sql assignment applied ai

Home - dataenthusiast092/Applied-AI-Assignments-Case-Studies GitHub Wiki

As a part of completing the coursework prescribed by the Applied-AI-Course , I have compiled all the latest work submitted by me on the Baymax classroom.

Optional Assignment

Initially 2 optional assignments were done:

• Python Practice Questions
• Pandas Practice Questions

Additionally, a mandatory SQL assignment was done.

Data Visualization

In the beginning of the course, Exploratory Data Analysis and Dimensionality Reduction assignments were done.

Supervised Learning

Later on, when diving into the course total 7 Supervised Machine Learning assignments were done:

• k-Nearest Neighbour
• Naive Bayes
• Logistic Regression
• SGD for Linear Regression
• Support Vector Machines
• Decision Trees
• Random Forest- Gradient Boosted Decision Trees

Unsupervised Learning

Also, Unsupervised Machine Learning assignments were done:

• k-Means, Agglomerative Hierarchical and DBSCAN Clustering
• Truncated Singular Value Decomposition

Deep Learning

Later on Deep Learning was done:

• Keras and Tensorflow

Supervised Learning Case Studies

Some Supervised Machine Learning Case Studies were also done:

• Quora Question pair Similarity
• Personalized Cancer Diagnosis
• Facebook Friend Recommendation

Work under construction..

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How to utilise Appliedaicourse

Python for data science introduction, python for data science: data structures, python for data science: functions, python for data science: numpy, python for data science: matplotlib, python for data science: pandas, python for data science: computational complexity, module 1: live sessions.

How AI & SQL Can Accelerate Your Data Analysis Workflow

• data analysis

Table of Contents

The Growing Role of AI Across Industries

1.   enhancing efficiency and accuracy, 2.   reducing human errors, 3.   managing big data efficiently, 4.   uncovering patterns in unstructured data, 1.   automated data cleaning, 2.   powerful, efficient queries, 3.   data visualization, 4.   pattern recognition and predictive analytics, 5.   unstructured data analysis, ai for data analysis: the final word.

Did you know that using AI for data analysis can be a total game changer for SQL developers and data analysts? In this article, we’ll learn about how AI can help you level up your data analysis skills and why it’s a must-have tool in every data professional’s toolkit.

Artificial Intelligence (AI) has emerged as a game-changer in the workforce, transforming industries and redefining how professionals deal with data. Specifically, the use of AI for data analysis is becoming increasingly critical in making informed decisions.

Before we get into the various use cases, let's clear up some common misconceptions about AI. AI should be viewed as a tool, not a threat. Contrary to popular belief, AI is designed to help us rather than replace us. Humans possess skills and abilities that are difficult (if not impossible) to replicate with AI; see why in our article 7 Reasons Why AI Won't Replace SQL Developers . As SQL developers and data analysts, we can leverage AI to overcome the limitations of traditional data analysis, improving our  efficiency and accuracy and helping us stay competitive.

If you're looking for a complete course that will take you from SQL zero to SQL hero while embracing AI, check out our SQL from A to Z learning track. This 7-course track covers both fundamental and advanced SQL abilities, giving you all you need to succeed in your career. So, let's dive in and see how AI can help you with your data analysis activities!

AI has grown across numerous industries, leaving a lasting impression on our world. It has become an indispensable resource for businesses seeking to make data-driven decisions.

For example, recent healthcare research highlights AI's significance in improving medical imaging for faster disease identification and diagnosis; this results in better patient outcomes. In the banking sector, AI excels at real-time analysis of massive datasets. This is critical to detecting fraud and analyzing risks, and AI’s accuracy is constantly improving. AI-powered recommendation systems, such as those used by Amazon and Netflix, can personalize product recommendations based on user behavior, improving consumer experiences and driving sales. These examples demonstrate the widespread impact of AI across a variety of sectors.

How AI Can Overcome Challenges of Traditional Data Analysis

As the world of data analysis advances, the challenges that traditional methods face become more obvious. Traditional methodologies are struggling to keep up with the sheer quantity and complexity of datasets. Because data is an essential resource for organizations to remain competitive, accurate and timely data analysis is critical. For more information on why data is an essential resource, check out our article Why Is Data a Valuable Resource for Your Business?

In response to the challenges of traditional data analysis, AI emerges as a powerful solution, transforming workflows and assisting professionals in their daily activities. Let's look at how AI can improve your workflow and productivity by overcoming the challenges of traditional data analysis:

Manual data cleaning, preparation, and analysis can take a significant amount of time and effort. In addition, manually conducting sophisticated data manipulations and calculations can also take a long time, thus producing delays in decision-making processes.

AI is a powerful solution to the time-consuming procedure of data processing. Let’s say a company has to analyze enormous amounts of customer feedback data to uncover trends and insights. Manual processing of this task could take days or even weeks.

Now, AI-powered natural language processing (NLP) algorithms allow data to be quickly reviewed; attitudes can be extracted and major themes discovered in hours or even minutes. This enables the team to make better-informed decisions faster and to focus on more complex analysis, resulting in increased efficiency and production.

Due to the volume and complexity of today’s data, human errors are nearly unavoidable in traditional data processing. Even the most experienced data analysts can make mistakes when cleaning, converting, or interpreting data.

These flaws can lead to incorrect conclusions and untrustworthy insights, jeopardizing the analysis's accuracy and integrity. The consequences of such errors can range from slight inconsistencies to serious financial or operational effects.

AI can also be used to reduce human error, particularly in operations demanding repeated or sophisticated data processing. In manufacturing, for example, AI-powered quality control systems can detect faults by evaluating product images in real time. They could use computer vision to detect any deviations from standard quality.

The risk of human error is reduced by automating this technique, resulting in greater product quality and increased efficiency. With AI handling repetitive and error-prone tasks, human workers can focus on creative and strategic areas, increasing overall productivity and quality.

The digital age has resulted in an explosion of data, which is commonly referred to as Big Data . Traditional databases and analysis tools may have difficulty working with the sheer volume and variety of Big Data, resulting in performance bottlenecks and lengthy processing times. Big Data management necessitates large storage and processing resources, which can lead to financial issues for enterprises.

Because AI can speedily analyze and process data, it is a game-changing tool for fast and efficient Big Data handling. Consider a retailer with millions of daily transactions. Handling and extracting insights from such massive amounts of data would be a massive undertaking, frequently leading to delayed decision-making.

By utilizing AI-powered data analysis technologies such as machine learning algorithms and natural language processing, this company can quickly find patterns, trends, and client preferences. Understanding consumer behavior enables the firm to optimize inventory management, develop focused marketing campaigns, and provide better customer experiences – thus allowing the retailer to remain competitive.

Unstructured data – such as text, images, and videos – holds immense potential for valuable insights. However, extracting patterns and correlations from unstructured data is a complex and challenging task for traditional data analysis methods. Human analysts must spend significant effort and expertise in preprocessing and transforming unstructured data into a structured format that’s suitable for analysis.

Instead, professionals can use AI to find patterns in this unstructured data. Imagine that our retailer wants to understand customers’ product opinions based on Internet reviews and social media remarks. Typical data analysis approaches would struggle to extract meaningful insights from these massive amounts of unstructured text data.

By leveraging NLP algorithms, the firm can analyze and categorize client sentiments, identify patterns, and discover common themes. As a result, they can make faster data-driven decisions, improve their products, and increase customer satisfaction – all based on the rich patterns buried inside unstructured data.

AI For SQL Developers & Data Analysts

By now you’re probably curious as to how AI can help your daily SQL development or data analysis responsibilities. Or if you’re simply interested in these careers, check out the article How to Become a Database Analyst or How to Become a SQL Developer . Now, back to how AI can help power up your SQL.

AI can automate data cleaning, a crucial step in the data preparation process. The detection and correction of errors, inconsistencies, and missing values in huge databases can be automated using AI-powered systems.

Consider a hospital that has a massive database of patient records. Data analysts can use AI to automatically find and correct anomalies in patient ages, addresses, or medical codes – ensuring that the data is accurate and reliable for future research. This saves time and effort. It also improves data accuracy, resulting in more precise insights and informed decision-making.

Using AI-powered query optimization techniques, we can improve SQL queries; this results in faster execution times and lower resource usage. This allows SQL developers and data analysts to more efficiently extract insights from vast databases.

Additionally, SQL professionals may use AI to save time, increase productivity, and gain deeper insights. This allows them to make more educated decisions and improve overall operations.

Consider a huge dataset with complex relationships and various dimensions. Manual visualization and exploration may be time-consuming and restricted if you want to discover its insights. By using AI-powered data visualization tools (such as automatic charting and dynamic dashboards), patterns and trends can be readily recognized.

AI can provide improved visualizations, making it easier for data analysts to interpret and convey their findings successfully. As a result their workflows will be accelerated, fostering deeper insights and allowing better communication of complicated data-driven stories with clarity and impact.

By leveraging AI-powered algorithms, patterns and relationships can be more easily identified in large datasets. Imagine you have sales data; by employing advanced machine learning algorithms, AI can analyze customer behavior rapidly. This includes such critical information as consumer purchasing preferences, seasonal trends, and peak sales times.

With this knowledge, marketing strategies can be enhanced, future sales forecasted, and better data-driven decisions can be made. In short, the business can more efficiently meet customer requirements.

AI's ability to analyze text, image, and video data opens up new possibilities for data-driven decision-making. Consider a healthcare organization that collects patient input via a variety of methods, such as written notes, audio recordings, and photographs. Manually analyzing this unstructured data would be time-consuming and impractical.

Using NLP and AI picture recognition technologies, the hospital can quickly extract useful insights from this data. AI can classify emotions, recognize recurring themes, and even spot patterns in medical imagery. As a result, a full picture of patient experiences can be found. This helps hospital administrators identify areas for improvement and make better data-driven decisions that improve the overall service quality.

We've looked at a variety of AI use cases for SQL developers and data analysts in this article, emphasizing the growing role of AI for data analysis. However,  bear in mind that these are only a few examples of how AI could improve our data analysis operations. AI's rapid development continues to show new potential and applications for our day-to-day responsibilities.

Professionals that want to stay competitive must stay up to date on the latest AI trends and discoveries. Embracing AI as a productivity booster and useful tool in your data analysis journey will yield considerable long-term benefits. In today's data-driven environment, AI is not a threat but a valuable ally, complimenting human skills and propelling us to success!

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Exploring AI Tools for Writing SQL Queries - Are they reliable?

By: Rajendra Gupta   |   Updated: 2024-03-22   |   Comments (2)   |   Related: More > Artificial Intelligence

Organizations are using AI technologies to improve efficiency, automation, and saving time. AI is making its place in every domain, such as generating content, visuals (audio/video), music, and analysis tools. How can you use these AI technologies for writing SQL code? Is it beneficial to be dependent on these tools for your development work? Let's explore thoughts in this tip.

Artificial Intelligence (AI) is multiplying, revolutionizing the world with its implementation and usage. According to Forbes, the AI market is projected to reach $1811.8 billion by 2030 compared to $136.55 billion in 2022.

Image Source Forbes

Developers spend time manually writing queries using query editor tools such as SQL Server Management Studio and Azure Data Studio. These tools have functionalities to help developers write queries. A few useful features include:

• SQL Code editor with IntelliSense features such as keyword completion, code navigation, and code snippets.
• Smart Code snippets can help you give syntax for creating databases, tables, stored procedures, functions, views, users, and logins.

Role of ChatGPT and Bard in Writing Queries for Developers

AI has moved a step ahead. It can write the code on our behalf. We will cover two popular AI tools - OpenAI's ChatGPT and Google's Bard. A short background of these AI tools:

• ChatGPT receives around 1.7 billion website hits monthly. It is expected to have a revenue of $1 billion in 2024. Source: Demandsage.com
• Bard received 142 million monthly visitors at the beginning of 2024.

ChatGPT, Bard, or any other AI tool cannot replace developers since it does not know the database structure, schema, indexing, and workload requirements. Suppose you want to use ChatGPT and provide it with the following instructions:

ChatGPT worked on the requirement and gave me the following query.

However, if you try to run this query on the WideWorldImporters database, you get the following errors:

Google Bard gives a different query for the same requirement but still has an error:

Why do we get these errors? Well! This is due to the limitation of AI tools. However, before we look into the limitations or disadvantages, let's look at a few use cases or advantages of using artificial intelligence tools.

Advantages of Artificial Intelligence Tools

Learning tools.

AI tools are great for beginners to learn a new topic or technology. It can give concise information with example codes, syntax, and query structures. It is a very interactive tool that allows you to ask questions and provides real-time instructions in plain English.

Learning Different Ways of Writing Code

AI tools can help you write SQL queries in different ways using other solutions. It can also help to debug the SQL queries with possible solutions or errors.

Speed Up Coding Process

Understanding the code snippets can help developers write code faster. These tools also explain the code line by line based on the context.

Works for Multiple Programming Languages

As a developer, you might be good at a specific language. For example, suppose you are good at T-SQL, i.e., Microsoft SQL Server query language. However, you might need to learn or write code in different languages, such as MySQL, per organization requirements. The AI tools can help you understand the query differences between these multiple programming languages and make switching or working with various languages easy. You can start a session like the one below.

Help with Query Optimization

AI tools can suggest the required indexes and explain why. It can offer best practices or alternatives for writing the query for optimized behavior. For example, an AI tool can suggest an index for a specific query.

Why We Should Not Rely on AI Tools for Development

AI tools are suitable for help, but these limitations and challenges should make you think before using these tools.

Absence of Comprehensive Understanding

AI tools can help you understand coding languages. However, they cannot write the code on the developer's behalf because it lacks context information about the project, application, business requirements, constraints, or domain-specific knowledge. Developers understand the organization's requirements, which is critical for effective code development.

Experience makes a developer effective. Code can work differently in different environments. For example, a stored procedure might work well in a small-scale application, but needs to be tuned differently for a large-scale setup processing large chunks of data. Developers with good experience in their technology can understand the workload and write or adjust their queries to meet scaling demands.

Limited Knowledge

Every AI model has a cut-off date for its knowledge model. For example, if you ask ChatGPT (free version) this question: When was ChatGPT last trained for its knowledge?

It gives the following answer:

This article was written in January 2024. As you can see, ChatGPT's knowledge was updated in January 2022. Therefore, it will not have any information after the changes since January 2022.

For example, if I ask: What is the latest in SQL Server 2022?

As per the ChatGPT AI tool, SQL Server 2022 has not yet been released.

Security Concerns

Developers must avoid sharing the organization's data, financial details, PII information, private or confidential data, or intellectual property with AI tools to avoid data breaches or unauthorized access to sensitive information. Data breaches or privacy leaks are severe concerns for any organization in the digital world.

Misinterpretation of Information

AI tools work as per user inputs. Sometimes, you might give input that is correct in your understanding. AI generates the code without syntax or errors, but it might not be a desired outcome as AI does not have human intelligence.

Experience Can Never Be Replaced

If you look at websites like MSSQLTips.com , they provide content from experienced professionals who have real-world experience working with big clients and environments. These articles are not just rudimentary; they give end-to-end solutions with possible errors. Therefore, exploring these blogs for reliable, accurate, and trustworthy resources is required.

• Explore MSSQLTips for database-related solutions.
• Be careful while using AI-based tools. Use them for learning, and then write your code with your application requirements. Do not mindlessly follow the AI tools without verifying their contents.

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The Future of Data Analysis is Here: A Look at AI-Driven SQL Generation

• Ndz Anthony
• August 31, 2023

Think of a world where you could extract insights from your data without having to write a single line of SQL code. Sounds fascinating, right? This is no longer a figment of imagination, but a reality that’s being shaped by the convergence of Artificial Intelligence (AI) and SQL—- “AI-Driven SQL Generation”.

AI is now stepping in to automate SQL query generation , a breakthrough that’s set to revolutionize the way we approach data analysis. In this blog post, we’ll journey through this exciting development, exploring how it works, its benefits, and the transformative impact it’s having on the data analytics field.

SQL with AI: How Do They Get Along?

AI and SQL, at first glance, might seem like an odd pair. SQL is a structured language with clear rules and syntax, while AI is all about learning from data and making predictions. But when these two come together, they create a powerful tool for data analysis.

AI can be used to generate SQL, turning a natural language question into a SQL query. This process, known as AI-powered SQL generation, is transforming the way we interact with databases.

The benefits of this approach are manifold.

• For one, it significantly reduces the time and effort required to write SQL queries. This is especially beneficial for complex queries that would otherwise require a deep understanding of the database structure and a lot of time to write.
• Moreover, AI-driven SQL generation can handle a much wider range of queries than a human could. It can easily deal with complex joins, nested queries, and other advanced SQL features. This makes it a powerful tool for exploring large and complex databases.
• Lastly, AI-driven SQL generation often helps reduce errors in SQL queries. By automating the query writing process, it eliminates the risk of human error, such as typos or syntax errors. The AI can also check the query for logical errors, such as asking for data that doesn’t exist in the database.

Is this Technology Necessary? Won’t it replace human SQL experts?

A common question that often pops up when discussing AI topics is — “Do we really need this technology? And what happens to the SQL experts in the industry?” Let’s tackle these concerns head-on.

Is this Technology Necessary?

The volume of data we’re dealing with is enormous. Traditional SQL query generation, while effective, can be time-consuming and complex, especially when dealing with large datasets. AI-driven SQL generation is a tool that is here to help us manage this data deluge more effectively. It’s not about replacing human effort but about augmenting it.

AI can automate the routine task of writing SQL queries, freeing up time for data analysts to focus on more strategic tasks. It can as well handle complex queries more efficiently, helping us uncover insights that we might miss otherwise. So, yes, this technology is not just necessary, it’s a requirement.

The Role of Human SQL Experts

Now, let’s address the elephant in the room – will AI replace human SQL experts? The short answer is no. While AI is a powerful tool, it’s just that – a tool. It can automate tasks and provide insights, but it doesn’t replace the need for human expertise.

As a SQL expert, your role is not just about writing queries. It’s about understanding the business context, asking the right questions, interpreting the results, and making strategic decisions. These are skills that AI can’t replicate.

Moreover, AI tools need guidance. They need human experts to train them, provide the right data, and interpret the results. So, rather than replacing SQL experts, AI is creating new opportunities for them to leverage their skills in more strategic ways.

In the next section, we’ll compare traditional SQL generation and AI-powered SQL generation. Let’s see how they stack up!

Traditional SQL Generation vs AI-Powered SQL Generation

In the traditional approach to SQL generation, data analysts and engineers manually write SQL queries. This process requires a deep understanding of SQL syntax and the structure of the database. It’s a meticulous task, and the complexity increases with the size and complexity of the data.

Let’s present the comparison of the two in a more structured way. Here’s a tabular breakdown:

In addition to these points, it’s worth noting that the rise of AI-powered SQL generation doesn’t spell the end for SQL experts. Quite the contrary, it presents an opportunity for these professionals to upskill and work in tandem with AI, leveraging the best of both worlds to drive data-driven decision making.

How SQL Generation Works in Datameer

Transitioning from the comparison, it’s clear that AI-powered SQL generation has a significant edge. But how does this process actually work in a tool like Datameer ? Datameer is a tool designed and optimized for Snowflake. This means that if you’re a Snowflake user, Datameer is a perfect fit for you. Here’s a step-by-step guide on how to generate SQL using Datameer:

Step 1: Connect Datameer to Your Snowflake Data Source

First, you’ll need to connect Datameer to your Snowflake data source. This is a straightforward process, thanks to Datameer’s direct integration with Snowflake .

Step 2: Define Your Data Transformation

Next, you’ll define your data transformation. This could be anything from a simple filter operation to a complex join of multiple tables. You can do this visually using Datameer’s intuitive interface, or you can write it out in SQL if you prefer.

Step 3: Run the Transformation

Once you’ve defined your transformation, you can run it. Datameer executes all data workflows directly inside of Snowflake, taking full advantage of Snowflake’s efficient and scalable compute capacity. For this example here below, I defined a join operation.

Step 4: Review the Generated SQL

After the transformation is complete, you can review the SQL that Datameer generated. To do this, you’ll need to log into your Snowflake account. This is a great way to learn how to write more efficient SQL and to ensure that the transformation did what you expected.

Step 5: Use the Results

Finally, you can use the results of your transformation. Whether you’re creating a report, feeding the data into a machine learning model, or just exploring the data, you can be confident that the data is accurate and up-to-date.

Remember, Datameer’s machine learning algorithms are continuously improving the accuracy and efficiency of SQL generation. This means that the more you use Datameer, the better it gets at generating SQL.

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Master SQL With AI-Powered SQL Query Builder

In the fast-paced world of technology, the ability to manage databases efficiently is crucial for developers and data analysts. However, generating accurate SQL queries remains a significant challenge, often leading to time-consuming revisions and debugging. Enter the AI-powered SQL query builder, a revolutionary tool designed to transform database management by leveraging artificial intelligence to produce highly accurate SQL queries instantly.

What is an AI-Powered SQL Query Builder?

An AI-powered SQL query builder is a cutting-edge tool that utilizes artificial intelligence to automate the generation of SQL queries. By understanding the structure of your database and the requirements of your query, it can instantly produce accurate and optimized SQL code, significantly reducing manual effort and error rates.

The Importance of Accurate SQL Queries

Accuracy in SQL queries is paramount. A single error can lead to incorrect data retrieval, application crashes, or even data corruption. In the realm of database management, the stakes are high, and the margin for error is slim. Ensuring the accuracy of SQL queries is essential for maintaining the integrity and performance of your database systems.

Features of Our AI-Powered SQL Query Builder

Our AI-powered SQL query builder is packed with features designed to address the common challenges of database management, making it an indispensable tool for modern developers and data analysts.

Generate Accurate SQL Queries Instantly

Thanks to its advanced AI algorithms, our tool can generate highly accurate SQL queries within seconds. This not only saves valuable time but also significantly reduces the likelihood of errors, ensuring your database operations run smoothly.

Compatibility with Major Database Engines

Our SQL query builder is designed to work seamlessly with all major database engines, including MySQL, PostgreSQL, SQL Server, and more. This ensures wide applicability and flexibility, regardless of the database technology you use.

User-Friendly Interface

We believe that powerful tools should also be accessible. That's why our SQL query builder features an intuitive, user-friendly interface that makes generating SQL queries a breeze, even for beginners. Experienced users will also appreciate the efficiency gains and advanced features.

SQL Query Builder Online

Accessibility is key in today's mobile world. Our SQL query builder is available online, meaning you can generate accurate SQL queries from anywhere, at any time, without the need for software installation.

Benefits of Using an AI-Powered SQL Query Builder

The advantages of integrating an AI-powered SQL query builder into your database management processes are numerous.

Increased Efficiency and Productivity

Automating the generation of SQL queries can drastically increase your efficiency and productivity, allowing you to focus on more strategic tasks and projects.

Enhanced Accuracy and Reliability

With AI-powered generation, you can trust that your SQL queries will be accurate and reliable, reducing the risk of errors and the need for time-consuming debugging.

Cost-Effective Database Management

By minimizing errors and saving time, our SQL query builder can lead to more cost-effective database management, optimizing your resources and budget.

Our AI-powered SQL query builder is particularly beneficial in scenarios such as complex database projects, where accuracy and efficiency are paramount, or when working with multiple database engines, requiring a versatile and reliable tool.

The AI-powered SQL query builder represents a significant leap forward in database management technology. By automating the generation of accurate SQL queries, it offers a host of benefits, from increased efficiency and productivity to enhanced accuracy and cost-effectiveness.

Ready to revolutionize your database management processes? Explore our AI-powered SQL query builder online and discover how it can transform your approach to generating SQL queries, making your work faster, more accurate, and more efficient.

Learn How to Use AI for SQL

Learn to generate SQL with AI, transform natural language to SQL, and utilize LLMs for SQL operations in our innovative course.

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Prerequisites

• Intro to SQL
• Learn SQL: Aggregate Functions
• Learn SQL: Multiple Tables

About this course

Discover how to enhance your database analytics using AI technology. This course will guide you on how to employ AI systems like ChatGPT or Gemini for everything from brainstorming analytics ideas to writing and analyzing SQL queries. Along the way, we’ll dive into the ethics of using AI as well as potential pitfalls such as hallucinations, data security, and legal questions. Harness the combined power of human expertise and artificial intelligence to unlock new insights from your data.

Skills you'll gain

Generate SQL with AI

Use AI to debug and explain complex queries

Navigate potential pitfalls in working with AI for SQL

Learn AI for SQL

Learn how to use AI for SQL analytics.

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Frequently asked questions about How to Use AI for SQL

What is sql.

SQL (Structured Query Language) is a programming language used to manage data stored in relational databases, which store structured data in tables. Its syntax is easy to read, so it’s easy to pick up on even if you’re completely new to programming, and it’s even useful for non-technical careers.

What does SQL do?

What kind of jobs use sql, why should i use ai to help me write sql, join over 50 million learners and start learn how to use ai for sql today, looking for something else, related resources, ai prompting best practices, design a custom game with generative ai, detecting hallucinations in generative ai, related courses and paths, learn how to use ai for coding, intro to ai strategy, analyze data with sql, browse more topics.

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TEXT 2 SQL .AI

Generate sql with ai, improve your sql skills and save time using ai by generating optimized sql queries effortlessly.

Translate, explain and fix complex SQL queries using your native language

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Frequently Asked Questions

• Generating SQL queries from text descriptions in multiple languages.
• Translating SQL queries into plain English explanations.
• Fixing errors in SQL queries.
• Customizable integration of database schemas (tables, columns, types).
• Support for various SQL dialects including PostgreSQL, MySQL, MS SQL, MariaDB, Snowflake, BigQuery, SQLite, DB2, Hive, Spark, Redshift, PL/SQL, Clickhouse, Dynamo and more!
• Generating and explaining formulas for Microsoft Excel and Google Sheets.
• Generating and explaining regex expressions with AI.
• Multilingual support across major global languages (English, Spanish, French, German, Portuguese, Italian, Russian, Chinese, Japanese, Korean, Arabic, Hindi).
• Easy API integration for extending application capabilities.

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