TABLE OF CONTENTS
- DA-101 SQL
- Topic 1: Getting Started with Data Analytics
- Topic 2: SQL Basics
- Topic 3: ER Diagram Basics
- Topic 4: SQL Group & Filter
- Topic 5: SQL Joins & Queries
- Topic 6: SQL Data Manipulation
- Topic 7: SQL Subqueries & Views
- Topic 8: SQL Optimization
- Topic 9: Integrating AI with SQL for Smarter Queries
- SQL Interview Questions
DA-101 SQL
Sprint summary
Getting Started
Topic 1: Getting Started with Data Analytics
- Introduction to DA-101
DA-101 is an introductory course focused on data analytics concepts and tools, aimed at helping individuals understand how to analyze and interpret data effectively.
Where is it used?
- Data Analytics is used in various industries such as finance, healthcare, marketing, and technology for decision-making and strategy development .
How is it used?
- Identify the data sources relevant to the analysis.
- Collect and clean the data to ensure accuracy.
- Use analytical tools to explore and visualize the data.
- Interpret the results to derive insights.
- Communicate findings to stakeholders for informed decision-making.
Takeaways / best practices:
- Always ensure data quality before analysis.
- Use visualizations to make data insights more accessible.
- Continuously update skills and tools to keep up with industry trends.
- Collaborate with stakeholders to align analytics with business goals.
- Introduction to databases
What is it?
A database is an organized collection of structured information or data, typically stored electronically in a computer system.
Where is it used?
Databases are used in various fields such as finance, healthcare, e-commerce, and social media for storing, managing, and analyzing data.
How is it used?
- Identify the data requirements for analysis.
- Choose the appropriate database management system (DBMS).
- Design the database schema to structure the data.
- Populate the database with relevant data.
- Use SQL or other query languages to retrieve and manipulate data.
- Analyze the data using analytical tools or software.
- Visualize the results to derive insights.
Takeaways / best practices
- Ensure data integrity and accuracy by implementing validation rules.
- Regularly back up the database to prevent data loss.
- Optimize queries for better performance.
- Use indexing to speed up data retrieval.
- Maintain proper documentation for database structure and usage.
- What is SQL
SQL, or Structured Query Language, is a programming language designed for managing and manipulating relational databases, essential for data analytics.
Where is it used?
- Data extraction from databases
- Data manipulation and transformation
- Reporting and data visualization tools
- Data warehousing and ETL processes
How is it used?
- Connect to a database using a database management system (DBMS).
- Write SQL queries to select, insert, update, or delete data.
- Use aggregate functions to summarize data (e.g., COUNT, SUM, AVG).
- Filter data using WHERE clauses to focus on specific subsets.
- Join multiple tables to combine related data.
- Export results for further analysis or reporting.
Takeaways / best practices:
- Always validate and sanitize inputs to prevent SQL injection.
- Use clear and descriptive naming conventions for tables and columns.
- Optimize queries for performance by indexing frequently accessed columns.
- Regularly back up databases to prevent data loss.
- Document SQL queries for future reference and collaboration.
- Your first SQL query
Your first SQL query is a basic command to retrieve data from a database, often used to analyze information or user reviews.
The SHOW keyword can be used to get various information, such as
Columns in a table
Tables in a database
List of databases
And much more
- Where is it used?
- In data analysis for business intelligence, reporting, and decision-making.
- How is it used?
- Identify the database and table containing the relevant data.
- Write a SELECT statement to specify the columns you want to retrieve:
-Select specific column data from a given table:
-Select all columns from given table
- Use WHERE clauses to filter data based on specific conditions.
- Execute the query to fetch results.
- Analyze the output for insights or trends.
- Takeaways / best practices:
- Always validate your data sources before querying.
- Use clear and descriptive column names in your SELECT statements.
- Optimize queries for performance by limiting the amount of data retrieved.
- Comment your SQL code for clarity and future reference.
- Test queries with small datasets before running on larger tables.
Topic 2: SQL Basics
- Introduction to SQL Commands
SQL commands are instructions used to communicate with a database. They can be categorized into several types:
1. **Data Query Language (DQL)**: Used to query and retrieve data. Example: `SELECT`.
2. **Data Definition Language (DDL)**: Used to define and manage database structures. Example: `CREATE`
-Create table Users with 3 columns named user_id, name and email:
3. **Data Manipulation Language (DML)**: Used to manipulate data within the database. Examples: `INSERT`, `UPDATE`, `DELETE`:
-Add data into table Users:
-Add data in table Users:
-Delete data from table Users:
These commands allow users to create, read, update, and delete data in a structured way.
- Filtering Data
Filtering data in data analytics refers to the process of selecting a subset of data based on specific criteria to focus analysis on relevant information.
Syntax:
Example:
Output:
Where is it used?
- Business intelligence
- Market research
- Financial analysis
- Data cleaning and preparation
How is it used?
- Define the criteria for filtering (e.g., date range, specific values).
- Apply the filter to the dataset using analytical tools or programming languages.
- Review the filtered data to ensure it meets the analysis requirements.
- Perform analysis on the filtered dataset.
- Visualize or report findings based on the filtered data.
Takeaways / best practices:
- Clearly define filtering criteria to avoid ambiguity.
- Use multiple filters judiciously to refine data without losing important context.
- Regularly review and update filters as data and analysis needs evolve.
- Document filtering processes for reproducibility and transparency.
- Sorting Data
Sorting data is the process of arranging data in a specific order, typically ascending or descending, based on one or more attributes.
Syntax:
ASC to sort in the ascending order
DESC to sort in the descending order
Sorting on multiple columns
Example
Output:
Where is it used?
- Data cleaning and preparation
- Data visualization
- Reporting and analysis
- Machine learning preprocessing
How is it used?
- Identify the dataset to be sorted.
- Choose the attribute(s) for sorting.
- Select the order (ascending or descending).
- Apply sorting algorithms or functions using data analysis tools or programming languages.
- Review the sorted data for accuracy and relevance.
Takeaways / best practices:
- Always sort data based on the context of analysis.
- Ensure data integrity before sorting to avoid misinterpretation.
- Use efficient sorting algorithms for large datasets to optimize performance.
- Document sorting criteria for reproducibility and transparency in analysis.
- Limiting Results
Limiting results in data analytics refers to the process of restricting the output of a query or analysis to a specific subset of data to enhance focus and relevance.
Syntax:
Replace 5 with the desired number of rows
Example:
Output:
Where is it used?
- Database queries
- Data visualization tools
- Reporting and dashboards
How is it used?
- Define the criteria for limiting results (e.g., date range, specific categories).
- Use filtering options in data analysis tools or SQL queries.
- Apply aggregation functions to summarize data if needed.
- Review the limited results to ensure they meet the analysis objectives.
- Adjust criteria as necessary to refine the output.
Takeaways / best practices:
- Always define clear objectives for limiting results to avoid unnecessary data.
- Use multiple criteria for more precise filtering.
- Regularly review and update limiting parameters to reflect changing data needs.
- Document the rationale for limiting results to maintain transparency in analysis.
Topic 3: ER Diagram Basics
- Introduction to ER diagram
An ER diagram, or Entity-Relationship diagram, is a visual representation of the relationships between entities in a database, commonly used in data analytics to model data structures.
Where is it used?
- Database design
- Data modeling
- System analysis
- Business process modeling
How is it used?
- Identify entities relevant to the data being analyzed.
- Define attributes for each entity to capture necessary information.
- Establish relationships between entities to show how they interact.
- Use symbols to represent entities (rectangles), attributes (ovals), and relationships (diamonds).
- Review and refine the diagram to ensure accuracy and completeness.
- Implement the design in a database management system.
Takeaways / best practices:
- Keep the diagram simple and clear for better understanding.
- Use consistent naming conventions for entities and attributes.
- Regularly update the ER diagram as the data model evolves.
- Involve stakeholders in the design process to ensure all requirements are captured.
- Validate the diagram against real-world scenarios to ensure its practicality.
- Entities, Attributes, and Relationships
Entities: Distinct objects or things in a dataset that can be identified and have a unique existence.
Attributes: Characteristics or properties that describe entities and provide more information about them.
Relationships: Connections or associations between entities that illustrate how they interact or relate to one another.
Where is it used?
- In database design, data modeling, and data analytics to structure and analyze data effectively.
How is it used?
- Identify entities relevant to the analysis.
- Define attributes for each entity to capture necessary details.
- Establish relationships between entities to understand interactions.
- Use this structured data model to perform queries and derive insights.
Takeaways / best practices:
- Clearly define entities to avoid ambiguity.
- Ensure attributes are relevant and necessary for analysis.
- Map relationships accurately to reflect real-world connections.
- Regularly review and update the data model as business needs evolve.
- Types of Relationships
Types of Relationships in Data Analytics refer to the various ways in which data variables can interact or correlate with each other:
-One to One (1:1):
What will data table look like for 1:1:
-One to Many (1:M):
What will data table look like for 1:M:
-Many to One (M:1):
What will data table look like for M:1:
-Many to Many (M:N):
What will data table look like for M:N:
Where is it used?
- In predictive modeling
- In exploratory data analysis
- In data visualization
How is it used?
- Identify variables of interest
- Analyze data to determine correlation (positive, negative, or no correlation)
- Use statistical methods (like regression analysis) to quantify relationships
- Visualize relationships using scatter plots or correlation matrices
- Interpret results to inform decision-making
Takeaways / best practices:
- Always visualize relationships to gain insights.
- Use appropriate statistical tests to validate findings.
- Be cautious of spurious correlations; correlation does not imply causation.
- Consider the context of the data when interpreting relationships.
- Keys in ER diagrams
Keys in ER diagrams are attributes that uniquely identify entities within a database.
Primary Key (PK): A unique identifier for each record in a table, ensuring that no two rows are identical.
Foreign Key (FK): A field that links one table to another, establishing a relationship
Where is it used?
- In database design and data modeling for relational databases.
How is it used?
- Identify the primary key for each entity to ensure uniqueness.
- Use foreign keys to establish relationships between entities.
- Ensure that keys are properly indexed for efficient querying.
- Regularly review and update keys as the data model evolves.
Takeaways / best practices:
- Always define a primary key for each entity to maintain data integrity.
- Use meaningful keys that reflect the data they represent.
- Avoid using composite keys unless necessary for uniqueness.
- Regularly audit keys to ensure they meet current data requirements.
- Reading ER diagram
- An ER diagram (Entity-Relationship diagram) is a visual representation of the relationships between data entities in a database.
- Where is it used?
- Database design
- Data modeling
- System analysis
- Data integration
- How is it used?
- Identify entities (e.g., customers, products).
- Define relationships between entities (e.g., purchases, orders).
- Determine attributes for each entity (e.g., customer name, product price).
- Create a visual diagram to represent entities and relationships.
- Use the diagram to guide database creation and data analytics processes.
- Takeaways / best practices:
- Keep the diagram simple and clear for better understanding.
- Use consistent naming conventions for entities and attributes.
- Regularly update the ER diagram to reflect changes in data requirements.
- Collaborate with stakeholders to ensure all necessary entities and relationships are included.
Topic 4: SQL Group & Filter
- Aggregation Functions
Aggregation functions are operations that process multiple values to produce a single summary value, commonly used in data analytics to derive insights from datasets.
Where is it used?
- Data summarization in reporting
- Statistical analysis
- Data visualization
- Business intelligence tools
How is it used?
- Identify the dataset to analyze.
- Choose the relevant aggregation function (e.g., SUM, AVG, COUNT).
- Apply the function to the desired data column(s).
- Group data if necessary (e.g., by categories or time periods).
- Interpret the resulting summary value for insights.
Takeaways / best practices:
- Select appropriate aggregation functions based on the analysis goal.
- Be mindful of data types and ensure compatibility with chosen functions.
- Use grouping wisely to avoid misleading summaries.
- Validate results by cross-checking with raw data.
- Document the aggregation process for transparency and reproducibility.
- Grouping
Grouping in data analytics refers to the process of organizing data into subsets based on shared characteristics or attributes for analysis.
The GROUP BY clause helps us organize data into categories
Syntax:
Where is it used?
- Business intelligence
- Market research
- Customer segmentation
- Financial analysis
How is it used?
- Identify the variable(s) for grouping.
- Select the appropriate grouping method (e.g., by category, range).
- Apply the grouping method to the dataset.
- Analyze the grouped data to extract insights.
- Visualize the results for better understanding.
Takeaways / best practices:
- Ensure clarity on the purpose of grouping before starting.
- Choose relevant attributes for effective grouping.
- Avoid over-complicating groups; keep them meaningful.
- Validate the results to ensure accuracy and relevance.
- Use visualizations to communicate findings effectively.
- Filtering Grouped data
Filtering grouped data refers to the process of applying conditions to subsets of data that have been aggregated based on specific criteria, allowing analysts to focus on relevant insights.
The HAVING clause in SQL filters grouped data based on conditions applied to aggregated results.
Syntax:
Where is it used?
- Data analysis in business intelligence
- Reporting and dashboard creation
- Statistical analysis and research
How is it used?
- Group the data based on relevant categories (e.g., by region, product, or time period).
- Apply aggregation functions (e.g., sum, average, count) to summarize the grouped data.
- Define filtering criteria to isolate specific groups or values of interest.
- Execute the filtering operation to retrieve the desired subset of the grouped data.
- Analyze the filtered results to draw insights or make decisions.
Takeaways / best practices:
- Always define clear objectives for filtering to avoid unnecessary complexity.
- Use meaningful groupings that align with the analysis goals.
- Ensure that filtering criteria are relevant and based on data quality.
- Document the filtering process for transparency and reproducibility.
- Regularly review and adjust filtering criteria as business needs evolve.
Topic 5: SQL Joins & Queries
- Joins
Joins are operations in data analytics that combine records from two or more tables based on related columns.
Where is it used?
- In relational databases
- Data warehousing
- Data integration and ETL processes
- Business intelligence reporting
How is it used?
- Identify the tables to be joined.
- Determine the common key or column(s) for the join.
- Choose the type of join (INNER, LEFT, RIGHT, FULL OUTER).
- Execute the join operation using SQL or data manipulation tools.
- Analyze the resulting dataset for insights.
INNER JOIN:
Returns records that have matching values in both tables
Same as the JOIN you used in the previous activity
Syntax:
Example:
LEFT JOIN:
Returns all records from the left table, and the matched records from the right table
Syntax:
Example:
RIGHT JOIN:
The opposite of LEFT JOIN
Returns all records from the right table, and the matched records from the left table
Syntax:
Example:
FULL OUTER JOIN:
Combination of LEFT JOIN and RIGHT JOIN
Returns all records when there is a match in either left or right table
Syntax:
Example:
Takeaways / best practices:
- Always understand the data relationships before joining.
- Use the appropriate type of join to avoid data loss or duplication.
- Limit the number of joined tables to improve performance.
- Test joins with sample data to ensure accuracy.
- Document the join logic for future reference and clarity.
- Exploring Relationships
Exploring relationships in data analytics involves analyzing the connections and interactions between different variables within a dataset.
Where is it used?
- Market research
- Social network analysis
- Healthcare studies
- Financial forecasting
How is it used?
- Identify variables of interest.
- Collect relevant data.
- Use statistical methods (e.g., correlation, regression) to analyze relationships.
- Visualize data through charts or graphs to illustrate findings.
- Interpret results to draw conclusions about the relationships.
Takeaways / best practices:
- Always ensure data quality and relevance.
- Use appropriate statistical methods for analysis.
- Visualizations should be clear and informative.
- Consider the context of the data when interpreting relationships.
- Be cautious of inferring causation from correlation.
- Combining Query Results
Combining query results refers to the process of merging data from multiple queries to create a comprehensive dataset for analysis.
UNION: Combines results, removes duplicates.
Syntax:
Example:
Query:
Output:
UNION ALL: Combines results without removing duplicates.
Syntax:
Query:
Output:
Where is it used?
- Data warehousing
- Business intelligence tools
- Reporting and dashboard creation
How is it used?
- Identify the queries that need to be combined.
- Ensure that the data structures (schemas) of the queries are compatible.
- Use SQL commands like JOIN, UNION, or subqueries to merge the results.
- Validate the combined dataset for accuracy and completeness.
- Analyze the combined data to derive insights.
Takeaways / best practices:
- Always check for data consistency and integrity before combining.
- Use clear naming conventions for combined datasets to avoid confusion.
- Document the logic used for combining queries for future reference.
- Optimize queries for performance to handle large datasets efficiently.
- Regularly review and update combined queries as data sources change.
- Optimizing Query Readability
Optimizing query readability refers to the practice of structuring and writing database queries in a clear and understandable manner to enhance comprehension and maintainability.
Where is it used?
- Data analytics projects
- Business intelligence tools
- Database management systems
How is it used?
- Use meaningful aliases for tables and columns.
- Organize queries with proper indentation and line breaks.
- Comment complex logic or calculations for clarity.
- Use consistent naming conventions throughout the query.
- Break down complex queries into smaller, manageable subqueries or common table expressions (CTEs).
Takeaways / best practices:
- Prioritize clarity over brevity; aim for easily understandable queries.
- Regularly review and refactor queries for improved readability.
- Collaborate with team members to establish and follow a standard query style guide.
- Document the purpose and logic of queries for future reference.
Topic 6: SQL Data Manipulation
- String Functions
String functions are operations that manipulate and analyze text data within datasets.
Common String Functions:
Where is it used?
- Data cleaning and preprocessing
- Text analysis and natural language processing
- Data transformation and feature engineering
How is it used?
- Identify and remove unwanted characters or whitespace
- Extract substrings or specific patterns using regular expressions
- Convert text to a consistent case (e.g., upper, lower)
- Concatenate multiple strings into a single string
- Split strings into lists based on delimiters
- Replace specific characters or substrings with others
Takeaways / best practices:
- Always validate and clean text data to ensure accuracy.
- Use regular expressions for complex pattern matching.
- Be mindful of case sensitivity when comparing strings.
- Document any transformations applied to maintain data integrity.
- Optimize string operations for performance, especially with large datasets.
- Data Manipulation
Data manipulation refers to the process of adjusting, organizing, or transforming data to make it more suitable for analysis.
INSERT:
Add a new row to a table
Query:
UPDATE:
Modify data in an existing row
Query:
DELETE:
Remove rows from a table based on a condition
Query:
Where is it used?
- Data analytics
- Business intelligence
- Data science
- Database management
How is it used?
- Import data from various sources (e.g., databases, spreadsheets)
- Clean the data by removing duplicates and correcting errors
- Transform data formats (e.g., converting dates, normalizing values)
- Aggregate data to summarize information (e.g., calculating averages, totals)
- Filter data to focus on relevant subsets
- Join multiple datasets to enrich analysis
- Visualize data to identify patterns and insights
Takeaways / best practices:
- Always back up original data before manipulation.
- Document each step of the manipulation process for reproducibility.
- Use consistent naming conventions for variables and datasets.
- Validate results after manipulation to ensure accuracy.
- Automate repetitive tasks to save time and reduce errors.
Topic 7: SQL Subqueries & Views
- Subqueries
- A subquery is a query nested within another SQL query, used to retrieve data that will be used in the main query.
Syntax:
- Where is it used?
- In data retrieval to filter results based on conditions from another query.
- In calculations or aggregations that depend on other data sets.
- How is it used?
- Identify the main query that requires additional data.
- Write the subquery to fetch the necessary data.
- Place the subquery in the appropriate clause of the main query (e.g., SELECT, WHERE, FROM).
- Ensure the subquery returns a single value or a set of values that the main query can utilize.
- Execute the main query to retrieve the final results.
- Takeaways / best practices:
- Keep subqueries simple to enhance readability and performance.
- Use subqueries judiciously; consider joins for better efficiency in some cases.
- Test subqueries independently to ensure they return the expected results.
- Avoid using subqueries in the SELECT clause if possible, as they can lead to performance issues.
- Correlated queries
Correlated queries are subqueries that depend on the outer query for their values, often used to filter or aggregate data based on related conditions.
Syntax:
Where is it used?
- In SQL databases for complex data retrieval.
- In data analysis for deriving insights from related datasets.
How is it used?
- Identify the main query that requires additional filtering or calculations.
- Write a subquery that references columns from the main query.
- Ensure the subquery executes for each row processed by the main query.
- Use the results of the subquery to refine or enhance the output of the main query.
Example:
Query:
Output:
Takeaways / best practices:
- Use correlated queries judiciously, as they can be less efficient than non-correlated queries.
- Optimize performance by minimizing the number of rows processed in the outer query.
- Test and analyze execution plans to identify potential bottlenecks.
- Consider alternative approaches, such as joins, when appropriate for better performance.
- Views
Views in data analytics are virtual tables created by querying data from one or more tables, allowing users to simplify complex queries and present data in a specific format.
Syntax:
Example:
Table: Same table which is used for correlated queries.
Creating View:
Query the View:
Output:
Where is it used?
- In databases for reporting and data analysis.
- In business intelligence tools for dashboard creation.
How is it used?
- Define the data requirements and desired output.
- Write a SQL query to select and manipulate the data.
- Create a view using the SQL query.
- Access the view as if it were a regular table for analysis or reporting.
Takeaways / best practices:
- Use views to encapsulate complex logic and simplify data access.
- Ensure views are optimized for performance to avoid slow queries.
- Regularly review and update views to reflect changes in underlying data structures.
- Limit the use of views for sensitive data to maintain security and compliance.
Topic 8: SQL Optimization
- Query Optimization
Query optimization is the process of improving the efficiency of a database query to reduce execution time and resource consumption.
Query Optimization with Filters:
Query Optimization with Column Selection
Where is it used?
- Data analytics platforms
- Business intelligence tools
- Database management systems
How is it used?
- Analyze the query execution plan to identify bottlenecks.
- Rewrite queries for better performance (e.g., using joins instead of subqueries).
- Use appropriate indexing to speed up data retrieval.
- Limit the amount of data processed by filtering early in the query.
- Optimize database schema for efficient data access.
Takeaways / best practices:
- Always analyze query performance before and after optimization.
- Use indexing judiciously to balance read and write performance.
- Regularly review and refactor queries as data and usage patterns change.
- Monitor database performance metrics to identify areas for improvement.
- Explain Keyword
- What is it? The "Explain" keyword in data analytics is used to provide insights into how a model makes predictions by detailing the relationships between input features and output predictions.
- Where is it used? It is commonly used in machine learning frameworks and data visualization tools to interpret model behavior and enhance transparency.
- How is it used?
- Select the model or algorithm you want to analyze.
- Use the "Explain" function or method provided by the analytics tool or library.
- Input the data for which you want to generate explanations.
- Review the output, which may include feature importance, decision paths, or visualizations.
- Interpret the results to understand model decisions and improve model performance.
- Takeaways / best practices:
- Always validate the explanations against domain knowledge.
- Use visualizations to make complex explanations more understandable.
- Regularly update explanations as models evolve or new data is introduced.
- Ensure that explanations are accessible to stakeholders with varying levels of technical expertise.
- Indexing
Indexing in data analytics refers to the process of creating a data structure that improves the speed of data retrieval operations on a database.
Where is it used?
- Databases
- Data warehouses
- Search engines
How is it used?
- Identify the columns frequently used in queries.
- Create an index on those columns to enhance retrieval speed.
- Monitor query performance to determine if additional indexes are needed.
- Regularly update and maintain indexes to ensure efficiency.
Takeaways / best practices:
- Use indexing selectively to avoid excessive overhead.
- Regularly analyze query performance to optimize indexing strategy.
- Consider the trade-off between read and write performance when indexing.
- Keep indexes updated to reflect changes in the underlying data.
- When to Index?
When to Index? refers to the strategic decision of creating indexes in databases to optimize query performance in data analytics.
Where is it used?
- In relational databases
- In data warehouses
- In big data platforms
How is it used?
- Identify frequently queried columns.
- Analyze query performance and execution plans.
- Create indexes on those columns to speed up data retrieval.
- Monitor the impact of indexes on performance.
- Adjust or remove indexes that are not beneficial.
Takeaways / best practices:
- Only index columns that are frequently used in search conditions.
- Avoid over-indexing, as it can slow down write operations.
- Regularly review and optimize indexes based on changing query patterns.
- Use composite indexes for queries involving multiple columns.
- Test performance before and after indexing to ensure effectiveness.
Topic 9: Integrating AI with SQL for Smarter Queries
- Using AI with SQL
Using AI with SQL in data analytics involves integrating artificial intelligence techniques with SQL queries to enhance data processing, analysis, and insights generation.
SQL Query Builder tool
https://workik.com/ai-powered-sql-query-generator
Where is it used?
- Business intelligence tools
- Data warehousing
- Predictive analytics
- Customer relationship management (CRM) systems
How is it used?
- Identify data sources and define the analytical objectives.
- Use SQL to extract and preprocess data from databases.
- Apply AI algorithms (like machine learning models) to analyze the data.
- Generate insights and predictions based on AI analysis.
- Visualize results using BI tools for better decision-making.
Takeaways / best practices:
- Ensure data quality and integrity before analysis.
- Use AI models that are interpretable and relevant to the business context.
- Continuously monitor and update models to adapt to new data trends.
- Collaborate with domain experts to validate findings and insights.
- Document processes and results for transparency and reproducibility.
SQL Interview Questions:
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