Data Analytics

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Quick recap of essential SQL basics 😄👇 SQL is a domain-specific language used for managing and querying relational databases. It's crucial for interacting with databases, retrieving, storing, updating, and deleting data. Here are some fundamental SQL concepts: 1. Database - A database is a structured collection of data. It's organized into tables, and SQL is used to manage these tables. 2. Table - Tables are the core of a database. They consist of rows and columns, and each row represents a record, while each column represents a data attribute. 3. Query - A query is a request for data from a database. SQL queries are used to retrieve information from tables. The SELECT statement is commonly used for this purpose. 4. Data Types - SQL supports various data types (e.g., INTEGER, TEXT, DATE) to specify the kind of data that can be stored in a column. 5. Primary Key - A primary key is a unique identifier for each row in a table. It ensures that each row is distinct and can be used to establish relationships between tables. 6. Foreign Key - A foreign key is a column in one table that links to the primary key in another table. It creates relationships between tables in a database. 7. CRUD Operations - SQL provides four primary operations for data manipulation: - Create (INSERT) - Add new records to a table. - Read (SELECT) - Retrieve data from one or more tables. - Update (UPDATE) - Modify existing data. - Delete (DELETE) - Remove records from a table. 8. WHERE Clause - The WHERE clause is used in SELECT, UPDATE, and DELETE statements to filter and conditionally manipulate data. 9. JOIN - JOIN operations are used to combine data from two or more tables based on a related column. Common types include INNER JOIN, LEFT JOIN, and RIGHT JOIN. 10. Index - An index is a database structure that improves the speed of data retrieval operations. It's created on one or more columns in a table. 11. Aggregate Functions - SQL provides functions like SUM, AVG, COUNT, MAX, and MIN for performing calculations on groups of data. 12. Transactions - Transactions are sequences of one or more SQL statements treated as a single unit. They ensure data consistency by either applying all changes or none. 13. Normalization - Normalization is the process of organizing data in a database to minimize data redundancy and improve data integrity. 14. Constraints - Constraints (e.g., NOT NULL, UNIQUE, CHECK) are rules that define what data is allowed in a table, ensuring data quality and consistency. Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz Share with credits: https://t.me/sqlspecialist Hope it helps :)

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 :)

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Business and Analytical Questions Q10: How do you define KPIs for a business process? KPIs (Key Performance Indicators) should be SMART (Specific, Measurable, Achievable, Relevant, Time-bound). Example: Operational KPI: Average processing time per request. Financial KPI: Monthly revenue growth rate. Q11: Give an example of how you used data to drive a business decision. At my previous job, I analyzed customer churn rates using SQL and Power BI. I found that customers leaving had lower engagement rates. We introduced loyalty programs, reducing churn by 15%. Scenario-Based & Behavioral Questions Q12: How do you handle a situation where different business units have conflicting reporting requirements? Understand each team's objectives. Standardize KPI definitions. Create customized dashboards based on common metrics. Align with senior stakeholders to prioritize key metrics. Q13: What would you do if your report is showing incorrect numbers? Check source data (duplicates, missing values). Validate ETL transformations. Review calculations in Power BI/Tableau. Compare outputs against historical trends. Industry-Specific Questions (Credit Reporting & Financial Services) Q14: What are some key credit risk metrics used in financial services? Credit Utilization Ratio = (Credit Used / Credit Limit) * 100 Debt-to-Income (DTI) Ratio = (Total Debt / Total Income) Delinquency Rate = % of accounts overdue Q15: How do you ensure compliance and data security in reporting? Follow GDPR, CCPA, and PCI-DSS regulations. Use role-based access control (RBAC). Encrypt sensitive data and restrict PII (Personally Identifiable Information) exposure. General HR Questions Q16: Why do you want to work at XYZ company? XYZ is a leader in data analytics and financial insights. I’m excited about leveraging my BI expertise to contribute to global delivery operations and improve KPI reporting. Q17: Tell me about a challenging project and how you handled it. I once worked on a real-time dashboard project with inconsistent data sources. I collaborated with IT to automate data ingestion and improved accuracy by 30%. Q18: Where do you see yourself in five years? I see myself growing into a leadership role in business intelligence, contributing to strategic decision-making through advanced data insights. Final Tips for Interview Preparation: ✅ Practice SQL queries and Power BI dashboards ✅ Review credit reporting metrics and industry knowledge ✅ Be ready with real-world case studies from your past experience React with ♥️ if you want me to post mock interview questions or scenario-based Interview Questions related to data analytics

Thanks for the amazing response Here are the Answers for above Interview Questions Technical Questions (Data Analysis & Reporting) SQL Questions Q1: How do you write a query to fetch the top 5 highest revenue-generating customers? SELECT customer_id, SUM(revenue) AS total_revenue FROM sales GROUP BY customer_id ORDER BY total_revenue DESC LIMIT 5; Q2: Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN. INNER JOIN: Returns only the matching records from both tables. LEFT JOIN: Returns all records from the left table and matching records from the right table. If no match is found, it returns NULLs. FULL OUTER JOIN: Returns all records from both tables, with NULLs where there is no match. SELECT a.customer_id, a.order_id, b.payment_id FROM orders a INNER JOIN payments b ON a.order_id = b.order_id; Q3: How would you optimize a slow-running query? Use Indexes on frequently queried columns. Avoid SELECT * and only select required columns. Use EXPLAIN ANALYZE to check query performance. Optimize JOINs and use WHERE instead of HAVING. Consider using Partitioning and Materialized Views for large datasets. Q4: What are CTEs and when would you use them? A Common Table Expression (CTE) is a temporary result set used within a query. It improves readability and avoids redundant subqueries. WITH sales_summary AS ( SELECT customer_id, SUM(revenue) AS total_revenue FROM sales GROUP BY customer_id ) SELECT * FROM sales_summary WHERE total_revenue > 10000; Data Visualization (Power BI / Tableau / Excel) Q5: How would you create a dashboard to track key performance metrics? 1. Identify the KPIs (e.g., revenue, customer retention, processing time). 2. Extract data using SQL or ETL tools. 3. Clean and transform data in Power Query (Power BI) or Alteryx. 4. Use visualizations like bar charts, line graphs, and KPI cards. 5. Add filters and slicers for user interactivity. 6. Automate data refresh and ensure data integrity. Q6: Explain the difference between measures and calculated columns in Power BI. Measures: Dynamic calculations used in reports (e.g., SUM, AVERAGE). Computed only when needed. Calculated Columns: Static calculations stored in the dataset. Used when a value is needed in a row-wise manner. -- Measure: Total Sales = SUM(Sales[Revenue]) -- Calculated Column: Sales Category = IF(Sales[Revenue] > 10000, "High", "Low") Q7: How do you handle missing data in Tableau? Use Filters to remove nulls. Use IFNULL() or ZN() functions to replace nulls. Interpolate missing values using LODs (Level of Detail Expressions). Use DATA BLENDING to merge datasets where missing data exists. IFNULL(SUM(Sales), 0) ETL & Data Processing (Alteryx, Power BI, Excel) Q8: What is ETL, and how does it relate to BI? ETL (Extract, Transform, Load) is the process of extracting data from various sources, transforming it for consistency, and loading it into a BI system for analysis. Q9: Have you used Alteryx for data transformation? Explain a complex workflow you built. Yes, I built an Alteryx workflow to: 1. Connect to multiple data sources (Excel, SQL). 2. Clean and merge datasets. 3. Create new KPIs and aggregate data. 4. Output to Power BI for visualization. Like this post if you want me to post remaining answers in the next post ❤️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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SQL for Data Analysts: From Basics to Advanced 🔹 Basics of SQL ➊ SQL Syntax & Basic Queries ↳ SELECT, FROM, WHERE for data retrieval ↳ Filtering data using AND, OR, BETWEEN, LIKE, IN ➋ Sorting & Limiting Data ↳ ORDER BY for sorting results ↳ LIMIT & OFFSET for pagination ➌ Data Filtering & Aggregation ↳ COUNT(), SUM(), AVG(), MIN(), MAX() ↳ Grouping data using GROUP BY and HAVING ➍ Joins & Relationships ↳ INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN ↳ Self-joins & cross-joins for complex relationships ➎ Subqueries & CTEs ↳ Writing subqueries for better query organization ↳ Using WITH to create Common Table Expressions (CTEs) 🔹 Intermediate SQL for Data Analysis ➏ Window Functions for Advanced Aggregation ↳ ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE() ↳ LEAD() & LAG() for time-based analysis ➐ String & Date Functions ↳ CONCAT(), UPPER(), LOWER(), TRIM(), SUBSTRING() ↳ DATEPART(), DATEDIFF(), EXTRACT() for date manipulation ➑ Case Statements & Conditional Logic ↳ CASE WHEN for conditional transformations ↳ Nested CASE statements for advanced logic ➒ Pivoting & Unpivoting Data ↳ PIVOT() for transforming row-based data into columns ↳ UNPIVOT() for restructuring wide tables ➓ Handling Missing Data & NULL Values ↳ Using COALESCE() & NULLIF() ↳ Filtering and replacing NULL values 🔹 Advanced SQL for Data Analysts ⓫ Optimizing SQL Queries ↳ Using Indexes to improve performance ↳ Understanding EXPLAIN & query execution plans ⓬ Recursive Queries & Hierarchical Data ↳ WITH RECURSIVE for hierarchical relationships ↳ Organizing parent-child relationships in tables ⓭ Stored Procedures & Functions ↳ Writing reusable stored procedures ↳ Creating user-defined functions (UDFs) ⓮ Working with JSON & Semi-Structured Data ↳ Extracting and parsing JSON data using JSON_VALUE() ↳ Handling nested structures in SQL ⓯ Time Series & Trend Analysis ↳ Calculating moving averages ↳ Performing time-based aggregations ⓰ SQL in Python ↳ Connecting databases using SQLAlchemy ↳ Running SQL queries in pandas.read_sql() ↳ Merging SQL and Pandas for advanced analysis 🚀 The best way to master SQL is to work on real-world datasets and optimize queries for performance! Free SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Python for Data Analysts: From Basics to Advanced Level 🔹 Basics of Python ➊ Python Syntax & Data Types ↳ Variables, data types (int, float, string, bool) ↳ Type conversion and basic operations ➋ Control Flow & Loops ↳ if-else, for, while loops ↳ List comprehensions for efficient iteration ➌ Functions & Lambda Expressions ↳ Defining functions and using *args & **kwargs ↳ Anonymous functions with lambda ➍ Error Handling ↳ try-except for handling errors gracefully ↳ Raising custom exceptions 🔹 Intermediate Python for Data Analytics ➎ Working with Lists, Tuples, and Dictionaries ↳ List, tuple, and dictionary operations ↳ Dictionary and list comprehensions ➏ String Manipulation & Regular Expressions ↳ String formatting and manipulation ↳ Extracting patterns with re module ➐ Date & Time Handling ↳ Working with datetime and pandas.to_datetime() ↳ Formatting, extracting, and calculating time differences ➑ File Handling (CSV, JSON, Excel) ↳ Reading and writing structured files using pandas ↳ Handling large files efficiently using chunks 🔹 Data Analysis with Python ➒ Pandas for Data Manipulation ↳ Reading, cleaning, filtering, and transforming data ↳ Aggregations using .groupby(), .pivot_table() ↳ Merging and joining datasets ➓ NumPy for Numerical Computing ↳ Creating and manipulating arrays ↳ Vectorized operations for performance optimization ⓫ Handling Missing Data ↳ .fillna(), .dropna(), .interpolate() ↳ Imputing missing values for better analytics ⓬ Data Visualization with Matplotlib & Seaborn ↳ Creating plots (line, bar, scatter, histogram) ↳ Customizing plots for presentations ↳ Heatmaps for correlation analysis 🔹 Advanced Topics for Data Analysts ⓭ SQL with Python ↳ Connecting to databases using sqlalchemy ↳ Writing and executing SQL queries in Python (pandas.read_sql()) ↳ Merging SQL and Pandas for analysis ⓮ Working with APIs & Web Scraping ↳ Fetching data from APIs using requests ↳ Web scraping using BeautifulSoup and Selenium ⓯ ETL (Extract, Transform, Load) Pipelines ↳ Automating data ingestion and transformation ↳ Cleaning and loading data into databases ⓰ Time Series Analysis ↳ Working with time-series data in Pandas ↳ Forecasting trends using moving averages ⓱ Machine Learning Basics for Data Analysts ↳ Introduction to Scikit-learn (Linear Regression, KNN, Clustering) ↳ Feature engineering and model evaluation 🚀 The best way to learn Python is by working on real-world projects!

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Data Analyst Interview Questions & Tips Be prepared with a mix of technical, analytical, and business-oriented interview questions. 1. Technical Questions (Data Analysis & Reporting) SQL Questions: How do you write a query to fetch the top 5 highest revenue-generating customers? Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN. How would you optimize a slow-running query? What are CTEs and when would you use them? Data Visualization (Power BI / Tableau / Excel) How would you create a dashboard to track key performance metrics? Explain the difference between measures and calculated columns in Power BI. How do you handle missing data in Tableau? What are DAX functions, and can you give an example? ETL & Data Processing (Alteryx, Power BI, Excel) What is ETL, and how does it relate to BI? Have you used Alteryx for data transformation? Explain a complex workflow you built. How do you automate reporting using Power Query in Excel? 2. Business and Analytical Questions How do you define KPIs for a business process? Give an example of how you used data to drive a business decision. How would you identify cost-saving opportunities in a reporting process? Explain a time when your report uncovered a hidden business insight. 3. Scenario-Based & Behavioral Questions Stakeholder Management: How do you handle a situation where different business units have conflicting reporting requirements? How do you explain complex data insights to non-technical stakeholders? Problem-Solving & Debugging: What would you do if your report is showing incorrect numbers? How do you ensure the accuracy of a new KPI you introduced? Project Management & Process Improvement: Have you led a project to automate or improve a reporting process? What steps do you take to ensure the timely delivery of reports? 4. Industry-Specific Questions (Credit Reporting & Financial Services) What are some key credit risk metrics used in financial services? How would you analyze trends in customer credit behavior? How do you ensure compliance and data security in reporting? 5. General HR Questions Why do you want to work at this company? Tell me about a challenging project and how you handled it. What are your strengths and weaknesses? Where do you see yourself in five years? How to Prepare? Brush up on SQL, Power BI, and ETL tools (especially Alteryx). Learn about key financial and credit reporting metrics.(varies company to company) Practice explaining data-driven insights in a business-friendly manner. Be ready to showcase problem-solving skills with real-world examples. React with ❤️ if you want me to also post sample answer for the above questions

Business Intelligence & Reporting Business Intelligence (BI) and reporting involve transforming raw data into actionable insights using visualization tools like Power BI, Tableau, and Google Data Studio. 1️⃣ Power BI & Tableau Basics These tools help create interactive dashboards, reports, and visualizations. Power BI: Uses DAX (Data Analysis Expressions) for calculations and Power Query for data transformation. Tableau: Uses calculated fields and built-in functions for dynamic reporting. 2️⃣ Essential Features in Power BI & Tableau 🔹 Dashboards: Interactive visualizations combining multiple reports. 🔹 Filters & Slicers: Allow users to focus on specific data. 🔹 Drill-through & Drill-down: Navigate from high-level to detailed data. 🔹 Calculated Fields: Custom metrics for analysis. 🔹 Data Blending: Combine multiple sources into a single report. 3️⃣ Power BI Key Concepts ✔ DAX (Data Analysis Expressions): Used for creating custom calculations. Example: Calculate Total Sales Total_Sales = SUM(Sales[Revenue]) Create a Year-over-Year Growth Rate YoY Growth = ( [Current Year Sales] - [Previous Year Sales] ) / [Previous Year Sales] ✔ Power Query: Used for data cleaning and transformation. Remove duplicates Merge datasets Pivot/Unpivot data ✔ Power BI Visuals Bar, Line, Pie Charts KPI Indicators Maps (for geographic analysis) 4️⃣ Tableau Key Concepts ✔ Calculated Fields: Used to create new metrics. Example: Total Profit Calculation SUM([Sales]) - SUM([Cost]) Sales Growth Percentage (SUM([Sales]) - LOOKUP(SUM([Sales]), -1)) / LOOKUP(SUM([Sales]), -1) ✔ Tableau Filters Dimension Filter (Category, Region) Measure Filter (Sales > $10,000) Top N Filter (Top 10 Products by Sales) ✔ Dashboards in Tableau Drag & drop visualizations Add filters and parameters Customize tooltips 5️⃣ Google Data Studio (Looker Studio) A free tool for creating interactive reports. ✔ Connects to Google Sheets, BigQuery, and SQL databases. ✔ Drag-and-drop report builder. ✔ Custom calculations using formulas like in Excel. Example: Create a Revenue per Customer metric: SUM(Revenue) / COUNT(DISTINCT Customer_ID) 6️⃣ Best Practices for BI Reporting ✅ Keep Dashboards Simple → Only show key KPIs. ✅ Use Consistent Colors & Formatting → Makes insights clear. ✅ Optimize Performance → Avoid too many calculations on large datasets. ✅ Enable Interactivity → Filters, drill-downs, and slicers improve user experience. Mini Task for You: In Power BI, create a DAX formula to calculate the Cumulative Sales over time. 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 :) #sql

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Exploratory Data Analysis (EDA) EDA is the process of analyzing datasets to summarize key patterns, detect anomalies, and gain insights before applying machine learning or reporting. 1️⃣ Descriptive Statistics Descriptive statistics help summarize and understand data distributions. In SQL: Calculate Mean (Average):

SELECT AVG(salary) AS average_salary FROM employees; 
Find Median (Using Window Functions) SELECT salary FROM ( SELECT salary, ROW_NUMBER() OVER (ORDER BY salary) AS row_num, COUNT(*) OVER () AS total_rows FROM employees ) subquery WHERE row_num = (total_rows / 2); 

Find Mode (Most Frequent Value)

SELECT department, COUNT(*) AS count FROM employees GROUP BY department ORDER BY count DESC LIMIT 1; 

Calculate Variance & Standard Deviation

SELECT VARIANCE(salary) AS salary_variance, STDDEV(salary) AS salary_std_dev FROM employees; 

In Python (Pandas): Mean, Median, Mode

df['salary'].mean() df['salary'].median() df['salary'].mode()[0]

Variance & Standard Deviation

df['salary'].var() df['salary'].std()

2️⃣ Data Visualization Visualizing data helps identify trends, outliers, and patterns. In SQL (For Basic Visualization in Some Databases Like PostgreSQL): Create Histogram (Approximate in SQL)

SELECT salary, COUNT(*) FROM employees GROUP BY salary ORDER BY salary; 

In Python (Matplotlib & Seaborn): Bar Chart (Category-Wise Sales)

import matplotlib.pyplot as plt 
import seaborn as sns 
df.groupby('category')['sales'].sum().plot(kind='bar') 
plt.title('Total Sales by Category') 
plt.xlabel('Category') 
plt.ylabel('Sales') 
plt.show() 

Histogram (Salary Distribution)

sns.histplot(df['salary'], bins=10, kde=True) 
plt.title('Salary Distribution') 
plt.show() 

Box Plot (Outliers in Sales Data)

sns.boxplot(y=df['sales']) 
plt.title('Sales Data Outliers') 
plt.show()

Heatmap (Correlation Between Variables)

sns.heatmap(df.corr(), annot=True, cmap='coolwarm') plt.title('Feature Correlation Heatmap') plt.show() 

3️⃣ Detecting Anomalies & Outliers Outliers can skew results and should be identified. In SQL: Find records with unusually high salaries

SELECT * FROM employees WHERE salary > (SELECT AVG(salary) + 2 * STDDEV(salary) FROM employees); 

In Python (Pandas & NumPy): Using Z-Score (Values Beyond 3 Standard Deviations)

from scipy import stats df['z_score'] = stats.zscore(df['salary']) df_outliers = df[df['z_score'].abs() > 3] 

Using IQR (Interquartile Range)

Q1 = df['salary'].quantile(0.25) 
Q3 = df['salary'].quantile(0.75) 
IQR = Q3 - Q1 
df_outliers = df[(df['salary'] < (Q1 - 1.5 * IQR)) | (df['salary'] > (Q3 + 1.5 * IQR))] 

4️⃣ Key EDA Steps Understand the Data → Check missing values, duplicates, and column types Summarize Statistics → Mean, Median, Standard Deviation, etc. Visualize Trends → Histograms, Box Plots, Heatmaps Detect Outliers & Anomalies → Z-Score, IQR Feature Engineering → Transform variables if needed Mini Task for You: Write an SQL query to find employees whose salaries are above two standard deviations from the mean salary. Here you can find the roadmap for data analyst: 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 :) #sql

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