✅ 📚 Python Libraries You Should Know

1. NumPy – Numerical computing
- Arrays, matrices, broadcasting
- Fast operations on large datasets
- Useful in data science & ML

2. Pandas – Data analysis & manipulation
- DataFrames and Series
- Reading/writing CSV, Excel
- GroupBy, filtering, merging

3. Matplotlib – Data visualization
- Line, bar, pie, scatter plots
- Custom styling & labels
- Save plots as images

4. Seaborn – Statistical plotting
- Built on Matplotlib
- Heatmaps, histograms, violin plots
- Great for EDA

5. Requests – HTTP library
- Make GET, POST requests
- Send headers, params, and JSON
- Used in web scraping and APIs

6. BeautifulSoup – Web scraping
- Parse HTML/XML easily
- Find elements using tags, class
- Navigate and extract data

7. Flask – Web development microframework
- Lightweight and fast
- Routes, templates, API building
- Great for small to medium apps

8. Django – High-level web framework
- Full-stack: ORM, templates, auth
- Scalable and secure
- Ideal for production-ready apps

9. SQLAlchemy – ORM for databases
- Abstract SQL queries in Python
- Connect to SQLite, PostgreSQL, etc.
- Schema creation & query chaining

10. Pytest – Testing framework
- Simple syntax for test cases
- Fixtures, asserts, mocking
- Supports plugins

11. Scikit-learn – Machine Learning
- Preprocessing, classification, regression
- Train/test split, pipelines
- Built on NumPy & Pandas

12. TensorFlow / PyTorch – Deep learning
- Neural networks, backpropagation
- GPU support
- Used in real AI projects

13. OpenCV – Computer vision
- Image processing, face detection
- Filters, contours, image transformations
- Real-time video analysis

14. Tkinter – GUI development
- Build desktop apps
- Buttons, labels, input fields
- Easy drag-and-drop interface

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🔹 Top 10 SQL Functions/Commands Commonly Used in Data Analysis 📊

1️⃣ SELECT
– Used to retrieve specific columns from a table.
SELECT name, age FROM users;

2️⃣ WHERE
– Filters rows based on a condition.
SELECT × FROM sales WHERE region = 'North';

3️⃣ GROUP BY
– Groups rows that have the same values into summary rows.
SELECT region, SUM(sales) FROM sales GROUP BY region;

4️⃣ ORDER BY
– Sorts the result by one or more columns.
SELECT * FROM customers ORDER BY created_at DESC;

5️⃣ JOIN
– Combines rows from two or more tables based on a related column.
SELECT a.name, b.salary
FROM employees a
JOIN salaries b ON a.id = b.emp_id;

6️⃣ COUNT() / SUM() / AVG() / MIN() / MAX()
– Common aggregate functions for metrics and summaries.
SELECT COUNT(×) FROM orders WHERE status = 'completed';

7️⃣ HAVING
– Filters after a GROUP BY (unlike WHERE, which filters before).
SELECT department, COUNT() FROM employees GROUP BY department HAVING COUNT() > 10;

8️⃣ LIMIT
– Restricts number of rows returned.
SELECT * FROM products LIMIT 5;

9️⃣ CASE
– Implements conditional logic in queries.
SELECT name,
CASE
WHEN score >= 90 THEN 'A'
WHEN score >= 75 THEN 'B'
ELSE 'C'
END AS grade
FROM students;

🔟 DATE functions (NOW(), DATE_PART(), DATEDIFF(), etc.)
– Handle and extract info from dates.
SELECT DATE_PART('year', order_date) FROM orders;

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Quick Recap of Essential Python Concepts 😄👇

Python is a versatile and beginner-friendly programming language widely used in data science, web development, and automation. Here's a quick overview of some fundamental concepts:

1.  Variables:
    *   Variables are used to store data values. They are assigned using the = operator.  Example: x = 10, name = "Alice"

2.  Data Types:
    *   Python has several built-in data types:
        *   Integer (int): Whole numbers (e.g., 1, -5).
        *   Float (float): Decimal numbers (e.g., 3.14, -2.5).
        *   String (str): Textual data (e.g., "Hello", 'Python').
        *   Boolean (bool): True or False values.
        *   List: Ordered collection of items (e.g., [1, 2, "apple"]).
        *   Tuple: Ordered, immutable collection (e.g., (1, 2, "apple")).
        *   Dictionary: Key-value pairs (e.g., {"name": "Alice", "age": 30}).

3.  Operators:
    *   Python supports various operators for performing operations:
        *   Arithmetic Operators: +, -, *, /, // (floor division), % (modulus), * (exponentiation).
        *   Comparison Operators: ==, !=, >, <, >=, <=.
        *   Logical Operators: and, or, not.
        *   Assignment Operators: =, +=, -=, *=, /=, etc.

4.  Control Flow:
    *   Control flow statements determine the order in which code is executed:
        *   if, elif, else: Conditional execution.
        *   for loop: Iterating over a sequence (list, string, etc.).
        *   while loop: Repeating a block of code as long as a condition is true.

5.  Functions:
    *   Functions are reusable blocks of code defined using the def keyword.

        def greet(name):
            print("Hello, " + name + "!")
        greet("Bob")  # Output: Hello, Bob!
        

6.  Lists:
    *   Lists are ordered, mutable (changeable) collections.
    *   Create: my_list = [1, 2, 3, "a"]
    *   Access: my_list[0] (first element)
    *   Modify: my_list.append(4), my_list.remove(2)

7.  Dictionaries:
    *   Dictionaries store key-value pairs.
    *   Create: my_dict = {"name": "Alice", "age": 30}
    *   Access: my_dict["name"] (gets "Alice")
    *   Modify: my_dict["city"] = "New York"

8.  Loops:
    *  For Loops:

        my_list = [1, 2, 3]
        for item in my_list:
            print(item)
        

*   While Loops:

        count = 0
        while count < 5:
            print(count)
            count += 1
        

9.  String Manipulation:
    *   Slicing: my_string[1:4] (extracts a portion of the string)
    *   Concatenation: "Hello" + " " + "World"
    *   Useful Methods: .upper(), .lower(), .strip(), .replace(), .split()

10. Modules and Libraries:
    *   import statement is used to include code from external modules (libraries).
    *   Example:

        import math
        print(math.sqrt(16))  # Output: 4.0
        

Python Programming Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

Hope it helps :)

It takes time to learn Excel.

It takes time to master SQL.

It takes time to understand Power BI.

It takes time to analyze complex datasets.

It takes time to create impactful dashboards.

It takes time to work on real-world data projects.

It takes time to build a strong LinkedIn profile.

It takes time to prepare for technical and behavioral interviews.

Here’s one tip from someone who’s been through it all:

Be Patient. Good things take time ☺️

Keep building your skills and showcasing your value. Your time will come!

Tableau Cheat Sheet ✅

This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting to Data
   - Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).

2. Data Preparation
   - Data Interpreter: Clean data automatically using the Data Interpreter.
   - Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
   - Union Data: Stack data from multiple tables with the same structure.

3. Creating Views
   - Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
   - Show Me: Use the *Show Me* panel to select different visualization types.

4. Types of Visualizations
   - Bar Chart: Compare values across categories.
   - Line Chart: Display trends over time.
   - Pie Chart: Show proportions of a whole (use sparingly).
   - Map: Visualize geographic data.
   - Scatter Plot: Show relationships between two variables.

5. Filters
   - Dimension Filters: Filter data based on categorical values.
   - Measure Filters: Filter data based on numerical values.
   - Context Filters: Set a context for other filters to improve performance.

6. Calculated Fields
   - Create calculated fields to derive new data:
     - Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales])

7. Parameters
   - Use parameters to allow user input and control measures dynamically.

8. Formatting
   - Format fonts, colors, borders, and lines using the Format pane for better visual appeal.

9. Dashboards
   - Combine multiple sheets into a dashboard using the *Dashboard* tab.
   - Use dashboard actions (filter, highlight, URL) to create interactivity.

10. Story Points
    - Create a story to guide users through insights with narrative and visualizations.

11. Publishing & Sharing
    - Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.

12. Export Options
    - Export to PDF or image for offline use.

13. Keyboard Shortcuts
    - Show/Hide Sidebar: Ctrl+Alt+T
    - Duplicate Sheet: Ctrl + D
    - Undo: Ctrl + Z
    - Redo: Ctrl + Y

14. Performance Optimization
    - Use extracts instead of live connections for faster performance.
    - Optimize calculations and filters to improve dashboard loading times.

Best Resources to learn Tableau: https://whatsapp.com/channel/0029VasYW1V5kg6z4EHOHG1t

Hope you'll like it

If I Were to Start My Data Science Career from Scratch, Here's What I Would Do 👇

1️⃣ Master Advanced SQL

Foundations: Learn database structures, tables, and relationships.

Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.

Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.

JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.

Advanced Concepts: CTEs, window functions, and query optimization.

Metric Development: Build and report metrics effectively.


2️⃣ Study Statistics & A/B Testing

Denoscriptive Statistics: Know your mean, median, mode, and standard deviation.

Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.

Probability: Understand basic probability and Bayes' theorem.

Intro to ML: Start with linear regression, decision trees, and K-means clustering.

Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.

A/B Testing: Design experiments—hypothesis formation, sample size calculation, and sample biases.


3️⃣ Learn Python for Data

Data Manipulation: Use pandas for data cleaning and manipulation.

Data Visualization: Explore matplotlib and seaborn for creating visualizations.

Hypothesis Testing: Dive into scipy for statistical testing.

Basic Modeling: Practice building models with scikit-learn.


4️⃣ Develop Product Sense

Product Management Basics: Manage projects and understand the product life cycle.

Data-Driven Strategy: Leverage data to inform decisions and measure success.

Metrics in Business: Define and evaluate metrics that matter to the business.


5️⃣ Hone Soft Skills

Communication: Clearly explain data findings to technical and non-technical audiences.

Collaboration: Work effectively in teams.

Time Management: Prioritize and manage projects efficiently.

Self-Reflection: Regularly assess and improve your skills.


6️⃣ Bonus: Basic Data Engineering

Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.

ETL: Set up extraction jobs, manage dependencies, clean and validate data.

Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.

I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

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Essential Python and SQL topics for data analysts 😄👇

Python Topics:

Python Resources - @pythonanalyst

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
   - Denoscriptive 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:

SQL Resources - @sqlanalyst

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!

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

Top 10 SQL statements & functions used for data analysis

SELECT: To retrieve data from a database.
FROM: To specify the table or tables from which to retrieve data.
WHERE: To filter data based on specified conditions.
GROUP BY: To group rows with similar values into summary rows.
HAVING: To filter grouped data based on conditions.
ORDER BY: To sort the result set by one or more columns.
COUNT(): To count the number of rows or non-null values in a column.
SUM(): To calculate the sum of values in a numeric column.
AVG(): To calculate the average of values in a numeric column.
JOIN: To combine data from multiple tables based on a related column.

These SQL statements and functions are fundamental for data analysis and querying relational databases effectively.

Hope it helps :)

To effectively learn SQL for a Data Analyst role, follow these steps:

1. Start with a basic course: Begin by taking a basic course on YouTube to familiarize yourself with SQL syntax and terminologies. I recommend the "Learn Complete SQL" playlist from the "techTFQ" YouTube channel.

2. Practice syntax and commands: As you learn new terminologies from the course, practice their syntax on the "w3schools" website. This site provides clear examples of SQL syntax, commands, and functions.

3. Solve practice questions: After completing the initial steps, start solving easy-level SQL practice questions on platforms like "Hackerrank," "Leetcode," "Datalemur," and "Stratascratch." If you get stuck, use the discussion forums on these platforms or ask ChatGPT for help. You can paste the problem into ChatGPT and use a prompt like:
- "Explain the step-by-step solution to the above problem as I am new to SQL, also explain the solution as per the order of execution of SQL."

4. Gradually increase difficulty: Gradually move on to more difficult practice questions. If you encounter new SQL concepts, watch YouTube videos on those topics or ask ChatGPT for explanations.

5. Consistent practice: The most crucial aspect of learning SQL is consistent practice. Regular practice will help you build and solidify your skills.

By following these steps and maintaining regular practice, you'll be well on your way to mastering SQL for a Data Analyst role.

🚀 Essential Python/ Pandas snippets to explore data:
 
1.   .head() - Review top rows
2.   .tail() - Review bottom rows
3.   .info() - Summary of DataFrame
4.   .shape - Shape of DataFrame
5.   .describe() - Denoscriptive stats
6.   .isnull().sum() - Check missing values
7.   .dtypes - Data types of columns
8.   .unique() - Unique values in a column
9.   .nunique() - Count unique values
10.   .value_counts() - Value counts in a column
11.   .corr() - Correlation matrix