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Quick Recap of Essential SQL Concepts 1️⃣ FROM clause: Specifies the tables from which data will be retrieved. 2️⃣ WHERE clause: Filters rows based on specified conditions. 3️⃣ GROUP BY clause: Groups rows that have the same values into summary rows. 4️⃣ HAVING clause: Filters groups based on specified conditions. 5️⃣ SELECT clause: Specifies the columns to be retrieved. 6️⃣ WINDOW functions: Functions that perform calculations across a set of table rows. 7️⃣ AGGREGATE functions: Functions like COUNT, SUM, AVG that perform calculations on a set of values. 8️⃣ UNION / UNION ALL: Combines the result sets of multiple SELECT statements. 9️⃣ ORDER BY clause: Sorts the result set based on specified columns. 🔟 LIMIT / OFFSET (or FETCH / OFFSET in some databases): Controls the number of rows returned and starting point for retrieval. Here you can find quick SQL Revision Notes👇 https://topmate.io/analyst/864817 Hope it helps :)

How to annoy a data analyst in 2024: ☑ Assume the analysis you're asking is "just a quick SQL thing." ☑ Ask to "tweak" a finished dashboard. It's never just a small change. ☑ Question why the numbers in their carefully crafted dashboard don't match your hastily pulled spreadsheet. ☑ Assume all data is clean, structured, and readily available. Spoiler: it's not. ☑ After receiving a detailed, interactive dashboard, ask, "Can I just get this as a printable PDF?" 🤦🏽♂️🤦🏽♂️

Breaking into Data Analysis can be very confusing in 2024! Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R? Fundamental principles are more important than tools: Understanding data cleaning and preprocessing is more important than SQL vs NoSQL. Understanding data visualization concepts is more important than Tableau vs Power BI. Understanding statistical analysis is more important than Excel vs R. Understanding programming for data manipulation is more important than Python vs R. Knowing these will allow you to pick up new emerging tools easily. Stick to fundamentals first. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

Hey guys 👋 I was working on something big from last few days. Finally, I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 If you go on purchasing these books, it will cost you more than 15000 but I kept the minimal price for everyone's benefit. I hope these resources will help you in data analytics journey. I will add more resources here in the future without any additional cost. All the best for your career ❤️

Top 5 Tools to master Data Analytics 1. Python: - Versatile programming language. - Offers powerful libraries like Pandas, NumPy, and Scikit-learn. - Used for data manipulation, analysis, and machine learning tasks. 2. R: - Statistical programming language. - Provides extensive statistical capabilities. - Popular for data analysis in academia. - Offers visualization libraries like ggplot2. 3. SQL (Structured Query Language): - Essential for working with relational databases. - Allows querying, manipulation, and management of data. - Standard language for database management systems. 4. Tableau: - Data visualization tool. - Enables creation of interactive dashboards. - Helps in communicating insights effectively. - Widely used in business intelligence. 5. Apache Spark: - Framework for large-scale data processing. - Offers distributed computing capabilities. - Libraries like Spark SQL and MLlib for data manipulation and machine learning. - Ideal for processing big data efficiently. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Like if it helps :)

Don't make this mistake as a beginner data analyst: Not learning SQL There's a reason it's been around for 40+ years. Get started with: - SQL basics (syntax + structure) - Data Manipulation (JOINs, GROUP BY etc) - Aggregation Functions (SUM, AVG etc)

Hey guys 👋 Since many of you requested for data analytics recorded video lectures, here you go! 👇👇 https://topmate.io/analyst/1068350 It contains comprehensive recorded video lectures on Data Analytics, covering key tools and languages like SQL, Python, Excel, and Power BI along with hands-on projects to ensure you gain practical experience alongside theoretical knowledge. Please use the above link to avail them!👆 NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it. Hope this helps in your data analytics journey... All the best!👍✌️

Data Analyst: Analyzes data to provide insights and reports for decision-making. Data Scientist: Builds models to predict outcomes and uncover deeper insights from data. Data Engineer: Creates and maintains the systems that store and process data.

🚀Roadmap to Becoming a Data Analyst🚀 Start your journey with these key steps:- 1️⃣ SQL: Master querying and managing data from databases. 2️⃣ Python: Use Python for data manipulation and automation. 3️⃣ Visualization: Present data using Matplotlib/Seaborn. 4️⃣ Excel: Handle data and create quick insights. 5️⃣ Power BI/Tableau: Build interactive dashboards. 6️⃣ Statistics: Understand key concepts for data interpretation. 7️⃣ Data Analytics: Apply everything in real-world projects! #DataAnalyst

Hey guys 👋 I was working on something big from last few days. Finally, I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 If you go on purchasing these books, it will cost you more than 15000 but I kept the minimal price for everyone's benefit. I hope these resources will help you in data analytics journey. I will add more resources here in the future without any additional cost. All the best for your career ❤️

How Data Analytics Helps to Grow Business to Best Analytics are the analysis of raw data to draw meaningful insights from it. In other words, applying algorithms, statistical models, or even machine learning on large volumes of data will seek to discover patterns, trends, and correlations. In this way, the bottom line is to support businesses in making much more informed, data-driven decisions. In simple words, think about running a retail store. You’ve got years of sales data, customer feedback, and inventory reports. However, do you know which are the best-sellers or where you’re losing money? By applying data analytics, you would find out some hidden opportunities, adjust your strategies, and improve your business outcome accordingly. read more......

7/ Use metaphors or analogies to explain difficult concepts. Don't use professional jargon. 8/ Include both the big picture and the details—it appeals to different stakeholders. 9/ Conclude with a call to action—what should they do next?

4/ Visualise trends over time to tell a story. 5/ Add context to your data—it makes your insights relevant. 6/ Speak the language of your audience—simplify complex terms.

9 secrets about Data Storytelling every analyst should know (number 6 is a must): 1/ Start with the end in mind—what’s the key takeaway? 2/ Don’t just present numbers—explain the 'so what' behind them. 3/ Data should drive decisions—frame your analysis as a solution to a problem. #DataAnalytics

Must Study: These are the important Questions for Data Analyst ✅ SQL 1. How do you handle NULL values in SQL queries, and why is it important? 2. What is the difference between INNER JOIN and OUTER JOIN, and when would you use each? 3. How do you implement transaction control in SQL Server? Excel 1. How do you use pivot tables to analyze large datasets in Excel? 2. What are Excel's built-in functions for statistical analysis, and how do you use them? 3. How do you create interactive dashboards in Excel? Power BI 1. How do you optimize Power BI reports for performance? 2. What is the role of DAX (Data Analysis Expressions) in Power BI, and how do you use it? 3. How do you handle real-time data streaming in Power BI? Python 1. How do you use Pandas for data manipulation, and what are some advanced features? 2. How do you implement machine learning models in Python, from data preparation to deployment? 3. What are the best practices for handling large datasets in Python? Data Visualization 1. How do you choose the right visualization technique for different types of data? 2. What is the importance of color theory in data visualization? 3. How do you use tools like Tableau or Power BI for advanced data storytelling? I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

TOP CONCEPTS FOR INTERVIEW PREPARATION!! 🚀TOP 10 SQL Concepts for Job Interview 1. Aggregate Functions (SUM/AVG) 2. Group By and Order By 3. JOINs (Inner/Left/Right) 4. Union and Union All 5. Date and Time processing 6. String processing 7. Window Functions (Partition by) 8. Subquery 9. View and Index 10. Common Table Expression (CTE) 🚀TOP 10 Statistics Concepts for Job Interview 1. Sampling 2. Experiments (A/B tests) 3. Descriptive Statistics 4. p-value 5. Probability Distributions 6. t-test 7. ANOVA 8. Correlation 9. Linear Regression 10. Logistics Regression 🚀TOP 10 Python Concepts for Job Interview 1. Reading data from file/table 2. Writing data to file/table 3. Data Types 4. Function 5. Data Preprocessing (numpy/pandas) 6. Data Visualisation (Matplotlib/seaborn/bokeh) 7. Machine Learning (sklearn) 8. Deep Learning (Tensorflow/Keras/PyTorch) 9. Distributed Processing (PySpark) 10. Functional and Object Oriented Programming Like ❤️ the post if it was helpful to you!!!

Goldman Sachs senior data analyst interview asked questions SQL 1 find avg of salaries department wise from table 2 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'. 3 newest joinee for every department (solved using lead lag) POWER BI 1. What does Filter context in DAX mean? 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. How do you calculate the total sales for a specific product using DAX? 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. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

Myntra interview questions for Data Analyst 2024. 1. You have a dataset with missing values. How would you use a combination of Pandas and NumPy to fill missing values based on the mean of the column? 2. How would you create a new column in a Pandas DataFrame by normalizing an existing numeric column using NumPy’s np.min() and np.max()? 3. Explain how to group a Pandas DataFrame by one column and apply a NumPy function, like np.std() (standard deviation), to each group. 4. How can you convert a time-series column in a Pandas DataFrame to NumPy’s datetime format for faster time-based calculations? 5. How would you identify and remove outliers from a Pandas DataFrame using NumPy’s Z-score method (scipy.stats.zscore)? 6. How would you use NumPy’s percentile() function to calculate specific quantiles for a numeric column in a Pandas DataFrame? 7. How would you use NumPy's polyfit() function to perform linear regression on a dataset stored in a Pandas DataFrame? 8. How can you use a combination of Pandas and NumPy to transform categorical data into dummy variables (one-hot encoding)? 9. How would you use both Pandas and NumPy to split a dataset into training and testing sets based on a random seed? 10. How can you apply NumPy's vectorize() function on a Pandas Series for better performance? 11. How would you optimize a Pandas DataFrame containing millions of rows by converting columns to NumPy arrays? Explain the benefits in terms of memory and speed. 12. How can you perform complex mathematical operations, such as matrix multiplication, using NumPy on a subset of a Pandas DataFrame? 13. Explain how you can use np.select() to perform conditional column operations in a Pandas DataFrame. 14. How can you handle time series data in Pandas and use NumPy to perform statistical analysis like rolling variance or covariance? 15. How can you integrate NumPy's random module (np.random) to generate random numbers and add them as a new column in a Pandas DataFrame? 16. Explain how you would use Pandas' applymap() function combined with NumPy’s vectorized operations to transform all elements in a DataFrame. 17. How can you apply mathematical transformations (e.g., square root, logarithm) from NumPy to specific columns in a Pandas DataFrame? 18. How would you efficiently perform element-wise operations between a Pandas DataFrame and a NumPy array of different dimensions? 19. How can you use NumPy functions like np.linalg.inv() or np.linalg.det() for linear algebra operations on numeric columns of a Pandas DataFrame? 20. Explain how you would compute the covariance matrix between multiple numeric columns of a DataFrame using NumPy. 21. What are the key differences between a Pandas DataFrame and a NumPy array? When would you use one over the other? 22. How can you convert a NumPy array into a Pandas DataFrame, and vice versa? Provide an example. You can find the answers here 👇👇 https://medium.com/@data_analyst/myntra-data-analyst-interview-questions-with-answers-97ed86953204 I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊