๐ Excel vs SQL vs Python (Pandas):
1๏ธโฃ Filtering Data
โณ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
โณ SQL: SELECT * FROM table WHERE column > 50;
โณ Python: df_filtered = df[df['column'] > 50]
2๏ธโฃ Sorting Data
โณ Excel: Data โ Sort (or =SORT(A2:A100, 1, TRUE))
โณ SQL: SELECT * FROM table ORDER BY column ASC;
โณ Python: df_sorted = df.sort_values(by="column")
3๏ธโฃ Counting Rows
โณ Excel: =COUNTA(A:A)
โณ SQL: SELECT COUNT(*) FROM table;
โณ Python: row_count = len(df)
4๏ธโฃ Removing Duplicates
โณ Excel: Data โ Remove Duplicates
โณ SQL: SELECT DISTINCT * FROM table;
โณ Python: df_unique = df.drop_duplicates()
5๏ธโฃ Joining Tables
โณ Excel: Power Query โ Merge Queries (or VLOOKUP/XLOOKUP)
โณ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
โณ Python: df_merged = pd.merge(df1, df2, on="id")
6๏ธโฃ Ranking Data
โณ Excel: =RANK.EQ(A2, $A$2:$A$100)
โณ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
โณ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7๏ธโฃ Moving Average Calculation
โณ Excel: =AVERAGE(B2:B4) (manually for rolling window)
โณ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
โณ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8๏ธโฃ Running Total
โณ Excel: =SUM($B$2:B2) (drag down)
โณ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
โณ Python: df["running_total"] = df["value"].cumsum()
1๏ธโฃ Filtering Data
โณ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
โณ SQL: SELECT * FROM table WHERE column > 50;
โณ Python: df_filtered = df[df['column'] > 50]
2๏ธโฃ Sorting Data
โณ Excel: Data โ Sort (or =SORT(A2:A100, 1, TRUE))
โณ SQL: SELECT * FROM table ORDER BY column ASC;
โณ Python: df_sorted = df.sort_values(by="column")
3๏ธโฃ Counting Rows
โณ Excel: =COUNTA(A:A)
โณ SQL: SELECT COUNT(*) FROM table;
โณ Python: row_count = len(df)
4๏ธโฃ Removing Duplicates
โณ Excel: Data โ Remove Duplicates
โณ SQL: SELECT DISTINCT * FROM table;
โณ Python: df_unique = df.drop_duplicates()
5๏ธโฃ Joining Tables
โณ Excel: Power Query โ Merge Queries (or VLOOKUP/XLOOKUP)
โณ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
โณ Python: df_merged = pd.merge(df1, df2, on="id")
6๏ธโฃ Ranking Data
โณ Excel: =RANK.EQ(A2, $A$2:$A$100)
โณ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
โณ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7๏ธโฃ Moving Average Calculation
โณ Excel: =AVERAGE(B2:B4) (manually for rolling window)
โณ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
โณ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8๏ธโฃ Running Total
โณ Excel: =SUM($B$2:B2) (drag down)
โณ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
โณ Python: df["running_total"] = df["value"].cumsum()
โค23๐1๐ฅ1
How do analysts use SQL in a company?
SQL is every data analystโs superpower! Here's how they use it in the real world:
Extract Data
Pull data from multiple tables to answer business questions.
Example:
(P.S. Avoid SELECT *โyour future self (and the database) will thank you!)
Clean & Transform
Use SQL functions to clean raw data.
Think TRIM(), COALESCE(), CAST()โlike giving data a fresh haircut.
Summarize & Analyze
Group and aggregate to spot trends and patterns.
GROUP BY, SUM(), AVG() โ your best friends for quick insights.
Build Dashboards
Feed SQL queries into Power BI, Tableau, or Excel to create visual stories that make data talk.
Run A/B Tests
Evaluate product changes and campaigns by comparing user groups.
SQL makes sure your decisions are backed by data, not just gut feeling.
Use Views & CTEs
Simplify complex queries with Views and Common Table Expressions.
Clean, reusable, and boss-approved.
Drive Decisions
SQL powers decisions across Marketing, Product, Sales, and Finance.
When someone asks โWhatโs working?โโyouโve got the answers.
And remember: write smart queries, not lazy ones. Say no to SELECT * unless you really mean it!
Hit โฅ๏ธ if you want me to share more real-world examples to make data analytics easier to understand!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
SQL is every data analystโs superpower! Here's how they use it in the real world:
Extract Data
Pull data from multiple tables to answer business questions.
Example:
SELECT name, revenue FROM sales WHERE region = 'North America';
(P.S. Avoid SELECT *โyour future self (and the database) will thank you!)
Clean & Transform
Use SQL functions to clean raw data.
Think TRIM(), COALESCE(), CAST()โlike giving data a fresh haircut.
Summarize & Analyze
Group and aggregate to spot trends and patterns.
GROUP BY, SUM(), AVG() โ your best friends for quick insights.
Build Dashboards
Feed SQL queries into Power BI, Tableau, or Excel to create visual stories that make data talk.
Run A/B Tests
Evaluate product changes and campaigns by comparing user groups.
SQL makes sure your decisions are backed by data, not just gut feeling.
Use Views & CTEs
Simplify complex queries with Views and Common Table Expressions.
Clean, reusable, and boss-approved.
Drive Decisions
SQL powers decisions across Marketing, Product, Sales, and Finance.
When someone asks โWhatโs working?โโyouโve got the answers.
And remember: write smart queries, not lazy ones. Say no to SELECT * unless you really mean it!
Hit โฅ๏ธ if you want me to share more real-world examples to make data analytics easier to understand!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค8
Complete roadmap to learn Python for data analysis
Step 1: Fundamentals of Python
1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)
2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions
3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions
4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)
Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)
2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully
3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation
Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations
2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data
3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn
Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering
2. Exploratory Data Analysis (EDA)
- Descriptive statistics
- Data visualization techniques
- Identifying patterns and outliers
3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions
Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models
2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models
3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)
Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects
2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects
๐จโ๐ป FREE Resources to Learn & Practice Python
1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://t.iss.one/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.iss.one/pythonfreebootcamp/134
7. https://t.iss.one/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://t.iss.one/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://t.iss.one/pythonspecialist/33
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
Step 1: Fundamentals of Python
1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)
2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions
3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions
4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)
Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)
2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully
3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation
Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations
2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data
3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn
Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering
2. Exploratory Data Analysis (EDA)
- Descriptive statistics
- Data visualization techniques
- Identifying patterns and outliers
3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions
Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models
2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models
3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)
Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects
2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects
๐จโ๐ป FREE Resources to Learn & Practice Python
1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://t.iss.one/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.iss.one/pythonfreebootcamp/134
7. https://t.iss.one/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://t.iss.one/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://t.iss.one/pythonspecialist/33
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
โค10
SQL (Structured Query Language) is the universal language of databases. Whether you're analyzing sales data, optimizing marketing campaigns, or tracking user behavior, SQL is your go-to tool for:
โ Accessing and managing data efficiently
โ Writing queries to extract insights
โ Building a strong foundation for advanced tools like Python, R, or Power BI
In short, SQL is the bridge between raw data and actionable insights. ๐
SQL Topics to Learn for Data Analyst/Business Analyst Roles
1. Basic:
* SELECT statements
* WHERE clause
* JOINs (INNER, LEFT, RIGHT, FULL)
* GROUP BY and HAVING
* ORDER BY
* Basic Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
2. Intermediate:
* Subqueries
* CASE statements
* UNION and UNION ALL
* Common Table Expressions (CTEs)
* Window Functions (ROW_NUMBER, RANK, DENSE_RANK, OVER)
* Data Manipulation (INSERT, UPDATE, DELETE)
* Indexes and Performance Tuning
3. Advanced:
* Advanced Window Functions (LEAD, LAG, NTILE)
* Complex Subqueries and Correlated Subqueries
* Advanced Performance Tuning
SQL is not just a skillโitโs the foundation of your data career. ๐
Here you can find essential SQL Interview Resources๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more ๐โค๏ธ
Hope it helps :)
โ Accessing and managing data efficiently
โ Writing queries to extract insights
โ Building a strong foundation for advanced tools like Python, R, or Power BI
In short, SQL is the bridge between raw data and actionable insights. ๐
SQL Topics to Learn for Data Analyst/Business Analyst Roles
1. Basic:
* SELECT statements
* WHERE clause
* JOINs (INNER, LEFT, RIGHT, FULL)
* GROUP BY and HAVING
* ORDER BY
* Basic Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
2. Intermediate:
* Subqueries
* CASE statements
* UNION and UNION ALL
* Common Table Expressions (CTEs)
* Window Functions (ROW_NUMBER, RANK, DENSE_RANK, OVER)
* Data Manipulation (INSERT, UPDATE, DELETE)
* Indexes and Performance Tuning
3. Advanced:
* Advanced Window Functions (LEAD, LAG, NTILE)
* Complex Subqueries and Correlated Subqueries
* Advanced Performance Tuning
SQL is not just a skillโitโs the foundation of your data career. ๐
Here you can find essential SQL Interview Resources๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more ๐โค๏ธ
Hope it helps :)
โค7
Want to become a Data Scientist?
Hereโs a quick roadmap with essential concepts:
1. Mathematics & Statistics
Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.
Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.
Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.
2. Programming
Python or R: Choose a primary programming language for data science.
Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.
R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.
SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.
3. Data Wrangling & Preprocessing
Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.
4. Data Visualization
Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.
5. Machine Learning
Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.
6. Advanced Machine Learning & Deep Learning
Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.
7. Natural Language Processing (NLP)
Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.
8. Big Data Tools (Optional)
Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.
9. Data Science Workflows & Pipelines (Optional)
ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).
10. Model Validation & Tuning
Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.
11. Time Series Analysis
Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.
12. Experimentation & A/B Testing
Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.
ENJOY LEARNING ๐๐
#datascience
Hereโs a quick roadmap with essential concepts:
1. Mathematics & Statistics
Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.
Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.
Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.
2. Programming
Python or R: Choose a primary programming language for data science.
Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.
R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.
SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.
3. Data Wrangling & Preprocessing
Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.
4. Data Visualization
Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.
5. Machine Learning
Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.
6. Advanced Machine Learning & Deep Learning
Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.
7. Natural Language Processing (NLP)
Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.
8. Big Data Tools (Optional)
Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.
9. Data Science Workflows & Pipelines (Optional)
ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).
10. Model Validation & Tuning
Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.
11. Time Series Analysis
Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.
12. Experimentation & A/B Testing
Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.
ENJOY LEARNING ๐๐
#datascience
โค10
Essential Python and SQL topics for data analysts ๐๐
Python Topics:
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters
2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY
3. Data Filtering
- WHERE Clause
- ORDER BY
4. Data Joins
- JOIN Operations
- Subqueries
5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization
6. Database Management
- Connecting to Databases
- SQLAlchemy
7. Database Design
- Data Types
- Normalization
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Python Resources - https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
SQL Resources - https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Hope it helps :)
Python Topics:
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters
2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY
3. Data Filtering
- WHERE Clause
- ORDER BY
4. Data Joins
- JOIN Operations
- Subqueries
5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization
6. Database Management
- Connecting to Databases
- SQLAlchemy
7. Database Design
- Data Types
- Normalization
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Python Resources - https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
SQL Resources - https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Hope it helps :)
โค4
Complete step-by-step syllabus of #Excel for Data Analytics
Introduction to Excel for Data Analytics:
Overview of Excel's capabilities for data analysis
Introduction to Excel's interface: ribbons, worksheets, cells, etc.
Differences between Excel desktop version and Excel Online (web version)
Data Import and Preparation:
Importing data from various sources: CSV, text files, databases, web queries, etc.
Data cleaning and manipulation techniques: sorting, filtering, removing duplicates, etc.
Data types and formatting in Excel
Data validation and error handling
Data Analysis Techniques in Excel:
Basic formulas and functions: SUM, AVERAGE, COUNT, IF, VLOOKUP, etc.
Advanced functions for data analysis: INDEX-MATCH, SUMIFS, COUNTIFS, etc.
PivotTables and PivotCharts for summarizing and analyzing data
Advanced data analysis tools: Goal Seek, Solver, What-If Analysis, etc.
Data Visualization in Excel:
Creating basic charts: column, bar, line, pie, scatter, etc.
Formatting and customizing charts for better visualization
Using sparklines for visualizing trends in data
Creating interactive dashboards with slicers and timelines
Advanced Data Analysis Features:
Data modeling with Excel Tables and Relationships
Using Power Query for data transformation and cleaning
Introduction to Power Pivot for data modeling and DAX calculations
Advanced charting techniques: combination charts, waterfall charts, etc.
Statistical Analysis in Excel:
Descriptive statistics: mean, median, mode, standard deviation, etc.
Hypothesis testing: t-tests, chi-square tests, ANOVA, etc.
Regression analysis and correlation
Forecasting techniques: moving averages, exponential smoothing, etc.
Data Visualization Tools in Excel:
Introduction to Excel add-ins for enhanced visualization (e.g., Power Map, Power View)
Creating interactive reports with Excel add-ins
Introduction to Excel Data Model for handling large datasets
Real-world Projects and Case Studies:
Analyzing real-world datasets
Solving business problems with Excel
Portfolio development showcasing Excel skills
Free Resources: https://t.iss.one/excel_data
Hope this helps you ๐
Introduction to Excel for Data Analytics:
Overview of Excel's capabilities for data analysis
Introduction to Excel's interface: ribbons, worksheets, cells, etc.
Differences between Excel desktop version and Excel Online (web version)
Data Import and Preparation:
Importing data from various sources: CSV, text files, databases, web queries, etc.
Data cleaning and manipulation techniques: sorting, filtering, removing duplicates, etc.
Data types and formatting in Excel
Data validation and error handling
Data Analysis Techniques in Excel:
Basic formulas and functions: SUM, AVERAGE, COUNT, IF, VLOOKUP, etc.
Advanced functions for data analysis: INDEX-MATCH, SUMIFS, COUNTIFS, etc.
PivotTables and PivotCharts for summarizing and analyzing data
Advanced data analysis tools: Goal Seek, Solver, What-If Analysis, etc.
Data Visualization in Excel:
Creating basic charts: column, bar, line, pie, scatter, etc.
Formatting and customizing charts for better visualization
Using sparklines for visualizing trends in data
Creating interactive dashboards with slicers and timelines
Advanced Data Analysis Features:
Data modeling with Excel Tables and Relationships
Using Power Query for data transformation and cleaning
Introduction to Power Pivot for data modeling and DAX calculations
Advanced charting techniques: combination charts, waterfall charts, etc.
Statistical Analysis in Excel:
Descriptive statistics: mean, median, mode, standard deviation, etc.
Hypothesis testing: t-tests, chi-square tests, ANOVA, etc.
Regression analysis and correlation
Forecasting techniques: moving averages, exponential smoothing, etc.
Data Visualization Tools in Excel:
Introduction to Excel add-ins for enhanced visualization (e.g., Power Map, Power View)
Creating interactive reports with Excel add-ins
Introduction to Excel Data Model for handling large datasets
Real-world Projects and Case Studies:
Analyzing real-world datasets
Solving business problems with Excel
Portfolio development showcasing Excel skills
Free Resources: https://t.iss.one/excel_data
Hope this helps you ๐
โค5๐ฅฐ1๐1
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SQL can be simpleโif you learn it the smart way..
If youโre aiming to become a data analyst, mastering SQL is non-negotiable.
Hereโs a smart roadmap to ace it:
1. Basics First: Understand data types, simple queries (SELECT, FROM, WHERE). Master basic filtering.
2. Joins & Relationships: Dive into INNER, LEFT, RIGHT joins. Practice combining tables to extract meaningful insights.
3. Aggregations & Functions: Get comfortable with COUNT, SUM, AVG, MAX, GROUP BY, and HAVING clauses. These are essential for summarizing data.
4. Subqueries & Nested Queries: Learn how to query within queries. This is powerful for handling complex datasets.
5. Window Functions: Explore ranking, cumulative sums, and sliding windows to work with running totals and moving averages.
6. Optimization: Study indexing and query optimization for faster, more efficient queries.
7. Real-World Scenarios: Apply your SQL knowledge to solve real-world business problems.
The journey may seem tough, but each step sharpens your skills and brings you closer to data analysis excellence. Stay consistent, practice regularly, and let SQL become your superpower! ๐ช
Here you can find essential SQL Interview Resources๐
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Hope it helps :)
If youโre aiming to become a data analyst, mastering SQL is non-negotiable.
Hereโs a smart roadmap to ace it:
1. Basics First: Understand data types, simple queries (SELECT, FROM, WHERE). Master basic filtering.
2. Joins & Relationships: Dive into INNER, LEFT, RIGHT joins. Practice combining tables to extract meaningful insights.
3. Aggregations & Functions: Get comfortable with COUNT, SUM, AVG, MAX, GROUP BY, and HAVING clauses. These are essential for summarizing data.
4. Subqueries & Nested Queries: Learn how to query within queries. This is powerful for handling complex datasets.
5. Window Functions: Explore ranking, cumulative sums, and sliding windows to work with running totals and moving averages.
6. Optimization: Study indexing and query optimization for faster, more efficient queries.
7. Real-World Scenarios: Apply your SQL knowledge to solve real-world business problems.
The journey may seem tough, but each step sharpens your skills and brings you closer to data analysis excellence. Stay consistent, practice regularly, and let SQL become your superpower! ๐ช
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โค10
Quick SQL functions cheat sheet for beginners
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, โฆ): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, โฆ): Returns the first non-null value.
Like for more free Cheatsheets โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalytics
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, โฆ): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, โฆ): Returns the first non-null value.
Like for more free Cheatsheets โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalytics
โค14
SQL Interview Questions with Answers
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like โStevenโ;
With this command, we will be able to extract all the records where the first name is like โStevenโ.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
React โค๏ธ for more
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like โStevenโ;
With this command, we will be able to extract all the records where the first name is like โStevenโ.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
React โค๏ธ for more
โค8๐2
๐ Excel vs SQL vs Python (Pandas):
1๏ธโฃ Filtering Data
โณ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
โณ SQL: SELECT * FROM table WHERE column > 50;
โณ Python: df_filtered = df[df['column'] > 50]
2๏ธโฃ Sorting Data
โณ Excel: Data โ Sort (or =SORT(A2:A100, 1, TRUE))
โณ SQL: SELECT * FROM table ORDER BY column ASC;
โณ Python: df_sorted = df.sort_values(by="column")
3๏ธโฃ Counting Rows
โณ Excel: =COUNTA(A:A)
โณ SQL: SELECT COUNT(*) FROM table;
โณ Python: row_count = len(df)
4๏ธโฃ Removing Duplicates
โณ Excel: Data โ Remove Duplicates
โณ SQL: SELECT DISTINCT * FROM table;
โณ Python: df_unique = df.drop_duplicates()
5๏ธโฃ Joining Tables
โณ Excel: Power Query โ Merge Queries (or VLOOKUP/XLOOKUP)
โณ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
โณ Python: df_merged = pd.merge(df1, df2, on="id")
6๏ธโฃ Ranking Data
โณ Excel: =RANK.EQ(A2, $A$2:$A$100)
โณ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
โณ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7๏ธโฃ Moving Average Calculation
โณ Excel: =AVERAGE(B2:B4) (manually for rolling window)
โณ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
โณ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8๏ธโฃ Running Total
โณ Excel: =SUM($B$2:B2) (drag down)
โณ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
โณ Python: df["running_total"] = df["value"].cumsum()
1๏ธโฃ Filtering Data
โณ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
โณ SQL: SELECT * FROM table WHERE column > 50;
โณ Python: df_filtered = df[df['column'] > 50]
2๏ธโฃ Sorting Data
โณ Excel: Data โ Sort (or =SORT(A2:A100, 1, TRUE))
โณ SQL: SELECT * FROM table ORDER BY column ASC;
โณ Python: df_sorted = df.sort_values(by="column")
3๏ธโฃ Counting Rows
โณ Excel: =COUNTA(A:A)
โณ SQL: SELECT COUNT(*) FROM table;
โณ Python: row_count = len(df)
4๏ธโฃ Removing Duplicates
โณ Excel: Data โ Remove Duplicates
โณ SQL: SELECT DISTINCT * FROM table;
โณ Python: df_unique = df.drop_duplicates()
5๏ธโฃ Joining Tables
โณ Excel: Power Query โ Merge Queries (or VLOOKUP/XLOOKUP)
โณ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
โณ Python: df_merged = pd.merge(df1, df2, on="id")
6๏ธโฃ Ranking Data
โณ Excel: =RANK.EQ(A2, $A$2:$A$100)
โณ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
โณ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7๏ธโฃ Moving Average Calculation
โณ Excel: =AVERAGE(B2:B4) (manually for rolling window)
โณ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
โณ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8๏ธโฃ Running Total
โณ Excel: =SUM($B$2:B2) (drag down)
โณ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
โณ Python: df["running_total"] = df["value"].cumsum()
โค8๐1
9 tips to get started with Data Analysis:
Learn Excel, SQL, and a programming language (Python or R)
Understand basic statistics and probability
Practice with real-world datasets (Kaggle, Data.gov)
Clean and preprocess data effectively
Visualize data using charts and graphs
Ask the right questions before diving into data
Use libraries like Pandas, NumPy, and Matplotlib
Focus on storytelling with data insights
Build small projects to apply what you learn
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
Learn Excel, SQL, and a programming language (Python or R)
Understand basic statistics and probability
Practice with real-world datasets (Kaggle, Data.gov)
Clean and preprocess data effectively
Visualize data using charts and graphs
Ask the right questions before diving into data
Use libraries like Pandas, NumPy, and Matplotlib
Focus on storytelling with data insights
Build small projects to apply what you learn
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
โค5
Commonly used Power BI DAX functions:
DATE AND TIME FUNCTIONS:
-
-
-
AGGREGATE FUNCTIONS:
-
-
-
-
-
-
-
FILTER FUNCTIONS:
-
-
-
-
TIME INTELLIGENCE FUNCTIONS:
-
-
-
-
-
TEXT FUNCTIONS:
-
-
-
INFORMATION FUNCTIONS:
-
-
-
LOGICAL FUNCTIONS:
-
-
-
RELATIONSHIP FUNCTIONS:
-
-
-
Remember, DAX is more about logic than the formulas.
DATE AND TIME FUNCTIONS:
-
CALENDAR-
DATEDIFF-
TODAY, DAY, MONTH, QUARTER, YEARAGGREGATE FUNCTIONS:
-
SUM, SUMX, PRODUCT-
AVERAGE-
MIN, MAX-
COUNT-
COUNTROWS-
COUNTBLANK-
DISTINCTCOUNTFILTER FUNCTIONS:
-
CALCULATE-
FILTER-
ALL, ALLEXCEPT, ALLSELECTED, REMOVEFILTERS-
SELECTEDVALUETIME INTELLIGENCE FUNCTIONS:
-
DATESBETWEEN-
DATESMTD, DATESQTD, DATESYTD-
SAMEPERIODLASTYEAR-
PARALLELPERIOD-
TOTALMTD, TOTALQTD, TOTALYTDTEXT FUNCTIONS:
-
CONCATENATE-
FORMAT-
LEN, LEFT, RIGHTINFORMATION FUNCTIONS:
-
HASONEVALUE, HASONEFILTER-
ISBLANK, ISERROR, ISEMPTY-
CONTAINSLOGICAL FUNCTIONS:
-
AND, OR, IF, NOT-
TRUE, FALSE-
SWITCHRELATIONSHIP FUNCTIONS:
-
RELATED-
USERRELATIONSHIP-
RELATEDTABLERemember, DAX is more about logic than the formulas.
โค5
Everyone thinks being a great data analyst is about advanced algorithms and complex dashboards.
But real data excellence comes from methodical habits that build trust and deliver real insights.
Here are 20 signs of a truly effective analyst ๐
โ They document every step of their analysis
โ Clear notes make their work reproducible and trustworthy.
โ They check data quality before the analysis begins
โ Garbage in = garbage out. Always validate first.
โ They use version control religiously
โ Every code change is tracked. Nothing gets lost.
โ They explore data thoroughly before diving in
โ Understanding context prevents costly misinterpretations.
โ They create automated scripts for repetitive tasks
โ Efficiency isnโt a luxuryโitโs a necessity.
โ They maintain a reusable code library
โ Smart analysts never solve the same problem twice.
โ They test assumptions with multiple validation methods
โ One test isnโt enough; they triangulate confidence.
โ They organize project files logically
โ Their work is navigable by anyone, not just themselves.
โ They seek peer reviews on critical work
โ Fresh eyes catch blind spots.
โ They continuously absorb industry knowledge
โ Learning never stops. Trends change too quickly.
โ They prioritize business-impacting projects
โ Every analysis must drive real decisions.
โ They explain complex findings simply
โ Technical brilliance is useless without clarity.
โ They write readable, well-commented code
โ Their work is accessible to others, long after they're gone.
โ They maintain robust backup systems
โ Data loss is never an option.
โ They learn from analytical mistakes
โ Errors become stepping stones, not roadblocks.
โ They build strong stakeholder relationships
โ Data is only valuable when people use it.
โ They break complex projects into manageable chunks
โ Progress happens through disciplined, incremental work.
โ They handle sensitive data with proper security
โ Compliance isnโt optionalโitโs foundational.
โ They create visualizations that tell clear stories
โ A chart without a narrative is just decoration.
โ They actively seek evidence against their conclusions
โ Confirmation bias is their biggest enemy.
The best analysts arenโt the ones with the most toolsโtheyโre the ones with the most rigorous practices.
But real data excellence comes from methodical habits that build trust and deliver real insights.
Here are 20 signs of a truly effective analyst ๐
โ They document every step of their analysis
โ Clear notes make their work reproducible and trustworthy.
โ They check data quality before the analysis begins
โ Garbage in = garbage out. Always validate first.
โ They use version control religiously
โ Every code change is tracked. Nothing gets lost.
โ They explore data thoroughly before diving in
โ Understanding context prevents costly misinterpretations.
โ They create automated scripts for repetitive tasks
โ Efficiency isnโt a luxuryโitโs a necessity.
โ They maintain a reusable code library
โ Smart analysts never solve the same problem twice.
โ They test assumptions with multiple validation methods
โ One test isnโt enough; they triangulate confidence.
โ They organize project files logically
โ Their work is navigable by anyone, not just themselves.
โ They seek peer reviews on critical work
โ Fresh eyes catch blind spots.
โ They continuously absorb industry knowledge
โ Learning never stops. Trends change too quickly.
โ They prioritize business-impacting projects
โ Every analysis must drive real decisions.
โ They explain complex findings simply
โ Technical brilliance is useless without clarity.
โ They write readable, well-commented code
โ Their work is accessible to others, long after they're gone.
โ They maintain robust backup systems
โ Data loss is never an option.
โ They learn from analytical mistakes
โ Errors become stepping stones, not roadblocks.
โ They build strong stakeholder relationships
โ Data is only valuable when people use it.
โ They break complex projects into manageable chunks
โ Progress happens through disciplined, incremental work.
โ They handle sensitive data with proper security
โ Compliance isnโt optionalโitโs foundational.
โ They create visualizations that tell clear stories
โ A chart without a narrative is just decoration.
โ They actively seek evidence against their conclusions
โ Confirmation bias is their biggest enemy.
The best analysts arenโt the ones with the most toolsโtheyโre the ones with the most rigorous practices.
โค11
If youโre a Data Analyst, chances are you use ๐๐๐ every single day. And if youโre preparing for interviews, youโve probably realized that it's not just about writing queries it's about writing smart, efficient, and scalable ones.
1. ๐๐ซ๐๐๐ค ๐๐ญ ๐๐จ๐ฐ๐ง ๐ฐ๐ข๐ญ๐ก ๐๐๐๐ฌ (๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐๐๐ฅ๐ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ)
Ever worked on a query that became an unreadable monster? CTEs let you break that down into logical steps. You can treat them like temporary views โ great for simplifying logic and improving collaboration across your team.
2. ๐๐ฌ๐ ๐๐ข๐ง๐๐จ๐ฐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ
Forget the mess of subqueries. With functions like ROW_NUMBER(), RANK(), LEAD() and LAG(), you can compare rows, rank items, or calculate running totals โ all within the same query. Total
3. ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ (๐๐๐ฌ๐ญ๐๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ)
Yes, they're old school, but nested subqueries are still powerful. Use them when you want to filter based on results of another query or isolate logic step-by-step before joining with the big picture.
4. ๐๐ง๐๐๐ฑ๐๐ฌ & ๐๐ฎ๐๐ซ๐ฒ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง
Query taking forever? Look at your indexes. Index the columns you use in JOINs, WHERE, and GROUP BY. Even basic knowledge of how the SQL engine reads data can take your skills up a notch.
5. ๐๐จ๐ข๐ง๐ฌ ๐ฏ๐ฌ. ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ
Joins are usually faster and better for combining large datasets. Subqueries, on the other hand, are cleaner when doing one-off filters or smaller operations. Choose wisely based on the context.
6. ๐๐๐๐ ๐๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ๐ฌ:
Want to categorize or bucket data without creating a separate table? Use CASE. Itโs ideal for conditional logic, custom labels, and grouping in a single query.
7. ๐๐ ๐ ๐ซ๐๐ ๐๐ญ๐ข๐จ๐ง๐ฌ & ๐๐๐๐๐ ๐๐
Most analytics questions start with "how many", "whatโs the average", or "which is the highest?". SUM(), COUNT(), AVG(), etc., and pair them with GROUP BY to drive insights that matter.
8. ๐๐๐ญ๐๐ฌ ๐๐ซ๐ ๐๐ฅ๐ฐ๐๐ฒ๐ฌ ๐๐ซ๐ข๐๐ค๐ฒ
Time-based analysis is everywhere: trends, cohorts, seasonality, etc. Get familiar with functions like DATEADD, DATEDIFF, DATE_TRUNC, and DATEPART to work confidently with time series data.
9. ๐๐๐ฅ๐-๐๐จ๐ข๐ง๐ฌ & ๐๐๐๐ฎ๐ซ๐ฌ๐ข๐ฏ๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ ๐๐จ๐ซ ๐๐ข๐๐ซ๐๐ซ๐๐ก๐ข๐๐ฌ
Whether it's org charts or product categories, not all data is flat. Learn how to join a table to itself or use recursive CTEs to navigate parent-child relationships effectively.
You donโt need to memorize 100 functions. You need to understand 10 really well and apply them smartly. These are the concepts I keep going back to not just in interviews, but in the real world where clarity, performance, and logic matter most.
1. ๐๐ซ๐๐๐ค ๐๐ญ ๐๐จ๐ฐ๐ง ๐ฐ๐ข๐ญ๐ก ๐๐๐๐ฌ (๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐๐๐ฅ๐ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ)
Ever worked on a query that became an unreadable monster? CTEs let you break that down into logical steps. You can treat them like temporary views โ great for simplifying logic and improving collaboration across your team.
2. ๐๐ฌ๐ ๐๐ข๐ง๐๐จ๐ฐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ
Forget the mess of subqueries. With functions like ROW_NUMBER(), RANK(), LEAD() and LAG(), you can compare rows, rank items, or calculate running totals โ all within the same query. Total
3. ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ (๐๐๐ฌ๐ญ๐๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ)
Yes, they're old school, but nested subqueries are still powerful. Use them when you want to filter based on results of another query or isolate logic step-by-step before joining with the big picture.
4. ๐๐ง๐๐๐ฑ๐๐ฌ & ๐๐ฎ๐๐ซ๐ฒ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง
Query taking forever? Look at your indexes. Index the columns you use in JOINs, WHERE, and GROUP BY. Even basic knowledge of how the SQL engine reads data can take your skills up a notch.
5. ๐๐จ๐ข๐ง๐ฌ ๐ฏ๐ฌ. ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ
Joins are usually faster and better for combining large datasets. Subqueries, on the other hand, are cleaner when doing one-off filters or smaller operations. Choose wisely based on the context.
6. ๐๐๐๐ ๐๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ๐ฌ:
Want to categorize or bucket data without creating a separate table? Use CASE. Itโs ideal for conditional logic, custom labels, and grouping in a single query.
7. ๐๐ ๐ ๐ซ๐๐ ๐๐ญ๐ข๐จ๐ง๐ฌ & ๐๐๐๐๐ ๐๐
Most analytics questions start with "how many", "whatโs the average", or "which is the highest?". SUM(), COUNT(), AVG(), etc., and pair them with GROUP BY to drive insights that matter.
8. ๐๐๐ญ๐๐ฌ ๐๐ซ๐ ๐๐ฅ๐ฐ๐๐ฒ๐ฌ ๐๐ซ๐ข๐๐ค๐ฒ
Time-based analysis is everywhere: trends, cohorts, seasonality, etc. Get familiar with functions like DATEADD, DATEDIFF, DATE_TRUNC, and DATEPART to work confidently with time series data.
9. ๐๐๐ฅ๐-๐๐จ๐ข๐ง๐ฌ & ๐๐๐๐ฎ๐ซ๐ฌ๐ข๐ฏ๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ ๐๐จ๐ซ ๐๐ข๐๐ซ๐๐ซ๐๐ก๐ข๐๐ฌ
Whether it's org charts or product categories, not all data is flat. Learn how to join a table to itself or use recursive CTEs to navigate parent-child relationships effectively.
You donโt need to memorize 100 functions. You need to understand 10 really well and apply them smartly. These are the concepts I keep going back to not just in interviews, but in the real world where clarity, performance, and logic matter most.
โค6๐6
Essential Skills Excel for Data Analysts ๐
1๏ธโฃ Data Cleaning & Transformation
Remove Duplicates โ Ensure unique records.
Find & Replace โ Quick data modifications.
Text Functions โ TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation โ Restrict input values.
2๏ธโฃ Data Analysis & Manipulation
Sorting & Filtering โ Organize and extract key insights.
Conditional Formatting โ Highlight trends, outliers.
Pivot Tables โ Summarize large datasets efficiently.
Power Query โ Automate data transformation.
3๏ธโฃ Essential Formulas & Functions
Lookup Functions โ VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions โ IF, AND, OR, IFERROR, IFS.
Aggregation Functions โ SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions โ CONCATENATE, TEXTJOIN, SUBSTITUTE.
4๏ธโฃ Data Visualization
Charts & Graphs โ Bar, Line, Pie, Scatter, Histogram.
Sparklines โ Miniature charts inside cells.
Conditional Formatting โ Color scales, data bars.
Dashboard Creation โ Interactive and dynamic reports.
5๏ธโฃ Advanced Excel Techniques
Array Formulas โ Dynamic calculations with multiple values.
Power Pivot & DAX โ Advanced data modeling.
What-If Analysis โ Goal Seek, Scenario Manager.
Macros & VBA โ Automate repetitive tasks.
6๏ธโฃ Data Import & Export
CSV & TXT Files โ Import and clean raw data.
Power Query โ Connect to databases, web sources.
Exporting Reports โ PDF, CSV, Excel formats.
Here you can find some free Excel books & useful resources: https://t.iss.one/excel_data
Hope it helps :)
#dataanalyst
1๏ธโฃ Data Cleaning & Transformation
Remove Duplicates โ Ensure unique records.
Find & Replace โ Quick data modifications.
Text Functions โ TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation โ Restrict input values.
2๏ธโฃ Data Analysis & Manipulation
Sorting & Filtering โ Organize and extract key insights.
Conditional Formatting โ Highlight trends, outliers.
Pivot Tables โ Summarize large datasets efficiently.
Power Query โ Automate data transformation.
3๏ธโฃ Essential Formulas & Functions
Lookup Functions โ VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions โ IF, AND, OR, IFERROR, IFS.
Aggregation Functions โ SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions โ CONCATENATE, TEXTJOIN, SUBSTITUTE.
4๏ธโฃ Data Visualization
Charts & Graphs โ Bar, Line, Pie, Scatter, Histogram.
Sparklines โ Miniature charts inside cells.
Conditional Formatting โ Color scales, data bars.
Dashboard Creation โ Interactive and dynamic reports.
5๏ธโฃ Advanced Excel Techniques
Array Formulas โ Dynamic calculations with multiple values.
Power Pivot & DAX โ Advanced data modeling.
What-If Analysis โ Goal Seek, Scenario Manager.
Macros & VBA โ Automate repetitive tasks.
6๏ธโฃ Data Import & Export
CSV & TXT Files โ Import and clean raw data.
Power Query โ Connect to databases, web sources.
Exporting Reports โ PDF, CSV, Excel formats.
Here you can find some free Excel books & useful resources: https://t.iss.one/excel_data
Hope it helps :)
#dataanalyst
โค10
๐๐จ๐ฐ ๐ญ๐จ ๐๐ซ๐๐ฉ๐๐ซ๐ ๐ญ๐จ ๐๐๐๐จ๐ฆ๐ ๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ
๐. ๐๐ฑ๐๐๐ฅ- Learn formulas, Pivot tables, Lookup, VBA Macros.
๐. ๐๐๐- Joins, Windows, CTE is the most important
๐. ๐๐จ๐ฐ๐๐ซ ๐๐- Power Query Editor(PQE), DAX, MCode, RLS
๐. ๐๐ฒ๐ญ๐ก๐จ๐ง- Basics & Libraries(mainly pandas, numpy, matplotlib and seaborn libraries)
5. Practice SQL and Python questions on platforms like ๐๐๐๐ค๐๐ซ๐๐๐ง๐ค or ๐๐๐๐๐ก๐จ๐จ๐ฅ๐ฌ.
6. Know the basics of descriptive statistics(mean, median, mode, Probability, normal, binomial, Poisson distributions etc).
7. Learn to use ๐๐/๐๐จ๐ฉ๐ข๐ฅ๐จ๐ญ ๐ญ๐จ๐จ๐ฅ๐ฌ like GitHub Copilot or Power BI's AI features to automate tasks, generate insights, and improve your projects(Most demanding in Companies now)
8. Get hands-on experience with one cloud platform: ๐๐ณ๐ฎ๐ซ๐, ๐๐๐, ๐จ๐ซ ๐๐๐
9. Work on at least two end-to-end projects.
10. Prepare an ATS-friendly resume and start applying for jobs.
11. Prepare for interviews by going through common interview questions on Google and YouTube.
I have curated top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
๐. ๐๐ฑ๐๐๐ฅ- Learn formulas, Pivot tables, Lookup, VBA Macros.
๐. ๐๐๐- Joins, Windows, CTE is the most important
๐. ๐๐จ๐ฐ๐๐ซ ๐๐- Power Query Editor(PQE), DAX, MCode, RLS
๐. ๐๐ฒ๐ญ๐ก๐จ๐ง- Basics & Libraries(mainly pandas, numpy, matplotlib and seaborn libraries)
5. Practice SQL and Python questions on platforms like ๐๐๐๐ค๐๐ซ๐๐๐ง๐ค or ๐๐๐๐๐ก๐จ๐จ๐ฅ๐ฌ.
6. Know the basics of descriptive statistics(mean, median, mode, Probability, normal, binomial, Poisson distributions etc).
7. Learn to use ๐๐/๐๐จ๐ฉ๐ข๐ฅ๐จ๐ญ ๐ญ๐จ๐จ๐ฅ๐ฌ like GitHub Copilot or Power BI's AI features to automate tasks, generate insights, and improve your projects(Most demanding in Companies now)
8. Get hands-on experience with one cloud platform: ๐๐ณ๐ฎ๐ซ๐, ๐๐๐, ๐จ๐ซ ๐๐๐
9. Work on at least two end-to-end projects.
10. Prepare an ATS-friendly resume and start applying for jobs.
11. Prepare for interviews by going through common interview questions on Google and YouTube.
I have curated top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
โค6
Roadmap to become a Data Analyst:
๐ Learn Excel
โ๐ Learn SQL
โ๐ Learn Python
โ๐ Learn Power BI / Tableau
โ๐ Learn Statistics & Probability
โ๐ Learn Data Transformation
โ๐ Learn Machine Learning Basics
โ๐ Build Projects & Portfolio
โโ Apply for Job
React โค๏ธ for More ๐
๐ Learn Excel
โ๐ Learn SQL
โ๐ Learn Python
โ๐ Learn Power BI / Tableau
โ๐ Learn Statistics & Probability
โ๐ Learn Data Transformation
โ๐ Learn Machine Learning Basics
โ๐ Build Projects & Portfolio
โโ Apply for Job
React โค๏ธ for More ๐
โค21