Hira Azmat

Most commonly asked question in interviews- 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐃𝐢𝐬𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 𝐨𝐟 𝐄𝐱𝐜𝐞𝐥, 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐝𝐨𝐞𝐬 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐮𝐩𝐨𝐧 𝐭𝐡𝐞𝐦? Here's what you can answer in interview- (Save this answer) 𝟏. 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐋𝐚𝐫𝐠𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬: Excel can slow down or crash when dealing with large amounts of data. In contrast, Power BI is built to handle big datasets efficiently. 𝐅𝐨𝐫 𝐞𝐱𝐚𝐦𝐩𝐥𝐞, if you're analyzing sales data from many regions and product categories over several years, Power BI can handle it smoothly without slowing down. 𝟐. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Excel has basic charts, while Power BI offers more advanced visual tools. These tools create interactive and attractive dashboards. This helps you understand your data better with dynamic visuals that you can customize. 𝟑. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐔𝐩𝐝𝐚𝐭𝐞𝐬: Excel requires manual updates, which can be time-consuming. Power BI automates these updates from different data sources. 𝐅𝐨𝐫 𝐞𝐱𝐚𝐦𝐩𝐥𝐞, if you're tracking real-time stock market data, Power BI updates your dashboard automatically to show the latest trends. 𝟒. 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Power BI makes it easy to share reports and dashboards online. This allows multiple people to access the same updated information at the same time. It improves teamwork and helps everyone make better decisions together unlike Excel's method of sharing files, which often causes confusion due to different versions being updated. Credit: Preeti Moolani 🙌 #data #dataanalyst #excel #powerbi

23

5 Comments

Let's enroll in this course and explore more about Microsoft Power BI 🟡 with Abu Bakar N. Alvi who is Power Bi consultant and Trainer🏆 Without data visualization, we can't get meaningful analytics from data. 📚 💻 The more we study this, the better we become.🥇

8

How to get a job as a Data Scientist? Getting a job as a Data Scientist involves several key steps: 1. **Educational Background**: Typically, a bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field is required. Many employers prefer candidates with a master’s degree (M.Tech, M.Sc, MBA, etc.) in Data Science, Machine Learning, or a similar discipline. 2. **Skills**: Develop proficiency in key skills such as programming (Python, R), statistical analysis, machine learning algorithms, data visualization tools (like Tableau, Power BI), and databases (SQL, NoSQL). 3. **Projects and Portfolios**: Build a portfolio of data science projects showcasing your skills. This could include Kaggle competitions, personal projects, or contributions to open-source projects. 4. **Certifications**: While not always mandatory, certifications from platforms like Coursera, edX, or Udacity can validate your skills and make your profile more attractive to employers. 5. **Networking**: Attend data science meetups, conferences, and workshops. Networking can help you learn about job openings and connect with professionals in the field. 6. **Internships**: Gain practical experience through internships. Many companies offer internships to students pursuing degrees related to data science. 7. **Job Portals and LinkedIn**: Regularly check job portals (like Naukri, LinkedIn, Indeed) for data science job postings. LinkedIn is also a great platform to connect with recruiters and showcase your profile. 8. **Prepare for Interviews**: Be prepared for technical interviews where you may be asked to solve problems related to data analysis, machine learning, and statistics. Practice coding exercises and be ready to discuss your projects. 9. **Soft Skills**: Data Scientists often work in teams, so communication skills and the ability to explain complex technical concepts to non-technical stakeholders are valuable. 10. **Continuous Learning**: Data science is a rapidly evolving field. Stay updated with the latest trends, tools, and techniques by reading blogs, research papers, and participating in online courses. By focusing on these steps, you can increase your chances of securing a job as a Data Scientist in India or anywhere else in the world. #freshers #AI #datascience #job #interview

25

2 Comments

‼️Most commonly asked question in interviews-‼️ 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐃𝐢𝐬𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 𝐨𝐟 𝐄𝐱𝐜𝐞𝐥, 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐝𝐨𝐞𝐬 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐮𝐩𝐨𝐧 𝐭𝐡𝐞𝐦? Here's what you can answer in interview- (Save this answer) 𝟏. 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐋𝐚𝐫𝐠𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬: Excel can slow down or crash when dealing with large amounts of data. In contrast, Power BI is built to handle big datasets efficiently. 𝐅𝐨𝐫 𝐞𝐱𝐚𝐦𝐩𝐥𝐞, if you're analyzing sales data from many regions and product categories over several years, Power BI can handle it smoothly without slowing down. 𝟐. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Excel has basic charts, while Power BI offers more advanced visual tools. These tools create interactive and attractive dashboards. This helps you understand your data better with dynamic visuals that you can customize. 𝟑. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐔𝐩𝐝𝐚𝐭𝐞𝐬: Excel requires manual updates, which can be time-consuming. Power BI automates these updates from different data sources. 𝐅𝐨𝐫 𝐞𝐱𝐚𝐦𝐩𝐥𝐞, if you're tracking real-time stock market data, Power BI updates your dashboard automatically to show the latest trends. 𝟒. 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Power BI makes it easy to share reports and dashboards online. This allows multiple people to access the same updated information at the same time. It improves teamwork and helps everyone make better decisions together unlike Excel's method of sharing files, which often causes confusion due to different versions being updated. 👉 Follow me for more updates Save the image for your better youtube sources Credit : preeti moolani #dataanalyts #dataanalytics #businessanalyst #businessanalytics #Powerbi #Excel

18

3 Comments

Are you looking for Available SQL Courses and Certifications to help you upskill Online?… If yes, then this is for you. ➞ These courses are actually free • Enroll and start learning something new 1. SQL for Data Analysis – Udacity https://lnkd.in/dbaDGHGy You should enroll if You have a general familiarity with working with data in spreadsheets. 2. Introduction to SQL– DataCamp https://lnkd.in/dSNWuRh6 You should enroll if You are a beginner and want to learn the SQL Basics. 3. Advanced SQL– Kaggle https://lnkd.in/dKa69WxE This is a Free Kaggle Course to learn advanced SQL. You should enroll if You have prior knowledge of SQL basics. 4. Oracle SQL – A Complete Introduction– Udemy https://lnkd.in/dByhYCte you will learn Functions, Data Types, Joins, set operators, subqueries, Inserting, updating, deleting data, and Creating tables. 5. Intermediate SQL Queries– DataCamp https://lnkd.in/dRKK2U6i You will learn the language’s syntax, which is shared by many types of databases like PostgreSQL, MySQL, SQL Server, and Oracle. This course covers everything you need to start working with databases right away. 6. Intro to Relational Databases– Udacity https://lnkd.in/dVpHuvVd In this Free Course, you will learn the basics of SQL and database design, as well as the Python API for connecting Python code to a database. 7. Advanced Databases and SQL Querying– Udemy https://lnkd.in/dPsv4vJr You should enroll if You are comfortable writing TSQL code and must know basic syntax like SELECT, INSERT, UPDATE, etc. Follow Abimbola Arowolo for more insightful post, kindly Repost to help others too🥂

151

41 Comments

Hello #dataanalyst ✍️Best practices for writing SQL 📊queries: 1- Write SQL keywords in capital letters. 2- Use table aliases with columns when you are joining multiple tables. 3- Never use select *, always mention list of columns in select clause. 4- Add useful comments wherever you write complex logic. Avoid too many comments. 5- Use joins instead of subqueries when possible for better performance. 6- Create CTEs instead of multiple sub queries , it will make your query easy to read. 7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability. 8- Never use order by in sub queries , It will unnecessary increase runtime. 9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance. Add yours 👇 Feel free to connect for any feedback or collaboration opportunities! #data #database #mysql #dataanalytics #datascience #businessanalyst #mysqlinterviewquestions #Dataanalysis #relationaldatabase #dataanalysis #databaseadmin #dataanalyst

1

Alhamdulillah, I finally achieved the Kaggle Expert Badge! After 41 Days of consistent hard work on Kaggle...! Worldwide Rank: 3177 of 61855 --------------------- Here's my strategy: Whenever I work on a project, like my current COVID-19 project, I review some codes. If a notebook is well-designed and written, I upvote it and add a short review. I grow organically, and that feeling is incredible. ❌ Don't's: Don’t rely on engagement groups. --------------------- My next goal is to become a Kaggle Master! 🏆 #KaggleExpert #DataScience #MachineLearning #KaggleJourney #ai #datascientist

29

21 Comments

SQL queries that are commonly asked during interviews: 1. Retrieve the total number of orders placed by each customer.    SELECT customer_id, COUNT(order_id) AS total_orders FROM Orders GROUP BY customer_id; 2. Get the average salary of employees by department.   SELECT department, AVG(salary) AS average_salary FROM Employee GROUP BY department; 3. List all employees who were hired in 2023.   SELECT * FROM Employee WHERE YEAR(hire_date) = 2023; 4. Find the highest sales amount recorded for each product.    SELECT product_id, MAX(sales_amount) AS highest_sales FROM Sales GROUP BY product_id; 5. Retrieve customers who have made purchases greater than $500.   SELECT customer_id FROM Orders WHERE total_amount > 500; 6. List products that have never been sold.    SELECT p.product_id, p.product_name FROM Products p LEFT JOIN Sales s ON p.product_id = s.product_id WHERE s.product_id IS NULL; 7. Get the count of distinct products sold each month.   SELECT MONTH(order_date) AS month, COUNT(DISTINCT product_id) AS distinct_products FROM Sales GROUP BY MONTH(order_date); 8. Find the employee(s) with the lowest salary.   SELECT * FROM Employee WHERE salary = (SELECT MIN(salary) FROM Employee); 9. Retrieve all customers and their corresponding orders.   SELECT c.customer_id, o.order_id FROM Customers c LEFT JOIN Orders o ON c.customer_id = o.customer_id; 10. Calculate the total revenue generated by each product.    SELECT product_id, SUM(quantity * price) AS total_revenue FROM Sales GROUP BY product_id; follow more for Priyanka SG #EXCEL #SQL #POWERBI #PYTHON #DATAANALYST #INTERVIEW #JOBS

551

34 Comments

Essential skills for today's world: 🌎 🌎 (MD Foysal Hosen Shihab ) 1. Digital and Technical Skills Data Analysis and Data Science: Proficiency in data visualization, statistical analysis, and tools like SQL, Python, and R. Machine Learning and Artificial Intelligence: Understanding of algorithms, neural networks, and hands-on experience with frameworks like TensorFlow or PyTorch. Programming: Coding skills in Python, JavaScript, Java, and C++; web development skills (HTML, CSS, JavaScript). Cybersecurity: Expertise in network security, ethical hacking, and common cybersecurity tools. Cloud Computing: Knowledge of AWS, Google Cloud, and Microsoft Azure for managing cloud infrastructure. 2. Soft Skills Adaptability and Flexibility: Ability to adjust to new roles, challenges, and technologies as needed. Emotional Intelligence: Skills in empathy, self-awareness, and managing relationships effectively. Critical Thinking and Problem Solving: Aptitude for analyzing situations and identifying effective solutions. Communication Skills: Clear and persuasive verbal and written communication, essential for virtual and cross-functional teamwork. Collaboration and Teamwork: Effective skills for working with diverse teams, both in-person and remotely. 3. Business and Analytical Skills Project Management: Familiarity with Agile, Scrum, or Lean methodologies and tools like Asana, Trello, and JIRA. Digital Marketing: Skills in SEO, social media marketing, content marketing, and tools like Google Analytics. Financial Literacy: Basic understanding of financial management, budgeting, and economic principles. Product Management: Competency in identifying market needs, developing product strategies, and analyzing customer feedback. 4. Creativity and Innovation Creative Thinking: Ability to brainstorm innovative solutions, valuable in design, marketing, and product development. Design Skills: Competency in graphic design, UX/UI design, and familiarity with Adobe Suite, Figma, or similar tools. Storytelling: Crafting compelling narratives to present ideas and engage audiences. 5. Leadership and Management Skills Strategic Thinking: Developing long-term goals and aligning projects with organizational vision. Decision-Making: Making informed choices and taking responsibility for outcomes. People Management: Skills in coaching, motivating, and inspiring team members toward a common goal. Conflict Resolution: Effectively managing and resolving disputes within teams or organizations. #iit bombay #buet #diu Daffodil International University-DIU

2

🛑 Stop paying for online courses ever again. Below is a simplified roadmap for learning Data Science for Free ✅ Share with everyone so that everyone get benifts No Payment required ✅ 🔹 7000+ Course Free Access : https://lnkd.in/dAcqjYEa <>. Google Data Analytics: 🔺https://lnkd.in/dZqUj2TV 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬: - Learn the basics of linear algebra, calculus, and Understand advanced concepts. Mathematics: https://lnkd.in/dtY59yDy 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: - Learn Python and R, the most popular programming languages. - Master essential libraries like NumPy, Pandas, Matplotlib. - Learn how to use databases like SQL and MongoDB. Python: https://lnkd.in/dzFhaaTF R language: https://lnkd.in/dKRAAwGi SQL: https://lnkd.in/d-6dV726 MongoDB: https://lnkd.in/drJDheaq 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: - Understand the fundamentals of probability and statistics. - Learn how to apply these concepts to real-world data problems. Probability: https://lnkd.in/dUqzjCgH Statistics: https://lnkd.in/dS7FmeQE 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: - Learn the basics of machine learning, including model construction, data exploration, and validation. - Explore intermediate concepts like handling missing values, categorical variables. - Dive into ensemble learning techniques like Random Forests. 🪢 https://lnkd.in/d6VamCnT 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: - Learn about artificial neural networks, convolutional neural networks, and recurrent neural networks. - Implement deep learning models using TensorFlow, or PyTorch. - Understand crucial concepts like stochastic gradient descent, dropout. 🪢 https://lnkd.in/dr79Bm9u 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: - Learn the art of feature engineering, from creating baseline models to encoding categorical variables, generating new features, and selecting the most impactful features for your models. Feature Engineering : https://lnkd.in/d8Z4hQYZ 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: - Learn how to deploy your data science models to production using cloud platforms like Microsoft Azure, or Google Cloud Platform. - Build web applications with Flask or Django. Microsoft Azure: https://lnkd.in/dk8acgqf Google Cloud: https://lnkd.in/d_3frTPn Flask: https://lnkd.in/dqdyyYeF Flask Project: https://lnkd.in/dBEZWjkA Django: https://lnkd.in/dbAfTy4q #datascience #datascienceroadmap #datascience

70

46 Comments

🎯 Mastering Business KPIs: A Business Analyst’s Guide A fantastic KPI framework was shared by codebasics and brilliantly explained by Zain Ul Hassan ! This comprehensive guide covers everything from Executive metrics to Project Management KPIs. Why this matters for data professionals: • Strategic metrics that drive business decisions • Cross-functional KPIs that tell the complete business story • Essential metrics for building impactful dashboards Pro Tip: Don’t just save this - practice implementing these KPIs in your projects. Understanding these metrics will significantly enhance your business acumen and make you a more effective data professional. 🔑 Remember: Each KPI tells a unique story. Master these, and you’ll speak the language of business fluently! #DataAnalytics #BusinessIntelligence #KPIs #DataScience Credit: codebasics.io | Inspired by Zain Ul Hassan insights

2

Top 5 Youtube channels to learn Data Science/ Analytics 👩‍💻 Hello LinkedIn Fam, Today I will be sharing you the most important Youtube channels which has helped me out through my journey to master data skills from scratch. 1. TechTFQ : AKA Thoufiq Mohammed 's youtube channel. It is one stop destination of all your SQL learning. He creates videos on basic to advance sql topics. Link: https://lnkd.in/e5Vcsruh 2. Alex the Analyst : Alex Freberg youtube channel. He is a legend for a reason. There is no questions that he had not answered in his youtube channel. Also, don't forget to check his data analytics project portfolio playlist, you gonna learn alot. Link: https://lnkd.in/eHbSmXsp 3. Zedstatistics: I adore his work and the way he teaches statistics unlike a number and makes it soo easy to understand. Link: https://lnkd.in/ghrAtubz 4. Luke Barousse : If you're a datanerd who's unaware of Luke's work then you're definitely missing out on some amazing insightful information about the data field. Go checkout his yt channel now. Link: https://lnkd.in/eCGTUxW4 5. Leila Gharani : Her videos gonna help you master the most underrated skills in the data science field i.e, excel, powerpoint and microsoft powerbi. Link: https://lnkd.in/gPuAZ9pE Checkout this amazing youtube channels and master each skill with confidence. Now that you have known my secrets, don't forget to follow Manisha Senapati for more valuable resources. #youtubelearning #youtubechannels #dataanalysis #dataskills #freelearning #knowledge #youtube #datascience #explorepage #linkedin

96

6 Comments

🛑 Stop paying for online courses ever again. Below is a simplified roadmap for learning Data Science for Free ✅ Share with everyone so that everyone get benifts No Payment required ✅ 🔹 7000+ Course Free Access : https://lnkd.in/dc7dUxkj <>. Google Data Analytics: 🔺https://lnkd.in/gAmTrjpu 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬: - Learn the basics of linear algebra, calculus, and Understand advanced concepts. Mathematics: https://lnkd.in/dRjKiiVa 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: - Learn Python and R, the most popular programming languages. - Master essential libraries like NumPy, Pandas, Matplotlib. - Learn how to use databases like SQL and MongoDB. Python: https://lnkd.in/gfyFSwMS R language: https://lnkd.in/gfiMfz5f SQL: https://lnkd.in/g2xpEzg5 MongoDB: https://lnkd.in/gQR-MWXp 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: - Understand the fundamentals of probability and statistics. - Learn how to apply these concepts to real-world data problems. Probability: https://lnkd.in/gYKVsHNY Statistics: https://lnkd.in/gkxWUYqV 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: - Learn the basics of machine learning, including model construction, data exploration, and validation. - Explore intermediate concepts like handling missing values, categorical variables. - Dive into ensemble learning techniques like Random Forests. 🪢 https://lnkd.in/g72_6deK 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: - Learn about artificial neural networks, convolutional neural networks, and recurrent neural networks. - Implement deep learning models using TensorFlow, or PyTorch. - Understand crucial concepts like stochastic gradient descent, dropout. 🪢 https://lnkd.in/gvNV8p_T 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: - Learn the art of feature engineering, from creating baseline models to encoding categorical variables, generating new features, and selecting the most impactful features for your models. Feature Engineering : https://lnkd.in/g8NAq7bh 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: - Learn how to deploy your data science models to production using cloud platforms like Microsoft Azure, or Google Cloud Platform. - Build web applications with Flask or Django. Microsoft Azure: https://lnkd.in/gjT9Jbnm Google Cloud: https://lnkd.in/gj9A4SWm Flask: https://lnkd.in/gNDcumpx Flask Project: https://lnkd.in/gzrZYRwe Django: https://lnkd.in/gMzFJKQc PDF Credit - Respective Owner #datascience #datascienceroadmap #datascience

617

71 Comments

Performing ad-hoc analysis as a data analyst is so common and important as well. Being a data analyst, you will spend most of your time in performing ad-hoc analysis for different departments. 𝗜𝘁 𝗮𝗹𝘀𝗼 𝗵𝗲𝗹𝗽𝘀 𝘆𝗼𝘂 𝗶𝗻 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘄𝗮𝘆𝘀 𝗮𝘀 𝗮 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁: • It improves your communication skills while working with multiple departments. • It makes you more adaptable as a data analyst because you perform analysis according to multiple angles. • Your prioritization skills improve as on daily basis you will have to prioritize tasks. • It improves your ability to work with cross-functional teams especially when you need to link data/requirements from multiple departments to reach to that actionable insight. I have worked with multiple startups worldwide especially from the US and UK and realised this over time that ad-hoc requests have improved me as a data analyst. Enjoy. Follow for more! #DataAnalytics #DataAnalyst #AdHocAnalysis #BusinessIntelligence #DataDriven #DataCommunity #CrossFunctional #ActionableInsights #DataAnalyticsUSA #DataAnalystUSA #TechJobsUSA #DataAnalyticsUK #DataAnalystUK #TechJobsUK #DataAnalyticsEurope #DataAnalystEurope #TechJobsEurope #GlobalDataAnalyst #AnalyticsWorldwide #CareerGrowth #DataCareers #AnalyticalThinking #DataCommunity #CommunicationSkills #Adaptability #DataInsights #DataSkills

10

4 Comments