Any organisation dealing with huge chunks of data uses data analysis. The organisation must find ways to glean relevant information from colossal unfiltered data. With the growth of artificial intelligence (AI), data science has undergone many changes. Data scientists can now use AI-powered data analysis tools to spot trends, optimise decision-making functions and automate processes.
What Is Data Science?
The IT world has seen a steady rise in the demand for data scientists in a market where organisations must change data into insights. Even IT professionals are learning data science to keep up with their competitors.
Data science involves an amalgamation of statistics, computer science and mathematics. It collects, analyses and interprets data to gain insights into the input data. Almost every organisation uses data science today for predicting user behaviour and identifying new opportunities. Organisations use data science for making decisions about product creation. Even governments use data science to improve the efficiency of public services.
What Is AI?
AI, or artificial intelligence, is a branch of computer science that uses technology to perform actions without human interaction. Machine learning and deep learning form the base of artificial intelligence. AI is an umbrella term under which many hardware and software components support natural language understanding, machine learning, natural language processing, and computer vision.
AI can perform various tasks such as understanding and translate written and spoken human language, making recommendations and analysing data. Modern computing heavily uses AI for all types of functions.
AI can identify patterns and tendencies inside datasets to make predictions and uncover valuable insights. Artificial intelligence can make complicated calculations from huge volumes of statistics with minimum human intervention. It not only saves time but also decreases the chance of human errors.
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How Does Data Science Use AI?
While data science and AI might have overlapping areas and are connected courtesy of sharing the same domain, they have distinct applications. A prevalent question linking data science and AI is, ‘How does it use AI’?
Using machine learning methods and leveraging advanced algorithms, data scientists can use AI-powered tools for various tasks. They can make precise predictions, create actionable insights, and discover hidden patterns.
Data scientists can extract the maximum value from their data using AI tools. Programmers develop algorithms to receive input data and use statistical models to predict outputs while updating the outputs when new data is available.
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Best AI Tools for Data Scientists in 2024
Data scientists can use a variety of AI-powered tools for predictive analysis. These tools are indispensable for all data-driven organisations in the world. Look at 10 of the most used AI tools for data scientists.
1. Tableau
Frequently topping the list of best AI tools for data scientists, is the tool called Tableau. It is a data visualisation and analytics platform through which data scientists can interact with their data. It does not require prior knowledge of coding to use. This AI-powered tool can help in the creation of reports which you can share across mobile and desktop platforms.
Tableau can easily handle large volumes of data efficiently. VizQl is the query language for the Tableau platform. The main job of this language is to translate drag-and-drop dashboards into back-end queries. Users can use this tool to communicate data visually. It has AI-powered features such as Ask Data which users can use to ask questions in simple language to receive visual responses instantly.
2. Akkio
Akkio is a forecasting and business analytics tool. Users can predict potential outcomes from the data after the tool analyses it. Although the tool is mainly targeted at beginners, anyone can use it to analyse large chunks of data. Users can upload their dataset and choose the variable they wish to predict. Akkio creates a neural network surrounding that variable.
Like Tableau, users without any coding experience can use Akkio. It is a no-code machine learning platform that offers precise ratings for models. Users can instantly share the forecast with their team using a direct link. You can even use your phone to use Akkio. It is possible to include AI in any website by using Akkio.
3. Polymer
Another AI tool data scientists can use for their work is Polymer. This robust artificial intelligence tool uses a powerful AI to change data into a flexible, strong and streamlined database. Like the above tools, this tool also does not require users to have a working knowledge of coding.
Many IT professionals use Polymer to make spreadsheets interactive and searchable. The AI analyses the data and helps users understand it better. The user has to upload their spreadsheet to the Polymer platform, which immediately changes it into a coherent database. Navigating through this organised database is very easy.
4. Microsoft Power BI
Microsoft Power BI is a business intelligence (BI) platform which data scientists can use for analysing, sharing, aggregating and visualising data. The user interface is somewhat similar to Excel. Users require almost no training to use this versatile AI tool.
The common use of Microsoft Power BI is to search for insights into a company’s data. Users can share the data with other Power BI co-employees. The tool can transform and clean disparate data sets into a data model. It can create graphs and charts based on its analysis. Managers can use Power BI to create executive dashboards to find out insights about departments.
5. Google Cloud AutoML
This powerful AI tool is useful for building high-quality machine-learning models. The tool from Google has an intuitive graphical interface which any layman without coding experience can use. This tool is handy for users using other Google Cloud tools since they can seamlessly integrate Cloud AutoML with those tools.
This tool can automate the process of selecting ML models and hyperparameter tuning, thereby enhancing the tool’s efficiency. Cloud AutoML comprises various segments such as AutoML Vision, Cloud AutoML Natural Language and Vertex AI. Users can select the segment they want to use depending on their capabilities.
6. KNIME
The open-source KNIME (Konstanz Information Miner) platform is useful for data analytics. Users can use the tool’s help to analyse, transform and model data. It has a user-friendly interface suitable for beginners. The KNIME platform includes a comprehensive toolset for evaluating data and visualising data.
Most data scientists use this tool for its modular data pipelining approach. There is seamless integration with other machine learning libraries and algorithms for connecting various building blocks. These building blocks include tasks like statistical analysis, data preprocessing and feature engineering. The flexible nature of KNIME helps users design end-to-end analytical workflows inside a unified interface.
7. H20 AutoML
This low-code data science platform is a powerful tool for automating tedious aspects of machine learning. Data scientists can access many tools to automate the whole ML workflow. This low-code tool helps data scientists easily create highly accurate ML models.
There is very little need for manual effort on the part of the data scientist using H20 AutoML. It can readily handle large volumes of datasets. The tool supports multiple machine learning methods, such as time-series forecasting, regression and anomaly detection. H20 can work on ML algorithms— artificial neural networks, linear regression and gradient descent.
8. RapidMiner
This is a powerful machine-learning platform. Users can analyse data with the help of the user-friendly RapidMiner tool. A user with very little knowledge of machine learning can easily use this tool. It consists of a drag-and-drop mechanism that users can use to automate data preparation.
RapidMiner has a large library of pre-built artificial intelligence models. Users can take advantage of these models for efficient analysis of data. Organisations can use this tool for analysing employee performance, productivity, time management, and workflow-related data. Automated ML helps streamline the process of data analytics.
9. Amazon SageMaker
This machine learning platform is top-rated among data scientists for its end-to-end model development capabilities. Amazon SageMaker is a cloud-based platform that helps create ML models with less time. It offers high scalability and automatically deploys models.
There are built-in algorithms for several tasks, such as clustering and regression. Data scientists can easily share their work and collaborate with other data scientists. SageMaker is helpful for companies that work on large-scale projects. This tool seamlessly integrates with other AWS tools and services, especially benefiting a company using Amazon Web Services.
10. IBM Watson Studio
Data scientists use this AI tool from IBM for deploying, creating and managing artificial intelligence models while optimising decision-making methods. Users can seamlessly collaborate among themselves.
Users can use IBM Watson Studio’s help to leverage open-source platforms such as TensorFlow and PyTorch. They can use these along with IBM’s own tools for both visual and code-based data science. This tool supports well-known environments such as JupyterLab, CLIs and Jupyter Notebooks. Users can choose between no-code and code tools for training and building ML models. They can automate their models’ feedback loop to make them continually adapt to changes over time.
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Choosing the Right AI Tool for Data Science
Data scientists use large databases each day. Since there are so many applications of data science, you must choose the right AI tools. Knowing ‘how does data science use AI’ can help select the most suitable tool. Some factors to consider while deciding on the right tool are functionality, features, scalability and compatibility. Other factors include:
- The tool should accommodate both your present and foreseeable needs. It should match your goals for your business.
- The interface should be user-friendly and inclusive. The tool must be easy to understand so users can readily produce reports and dashboards.
- Keep in mind your budget and choose AI tools accordingly. Some tools are free, while others are paid. Keep track of the various pricing models.
- The tool should provide adequate security to the user so that there is no risk of data loss. Choose a tool which has uniform security protocols at all levels.
- Tools which support integrations can help prevent unnecessary disruptions. Check if you can integrate the tool seamlessly into your existing software, infrastructure or platforms. Compatibility is an essential factor.
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Top Data Science Skills to Learn
| Top Data Science Skills to Learn | ||
| 1 | Data Analysis Course | Inferential Statistics Courses |
| 2 | Hypothesis Testing Programs | Logistic Regression Courses |
| 3 | Linear Regression Courses | Linear Algebra for Analysis |
Conclusion
With the addition of AI to data science, organisations have rapidly changed their approach to analysing data and drawing insights. AI-powered tools such as Amazon SageMaker, Tableau, and KNIME can help automate tasks. Potential candidates seeking a career in data science must have a solid foundation in various AI tools.
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