Adrian Pang

4 Free Resources For SQL Interview Preparation ✅ Data Interview Pro by Emma Ding The first resource is the Data Interview Pro YouTube channel. This is really good to know what to expect in a SQL interview. It also will give you a very good review of the important concepts in addition to important tips to ace and pass the interview. ➡Link: https://lnkd.in/gCR-tPhR ✅ Leetcode The second resource to prepare for SQL interviews is Leetcode. LeetCode is a popular website that provides a platform for preparing for technical interviews, including those for SQL positions. The site offers a range of SQL problems, from beginner to advanced levels, which can help you prepare for SQL-related interview questions. ➡Link: https://leetcode.com/ ✅ Stratascratch The third resource is the Stratascratch platform. StrataScratch is a website that provides a platform for preparing for coding interviews, including those that involve SQL. The site offers a range of SQL problems, from beginner to advanced levels, which can help you prepare for SQL-related interview questions. ➡Link: https://lnkd.in/e4VTTfN ✅ DataLemur The fourth resource is the DataLemur website. This is a really good website if you are preparing for an SQL interview at a big company. It provides SQL questions from top companies from easy to medium and to hard questions. ➡Link: https://datalemur.com/ ➡ Do not miss the 60% discount on To Data & Beyond yearly subscription: https://lnkd.in/gmwFUqYr ➡ Check out To Data & Beyond's latest ebook: LLM Roadmap from Beginner to Advanced Level: https://lnkd.in/dp7_CssN

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Link For Registration 👇 https://lnkd.in/gP8xjYZT Want to land your Dream Data Science Job?  Join us on June 1st, 1 PM for a power-packed webinar where you'll learn insider tips on cracking top-tier interviews from Anirudh Peddada. Don't miss out on key insights and strategies to ace your next interview! Reasons Why you shouldn't MISS this Webinar? 🔑 Insider Tips & Strategies: Mr. Peddada will share the secrets to standing out in the ultra-competitive data science field. These are tips you won't find in textbooks or online courses! 🔍 Interview Insights: Learn what top-tier firms look for during interviews. Understand the skills and experiences that make candidates shine and how to present yourself in the best light. Topics that’ll be covered: 🎯Interview Landscape: Dive into how data science interviews are structured and what to expect. From technical challenges to behavioral questions, you'll get a complete overview. 🎯Tech Skills Mastery: Nail the core technical skills required for top-tier data science roles, like programming, statistical analysis, and machine learning. Mr. Peddada will share the best resources and study strategies. 🎯Ace Behavioral Interviews: Learn strategies to highlight your soft skills, problem-solving abilities, and cultural fit. Avoid common pitfalls that trip up many candidates. 🎯Winning Portfolios & Resumes: Discover how to make your application stand out. Get tips on showcasing your projects, skills, and experiences effectively. 🎯Real-Life Case Studies: Benefit from real-life examples and case studies that illustrate successful interview techniques and approaches. Understand what worked for others and how you can apply these insights to your own interviews. Don’t miss out on this chance to elevate your interview game and move closer to your dream job. Reserve your spot now and take the first step towards your success! #iidst #webinarbyiidst #DataScience #InterviewTips #IIDSTWebinar

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Learn how to write maintainable Python code! Sadra Yahyapour’s new article on Python's Generic Typing shows how to make type-checking dynamic, adding flexibility to your code. 🔗 Learn the basics, syntax, and best practices for using generics: https://hshno.de/X0YQJ52

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#Algorithms_Design_Techniques (Part-3) Classification by Design Approaches encompasses two main strategies for algorithm design: Top-Down Approach: This method involves breaking down a complex problem into smaller, more manageable sub-problems. The process continues recursively until each sub-problem is solvable, eventually leading to a solution for the original complex problem. Bottom-Up Approach: Conversely, the bottom-up approach, also known as the reverse of the top-down approach, involves solving individual components of a complex program using a programming language. These components are then combined to form the complete program. In the top-down approach, the system is designed starting from the highest level of abstraction and gradually refining it to lower levels, whereas the bottom-up approach builds the system by starting with individual components and integrating them into a larger system. Each approach has its own advantages and disadvantages, with the choice often dependent on the specific problem at hand. Additionally, algorithms can be classified into other categories, including: Randomized Algorithms: These algorithms utilize random choices to achieve faster solutions. An example is the Randomized Quicksort Algorithm. Classification by Complexity: Algorithms are categorized based on the time taken to solve a problem for a given input size, known as time complexity analysis. Some algorithms have linear time complexity (O(n)), while others have exponential time complexity. Classification by Research Area: Each field in computer science has its own set of problems that require efficient algorithms. Examples include Sorting Algorithms, Searching Algorithms, and Machine Learning algorithms. Branch and Bound Enumeration and Backtracking: These techniques are commonly used in Artificial Intelligence. In conclusion, the classification of algorithms plays a vital role in organizing, understanding, and comparing different algorithms. Whether categorized by design method, complexity, or research area, these classifications aid in problem-solving, performance comparison, and algorithm reuse. Moreover, they are essential for research and development in various fields of computer science, contributing to the efficiency and effectiveness of problem-solving processes.

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𝗜𝗺𝗮𝗴𝗶𝗻𝗲 𝘁𝗵𝗶𝘀: 𝗬𝗼𝘂'𝗿𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝗮 𝗴𝗿𝗼𝘂𝗻𝗱𝗯𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁, 𝗮𝗻𝗱 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝗳𝗮𝗹𝗹𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝗽𝗹𝗮𝗰𝗲. 𝗕𝘂𝘁 𝘁𝗵𝗲𝗻, 𝘆𝗼𝘂 𝗵𝗶𝘁 𝗮 𝘄𝗮𝗹𝗹—𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴’𝘀 𝗺𝗶𝘀𝘀𝗶𝗻𝗴. ✨ You realize that to truly excel, you need more than just basic knowledge; you need the right tools. That’s when you discover the world of Python packages — powerful, flexible, and absolutely essential for any data scientist. 🎯 𝗘𝗻𝘁𝗲𝗿 𝘁𝗵𝗲 𝘁𝗼𝗽 𝗣𝘆𝘁𝗵𝗼𝗻 𝗽𝗮𝗰𝗸𝗮𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗸𝗻𝗼𝘄: 𝟭.𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄 - TensorFlow becomes your go-to framework to bring your deep learning model to life. 𝟮.𝗦𝗰𝗶𝗣𝘆 - SciPy steps in to handle the heavy lifting when you are faced with complex computations. 𝟯.𝗡𝘂𝗺𝗣𝘆 - Every time you work with numerical data, NumPy is there, ensuring precision and speed in every calculation. 𝟰.𝗞𝗲𝗿𝗮𝘀 - Keras helps you build and train neural networks with ease, like a seasoned pro. 𝟱.𝗠𝗮𝘁𝗽𝗹𝗼𝘁𝗹𝗶𝗯 - Matplotlib helps you create stunning visualizations that captivate and inform. 𝟲.𝗣𝗮𝗻𝗱𝗮𝘀 - Difficulties with Data Manipulation ? Pandas come to your rescue. 𝟳.𝗦𝗲𝗮𝗯𝗼𝗿𝗻 - Seaborn adds that extra touch of elegance to your visualizations. 𝟴.𝗣𝗹𝗼𝘁𝗹𝘆 - You need your data to interact with your audience, and Plotly provides the perfect platform for creating dynamic, interactive plots and dashboards. 𝟵.𝗦𝘁𝗮𝘁𝘀𝗺𝗼𝗱𝗲𝗹𝘀 - Statsmodels equips you with the tools to model and test your hypothesis with confidence. 𝟭𝟬.𝗦𝗰𝗶𝗸𝗶𝘁-𝗟𝗲𝗮𝗿𝗻 - As you venture into machine learning, Scikit-Learn becomes your trusted companion. 💻 𝗕𝘂𝘁 𝗵𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗽𝗮𝗿𝘁: You don’t have to figure this out alone. At piCake, we don’t just teach you theory; we immerse you in real-world projects and collaborative group activities. You’ll master these tools by actually using them, guided by industry experts who know what it takes to succeed. 🌐 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝘆𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀? Visit www.picake.in to explore our courses, designed to take you from novice to pro. 💬 𝗡𝗼𝘄, 𝗹𝗲𝘁’𝘀 𝘁𝗮𝗹𝗸—𝗪𝗵𝗶𝗰𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 𝗽𝗮𝗰𝗸𝗮𝗴𝗲 𝗱𝗼 𝘆𝗼𝘂 𝗿𝗲𝗹𝘆 𝗼𝗻 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁? Share your thoughts in the comments below! #datascientist #pythonprogramming #dataanalytics #fullstackdeveloper #softwaredevelopment #pythoncode #dsa #webdevelopers #fullstackdeveloper #python3 #datastructure #programmerslife #programmerslife #softwareengineering #tensorflow #scikitlearn #numpy #pandas #keras #seaborn #matplotlib #plotly #statsmodels #scipy #linkedin #followers

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“𝗦𝗤𝗟 𝗡𝘂𝗺𝗲𝗿𝗶𝗰 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗚𝘂𝗶𝗱𝗲: 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀" 𝟭. 𝗪𝗵𝗮𝘁 𝗔𝗿𝗲 𝗦𝗤𝗟 𝗡𝘂𝗺𝗲𝗿𝗶𝗰 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀? SQL Numeric Functions enable precise mathematical operations, making it easier to analyze and transform numerical data. 𝟮. 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗡𝘂𝗺𝗲𝗿𝗶𝗰 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 These include basic arithmetic, rounding, logarithmic, trigonometric, and advanced functions like power and roots, catering to diverse needs. 𝟯. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 Use them for tasks like calculating totals, formatting numbers, generating random values, or performing trigonometric and logarithmic calculations. 𝟰. 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 Optimize queries with indexed columns, test edge cases, and rely on SQL functions for calculations to improve performance. [Explore More In The Post] Follow Future Tech Skills for more such information and don’t forget to save this post for later Join our group to discover more about Data Analytics, Data Science, Development & QA : https://lnkd.in/gUBDWHqe #sql #sqlserver #sqldatabase #sqldeveloper #sqlqueries #dataanalytics #datascience #dataanalyst

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Machine Learning Quiz: 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫 𝐐𝐮𝐢𝐳 Quiz link: https://lnkd.in/gtxsCGHG 🤖 🤔 Want to check your knowledge on Deep Learning? Take this multiple-choice quiz on "Deep Learning Beginner Quiz" to test your knowledge. Do share your scores in the comment section below 👇👇 At AIML.com: 💯 Get all your Quiz scores in real time 👉 Detailed answer explanations for quizzes for a deeper understanding 🕺 A reason to put your hands in the air; It's all FREE! Share the knowledge by clicking Repost. Learning resources for this quiz: https://lnkd.in/gaieUFfb -- Preparing for ML interviews? Join us on: https://aiml.com, the world's largest repository of Machine Learning interview questions and quizzes #neuralnetworks #deeplearning #mlquiz

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🔳 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗼𝗳 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 𝗮𝗻𝗱 𝗮𝗯𝘀𝘁𝗿𝗮𝗰𝘁 𝗰𝗹𝗮𝘀𝘀𝗲𝘀 𝗶𝗻 𝗝𝗮𝘃𝗮! Interfaces are blueprints for classes that define a set of methods that implementing classes must implement. They provide a connection that classes must adhere to descendent class. 📌 𝐍𝐨𝐭𝐞: 🔷 Interfaces cannot contain instance variables. 🔷 All methods declared in an interface are implicitly public, abstract, and final. 🔷 Interfaces cannot have constructors. 🔷 A class can implement multiple interfaces. ⬛ 𝐀𝐛𝐬𝐭𝐫𝐚𝐜𝐭 𝐂𝐥𝐚𝐬𝐬𝐞𝐬 Abstract classes are classes that cannot be instantiated directly. They can contain both abstract and non-abstract methods. 📌 𝐍𝐨𝐭𝐞: 🔷 Abstract classes can contain instance variables and constructors. 🔷 Abstract classes can have both abstract and non-abstract methods. 🔷 Abstract methods are declared with the abstract keyword and do not have a body. 🔷 A class that extends an abstract class must either implement all abstract methods or be declared abstract itself. 📩 𝐖𝐡𝐢𝐜𝐡 𝐨𝐧𝐞 𝐰𝐞 𝐮𝐬𝐞 𝐚𝐭 𝐰𝐡𝐚𝐭 𝐭𝐢𝐦𝐞: ⏯️ 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞𝐬: Use interfaces when you want to define a contract that multiple unrelated classes can implement. Interfaces are ideal for defining common behaviors or properties that classes can share without having a common ancestor. ⏯️ 𝐀𝐛𝐬𝐭𝐫𝐚𝐜𝐭 𝐂𝐥𝐚𝐬𝐬𝐞𝐬: Use abstract classes when you want to provide a partial implementation of a class and force subclasses to implement specific methods. Abstract classes are useful for creating hierarchies of related classes with common functionality. Like❤️, comment ✍and repost♻️🌹 #java #javaprogramming

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What Is KNN Classification And How Can This Analysis Help An Enterprise? https://bit.ly/3Kmfgld #CreditLoanApprovalAnalysis #DataLiteracy #FraudAnalysis #KNNClassificationAnalysis #TrainingforCitizenDataScientist #CitizenDataScientist #Smarten #Analysis

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What Is KNN Classification And How Can This Analysis Help An Enterprise? https://bit.ly/3Kmfgld #CreditLoanApprovalAnalysis #DataLiteracy #FraudAnalysis #KNNClassificationAnalysis #TrainingforCitizenDataScientist #CitizenDataScientist #Smarten #Analysis

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🚀 𝑩𝒆𝒈𝒊𝒏𝒏𝒆𝒓’𝒔 𝑹𝒐𝒂𝒅𝒎𝒂𝒑 𝒕𝒐 𝑳𝒆𝒂𝒓𝒏 𝑫𝒂𝒕𝒂 𝑺𝒄𝒊𝒆𝒏𝒄𝒆 🌐 Are you ready to kickstart your Data Science journey? Here’s a roadmap to guide you through the essentials—perfect for beginners and those looking to sharpen their skills. Let’s dive in: 1️⃣ 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐫𝐭𝐞𝐝: Understand the basics of data science and explore the exciting opportunities it offers. 2️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬 𝐨𝐟 𝐌𝐚𝐭𝐡 & 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: 🧠 Focus on linear algebra, probability, and descriptive stats for effective data analysis. 3️⃣ 𝐋𝐞𝐚𝐫𝐧 𝐊𝐞𝐲 𝐓𝐨𝐨𝐥𝐬: 🛠️ Get hands-on with SQL, Python, and R—essential for data wrangling and analysis. 4️⃣ 𝐃𝐞𝐞𝐩 𝐃𝐢𝐯𝐞 𝐢𝐧𝐭𝐨 𝐌𝐚𝐭𝐡 & 𝐒𝐭𝐚𝐭𝐬: 🔍 Explore data cleaning, feature engineering, and advanced models like: 🔹 Linear Regression 📈 🔹 Logistic Regression 📊 🔹 Decision Trees 🌳 🔹 SVM 🧬 🔹 KNN and more! 5️⃣ 𝐏𝐫𝐨𝐟𝐢𝐥𝐞 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠: 🖥️ Showcase your projects on GitHub, StackOverflow, and LinkedIn to boost your portfolio. 6️⃣ 𝐏𝐫𝐞𝐩𝐚𝐫𝐞 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬: 🤝 Practice mock interviews, solve problems, and sharpen your interview game. 7️⃣ 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞 𝐉𝐨𝐛 𝐌𝐚𝐫𝐤𝐞𝐭: 🧳 Learn about the various roles, responsibilities, and career paths in data science. Follow this roadmap, stay consistent, and you'll be on your way to becoming a Data Science pro! 🔥📊 ✨ Share this roadmap with your friends to help them kickstart their journey too! 😊📈

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📢 Hello LinkedIn community! 🌟💼 🌟 Day 41/50 Days of SQL Challenge 🌟 SQL Course 2024: SQL for Data Analysis and Data Science. https://lnkd.in/g45cbiXa SQL Bootcamp 2024: Master SQL & PostgreSQL - Hands-On Course https://lnkd.in/gaHnijmg SQL Introduction Course 2024: SQL Crash Course. https://lnkd.in/dFyqBHBB Today, let's solve a SQL challenge that involves reporting the primary department for each employee. 📊👥 The combination of employee_id and department_id forms the primary key for this table. The primary_flag column is an ENUM (category) with values 'Y' or 'N'. If the primary_flag is 'Y', it indicates that the department is the primary department for the employee, and if it's 'N', it means the department is not the primary. Employees can belong to multiple departments, and when they join other departments, they need to specify their primary department. Note that when an employee belongs to only one department, their primary_flag is 'N'. Solution: SELECT employee_id, CASE WHEN COUNT(*) > 1 AND COUNT(CASE WHEN primary_flag = 'Y' THEN 1 END) = 1 THEN MAX(CASE WHEN primary_flag = 'Y' THEN department_id END) WHEN COUNT(*) = 1 THEN MAX(department_id) ELSE NULL END AS department_id FROM Employee GROUP BY employee_id HAVING department_id IS NOT NULL; we group the records by employee_id. We use a CASE statement to handle different scenarios. If an employee belongs to more than one department and has exactly one department marked as the primary (primary_flag = 'Y'), we select that department as their primary department. If an employee belongs to only one department, we select their only department as their primary department. If an employee belongs to multiple departments but doesn't have a primary department (primary_flag = 'Y' for any department), we return NULL for their department. The HAVING clause is used to filter out the employees who don't have a primary department (NULL department_id). Result: +-------------+---------------+ | employee_id | department_id | +-------------+---------------+ | 1 | 1 | | 2 | 1 | | 3 | 3 | | 4 | 3 | +-------------+---------------+ employee 1 has only one department (department 1), and it is considered their primary department. Employee 2 belongs to departments 1 and 2, but department 1 is marked as their primary department. Employee 3 belongs to department 3, and it is their primary department. Employee 4 belongs to departments 2, 3, and 4, but department 3 is marked as their primary department. #SQL hashtag #Databases hashtag #DataAnalysis hashtag #LeetCode hashtag #SQL hashtag #DataAnalysis hashtag #DataManipulation hashtag #ContinuousLearning hashtag #Temotec hashtag #TemotecAcademy hashtag #TamerAhmed hashtag #50DaysOFSQL hashtag #50daysofsqls hashtag #SQLChallenges hashtag #DataAnalysis hashtag #SQLQueries hashtag #DataManipulation hashtag #ProblemSolving hashtag

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𝐃𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬 𝐚𝐧𝐝 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐭𝐡𝐞𝐭 𝐚𝐫𝐞 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐝 𝐭𝐨 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐡𝐲𝐩𝐨𝐭𝐡𝐞𝐬𝐞𝐬 𝐚𝐧𝐝 𝐚𝐜𝐡𝐢𝐞𝐯𝐞 𝐢𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥 𝐫𝐞𝐬𝐮𝐥𝐭𝐬. 1️⃣ 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 🔸 𝘋𝘦𝘴𝘤𝘳𝘪𝘱𝘵𝘪𝘷𝘦 𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘴: Mean, median, mode, standard deviation, variance, etc. 🔸 𝘐𝘯𝘧𝘦𝘳𝘦𝘯𝘵𝘪𝘢𝘭 𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘴: Hypothesis testing, t-tests, chi-square tests, ANOVA, etc. 🔸 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴: Linear, logistic, polynomial, multiple regression. 🔸 𝘔𝘶𝘭𝘵𝘪𝘷𝘢𝘳𝘪𝘢𝘵𝘦 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴: MANOVA, factor analysis, cluster analysis. 🔸 𝘛𝘪𝘮𝘦 𝘚𝘦𝘳𝘪𝘦𝘴 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴: ARIMA, moving averages, seasonal decomposition. 2️⃣ 𝐒𝐮𝐫𝐯𝐞𝐲 𝐚𝐧𝐝 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 🔸 𝘚𝘶𝘳𝘷𝘦𝘺 𝘋𝘢𝘵𝘢: Likert scale analysis, frequency distribution, cross-tabulation. 🔸 𝘌𝘹𝘱𝘦𝘳𝘪𝘮𝘦𝘯𝘵𝘢𝘭 𝘋𝘢𝘵𝘢: ANOVA, ANCOVA, mixed-effects models 3️⃣ 𝐆𝐞𝐧𝐨𝐦𝐢𝐜 𝐚𝐧𝐝 𝐁𝐢𝐨𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐜𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 🔸 𝘚𝘦𝘲𝘶𝘦𝘯𝘤𝘦 𝘈𝘭𝘪𝘨𝘯𝘮𝘦𝘯𝘵t: BLAST, ClustalW. 🔸 𝘎𝘦𝘯𝘦 𝘌𝘹𝘱𝘳𝘦𝘴𝘴𝘪𝘰𝘯 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴: Microarray, RNA-Seq analysis. 🔸 𝘍𝘶𝘯𝘤𝘵𝘪𝘰𝘯𝘢𝘭 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴: Pathway enrichment, gene ontology. 4️⃣ 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 🔸 𝘔𝘢𝘱𝘱𝘪𝘯𝘨 𝘛𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦𝘴: GIS software for spatial distribution. 🔸 𝘚𝘱𝘢𝘵𝘪𝘢𝘭 𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘴: Hotspot analysis, Kriging, Moran’s I. 5️⃣ 𝐄𝐜𝐨𝐧𝐨𝐦𝐢𝐜 𝐚𝐧𝐝 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 🔸 𝘌𝘤𝘰𝘯𝘰𝘮𝘦𝘵𝘳𝘪𝘤 𝘔𝘰𝘥𝘦𝘭𝘴: ARIMA, GARCH, cointegration tests. 🔸 𝘗𝘰𝘳𝘵𝘧𝘰𝘭𝘪𝘰 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴: Sharpe ratio, Markowitz model. 6️⃣ 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 🔸 𝘋𝘢𝘵𝘢 𝘔𝘪𝘯𝘪𝘯𝘨 𝘛𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦𝘴: Apriori algorithm, association rule learning. 🔸 𝘏𝘢𝘥𝘰𝘰𝘱/𝘚𝘱𝘢𝘳𝘬-𝘉𝘢𝘴𝘦𝘥 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴: Processing massive datasets. 7️⃣ 𝐈𝐦𝐚𝐠𝐢𝐧𝐠 𝐚𝐧𝐝 𝐒𝐢𝐠𝐧𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 🔸 𝘐𝘮𝘢𝘨𝘦 𝘗𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨: Fourier transform, edge detection, pixel-based analysis. 🔸 𝘚𝘪𝘨𝘯𝘢𝘭 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴: Spectral analysis, wavelet transforms, power spectrum analysis. 8️⃣ 𝐌𝐞𝐭𝐚-𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐚𝐧𝐝 𝐒𝐲𝐬𝐭𝐞𝐦𝐚𝐭𝐢𝐜 𝐑𝐞𝐯𝐢𝐞𝐰𝐬 𝘌𝘧𝘧𝘦𝘤𝘵 𝘚𝘪𝘻𝘦 𝘊𝘢𝘭𝘤𝘶𝘭𝘢𝘵𝘪𝘰𝘯: Forest plots, funnel plots. 𝘗𝘶𝘣𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘉𝘪𝘢𝘴 𝘛𝘦𝘴𝘵𝘪𝘯𝘨: Egger’s test, Begg’s test. 🔳 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐚𝐧𝐝 𝐓𝐨𝐨𝐥𝐬 𝐟𝐨𝐫 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 🔺 SPSS, Stata, R, Python: Statistical analysis and visualization. 🔺 MATLAB: Numerical computing and simulation. 🔺 Tableau, Power BI: Data visualization. 🔺 NVivo, MAXQDA: Qualitative data analysis. 🌟 Whether you're working on a PhD thesis, PG dissertation, or publication, ManuscriptEdge provides end-to-end support, ensuring data perfection. 📩 Contact us: ✉️ Email: [email protected] 📱 WhatsApp: +91 9797970209 We transform your raw data into meaningful results! 🌐

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🛑 Stop paying for online courses ever again. SQL Roadmap with FREE courses Learning SQL is essential for anyone dealing with relational databases or working in the field of data management. So You can take benifts from below ones 👇 🔺 7000+ Course Free Access : https://lnkd.in/dc7dUxkj <>. Google Data Analytics: 🔹https://lnkd.in/e3uPshjh Repost so that all can take benifits 1. Learn SQL Basics for Data Science Specialization In this course, you will learn to: -Master SQL Commands -Data Preparation -End-to-End Data Pipeline -Project Development and Presentation: 🔺Link: https://lnkd.in/eFfhe57d 2. Google Data Analytics Professional Certificate -This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 🔹Link: https://lnkd.in/euJUQjiP 3.SQL for Data Science What you'll learn -Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results. -Use SQL commands to filter, sort, and summarize data. -Create an analysis table from multiple queries using the UNION operator. -Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis. 🔺Link: https://lnkd.in/eAX2aehf 4.Databases and SQL for Data Science with Python What you'll learn -Analyze data within a database using SQL and Python. -Create a relational database and work with multiple tables using DDL commands. -Construct basic to intermediate level SQL queries using DML commands. Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins. 🔹Link: https://lnkd.in/emhYQ9A4 5. Introduction to Structured Query Language (SQL) What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses.What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses. 🔺Link: https://lnkd.in/ea7YQBpk Pdf credit- Respective Owner follow Suman Kumar for more Like 👍 Comment ✍️ repost 🔄 #sql #sqldatabase #freecertification #freecourses #dataanalytics #datasciences

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🛑 Stop paying for online courses ever again. SQL Roadmap with FREE courses Learning SQL is essential for anyone dealing with relational databases or working in the field of data management. So You can take benifts from below ones 👇 🔺 7000+ Course Free Access : https://lnkd.in/dc7dUxkj <>. Google Data Analytics: 🔹https://lnkd.in/e3uPshjh Repost so that all can take benifits 1. Learn SQL Basics for Data Science Specialization In this course, you will learn to: -Master SQL Commands -Data Preparation -End-to-End Data Pipeline -Project Development and Presentation: 🔺Link: https://lnkd.in/eFfhe57d 2. Google Data Analytics Professional Certificate -This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 🔹Link: https://lnkd.in/euJUQjiP 3.SQL for Data Science What you'll learn -Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results. -Use SQL commands to filter, sort, and summarize data. -Create an analysis table from multiple queries using the UNION operator. -Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis. 🔺Link: https://lnkd.in/eAX2aehf 4.Databases and SQL for Data Science with Python What you'll learn -Analyze data within a database using SQL and Python. -Create a relational database and work with multiple tables using DDL commands. -Construct basic to intermediate level SQL queries using DML commands. Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins. 🔹Link: https://lnkd.in/emhYQ9A4 5. Introduction to Structured Query Language (SQL) What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses.What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses. 🔺Link: https://lnkd.in/ea7YQBpk Pdf credit- Respective Owner Follow Suman Kumar for more Like 👍 Comment ✍️ Repost 🔄 #sql #sqldatabase #freecertification #freecourses #dataanalytics #datasciences#roadmap LinkedIn for Creators LinkedIn Learning

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