This website is using cookies to ensure you get the best experience possible on our website.
More info: Privacy & Cookies, Imprint
Data analytics plays a crucial role in artificial intelligence (AI). AI systems are typically based on machine learning, in which algorithms learn from large amounts of data to identify patterns, relationships and rules. Data analytics makes it possible to extract relevant information from existing data and make it useful for AI models.
Here are some important aspects of how data analytics is used in AI:
Data sourcing and preprocessing:
Data analytics involves sourcing, cleaning, and transforming raw data to prepare it for processing by AI models. This step is critical because the quality and representativeness of the data has a major impact on the performance of the AI.
Feature extraction: data analysis helps identify relevant features or attributes in the data that are important for AI model learning. By applying statistical methods or other techniques, relevant information can be extracted from the data.
Training data for machine learning: data analytics allows large amounts of training data to be analyzed and processed to train AI models. This includes labeling data to show the AI algorithms what results are expected.
Model selection and validation: data analysis helps evaluate and select appropriate models for specific AI tasks. By analyzing the performance of different models against validation data, the best models can be identified.
Model selection and validation.
Monitoring and adaptation: data analytics also plays an important role in monitoring and adapting AI models on the fly. By analyzing real-time data, the performance of the model can be evaluated and adjusted as needed.
Overall, data analytics is an essential part of the AI lifecycle. It helps AI gather and process relevant information to enable accurate predictions, decisions, or other tasks. Without thorough data analysis, many AI applications would not be able to deliver the desired results.
Artificial intelligence (AI) hourly rates can vary widely depending on experience, area of expertise, industry, location and type of service. AI encompasses a wide range of applications, including machine learning, deep learning, natural language processing, computer vision and more. Here are rough benchmarks for hourly rates in AI in different regions:
Europe:
Entry or junior AI specialist: €40 - €80 per hour
Experienced AI specialist: 80 € - 150 € per hour
Senior AI specialist or subject matter expert: 150 € and more per hour
USA:
Entry or junior AI specialist: $60 - $120 per hour
Experienced AI Specialist: $120 - $250 per hour
Senior AI specialist or subject matter expert: $250 and up per hour
These estimates are general and may vary depending on the area of expertise (e.g., machine learning, data science, computer vision), the reputation of the AI expert, and the type of service.
Since AI is a high-demand field, AI specialists should consider their own experience and expertise, as well as the value they can add to clients, when setting prices. Exact hourly rates may also depend on the complexity of projects and specific service offerings. It is advisable to research current market data and pricing in your region and industry to determine competitive hourly rates.
The salary of an IT specialist can vary significantly depending on specialty, experience, education, industry, location, and area of responsibility. The term "IT specialist" can encompass a wide range of occupations, including software developers, systems administrators, network engineers, database administrators, IT security experts and more. Here are rough benchmarks for IT specialist salaries in different regions:
Europe:
Entry or junior IT specialist: €25,000 - €45,000 per year
Experienced IT specialist: €45,000 - €70,000 per year
Senior IT specialist or subject matter expert: €70,000 and above per year
USA:
Entry or junior IT specialist: $50,000 - $70,000 per year
Experienced IT Specialist: $70,000 - $90,000 per year
Senior IT specialist or subject matter expert: $90,000 and up per year
These estimates are general guidelines and may vary by region, industry, and individual factors. Salaries for IT specialists can typically be higher if they focus on specialized areas such as cloud computing, IT security, artificial intelligence, machine learning, or data science.
To get an accurate estimate of salaries, consider the following.
To get accurate and up-to-date information about IT specialist salaries in your region and industry, it is recommended to use trusted salary surveys, job portals or professional associations.
The salary of a computer scientist can vary widely, as this term covers a wide range of roles and specialties within the IT field. Computer scientists can be software developers, system administrators, database administrators, network engineers, IT security experts, machine learning engineers, data scientists and more. Here are rough benchmarks for computer scientist salaries in different regions:
Europe:
Entry or junior computer scientist: €25,000 - €45,000 per year
Experienced computer scientist: €45,000 - €70,000 per year
Senior computer scientist or subject matter expert: €70,000 and up per year
USA:
Entry or junior computer scientist: $50,000 - $70,000 per year
Experienced Computer Scientist: $70,000 - $100,000 per year
Senior computer scientist or subject matter expert: $100,000 and up per year
These estimates are general guidelines and may vary by region, industry, specific role, and individual factors. Computer scientist salaries may be higher if they focus on specialized areas such as artificial intelligence, machine learning, cybersecurity, or other high-demand areas of expertise.
In a groundbreaking announcement, Microsoft Excel has unveiled a new chapter in data analytics by introducing Python integration to its platform. This momentous stride brings together the power of Python's analytical capabilities and the versatility of Excel's data organization and visualization tools. With the launch of Python in Excel, users can seamlessly merge Python and Excel analytics within the same workbook, ushering in a new era of efficiency and sophistication in data analysis.
A Fusion of Python and Excel: The Next Evolution in Data Analytics
From its inception, Microsoft Excel has been at the forefront of transforming data handling, analysis, and visualization. Now, with Python in Excel, Microsoft takes another leap forward, offering a Public Preview of this groundbreaking integration. The synergy between these two stalwarts in the data world allows users to directly input Python code into Excel cells, with the calculations executed in the Microsoft Cloud. The results, including plots and visualizations, are then seamlessly integrated into the Excel worksheet, all without requiring any intricate setup.
The initial roll-out of Python in Excel is available for those participating in the Microsoft 365 Insiders program, accessed through the Beta Channel in Excel for Windows.
Unveiling the Distinctive Features of Python in Excel
Catering to Analysts' Needs: Excel's familiar tools like formulas, charts, and PivotTables are utilized by millions for data analysis. Now, Python in Excel takes this a step further by natively integrating Python directly into the Excel grid. The new PY function enables users to input Python code directly into Excel cells, offering access to potent Python analytics alongside Excel's trusted features.
Unleashing Python's Power via Anaconda: Python in Excel leverages Anaconda Distribution for Python, a repository embraced by countless data practitioners globally. This integration facilitates access to popular Python libraries like pandas, Matplotlib, and scikit-learn, amplifying the analytical prowess available within Excel.
Security and Cloud Compatibility: Python in Excel operates securely in the Microsoft Cloud environment, utilizing Azure Container Instances for isolated execution. The integration ensures data privacy, restricting Python code's knowledge of users' identities and keeping workbook data isolated and secure.
Team Collaboration Made Effortless: Collaboration takes center stage with Python in Excel. Teams can interact with and refresh Python-powered analytics without grappling with complex installations or management of libraries. Collaboration tools like Microsoft Teams and Outlook seamlessly enable shared workbooks and foster a cohesive working environment.
Microsoft's Commitment to Python: The partnership across various Microsoft teams underscores the company's dedication to enhancing Python's accessibility and integration. Guido van Rossum, Python's creator and Microsoft Distinguished Engineer, lauds this milestone, highlighting the collaborative spirit.
Unlocking New Avenues in Data Analysis
Python in Excel opens up a realm of possibilities, transforming Excel from a traditional spreadsheet tool into an advanced analytical powerhouse. Advanced visualizations utilizing Python's renowned charting libraries, machine learning, predictive analytics, and even data cleaning are now within Excel users' grasp. This integration enhances the workflow of diverse sectors, from education and corporate analytics to financial analysis.
The Road Ahead
With Python in Excel making its debut through the Public Preview for the Microsoft 365 Insiders program, the future holds promise. Expectations are high as Microsoft works on refining the integration, expanding editing experiences, error management, documentation, and more. The integration's potential to revolutionize data analysis and collaboration ensures a keen eye on its evolution.
In this era of data-driven decision-making, Microsoft's Python in Excel heralds a transformative era where two juggernauts, Python and Excel, coalesce to empower analysts and organizations worldwide. The fusion of these platforms unlocks a future of unparalleled data exploration, analysis, and insight generation.