Jirka Zelenka

🚀 Want a #DataAnalystjob? Craft a standout #coverletter using #templates! Highlight your #skills in data, SQL & Python.📊💼 Read more - https://lnkd.in/gWn7ivrP . . #Rozgar #Blogpost #Newblog #JobpPortal #CareerTips #JobSearch #Resume #CareerSuccess #StandOut #Hiring #Analytics #Tips #DataAnalytics #Jobs #India #JobPortal #JobHunt #SuccessTips

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📊 Struggling with data overload? Join us for Data Headaches 101 and master the essentials of DBMS! 🚀 📅 Date: 12th - 14th November 2024 📍 Venue: C 103, C 104, C 105, C 106 🔗 Learn more: www.rsmartedu.in #DBMSWorkshop #RaiseSmart #RathinamGroup #DataManagement #TechSkills #StudentSuccess #FutureReady #TechLearning"

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💡 Boost Your SQL Skills with These 20 Medium-Level Interview Questions! Are you prepping for a SQL interview or just looking to sharpen your database management skills? Check it out now and get ready to ace your next tech interview! 💼✨ https://lnkd.in/gwBb_E9a #SQL #SQLInterview #DatabaseManagement #SQLQueries #TechInterview #Programming #DataScience #SQLTutorial #Coding #Database #SQLServer #DataAnalysis #TechSkills #SQLQuestions #CareerGrowth #InterviewPreparation #TechCareers #DataManagement #CodingInterview #LearnSQL

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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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🚀 Day 12 of SQL Practice with Analyst Builder by Alex Freberg! Today's challenge was all about string manipulation in SQL, where we had to fix an issue with incorrectly input data. The task was to separate a combined ID and First Name, where each ID was exactly 5 characters long. This situation is a common real-world scenario when data is not properly validated before being entered into a database. Key Steps: String Splitting: We used SQL string functions to extract the first 5 characters as the ID and the remaining as the First Name. SUBSTR and LENGTH Functions: By utilizing the SUBSTR function, we could effectively separate the ID and First Name into distinct columns. The first 5 characters (SUBSTR(column, 1, 5)) were extracted as the ID. The remainder of the string (SUBSTR(column, 6)) was identified as the First Name. This challenge was a great reminder of how powerful string manipulation is in SQL for cleaning and transforming messy data. 💡 💬 What stood out to me? It initially felt like a scenario where you’d need complex parsing logic, but SQL's built-in string functions like SUBSTR simplified the task, making it easy to apply. These challenges constantly push me to think critically about efficient ways to clean and manage data—a key skill for any data analyst! Challenges like these show how SQL enables us to take imperfect data and transform it into something clean and usable, streamlining the entire data pipeline! Hashtag#SQL Hashtag#DataAnalytics Hashtag#SQLPractice Hashtag#LearningSQL Hashtag#BusinessIntelligence Hashtag#DataCleaning Hashtag#TechSkills Hashtag#CareerGrowth Hashtag#AlexFreberg Hashtag#AnalystBuilder Hashtag#SQLQueries Hashtag#DataTransformation

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7 Skills Every Data Scientist Must Hone to Earn 7 Figures https://lnkd.in/gdCJN6BW This article describes numerous skills, such as interpersonal skills and data visualization, that can help you settle your career as a data scientist. #DataScientist #Python #DataScientistskills #MachineLearning #Cloudcomputing #AINews #AnalyticsInsight #AnalyticsInsightMagazine

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My go-to list of resources and interview tips to help you prepare for a career in Data Analytics ! Top Resources :- ➡️ SQL Practice Websites: Sharpen your SQL skills at [https://datalemur.com/], [https://lnkd.in/gkCpv7NA], and [https://lnkd.in/gt4hV9zf] with interactive exercises. Check out "techTFQ" YouTube channel for video Tutorials. ➡️ Pandas Practice: Explore [https://lnkd.in/g7mYVbbc] and [https://lnkd.in/gkXn-3R6] for hands-on experience with Python (Pandas). Check out [ https://lnkd.in/gbrajXDq] this playlist for pandas case studies. ➡️ Excel Tutorials: Master Excel with guidance from Leila Gharani's & Chandoo.org's youtube channel. ➡️ Tableau/Power BI Guides: Improve your visualization skills with tutorials on [https://lnkd.in/gHFCyDu4 - Tableau] and [https://lnkd.in/gyn-2vdK - Power BI]. Important Interview Tips:- ➡️ Excel: Be ready to discuss importing data, pivot tables, and key functions (SUMIFS, XLOOKUP, etc.) ➡️ SQL: Prepare for SQL challenges, like identifying employees retiring next year or analyzing monthly trends in transactions ➡️ Problem-Solving: Be prepared to show your approach to real-life data challenges ➡️ Soft Skills: Communicate clearly, collaborate well, and show adaptability Boosting Your Profile:- ➡️ Personal Projects: Share your own analytics projects or case studies ➡️ Networking: Connect with people in the industry and join discussions ➡️ Continuous Learning: Keep up with the latest in analytics tools and trends ➡️ Key Takeaway: Practical skills and interview readiness are essential for a successful career in data analytics 👉𝐑𝐞𝐟𝐞𝐫𝐫𝐚𝐥𝐬/𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬✅:: : https://lnkd.in/gcw-ziZm Join my 𝗙𝗥𝗘𝗘 WhatsApp channel where I share curated job/internship openings for data-related roles and industry updates. link in comment section! #dataanalyst #datascience

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Below I am sharing some interview questions that one of intern recently faced at Mercedes Benz for a Data Analyst role. These questions cover SQL, Python, and Power BI DAX. If you’re preparing for a Data Analyst interview, these might help you out:- Power BI (DAX): 1. Write a DAX measure to find the top 5 products based on product color from two tables: 'Product' (columns: 'product_id', 'product_color') and 'Sales' (columns: 'product_id', 'profit'). 2. Explain how to implement Row-Level Security (RLS) in Power BI. 3. Describe different types of filters in Power BI. 4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX. 5. Write a DAX formula to calculate the rolling 12 months of average sales using a 'sales': table with a 'DATE' column, 'product_id' and 'profit' column. Python: 1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys. 2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated. 3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated. 4. Write a function to add two numbers, take input from the user, and handle possible input errors such as non-numeric input and empty input. SQL: 1. Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'. 2. Explain the order of execution in SQL queries. For detailed answers to these questions, download our app from playstore and learn more to become successful data scientist. https://lnkd.in/g2ugYmmd #datascientist #datascience #interns #interviews #jobs #freshers #skilledfreshers #students #engineering #mca #bca #cse #btech #colleges #universities #career

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𝐂𝐚𝐧 𝐚 𝐅𝐫𝐞𝐬𝐡𝐞𝐫 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐭? 🤔 The short answer to this question is— it’s possible, but it's not the easy path. 🌟 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐲: 👉 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐤𝐢𝐥𝐥 𝐒𝐞𝐭 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐝: Analysis involves complex statistical analyses, technical tools, algorithms, and domain knowledge. Freshers often need to acquire these through rigorous learning and practical experience. 👉 𝐄𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐁𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝: Even though a relevant degree is not mandatory, many Analyst roles prefer candidates with a strong background in fields like Statistics, Computer Science, or Mathematics. 👉 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: Even with the right degree, hands-on experience is crucial. Internships, projects, and contributions to open-source projects can bridge the gap between theoretical knowledge and real-world application. 👉 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Data Science is an ever-evolving field. Whether you're a fresher or an experienced professional, continuous learning and upskilling are essential. Freshers might find it more practical to start in roles like Data Analyst, Business Analyst or Financial Analysts to build and grow their careers by building skills in this direction. You can learn more about the career paths in the Analyst roles by visiting the link below- https://lnkd.in/gepfmVk5 #DataScience #CareerAdvice #DataAnalyst #MachineLearning #DataAnalystJourney #CareerGrowth #Freshers #TechCareers

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SQL (Structured Query Language) is an important tool for data scientists. It is a programming language used to manipulate data stored in relational databases. SQL vs. NoSQL: Decoding the database dilemma to perfect solutions: https://hubs.la/Q02ysx_T0 #sql #nosql #programmers

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Excel is a powerful tool for managing and analyzing data. Formulas in Excel help with calculations and analysis. Here are five key formulas every data analyst should know.

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"In this article, I will discuss the SQL knowledge you should have to land an entry-level data science role and provide some resources and advice that helped me when I was learning SQL." SQL Knowledge You Need For Data Science by Egor Howell

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Series 1 of 30 #QueryWithQowiyy – Understanding Database Hello everyone, you are welcome to these new 30 days series, in this series we shall be learning SQL for the next 30 days (Oct. 2- Oct. 31), I’ll implore you to get yourself a book and pen, as the classes will require you to be putting down some things as we progress. Today, we’ll be learning and understand what’s database, and what is the relationship between SQL and Database. SQL is the acronym for Structured Query Language. It’s a language we used in interacting with database. What do we mean by database: Database can be seen as a Data storage/warehouse use for storing dataset for future purposes. Illustration: See database like a water tank which water is being pumped inside, and whenever we need water we just go to the tank and get our water through the tap. Database is the tank, SQL is the Tap, and Data Analyst is the Data fetcher. After understanding what database is, now let’s look at the types of databases. We have 2 common types of Databases namely; 1.     Relational/SQL database : This store dataset that are in tabular form, it has rows and columns e.g. MySQL, PostgrSQL, MS Access, Oracle, Microsoft Server etc. 2.     Non Relational/NoSQL Database : This store dataset that are non relational, e.g.   MongoDB, Cssandra etc. We also have some other database like Time series Database Why Database? We make use of Database for the following reasons, but not limited to them; 1.     It has the capability of storing large amounts of dataset unlike Excel that can only store a dataset of 1,048,576 rows. 2.     It makes data sharing easy as different people can access the same database from different locations. 3.     Data structure is highly maintained. 4.     Data security is guaranteed, as no one can access the database without the passcode 5.     It help reduces data redundancy. 6.     Data consistency, Now, I believe we now understand Database, God sparing our life, we dive deeper into SQL introduction tomorrow #QueryWithQowiyy, #SQL, #DataAnalyst, #Database

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SQL Practice Series - 18/18 🚀 Let's dive into some important SQL practice questions! 🚀 Today, I'm sharing the Eighteenth out of 18 crucial SQL questions that will help you solidify your understanding and prepare for real-world challenges. 📝 Question 18: Imagine you are a data analyst at Atliq Exclusive. Your task is to identify the top 5 customers in the Indian market who received the highest average pre-invoice discount percentages for the fiscal year 2021. This information is crucial for understanding customer behavior and optimizing discount. Solution 18: SELECT c.customer AS customer_name, c.customer_code, ROUND(AVG(f.pre_invoice_discount_pct),4) AS average_discount_percentage FROM fact_pre_invoice_deductions f JOIN dim_customer c ON f.customer_code = c.customer_code WHERE f.fiscal_year = 2021 AND c.market = 'India' GROUP BY c.customer, c.customer_code ORDER BY average_discount_percentage DESC LIMIT 5; This query calculates the average pre-invoice discount percentage for Indian customers in the fiscal year 2021. - It joins the fact_pre_invoice_deductions table with the dim_customer table to retrieve customer information. - The query calculates the average discount percentage (AVG(f.pre_invoice_discount_pct)) for each customer, rounding it to four decimal places. - It filters the data to include only customers in the Indian market for the fiscal year 2021. - The results are grouped by customer and sorted by the highest average discount percentage, displaying the top 5 customers. This query provides insight into which Indian customers received the highest average discounts in 2021. Question 17: https://lnkd.in/gb357X6M Stay tuned for more! #SQL #DataAnalysis #LearningJourney #PracticeMakesPerfect

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