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Essential SQL Knowledge for Entry-Level Data Science Roles
Meta releases its most powerful AI model yet
Welcome to learning edition of the Data Pragmatist, your dose of all things data science and AI.
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🔮 Meta releases its most powerful AI model yet LINK
Meta has released Llama 3.1 405B, its largest open-source AI model to date, featuring 405 billion parameters which enhance its problem-solving abilities.
Trained with 16,000 Nvidia H100 GPUs, Llama 3.1 405B is competitive with leading AI models like OpenAI’s GPT-4 and Anthropic’s Claude 3.5 Sonnet, though it has specific strengths and weaknesses.
Meta's new AI model is available for download or cloud usage and powers chatbots on platforms like WhatsApp and Meta.ai, showcasing capabilities in coding, mathematical queries, and multilingual document summarization.
💰 Google talks to acquire Wiz for $23 billion fall through LINK
Cybersecurity startup Wiz has turned down a $23 billion acquisition proposal from Alphabet, Google's parent company, opting instead to pursue an initial public offering.
Wiz co-founder Assaf Rappaport highlighted antitrust and investor concerns as key reasons for rejecting the deal, aiming for $1 billion in annual recurring revenue alongside the IPO.
Neither Wiz nor Alphabet have confirmed discussions about the deal, which would have been Google's largest acquisition, with Wiz providing cloud-based security solutions for enterprise clients.
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🧠 Essential SQL Knowledge for Entry-Level Data Science Roles
SQL (Structured Query Language) is a cornerstone skill for data scientists. According to a survey by 365DataScience of 1,000 LinkedIn data science job postings, 60% required SQL proficiency. This highlights its importance in the data science field. This article discusses the essential SQL knowledge required for entry-level data science roles and offers resources and advice to help you learn SQL effectively.
Basic SQL Functions
For entry-level roles, mastery of basic SQL functions is essential. Here are key commands with examples using an imaginary Employees table:
SELECT
SELECT * FROM Employees;
ALTER, INSERT, CREATE
CREATE TABLE Employees (
EmployeeID INT PRIMARY KEY,
FirstName NVARCHAR(50),
LastName NVARCHAR(50),
BirthDate DATE,
Salary DECIMAL(10, 2)
);
INSERT INTO Employees (EmployeeID, FirstName, LastName, BirthDate, Salary)
VALUES (1, 'John', 'Doe', '1980-01-01', 50000.00);
ALTER TABLE Employees ADD Department VARCHAR(50);
GROUP BY, ORDER BY
SELECT Department, COUNT(*) AS NumberOfEmployees
FROM Employees
GROUP BY Department;
SELECT * FROM Employees
ORDER BY LastName ASC, FirstName ASC;
WHERE, AND, OR, BETWEEN, IN, HAVING
SELECT * FROM Employees WHERE Salary > 40000 AND Department = 'Sales';
AVG, COUNT, MIN, MAX, SUM
SELECT
AVG(Salary) AS AverageSalary,
COUNT(EmployeeID) AS NumberOfEmployees,
MIN(Salary) AS MinimumSalary,
MAX(Salary) AS MaximumSalary,
SUM(Salary) AS TotalSalary
FROM Employees;
DISTINCT
SELECT DISTINCT Department FROM Employees;
JOIN Operations
SELECT e.EmployeeID, e.FirstName, e.LastName, d.DepartmentName
FROM Employees e
INNER JOIN Departments d ON e.DepartmentID = d.DepartmentID;
Advanced SQL Functions
Common Table Expressions (CTEs) and Subqueries
WITH SalesCTE AS ( SELECT EmployeeID, SUM(SalesAmount) AS TotalSales FROM Sales GROUP BY EmployeeID ) SELECT e.EmployeeID, e.FirstName, e.LastName, s.TotalSales FROM Employees e JOIN SalesCTE s ON e.EmployeeID = s.EmployeeID;
User Defined Functions (UDFs)
CREATE FUNCTION dbo.GetEmployeeFullName(@EmployeeID INT) RETURNS NVARCHAR(100) AS BEGIN DECLARE @FullName NVARCHAR(100); SELECT @FullName = FirstName + ' ' + LastName FROM Employees WHERE EmployeeID = @EmployeeID; RETURN @FullName; END;
Mastering SQL is indispensable for aspiring data scientists, given that 60% of job postings require this skill. By focusing on fundamental SQL commands like SELECT, INSERT, and JOIN, and understanding advanced functions such as CTEs and UDFs, you can effectively manipulate and analyze data. Utilizing resources like W3Schools, Tutorialspoint, and practice platforms like HackerRank will solidify your skills. Remember, consistent practice and patience are key. With dedication, you can acquire the necessary SQL knowledge to secure an entry-level data science role and enhance your career prospects.
Top AI SQL Tools
AI2sql
Overview: AI-driven SQL query generator using natural language.
Features: Supports MySQL, PostgreSQL, MongoDB, Oracle, integrated with GPT-3.
Performance: Launched in 2021, ranked fifth on Product Hunt, v2.0 received 142 upvotes.
AI Query
Overview: Uses GPT-3 to generate SQL queries from natural language.
Features: Supports Postgres, MySQL, MariaDB, SQL Server; visual interface for table structure.
Performance: Launched on Product Hunt in late 2022.
Outerbase
Overview: Modern SQL client with AI capabilities.
Features: Supports Postgres, MySQL, relational databases; spreadsheet-like interface.
Performance: Launched in 2023, minimal updates since April.
SQL Chat
Overview: Chat-based SQL client integrated with ChatGPT.
Features: Supports MySQL, PostgreSQL, SQL Server, TiDB Serverless; subscription model available.
Performance: Significant attention since Product Hunt launch.
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