Advertising on the Telegram channel «Data Analyst Interview»

1. Does SQL support programming language features? It is true that SQL is a language, but it does not support programming as it is not a programming language, it is a command language. We do not have some programming concepts in SQL like for loops or while loop, we only have commands which we can use to query, update, delete, etc. data in the database. SQL allows us to manipulate data in a database. 2. What is a trigger? Trigger is a statement that a system executes automatically when there is any modification to the database. In a trigger, we first specify when the trigger is to be executed and then the action to be performed when the trigger executes. Triggers are used to specify certain integrity constraints and referential constraints that cannot be specified using the constraint mechanism of SQL. 3. What are aggregate and scalar functions? For doing operations on data SQL has many built-in functions, they are categorized into two categories and further sub-categorized into seven different functions under each category. The categories are: Aggregate functions: These functions are used to do operations from the values of the column and a single value is returned. Scalar functions: These functions are based on user input, these too return a single value. 4. Define SQL Order by the statement? The ORDER BY statement in SQL is used to sort the fetched data in either ascending or descending according to one or more columns. By default ORDER BY sorts the data in ascending order. We can use the keyword DESC to sort the data in descending order and the keyword ASC to sort in ascending order. 5. What is the difference between primary key and unique constraints?  The primary key cannot have NULL values, the unique constraints can have NULL values. There is only one primary key in a table, but there can be multiple unique constraints. The primary key creates the clustered index automatically but the unique key does not.

𝟰 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 — 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 & 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱!😍 Ready to kickstart your career in Data Science—without spending a rupee?💰 These 4 beginner-friendly courses will help you build a strong foundation in data science by teaching you how to gather, clean, analyse, and visualise data📊📌 𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇 https://pdlink.in/45uXCtI An initiative supported by NASSCOM and the Government of India✅️

𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐝𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: 𝘐 𝘨𝘦𝘵 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘧𝘳𝘰𝘮 𝘥𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 𝘢𝘴𝘱𝘪𝘳𝘢𝘯𝘵𝘴 𝘢𝘯𝘥 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘴 𝘰𝘯 𝘩𝘰𝘸 𝘵𝘰 𝘨𝘢𝘪𝘯 𝘤𝘰𝘮𝘮𝘢𝘯𝘥 𝘰𝘧 𝘗𝘺𝘵𝘩𝘰𝘯. 📍𝐋𝐞𝐚𝐫𝐧 𝐂𝐨𝐫𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Master Python libraries for data analytics, like -pandas for dataframes, -NumPy for numerical operations, -Matplotlib/Seaborn for plotting, -scikit-learn for machine learning. 📍𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code. 📍𝐔𝐬𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦-𝐒𝐨𝐥𝐯𝐢𝐧𝐠 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance. 📍𝐃𝐨 𝐌𝐨𝐜𝐤 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Work on end-to-end Python analytics projects—data loading, cleaning, analysis, and visualization. 📍𝐋𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐏𝐚𝐬𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Review your previous Python projects to see where your code can be more efficient. Like this post if you need more resources like this 👍❤️

🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸 Join our channel today for free! Tomorrow it will cost 500$! https://t.me/+104RMnxC7U1kZTll You can join at this link! 👆👇 https://t.me/+104RMnxC7U1kZTll

A step-by-step guide to land a job as a data analyst Landing your first data analyst job is toughhhhh. Here are 11 tips to make it easier: - Master SQL. - Next, learn a BI tool. - Drink lots of tea or coffee. - Tackle relevant data projects. - Create a relevant data portfolio. - Focus on actionable data insights. - Remember imposter syndrome is normal. - Find ways to prove you’re a problem-solver. - Develop compelling data visualization stories. - Engage with LinkedIn posts from fellow analysts. - Illustrate your analytical impact with metrics & KPIs. - Share your career story & insights via LinkedIn posts. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Hope this helps you 😊

𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 😍 Secure Your Future with Top MNCs! 💻Learn Coding from IIT Alumni & Experts from Leading Tech Companies. ✨ 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:- ✅ Trusted by 7,500+ Students 🤝 500+ Hiring Partners 💼 Average Package: ₹7.2 LPA 🏆 Highest Package: ₹41 LPA Eligibility: BTech / BCA / BSc / MCA / MSc 🔗 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇:-  https://pdlink.in/4hO7rWY Hurry! Limited Seats Available. 🏃‍♀️

Data Analytics Interview Questions

𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄😍 𝗦𝗤𝗟:- https://pdlink.in/3SMHxaZ 𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3FJhizk 𝗝𝗮𝘃𝗮  :- https://pdlink.in/4dWkAMf 𝗗𝗦𝗔 :- https://pdlink.in/3FsDA8j  𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4jLOJ2a 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 :-  https://pdlink.in/4dFem3o 𝗖𝗼𝗱𝗶𝗻𝗴 :- https://pdlink.in/3F00oMw Get Your Dream Tech Job In Your Dream Company💫

Data-Driven Decision Making Data-driven decision-making (DDDM) involves using data analytics to guide business strategies instead of relying on intuition. Key techniques include A/B testing, forecasting, trend analysis, and KPI evaluation. 1️⃣ A/B Testing & Hypothesis Testing A/B testing compares two versions of a product, marketing campaign, or website feature to determine which performs better. ✔ Key Metrics in A/B Testing: Conversion Rate Click-Through Rate (CTR) Revenue per User ✔ Steps in A/B Testing: 1. Define the hypothesis (e.g., "Changing the CTA button color will increase clicks"). 2. Split users into Group A (control) and Group B (test). 3. Analyze differences using statistical tests. ✔ SQL for A/B Testing: Calculate average purchase per user in two test groups

    
        SELECT test_group, AVG(purchase_amount) AS avg_purchase  
FROM ab_test_results  
GROUP BY test_group;{}
    

Run a t-test to check statistical significance (Python)

    
        from scipy.stats import ttest_ind
t_stat, p_value = ttest_ind(group_A['conversion_rate'], group_B['conversion_rate'])
print(f"T-statistic: {t_stat}, P-value: {p_value}"){}
    

🔹 P-value < 0.05 → Statistically significant difference. 🔹 P-value > 0.05 → No strong evidence of difference. 2️⃣ Forecasting & Trend Analysis Forecasting predicts future trends based on historical data. ✔ Time Series Analysis Techniques: Moving Averages (smooth trends) Exponential Smoothing (weights recent data more) ARIMA Models (AutoRegressive Integrated Moving Average) ✔ SQL for Moving Averages: 7-day moving average of sales

    
        SELECT order_date,  
       sales,  
       AVG(sales) OVER (ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS moving_avg  
FROM sales_data;{}
    

✔ Python for Forecasting (Using Prophet)

    
        from fbprophet import Prophet
model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)
model.plot(forecast){}
    

3️⃣ KPI & Metrics Analysis KPIs (Key Performance Indicators) measure business performance. ✔ Common Business KPIs: Revenue Growth Rate → (Current Revenue - Previous Revenue) / Previous Revenue Customer Retention Rate → Customers at End / Customers at Start Churn Rate → % of customers lost over time Net Promoter Score (NPS) → Measures customer satisfaction ✔ SQL for KPI Analysis: Calculate Monthly Revenue Growth

    
        SELECT month,  
       revenue,  
       LAG(revenue) OVER (ORDER BY month) AS prev_month_revenue,  
       (revenue - prev_month_revenue) / prev_month_revenue * 100 AS growth_rate  
FROM revenue_data;{}
    

✔ Python for KPI Dashboard (Using Matplotlib)

    
        import matplotlib.pyplot as plt
plt.plot(df['month'], df['revenue_growth'], marker='o')
plt.title('Monthly Revenue Growth')
plt.xlabel('Month')
plt.ylabel('Growth Rate (%)')
plt.show(){}
    

4️⃣ Real-Life Use Cases of Data-Driven Decisions 📌 E-commerce: Optimize pricing based on customer demand trends. 📌 Finance: Predict stock prices using time series forecasting. 📌 Marketing: Improve email campaign conversion rates with A/B testing. 📌 Healthcare: Identify disease patterns using predictive analytics. Mini Task for You: Write an SQL query to calculate the customer churn rate for a subscription-based company. Data Analyst Roadmap: 👇 https://t.me/sqlspecialist/1159 Like this post if you want me to continue covering all the topics! ❤️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗔𝗿𝗲 𝗛𝗶𝗿𝗶𝗻𝗴 𝗡𝗼𝘄😍 💼 Roles in multiple domains 💰 Salary: 3 LPA to 20 LPA 🌍 PAN India | Remote & Onsite options 📩 Register & Upload Your CV Today 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇:- https://bit.ly/44qMX2k Select your experience & Complete The Registration Process ✅ Start applying to jobs that fit your profile and boost your career growth!