field. Data professionals can use the latest version of ChatGPT for data analysis, automated coding, data preprocessing and much more.
- DataCamp Workspace AI - DataCamp Workspace AI is another great AI tool that can optimize several coding tasks and democratize programming in an organization. Some of the features include fixing code errors, generating code based on natural language prompts and improving existing code.
- Mito - Mito is a Python-based spreadsheet app that makes it easier for data professionals to analyze and manipulate data sets. Mito provides a user-friendly and familiar spreadsheet format, similar to Excel, that automatically generates Pandas code in real-time when you make edits in your spreadsheet. Not only is Mito an efficient way to streamline spreadsheet workflows, but it also is a great way to learn Python.
The best part is these types of AI tools are only getting more advanced. In the near future, data scientists may be able to automate nearly all of their tedious tasks and focus on deeper, more strategic work. Additionally, AI will be able to help them with this work as they improve analysis and gain deeper insights into the data sets they work with.
The Traditional Data Science Stack
The truth is, the traditional data science stack likely isn’t going anywhere anytime soon. The most useful tools in a data science stack are useful for a reason. Rather than replacing the traditional data science stack altogether, AI tools and machine learning will iterate on these traditional models and improve them.
This is especially important for organizations that deal with big data, as the traditional stack is no longer sufficient to efficiently and effectively analyze these massive data sets. AI tools allow these organizations to stay competitive and optimize different stages of the data science stack. Not only does this save time and money, but it empowers data scientists to get better insights than they ever could before.
With that being said, let’s dive into some of the most common parts of the traditional data stack and look at how AI can help.
The first step in any data science stack is a database where collected data is stored. In its simplest function, it is the place where all of an organization’s structured and unstructured data is located to make it more accessible for retrieval and analysis.
There are numerous database options out there, each suited to the different data needs of an organization. Relational databases and NoSQL databases are some of the most common options in the modern data stack.
Regardless of the database your organization utilizes, AI tools can help automate and streamline several database-related tasks. A common AI automation for databases is automating the data preprocessing and cleansing processes. AI can also be used to detect anomalies and issues in databases, helping to ensure data is secure, consistent and accurate. Machine learning models can also be used to organize and categorize database assets, making them easier to query and access.
Overall, incorporating AI into your database layer can greatly enhance your data science software stack, allowing you to leverage advanced machine learning algorithms to deal with large amounts of data and eliminate a lot of tedious data science tasks.
2. ETL (extraction, transformation and loading)
In any data science workflow, ETL tools are essential. ETL stands for extraction, transformation and loading. Extraction is the process of gathering data from various sources or databases, while transformation involves cleaning, normalizing and reformatting the data. Loading involves putting the transformed data into a target database or application.
Some organizations also opt for the ELT model, which extracts the data, loads it into the target destination and then transforms it for analysis. The ELT process can sometimes offer advantages over the ETL model, such as cost-saving and improved data usability.
In either model, AI tools can be incorporated to enhance these processes. AI can greatly streamline much of the ETL process by automating transformation tasks and removing manual work for data scientists. Additionally, AI can be used to identify anomalies in data, ensuring bad data is weeded out before analysis. Another useful function of AI in the ETL layer is identifying the relationships and mappings between data points, which can help optimize the analysis process.
Data that has gone through the ETL process is ready for data warehousing, where data can be made easily accessible for analysis. For many organizations, cloud warehousing has been the ideal modern solution for their data stacks. Cloud warehousing offers numerous benefits, such as being more scalable than on-premises solutions. Although cloud environments aren’t the right solution for every business, more and more organizations are migrating data to the cloud.
Many cloud environments even have AI and machine learning tools built in, empowering organizations to utilize these tools for advanced data analysis. Machine learning, predictive analytics and natural language processing can be used to analyze large sets of data and provide in-depth insights into patterns and relationships between data points.
As always, automation is a big benefit for organizations that incorporate AI tools into their data warehousing layer.
4. Data Modeling
Data modeling is another critical facet of data science. It involves creating a visual representation of data to help analysts understand its relationships, structure and meaning. In traditional data stacks, data modeling was a manual and time-consuming process that required high levels of expertise. However, with the introduction of AI tools, the process has become much simpler.
AI tools can allow users to build machine learning models without the need for extensive coding or data science experience. This is particularly useful for small or medium-sized companies that don't have the resources to hire a full-time data science team. For example, Mito is a tool that can automatically create Pandas code as you edit and manipulate data in spreadsheets. This Pandas code can then be utilized in the Python ecosystem for advanced data modeling and analysis.
Tools like Mito not only streamline the data modeling process by automating Pandas code, but they make data science more accessible to all users. Instead of needing advanced coding knowledge, manipulating data in a familiar spreadsheet format is enough to get you the code you need for data modeling tasks.
In other words, incorporating AI into your data modeling workflow can help to reduce errors, increase accuracy and save time.
5. Visualizations and Insights
Following the data analysis steps, data scientists need to be able to communicate their findings to others in a way that is clear and easy to understand. That's where visualizations and insights come in. These tools enable you to take complex data and transform it into easy-to-digest visuals that can be easily shared with your team, stakeholders or clients.
Mito can come in handy for this step too. With Mito, you can generate charts and graphs without ever having to write any code. By streamlining these steps, data scientists can prepare their analyses in a fraction of the time and ensure that information is communicated to the right people.
As you can see, AI can be incorporated into nearly every layer of the modern data science stack. The more organizations can leverage AI and machine learning, the more time and money they’re able to save while also gaining deeper insights into their data like never before.
Python AI for Data Science
Python is one of the most popular and widely-used programming languages, so it’s no surprise that there are numerous AI tools and resources for Python users.
Data scientists can harness the power of Python to build AI-powered applications for their workflows. Python users can find a vast library of machine learning frameworks, data analysis tools and AI tools readily available. Tools like TensorFlow, PyTorch, Scikit-learn and Mito are making advanced machine learning and AI tools accessible to every organization.
While Python is one of the easier programming languages to learn, it is important to note that there may be a learning curve in incorporating these AI tools into your Python workflows. Make sure your data science team has the time to learn the basics of these tools and get the most benefit from them.
Incorporating AI into Your Python Workflow
If you’re looking to gain some of the benefits that AI tools can offer, here are some ways to incorporate AI into your Python workflow:
- Use pre-trained AI models - One of the easiest ways to integrate AI into your workflow is by using pre-trained models. However, if you want to get more in-depth with AI tools, you will need to take a more comprehensive approach.
- Understand AI concepts - For a more comprehensive approach, get a solid grip on the concepts and techniques of AI in data science. Now is also a good time to research what Python frameworks and libraries are available for AI development.
- Choose your AI framework or library - Choose the right framework or library for your data stack based on your research.
- Build your model - Build your AI tool or model using Python code to create and train the model. Training the model will involve feeding it data, adjusting its parameters and optimizing performance.
- Evaluation - Evaluate your model by testing it on specific data analysis tasks. Iterate your model based on the results.
- Deploy your AI tool - Once your AI model has been extensively trained and tested, it’s time to deploy it into your Python workflow for real-world use.
- Monitoring - Monitor the performance of your AI model and maintain it as necessary.<p>
Mito’s AI Tools Make Python More Powerful (and More Fun)
Remember, there are plenty of existing AI tools readily available to make your Python workflows more optimized and powerful. You can incorporate Mito into your workflows right away, allowing you to harness the power of Python and Pandas for more advanced data manipulation and analysis with an easy-to-use interface. On top of being more powerful, Mito is even fun to use. While data science tasks can sometimes feel like a slog, Mito will empower you to make the most of your data in a familiar spreadsheet format.
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