🚦Top 10 Data Science Tools🚦

Here we will examine the top best Data Science tools that are utilized generally by data researchers and analysts. But prior to beginning let us discuss about what is Data Science.

🛰What is Data Science ?

Data science is a quickly developing field that includes the utilization of logical strategies, calculations, and frameworks to extract experiences and information from organized and unstructured data .

🗽Top Data Science Tools that are normally utilized :

1.) Jupyter Notebook : Jupyter Notebook is an open-source web application that permits clients to make and share archives that contain live code, conditions, representations, and narrative text .

2.) Keras : Keras is a famous open-source brain network library utilized in data science. It is known for its usability and adaptability.
Keras provides a range of tools and techniques for dealing with common data science problems, such as overfitting, underfitting, and regularization.

3.) PyTorch : PyTorch is one more famous open-source AI library utilized in information science. PyTorch also offers easy-to-use interfaces for various tasks such as data loading, model building, training, and deployment, making it accessible to beginners as well as experts in the field of machine learning.

4.) TensorFlow : TensorFlow allows data researchers to play out an extensive variety of AI errands, for example, image recognition , natural language processing , and deep learning.

5.) Spark : Spark allows data researchers to perform data processing tasks like data control, investigation, and machine learning , rapidly and effectively.

6.) Hadoop : Hadoop provides a distributed file system (HDFS) and a distributed processing framework (MapReduce) that permits data researchers to handle enormous datasets rapidly.

7.) Tableau : Tableau is a strong data representation tool that permits data researchers to make intuitive dashboards and perceptions. Tableau allows users to combine multiple charts.

8.) SQL : SQL (Structured Query Language) SQL permits data researchers to perform complex queries , join tables, and aggregate data, making it simple to extricate bits of knowledge from enormous datasets. It is a powerful tool for data management, especially for large datasets.

9.) Power BI : Power BI is a business examination tool that conveys experiences and permits clients to make intuitive representations and reports without any problem.

10.) Excel : Excel is a spreadsheet program that broadly utilized in data science. It is an amazing asset for information the board, examination, and visualization .Excel can be used to explore the data by creating pivot tables, histograms, scatterplots, and other types of visualizations.

How to become a Data Analyst in 2025

7 Must-Have Tools for Data Analysts in 2025:

✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.

Perfect blend of code, visuals, and storytelling.

React with ❤️ for free tutorials on each tool

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Data Analyst interview questions 👇

Excel:
1. Explain the difference between the "COUNT", "COUNTA", "COUNTIF", and "COUNTIFS" functions in Excel. When would you use each of these functions, and provide examples?
2. How do you create a pivot chart in Excel, and what are some advantages of using pivot charts for data visualization?
3. Describe the purpose and usage of Excel's "Solver" tool. Can you provide an example of a problem you could solve using the Solver tool?
4. How would you use Excel's "Data Validation" feature to ensure data integrity in a spreadsheet? Provide examples of different types of data validation rules you might implement.
5. What are Excel tables, and how do they differ from regular data ranges? What advantages do tables offer in terms of data management and analysis?

SQL:
1. Discuss the concept of data aggregation in SQL. How do you use aggregate functions such as SUM, AVG, MIN, and MAX to summarize data in a query?
2. Explain the difference between a primary key and a foreign key in SQL. Why are these constraints important in database design?
3. How do you handle duplicates in a SQL query result? Can you demonstrate how to remove duplicates using the DISTINCT keyword or other techniques?
4. Describe the purpose and benefits of using stored procedures in SQL databases. Provide an example of a scenario where you would use a stored procedure.
5. What is SQL injection, and how can you prevent it in your SQL queries or applications? Discuss best practices for writing secure SQL code.

Power BI:
1. How does Power BI handle data refresh and scheduling for reports and dashboards? What options are available for configuring data refresh settings?
2. Describe the concept of row-level security in Power BI. How can you implement row-level security to restrict access to specific data based on user roles or permissions?
3. What is the Power Query Editor in Power BI, and how do you use it to transform and clean data imported from different sources?
4. Discuss the benefits of using Power BI's Direct Query mode versus Import mode for connecting to data sources. When would you choose one mode over the other?
5. How do you share reports and dashboards with other users in Power BI? What options are available for distributing and collaborating on Power BI content within an organization?

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Like if it helps :)

How to Think Like a Data Analyst 🧠📊

Being a great data analyst isn’t just about knowing SQL, Python, or Power BI—it’s about how you think.

Here’s how to develop a data-driven mindset:

1️⃣ Always Ask ‘Why?’ 🤔
Don’t just look at numbers—question them. If sales dropped, ask: Is it seasonal? A pricing issue? A marketing failure?

2️⃣ Break Down Problems Logically 🔍
Instead of tackling a problem all at once, divide it into smaller, manageable parts. Example: If customer churn is increasing, analyze trends by segment, region, and time period.

3️⃣ Be Skeptical of Data ⚠️
Not all data is accurate. Always check for missing values, biases, and inconsistencies before drawing conclusions.

4️⃣ Look for Patterns & Trends 📈
Raw numbers don’t tell a story until you find relationships. Compare trends over time, detect anomalies, and identify key influencers.

5️⃣ Keep Business Goals in Mind 🎯
Data without context is useless. Always tie insights to business impact—cost reduction, revenue growth, customer satisfaction, etc.

6️⃣ Simplify Complex Insights ✂️
Not everyone understands data like you do. Use visuals and clear language to explain findings to non-technical audiences.

7️⃣ Be Curious & Experiment 🚀
Try different approaches—A/B testing, new models, or alternative data sources. Experimentation leads to better insights.

8️⃣ Stay Updated & Keep Learning 📚
The best analysts stay ahead by learning new tools, techniques, and industry trends. Follow blogs, take courses, and practice regularly.

Thinking like a data analyst is a skill that improves with experience. Keep questioning, analyzing, and improving! 🔥

React with ❤️ if you agree with me

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

🔍 Real-World Data Analyst Tasks & How to Solve Them

As a Data Analyst, your job isn’t just about writing SQL queries or making dashboards—it’s about solving business problems using data. Let’s explore some common real-world tasks and how you can handle them like a pro!

📌 Task 1: Cleaning Messy Data

Before analyzing data, you need to remove duplicates, handle missing values, and standardize formats.

✅ Solution (Using Pandas in Python):

import pandas as pd  
df = pd.read_csv('sales_data.csv')  
df.drop_duplicates(inplace=True)  # Remove duplicate rows  
df.fillna(0, inplace=True)  # Fill missing values with 0  
print(df.head())

💡 Tip: Always check for inconsistent spellings and incorrect date formats!


📌 Task 2: Analyzing Sales Trends

A company wants to know which months have the highest sales.

✅ Solution (Using SQL):

SELECT MONTH(SaleDate) AS Month, SUM(Quantity * Price) AS Total_Revenue  
FROM Sales  
GROUP BY MONTH(SaleDate)  
ORDER BY Total_Revenue DESC;

💡 Tip: Try adding YEAR(SaleDate) to compare yearly trends!


📌 Task 3: Creating a Business Dashboard

Your manager asks you to create a dashboard showing revenue by region, top-selling products, and monthly growth.

✅ Solution (Using Power BI / Tableau):

👉 Add KPI Cards to show total sales & profit

👉 Use a Line Chart for monthly trends

👉 Create a Bar Chart for top-selling products

👉 Use Filters/Slicers for better interactivity

💡 Tip: Keep your dashboards clean, interactive, and easy to interpret!

Like this post for more content like this ♥️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗻 𝗮 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄?

𝗕𝗮𝘀𝗶𝗰 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻

-Brief introduction about yourself.

-Explanation of how you developed an interest in learning Power BI despite having a chemical background.


𝗧𝗼𝗼𝗹𝘀 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆

-Discussion about the tools you are proficient in.

-Detailed explanation of a project that demonstrated your proficiency in these tools.

𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻

Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project

Follow-up Question:

Was there any improvement in sales after building the report?

Provide a clear before and after scenario in sales post-report creation.

What areas did you identify where the company was losing sales, and what were your recommendations?

- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.

- How do you handle null values? Describe your approach to managing null values in datasets.


𝗦𝗤𝗟 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀

-Explain the order in which SQL clauses are executed.

-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).

-Explain window functions and how to rank values in SQL.

- Difference between JOIN and UNION.

-How to return unique values in SQL.

𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀

-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.

- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.

-Describe cases when you showcased team spirit.

-⭐ 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗔𝗽𝗽 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?

- Rate yourself on Excel, SQL, and Python out of 10.

- What are your strengths in data analytics?

Like if it helps :)

1. What are the different subsets of SQL?

Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions.

2. List the different types of relationships in SQL.

There are different types of relations in the database:
One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many – This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ.

3. What is a Stored Procedure?

A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.

4. What is Pattern Matching in SQL?

SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.

📌 DATA ANALYST INTERVIEW QUESTIONS

Data Analytics Roadmap
|
|-- Fundamentals
|   |-- Mathematics
|   |   |-- Descriptive Statistics
|   |   |-- Inferential Statistics
|   |   |-- Probability Theory
|   |
|   |-- Programming
|   |   |-- Python (Focus on Libraries like Pandas, NumPy)
|   |   |-- R (For Statistical Analysis)
|   |   |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
|   |-- Data Sources
|   |   |-- APIs
|   |   |-- Web Scraping
|   |   |-- Databases
|   |
|   |-- Data Storage
|   |   |-- Relational Databases (MySQL, PostgreSQL)
|   |   |-- NoSQL Databases (MongoDB, Cassandra)
|   |   |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
|   |-- Handling Missing Data
|   |-- Data Transformation
|   |-- Data Normalization and Standardization
|   |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
|   |-- Data Visualization Tools
|   |   |-- Matplotlib
|   |   |-- Seaborn
|   |   |-- ggplot2
|   |
|   |-- Identifying Trends and Patterns
|   |-- Correlation Analysis
|
|-- Advanced Analytics
|   |-- Predictive Analytics (Regression, Forecasting)
|   |-- Prescriptive Analytics (Optimization Models)
|   |-- Segmentation (Clustering Techniques)
|   |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
|   |-- Visualization Tools
|   |   |-- Power BI
|   |   |-- Tableau
|   |   |-- Google Data Studio
|   |
|   |-- Dashboard Design
|   |-- Interactive Visualizations
|   |-- Storytelling with Data
|
|-- Business Intelligence (BI)
|   |-- KPI Design and Implementation
|   |-- Decision-Making Frameworks
|   |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
|   |-- Tools and Frameworks
|   |   |-- Hadoop
|   |   |-- Apache Spark
|   |
|   |-- Real-Time Data Processing
|   |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
|   |-- Industry Applications
|   |   |-- E-commerce
|   |   |-- Healthcare
|   |   |-- Supply Chain
|
|-- Ethical Data Usage
|   |-- Data Privacy Regulations (GDPR, CCPA)
|   |-- Bias Mitigation in Analysis
|   |-- Transparency in Reporting

Free Resources to learn Data Analytics skills👇👇

1. SQL

https://mode.com/sql-tutorial/introduction-to-sql

https://t.iss.one/sqlspecialist/738

2. Python

https://www.learnpython.org/

https://t.iss.one/pythondevelopersindia/873

https://bit.ly/3T7y4ta

https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial

3. R

https://datacamp.pxf.io/vPyB4L

4. Data Structures

https://leetcode.com/study-plan/data-structure/

https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513

5. Data Visualization

https://www.freecodecamp.org/learn/data-visualization/

https://t.iss.one/Data_Visual/2

https://www.tableau.com/learn/training/20223

https://www.workout-wednesday.com/power-bi-challenges/

6. Excel

https://excel-practice-online.com/

https://t.iss.one/excel_data

https://www.w3schools.com/EXCEL/index.php

Join @free4unow_backup for more free courses

Like for more ❤️

ENJOY LEARNING 👍👍

Why learn SQL if ChatGPT can write it?

A few reasons why you should still learn SQL:
1️⃣ An understanding of the nuances of SQL is necessary to ask the Large Language Model (”LLM”) the right questions to get a good response.

2️⃣ You have to double check the LLMs response. Sometimes I get answers that uses features that have been deprecated (probably because the LLM was trained on older data). It still makes mistakes and overcomplicates problems.

3️⃣ Making changes to the query requires an understanding of SQL. Without it, you might get stuck. It's important to understand the query's purpose.

So what do I use these LLMs for?
I find it a good starting point for syntax or query structure. Like “how would I use a window function to get the latest record in a table?” But it doesn’t understand my company’s data models, table relationships, or business logic. This is where my SQL + business knowledge comes in.

Excel: Keyboard Shortcuts

~ Educational Purpose
~ Could be Useful to Someone

Guys, Big Announcement!

We’ve officially hit 5 Lakh followers on WhatsApp and it’s time to level up together! ❤️

I've launched a Python Learning Series — designed for beginners to those preparing for technical interviews or building real-world projects.

This will be a step-by-step journey — from basics to advanced — with real examples and short quizzes after each topic to help you lock in the concepts.

Here’s what we’ll cover in the coming days:

Week 1: Python Fundamentals

- Variables & Data Types

- Operators & Expressions

- Conditional Statements (if, elif, else)

- Loops (for, while)

- Functions & Parameters

- Input/Output & Basic Formatting


Week 2: Core Python Skills

- Lists, Tuples, Sets, Dictionaries

- String Manipulation

- List Comprehensions

- File Handling

- Exception Handling


Week 3: Intermediate Python

- Lambda Functions

- Map, Filter, Reduce

- Modules & Packages

- Scope & Global Variables

- Working with Dates & Time


Week 4: OOP & Pythonic Concepts

- Classes & Objects

- Inheritance & Polymorphism

- Decorators (Intro level)

- Generators & Iterators

- Writing Clean & Readable Code


Week 5: Real-World & Interview Prep

- Web Scraping (BeautifulSoup)

- Working with APIs (Requests)

- Automating Tasks

- Data Analysis Basics (Pandas)

- Interview Coding Patterns

You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527

Exploratory Data Analysis ( EDA)

Once you've learned/mastered the fundamentals of SQL, try learning these:


- 𝐉𝐎𝐈𝐍𝐬: LEFT, RIGHT, INNER, OUTER joins.
- 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Utilize SUM, COUNT, AVG, and others for efficient data summarization.
- 𝐂𝐀𝐒𝐄 𝐖𝐇𝐄𝐍 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬: Use conditional logic to tailor query results.
- 𝐃𝐚𝐭𝐞 𝐓𝐢𝐦𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Master manipulating dates and times for precise analysis.

Next, explore advanced methods to structure and reuse SQL code effectively:

- 𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬 (𝐂𝐓𝐄𝐬): Simplify complex queries into manageable parts to increase the readability.
- 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬: Nest queries for more granular data retrieval.
- 𝐓𝐞𝐦𝐩𝐨𝐫𝐚𝐫𝐲 𝐓𝐚𝐛𝐥𝐞𝐬: Create and manipulate temporary data sets for specific tasks.

Then, move on to advanced ones:

- 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Perform advanced calculations over sets of rows with ease.
- 𝐒𝐭𝐨𝐫𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐞𝐬: Create reusable SQL routines for streamlined operations.
- 𝐓𝐫𝐢𝐠𝐠𝐞𝐫𝐬: Automate database actions based on specific events.
- 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 𝐂𝐓𝐄𝐬: Solve complex problems using recursive queries.
- 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐐𝐮𝐞𝐫𝐢𝐞𝐬: Techniques to enhance performance and efficiency.

Powerful One-Liners in Python You Should Know!


1. Swap Two Numbers

n1, n2 = n2, n1


2. Reverse a String

reversed_string = input_string[::-1]


3. Factorial of a Number

fact = lambda n: [1, 0][n > 1] or fact(n - 1) * n


4. Find Prime Numbers (2 to 10)

primes = list(filter(lambda x: all(x % y != 0 for y in range(2, x)), range(2, 10)))


5. Check if a String is Palindrome

palindrome = input_string == input_string[::-1]


Free Python Resources: https://t.iss.one/pythonproz

📘 Free Power BI Course by Microsoft

https://learn.microsoft.com/en-us/power-bi/

Hope you'll like it

Like this post if you need more resources like this 👍❤️

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

I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

Like if you need similar content 😄👍

Here are 5 key Python libraries/ concepts that are particularly important for data analysts:

1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.

2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.

3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.

4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.

5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.

By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.

Credits: https://t.iss.one/free4unow_backup

Python Interview Q&A: https://topmate.io/coding/898340

Like for more ❤️

ENJOY LEARNING 👍👍

Learning data analytics in 2025 can be an exciting and rewarding journey. Here are some steps you can take to start learning data analytics:

1. Understand the Basics: Begin by familiarizing yourself with the basic concepts of data analytics, such as data types, data visualization, statistical analysis, and machine learning.

2. Take Online Courses: There are many online platforms that offer courses in data analytics, such as Coursera, Udemy, and edX. Look for courses that cover topics like data manipulation, data visualization, and predictive modeling.

3. Practice with Real Data: To truly understand data analytics, you need to practice with real datasets. You can find datasets on websites like Kaggle or UCI Machine Learning Repository to work on real-world projects.

4. Learn Tools and Software: Familiarize yourself with popular data analytics tools and software like Python, R, SQL, Tableau, and Power BI. These tools are commonly used in the industry for data analysis.

5. Join Data Analytics Communities: Join online communities like Reddit, LinkedIn groups, or local meetups to connect with other data analysts and learn from their experiences.

6. Build a Portfolio: Create a portfolio of your data analytics projects to showcase your skills to potential employers. Include detailed descriptions of the problem you solved, the data analysis techniques you used, and the results you achieved.

7. Stay Updated: Data analytics is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow industry blogs, attend webinars, and participate in online forums to stay informed.

Give credits while sharing: https://t.iss.one/learndataanalysis

Power BI DAX Cheatsheet 🚀

1️⃣ Basics of DAX (Data Analysis Expressions)

DAX is used to create custom calculations in Power BI.

It works with tables and columns, not individual cells.

Functions in DAX are similar to Excel but optimized for relational data.


2️⃣ Aggregation Functions

SUM(ColumnName): Adds all values in a column.

AVERAGE(ColumnName): Finds the mean of values.

MIN(ColumnName): Returns the smallest value.

MAX(ColumnName): Returns the largest value.

COUNT(ColumnName): Counts non-empty values.

COUNTROWS(TableName): Counts rows in a table.


3️⃣ Logical Functions

IF(condition, result_if_true, result_if_false): Conditional statement.

SWITCH(expression, value1, result1, value2, result2, default): Alternative to nested IF.

AND(condition1, condition2): Returns TRUE if both conditions are met.

OR(condition1, condition2): Returns TRUE if either condition is met.


4️⃣ Time Intelligence Functions

TODAY(): Returns the current date.

YEAR(TODAY()): Extracts the year from a date.

TOTALYTD(SUM(Sales[Amount]), Date[Date]): Year-to-date total.

SAMEPERIODLASTYEAR(Date[Date]): Returns values from the same period last year.

DATEADD(Date[Date], -1, MONTH): Shifts dates by a specified interval.


5️⃣ Filtering Functions

FILTER(Table, Condition): Returns a filtered table.

ALL(TableName): Removes all filters from a table.

ALLEXCEPT(TableName, Column1, Column2): Removes all filters except specified columns.

KEEPFILTERS(FilterExpression): Keeps filters applied while using other functions.


6️⃣ Ranking & Row Context Functions

RANKX(Table, Expression, [Value], [Order]): Ranks values in a column.

TOPN(N, Table, OrderByExpression): Returns the top N rows based on an expression.


7️⃣ Iterators (Row-by-Row Calculations)

SUMX(Table, Expression): Iterates over a table and sums calculated values.

AVERAGEX(Table, Expression): Iterates over a table and finds the average.

MAXX(Table, Expression): Finds the maximum value based on an expression.


8️⃣ Relationships & Lookup Functions

RELATED(ColumnName): Fetches a related column from another table.

LOOKUPVALUE(ColumnName, SearchColumn, SearchValue): Returns a value from a column where another column matches a value.


9️⃣ Variables in DAX

VAR variableName = Expression RETURN variableName

Improves performance by reducing redundant calculations.


🔟 Advanced DAX Concepts

Calculated Columns: Created at the column level, stored in the data model.

Measures: Dynamic calculations based on user interactions in Power BI visuals.

Row Context vs. Filter Context: Understanding how DAX applies calculations at different levels.

Free Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

React with ❤️ for free cheatsheets

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)