How much Statistics must I know to become a Data Scientist?

This is one of the most common questions

Here are the must-know Statistics concepts every Data Scientist should know:

𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆

↗️ Bayes' Theorem & conditional probability
↗️ Permutations & combinations
↗️ Card & die roll problem-solving

𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀

↗️ Mean, median, mode
↗️ Standard deviation and variance
↗️  Bernoulli's, Binomial, Normal, Uniform, Exponential distributions

𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀

↗️ A/B experimentation
↗️ T-test, Z-test, Chi-squared tests
↗️ Type 1 & 2 errors
↗️ Sampling techniques & biases
↗️ Confidence intervals & p-values
↗️ Central Limit Theorem
↗️ Causal inference techniques

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴

↗️ Logistic & Linear regression
↗️ Decision trees & random forests
↗️ Clustering models
↗️ Feature engineering
↗️ Feature selection methods
↗️ Model testing & validation
↗️ Time series analysis

Math & Statistics: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

Data Scientist Roadmap 📈

📂 Python Basics
∟📂 Numpy & Pandas
 ∟📂 Data Cleaning
  ∟📂 Data Visualization (Seaborn, Plotly)
   ∟📂 Statistics & Probability
    ∟📂 Machine Learning (Sklearn)
     ∟📂 Deep Learning (TensorFlow / PyTorch)
      ∟📂 Model Deployment
       ∟📂 Real-World Projects
        ∟✅ Apply for Data Science Roles

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SQL Basics for Beginners: Must-Know Concepts

1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.

2. SQL Syntax
SQL is written using statements, which consist of keywords like SELECT, FROM, WHERE, etc., to perform operations on the data.
- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g., SELECT, FROM).

3. SQL Data Types
Databases store data in different formats. The most common data types are:
- INT (Integer): For whole numbers.
- VARCHAR(n) or TEXT: For storing text data.
- DATE: For dates.
- DECIMAL: For precise decimal values, often used in financial calculations.

4. Basic SQL Queries
Here are some fundamental SQL operations:

- SELECT Statement: Used to retrieve data from a database.

     SELECT column1, column2 FROM table_name;
     

- WHERE Clause: Filters data based on conditions.

     SELECT * FROM table_name WHERE condition;
     

- ORDER BY: Sorts data in ascending (ASC) or descending (DESC) order.

     SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
     

- LIMIT: Limits the number of rows returned.

     SELECT * FROM table_name LIMIT 5;
     

5. Filtering Data with WHERE Clause
The WHERE clause helps you filter data based on a condition:

   SELECT * FROM employees WHERE salary > 50000;
   

You can use comparison operators like:
- =: Equal to
- >: Greater than
- <: Less than
- LIKE: For pattern matching

6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.

     SELECT COUNT(*) FROM table_name;
     

- SUM(): Adds up values in a column.

     SELECT SUM(salary) FROM employees;
     

- AVG(): Calculates the average value.

     SELECT AVG(salary) FROM employees;
     

- GROUP BY: Groups rows that have the same values into summary rows.

     SELECT department, AVG(salary) FROM employees GROUP BY department;
     

7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.

     SELECT employees.name, departments.department
     FROM employees
     INNER JOIN departments
     ON employees.department_id = departments.id;
     

- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.

     SELECT employees.name, departments.department
     FROM employees
     LEFT JOIN departments
     ON employees.department_id = departments.id;
     

8. Inserting Data
To add new data to a table, you use the INSERT INTO statement:

   INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
   

9. Updating Data
You can update existing data in a table using the UPDATE statement:

   UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
   

10. Deleting Data
To remove data from a table, use the DELETE statement:

    DELETE FROM employees WHERE name = 'John Doe';
    

Here you can find essential SQL Interview Resources👇
https://t.iss.one/DataSimplifier

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SQL Checklist for Data Analysts 🚀

🌱 Getting Started with SQL

👉 Install SQL database software (MySQL, PostgreSQL, or SQL Server)
👉 Set up your database environment and connect to your data

🔍 Load & Explore Data

👉 Understand tables, rows, and columns
👉 Use SELECT to retrieve data and LIMIT to get a sample view
👉 Explore schema and table structure with DESCRIBE or SHOW COLUMNS

🧹 Data Filtering Essentials

👉 Filter data using WHERE clauses
👉 Use comparison operators (=, >, <) and logical operators (AND, OR)
👉 Handle NULL values with IS NULL and IS NOT NULL

🔄 Transforming Data

👉 Sort data with ORDER BY
👉 Create calculated columns with AS and use arithmetic operators (+, -, *, /)
👉 Use CASE WHEN for conditional expressions

📊 Aggregation & Grouping

👉 Summarize data with aggregation functions: SUM, COUNT, AVG, MIN, MAX
👉 Group data with GROUP BY and filter groups with HAVING

🔗 Mastering Joins

👉 Combine tables with JOIN (INNER, LEFT, RIGHT, FULL OUTER)
👉 Understand primary and foreign keys to create meaningful joins
👉 Use SELF JOIN for analyzing data within the same table

📅 Date & Time Data

👉 Convert dates and extract parts (year, month, day) with EXTRACT
👉 Perform time-based analysis using DATEDIFF and date functions

📈 Quick Exploratory Analysis

👉 Calculate statistics to understand data distributions
👉 Use GROUP BY with aggregation for category-based analysis

📉 Basic Data Visualizations (Optional)

👉 Integrate SQL with visualization tools (Power BI, Tableau)
👉 Create charts directly in SQL with certain extensions (like MySQL's built-in charts)

💪 Advanced Query Handling

👉 Master subqueries and nested queries
👉 Use WITH (Common Table Expressions) for complex queries
👉 Window functions for running totals, moving averages, and rankings (ROW_NUMBER, RANK, LAG, LEAD)

🚀 Optimize for Performance

👉 Index critical columns for faster querying
👉 Analyze query plans and use optimizations
👉 Limit result sets and avoid excessive joins for efficiency

📂 Practice Projects

👉 Use real datasets to perform SQL analysis
👉 Create a portfolio with case studies and projects

Here you can find SQL Interview Resources👇
https://t.iss.one/DataSimplifier

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𝗛𝗼𝘄 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗢𝘃𝗲𝗿𝘄𝗵𝗲𝗹𝗺𝗲𝗱!)🧠

Let’s be honest:
SQL seems simple… until JOINs, Subqueries, and Window Functions come crashing in.

But mastering SQL doesn’t have to be hard.

You just need the right roadmap—and that’s exactly what this is.

Here’s a 5-step SQL journey to go from beginner to job-ready analyst👇

🔹 𝗦𝘁𝗲𝗽 𝟭: Nail the Basics (Learn to Think in SQL)

Start with the foundations:

✅ SELECT, WHERE, ORDER BY
✅ DISTINCT, LIMIT, BETWEEN, LIKE
✅ COUNT, SUM, AVG, MIN, MAX

Practice with small tables to build confidence.

Use platforms like:
➡️ W3Schools
➡️ Modesql
➡️ LeetCode (easy problems)

🔹 𝗦𝘁𝗲𝗽 𝟮: Understand GROUP BY and Aggregations (The Analyst’s Superpower)

This is where real-world queries begin. Learn:

✅ GROUP BY + HAVING
✅ Combining GROUP BY with COUNT/AVG
✅ Filtering aggregated data

Example:
"Find top 5 cities with the highest total sales in 2023"
That’s GROUP BY magic.

🔹 𝗦𝘁𝗲𝗽 𝟯: MASTER JOINS (Stop Getting Confused)
JOINS scare a lot of people. But they’re just pattern-matching across tables.

Learn one by one:
✅ INNER JOIN
✅ LEFT JOIN
✅ RIGHT JOIN
✅ FULL OUTER JOIN
✅ SELF JOIN
✅ CROSS JOIN (rare, but good to know)
Visualize them using Venn diagrams or draw sample tables—it helps!

🔹 𝗦𝘁𝗲𝗽 𝟰: Learn Subqueries and CTEs (Write Cleaner, Powerful SQL)

✅ Subqueries: Query inside another query
✅ CTEs (WITH clause): Cleaner and reusable queries
✅ Use them to break down complex problems

CTEs = the secret sauce to writing queries recruiters love.

🔹 𝗦𝘁𝗲𝗽 𝟱: Level Up with Window Functions (Your Entry into Advanced SQL)

If you want to stand out, this is it:

✅ ROW_NUMBER(), RANK(), DENSE_RANK()
✅ LAG(), LEAD(), NTILE()
✅ PARTITION BY and ORDER BY combo

Use these to:
➡️ Find top N per group
➡️ Track user behavior over time
➡️ Do cohort analysis

You don’t need 100 LeetCode problems.

You need 10 real-world queries done deeply.

Keep it simple. Keep it useful.

Roadmap to become Data Scientist

Top 20 #SQL INTERVIEW QUESTIONS

1️⃣ Explain Order of Execution of SQL query
2️⃣ Provide a use case for each of the functions Rank, Dense_Rank & Row_Number ( 💡 majority struggle )
3️⃣ Write a query to find the cumulative sum/Running Total
4️⃣ Find the Most selling product by sales/ highest Salary of employees
5️⃣ Write a query to find the 2nd/nth highest Salary of employees
6️⃣ Difference between union vs union all
7️⃣ Identify if there any duplicates in a table
8️⃣ Scenario based Joins question, understanding of Inner, Left and Outer Joins via simple yet tricky question
9️⃣ LAG, write a query to find all those records where the transaction value is greater then previous transaction value
1️⃣ 0️⃣ Rank vs Dense Rank, query to find the 2nd highest Salary of employee
( Ideal soln should handle ties)
1️⃣ 1️⃣ Write a query to find the Running Difference (Ideal sol'n using windows function)
1️⃣ 2️⃣ Write a query to display year on year/month on month growth
1️⃣ 3️⃣ Write a query to find rolling average of daily sign-ups
1️⃣ 4️⃣ Write a query to find the running difference using self join (helps in understanding the logical approach, ideally this question is solved via windows function)
1️⃣ 5️⃣ Write a query to find the cumulative sum using self join
(you can use windows function to solve this question)
1️⃣6️⃣ Differentiate between a clustered index and a non-clustered index?
1️⃣7️⃣ What is a Candidate key?
1️⃣8️⃣What is difference between Primary key and Unique key?
1️⃣9️⃣What's the difference between RANK & DENSE_RANK in SQL?
2️⃣0️⃣ Whats the difference between LAG & LEAD in SQL?

Access SQL Learning Series for Free: https://t.iss.one/sqlspecialist/523

Hope it helps :)

5 Key SQL Aggregate Functions for data analyst

🍞SUM(): Adds up all the values in a numeric column.

🍞AVG(): Calculates the average of a numeric column.

🍞COUNT(): Counts the total number of rows or non-NULL values in a column.

🍞MAX(): Returns the highest value in a column.

🍞MIN(): Returns the lowest value in a column.

SQL vs Mysql

Want to become a Data Scientist?

Here’s 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 👍👍