#Database Administrator

Comment “DATABASE” for the links. You Will Finally Understand Databases & SQL 📌 Watch these high-quality database videos: 1️⃣ Harvard CS50’s Intro to Databases with SQL A full university-level course covering SQL, relational databases, schemas, queries, joins, indexes, and real-world database fundamentals. 2️⃣ 7 Database Paradigms (Fireship) A fast, clear breakdown of different database types including relational, NoSQL, key-value, document, graph, and when to use each. 3️⃣ Database Design Course (Caleb Curry) A beginner-friendly guide to database design, normalization, relationships, primary keys, and planning databases the right way. If SQL feels confusing, database design feels abstract, or you’re unsure how real systems store and organize data, these videos connect everything together step by step. Great for learning SQL basics, understanding relational vs NoSQL databases, improving backend fundamentals, preparing for interviews, and building real projects that use databases properly. Save this if you want database concepts to finally make sense instead of memorizing queries.

Comment “INDEXING” to get the links! ⚡️ Querying a large database without understanding indexing is like flipping through a giant book page-by-page and hoping you find what you need before users rage-quit. If you don’t grasp index structures, query plans, and trade-offs, you’re building performance issues into production. This roadmap avoids that trap. 📚 What Is Database Indexing? A clear intro to what indexes actually do and why they transform query performance from minutes to milliseconds. ⏱ Indexing in 5 Minutes How B-trees, hash indexes, and query optimizers speed up data access — with zero hand-waving. 🎯 When Indexes Help — and When They Hurt Why adding indexes everywhere will destroy writes and storage… and how pros choose wisely. 💡 With these resources, you will: 🚀 Optimize read queries without rewriting business logic 🔍 Understand query execution plans to debug slow SQL 💾 Avoid over-indexing that tanks throughput ⚙️ Choose the right index type for the workload 🧠 Build databases that stay fast as data grows If you want to move from “my query is slow” to “I know exactly why — and how to fix it,” indexing fundamentals aren’t optional — they’re essential. 📌 Save this so you don’t forget it. 💬 Comment “INDEXING” and I’ll send you the full resource bundle. 👉 Follow for more Backend Engineering, Performance Tuning & System Design.

In my first years as a data scientist, I wasted hours on broken SQL, slow pandas scripts, messy Flask deployments, and “works on my machine” chaos. These 4 tools fixed that: • dbt → modular, documented SQL transformations • Polars → faster, cleaner alternative to pandas • FastAPI → quick, reliable model deployment • Docker → consistent environments, no more deployment nightmares If you’re just starting out, learning these early will save you months of frustration.

If there is something missing, feel free to mention it in the comments!Thank you!❤️ ✅ The list: #projectmanager #productmanager #businessanalyst #qualityassurancetester #userexperiencedesigner #userinterfacedesigner #technicalwriter #scrummaster #devopsengineer #systemanalyst #databaseadministrator #networkadministrator #salesengineer #technicalsupportspecialist #productowner #cybersecurityanalyst #dataanalyst #releasemanager #customersuccessmanager #trainingspecialist #itconsultant #businessintelligenceanalyst #informationsecurityanalyst #complianceanalyst #changemanagementspecialist #technicalrecruiter #itprojectcoordinator #userresearcher #itauditor #ittrainer

NetAdmin

This is the EXACT order I would learn Data Science in 2026. Hi 😊 my name is Dawn. I’ve been a Data Scientist at Meta, Patreon and other startups. And have coached 20+ clients into landing their dream Data jobs in the past year. 1️⃣ Learn SQL SQL is a must-have skill for every data professional because it’s the primary way you get data OUT of a database. It’s also a very easy coding language to learn, so I would start there. Use Interview Master to learn and practice SQL (link in bio): → Learn SQL: www.interviewmaster.ai/content/sql → Practice SQL: www.interviewmaster.ai/home 2️⃣ Start building Product Sense & Business Sense Product sense & business sense basically means you know how to use Data to solve real problems. I would start building this “soft” skill early because (1) it takes time to really learn this, and (2) as you’re learning Stats and Python, you already have context on how these might be used in the real world. I found the book: Cracking the PM Career to be super helpful before I landed my first Data Science job. 3️⃣ Learn Statistics How much Stats do you need for Data Science? Just the foundations, but you need to know it really really well. → Descriptive statistics → Common distributions → Probability and Bayes’ Theorem → Basic Machine Learning models → Experimentation concepts → A/B experiment design Check out Stanford’s Introduction to Statistics, which is free on Coursera. 4️⃣ Learn Python Python is the #1 skill for Data Scientists in 2025, but I put it 4th on this list because I find that it builds on skills 1-3. I learned Python on my own using DataCamp’s Python Data Fundamentals (link in bio). 5️⃣ Use AI-assisted coding tools Many data scientists are already using tools, like Claude Code & Cursor, to 2x their productivity. And also many companies are evaluating you on your use of AI during interviews. #datascience #datascientist

#Day1 Of SQL Learning in 60 Seconds Follow us @dataengineeringtamil #sql #database #DataEngineering #dataanalyst

Want my full toolkit? ➡️💬 Comment “LINK” and I will share all the other websites that I use when I am doing DSA! Kindly FOLLOW the account and then comment LINK as instagram does not allow sending DMs to non-followers. DSA doesn’t have to be boring text on a screen. 🙅‍♂️💻 If you are struggling to understand trees, graphs, or sorting algorithms, you need to see them in action. Visual learning > Rote memorization. Follow @techninjaah to level up your dev journey! ✅

📍Learning to code and becoming a data scientist without a background in computer science or mathematics is absolutely possible, but it will require dedication, time, and a structured approach. ✨👌🏻 🖐🏻Here’s a step-by-step guide to help you get started: 1. Start with the Basics: - Begin by learning the fundamentals of programming. Choose a beginner-friendly programming language like Python, which is widely used in data science. - Online platforms like Codecademy, Coursera, and Khan Academy offer interactive courses for beginners. 2. Learn Mathematics and Statistics: - While you don’t need to be a mathematician, a solid understanding of key concepts like algebra, calculus, and statistics is crucial for data science. - Platforms like Khan Academy and MIT OpenCourseWare provide free resources for learning math. 3. Online Courses and Tutorials: - Enroll in online data science courses on platforms like Coursera, edX, Udacity, and DataCamp. Look for beginner-level courses that cover data analysis, visualization, and machine learning. 4. Structured Learning Paths: - Follow structured learning paths offered by online platforms. These paths guide you through various topics in a logical sequence. 5. Practice with Real Data: - Work on hands-on projects using real-world data. Websites like Kaggle offer datasets and competitions for practicing data analysis and machine learning. 6. Coding Exercises: - Practice coding regularly to build your skills. Sites like LeetCode and HackerRank offer coding challenges that can help improve your programming proficiency. 7. Learn Data Manipulation and Analysis Libraries: - Familiarize yourself with Python libraries like NumPy, pandas, and Matplotlib for data manipulation, analysis, and visualization. For more look at the comment ⤵️ . . . #datascience #computerscience #datascientist #dataanalytics #dataanalyticstraining #python #softwaredeveloper #dataanalysis #bigdata #generativeai #codingbootcamp #businesswoman #veribilimi #codemotivation

Let’s get DIRTY!! 😉 It’s gonna be 80% of your job! Comment “clean” for my SQL skills cheat sheet, perfect for data projects and interview prep! Save these 4 tips to handle messy datasets w/ ease 😮‍💨 • Clean strings: remove extra spaces, weird quotes, and fix casing • Fix dates: standardize formats and remove stray characters • Recode variables: turn “CA”, “California”, and “calif” into one consistent value • Handle NULLs: don’t let missing data mess with your analysis #data #datacleaning #sql 🏷️ data, data cleaning, sql, data analytics, data science

What's the difference between database and data warehouse? Find out the full version in our video linked in bio. ______________ #AltexSoft_video #data #dataengineering #database #datawarehouse #dataengineer #warehouse #datascience #AI #datamanagement

want to become a data analyst in 2026? you don’t actually need a degree in maths or loads of prior experience 🚀 most companies care more about whether you can actually work with data to understand business problems than what you studied at uni or what niche skills you have. learn the basics: SQL, Excel and a repot building tool and learn how to pull insights and recommendations using these tools. if you’re looking to break into data analytics, switch careers into data, or land your first data analyst role - focus on building practical skills and learning how to explain your insights clearly. follow for more realistic career advice from a non-tech girlie working as a data analyst in the book industry #dataanalyst #womenindata #bookindustry #careerchange