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 👍👍

SQL Joins Simplified ✅

SQL Zero to Hero ✅

Commonly used Power BI DAX functions:

DATE AND TIME FUNCTIONS:
- CALENDAR
- DATEDIFF
- TODAY, DAY, MONTH, QUARTER, YEAR

AGGREGATE FUNCTIONS:
- SUM, SUMX, PRODUCT
- AVERAGE
- MIN, MAX
- COUNT
- COUNTROWS
- COUNTBLANK
- DISTINCTCOUNT

FILTER FUNCTIONS:
- CALCULATE
- FILTER
- ALL, ALLEXCEPT, ALLSELECTED, REMOVEFILTERS
- SELECTEDVALUE

TIME INTELLIGENCE FUNCTIONS:
- DATESBETWEEN
- DATESMTD, DATESQTD, DATESYTD
- SAMEPERIODLASTYEAR
- PARALLELPERIOD
- TOTALMTD, TOTALQTD, TOTALYTD

TEXT FUNCTIONS:
- CONCATENATE
- FORMAT
- LEN, LEFT, RIGHT

INFORMATION FUNCTIONS:
- HASONEVALUE, HASONEFILTER
- ISBLANK, ISERROR, ISEMPTY
- CONTAINS

LOGICAL FUNCTIONS:
- AND, OR, IF, NOT
- TRUE, FALSE
- SWITCH

RELATIONSHIP FUNCTIONS:
- RELATED
- USERRELATIONSHIP
- RELATEDTABLE

Remember, DAX is more about logic than the formulas.

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 👇

✅ They document every step of their analysis
➝ Clear notes make their work reproducible and trustworthy.

✅ They check data quality before the analysis begins
➝ Garbage in = garbage out. Always validate first.

✅ They use version control religiously
➝ Every code change is tracked. Nothing gets lost.

✅ They explore data thoroughly before diving in
➝ Understanding context prevents costly misinterpretations.

✅ They create automated noscripts for repetitive tasks
➝ Efficiency isn’t a luxury—it’s a necessity.

✅ They maintain a reusable code library
➝ Smart analysts never solve the same problem twice.

✅ They test assumptions with multiple validation methods
➝ One test isn’t enough; they triangulate confidence.

✅ They organize project files logically
➝ Their work is navigable by anyone, not just themselves.

✅ They seek peer reviews on critical work
➝ Fresh eyes catch blind spots.

✅ They continuously absorb industry knowledge
➝ Learning never stops. Trends change too quickly.

✅ They prioritize business-impacting projects
➝ Every analysis must drive real decisions.

✅ They explain complex findings simply
➝ Technical brilliance is useless without clarity.

✅ They write readable, well-commented code
➝ Their work is accessible to others, long after they're gone.

✅ They maintain robust backup systems
➝ Data loss is never an option.

✅ They learn from analytical mistakes
➝ Errors become stepping stones, not roadblocks.

✅ They build strong stakeholder relationships
➝ Data is only valuable when people use it.

✅ They break complex projects into manageable chunks
➝ Progress happens through disciplined, incremental work.

✅ They handle sensitive data with proper security
➝ Compliance isn’t optional—it’s foundational.

✅ They create visualizations that tell clear stories
➝ A chart without a narrative is just decoration.

✅ They actively seek evidence against their conclusions
➝ Confirmation bias is their biggest enemy.

The best analysts aren’t the ones with the most tools—they’re the ones with the most rigorous practices.

If you’re a Data Analyst, chances are you use 𝐒𝐐𝐋 every single day. And if you’re preparing for interviews, you’ve probably realized that it's not just about writing queries it's about writing smart, efficient, and scalable ones.

1. 𝐁𝐫𝐞𝐚𝐤 𝐈𝐭 𝐃𝐨𝐰𝐧 𝐰𝐢𝐭𝐡 𝐂𝐓𝐄𝐬 (𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬)

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 — great for simplifying logic and improving collaboration across your team.

2. 𝐔𝐬𝐞 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬

Forget the mess of subqueries. With functions like ROW_NUMBER(), RANK(), LEAD() and LAG(), you can compare rows, rank items, or calculate running totals — all within the same query. Total

3. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 (𝐍𝐞𝐬𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬)

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. 𝐈𝐧𝐝𝐞𝐱𝐞𝐬 & 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧

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. 𝐉𝐨𝐢𝐧𝐬 𝐯𝐬. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬

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. 𝐂𝐀𝐒𝐄 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬:

Want to categorize or bucket data without creating a separate table? Use CASE. It’s ideal for conditional logic, custom labels, and grouping in a single query.

7. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘

Most analytics questions start with "how many", "what’s the average", or "which is the highest?". SUM(), COUNT(), AVG(), etc., and pair them with GROUP BY to drive insights that matter.

8. 𝐃𝐚𝐭𝐞𝐬 𝐀𝐫𝐞 𝐀𝐥𝐰𝐚𝐲𝐬 𝐓𝐫𝐢𝐜𝐤𝐲

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. 𝐒𝐞𝐥𝐟-𝐉𝐨𝐢𝐧𝐬 & 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 𝐟𝐨𝐫 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐞𝐬

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 don’t 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.

Essential Skills Excel for Data Analysts 🚀

1️⃣ Data Cleaning & Transformation

Remove Duplicates – Ensure unique records.
Find & Replace – Quick data modifications.
Text Functions – TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation – Restrict input values.

2️⃣ Data Analysis & Manipulation

Sorting & Filtering – Organize and extract key insights.
Conditional Formatting – Highlight trends, outliers.
Pivot Tables – Summarize large datasets efficiently.
Power Query – Automate data transformation.

3️⃣ Essential Formulas & Functions

Lookup Functions – VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions – IF, AND, OR, IFERROR, IFS.
Aggregation Functions – SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions – CONCATENATE, TEXTJOIN, SUBSTITUTE.

4️⃣ Data Visualization
Charts & Graphs – Bar, Line, Pie, Scatter, Histogram.

Sparklines – Miniature charts inside cells.
Conditional Formatting – Color scales, data bars.
Dashboard Creation – Interactive and dynamic reports.

5️⃣ Advanced Excel Techniques
Array Formulas – Dynamic calculations with multiple values.
Power Pivot & DAX – Advanced data modeling.
What-If Analysis – Goal Seek, Scenario Manager.
Macros & VBA – Automate repetitive tasks.

6️⃣ Data Import & Export
CSV & TXT Files – Import and clean raw data.
Power Query – Connect to databases, web sources.
Exporting Reports – PDF, CSV, Excel formats.

Here you can find some free Excel books & useful resources: https://news.1rj.ru/str/excel_data

Hope it helps :)

#dataanalyst

𝐇𝐨𝐰 𝐭𝐨 𝐏𝐫𝐞𝐩𝐚𝐫𝐞 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭

𝟏. 𝐄𝐱𝐜𝐞𝐥- Learn formulas, Pivot tables, Lookup, VBA Macros.

𝟐. 𝐒𝐐𝐋- Joins, Windows, CTE is the most important

𝟑. 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈- Power Query Editor(PQE), DAX, MCode, RLS

𝟒. 𝐏𝐲𝐭𝐡𝐨𝐧- Basics & Libraries(mainly pandas, numpy, matplotlib and seaborn libraries)

5. Practice SQL and Python questions on platforms like 𝐇𝐚𝐜𝐤𝐞𝐫𝐑𝐚𝐧𝐤 or 𝐖𝟑𝐒𝐜𝐡𝐨𝐨𝐥𝐬.

6. Know the basics of denoscriptive statistics(mean, median, mode, Probability, normal, binomial, Poisson distributions etc).

7. Learn to use 𝐀𝐈/𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐭𝐨𝐨𝐥𝐬 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: 𝐀𝐳𝐮𝐫𝐞, 𝐀𝐖𝐒, 𝐨𝐫 𝐆𝐂𝐏

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 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊

Roadmap to become a Data Analyst:

📂 Learn Excel
∟📂 Learn SQL
∟📂 Learn Python
∟📂 Learn Power BI / Tableau
∟📂 Learn Statistics & Probability
∟📂 Learn Data Transformation
∟📂 Learn Machine Learning Basics
∟📂 Build Projects & Portfolio
∟✅ Apply for Job

React ❤️ for More 📊

Let's now understand the above Data Analyst Roadmap in detail: 🧠↗️

1️⃣ Learn Excel ⭐️

The foundation of data analysis. Learn formulas, pivot tables, charts, VLOOKUP/XLOOKUP, and conditional formatting. It helps in quick data cleaning and presenting insights.

Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i

2️⃣ Learn SQL 💻

Essential for working with databases. Focus on SELECT, JOIN, GROUP BY, WHERE, and subqueries to extract and manipulate data from relational databases.

SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

3️⃣ Learn Python 📱

A powerful tool for data manipulation and automation. Master libraries like pandas, numpy, matplotlib, and seaborn for data cleaning and visualization.

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

4️⃣ Learn Power BI / Tableau 📈

These tools help create interactive dashboards and visual reports. Learn how to import data, create filters, use DAX (Power BI), and design clear visualizations.

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

5️⃣ Learn Statistics & Probability 🛍

Know about denoscriptive stats (mean, median, mode), inferential stats, distributions, hypothesis testing, and correlation. Vital for making sense of data trends.

Statistics Resources: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

6️⃣ Learn Data Transformation 📈

Learn how to clean, shape, and prepare data for analysis. Use Python (pandas) or Power Query in Power BI, and understand ETL (Extract, Transform, Load) processes.

Data Cleaning: https://whatsapp.com/channel/0029VarxgFqATRSpdUeHUA27

7️⃣ Learn Machine Learning 🧠

Understand basic concepts like regression, classification, clustering, and decision trees. You don’t need to be an ML expert, just grasp how models work and when to use them.

Machine Learning: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O

8️⃣ Build Projects & Portfolio 🏹

Apply what you’ve learned to real datasets—like sales analysis, churn prediction, or dashboard creation. Showcase your work on GitHub or a personal website.

Data Analytics Projects: https://whatsapp.com/channel/0029VbAbnvPLSmbeFYNdNA29

9️⃣ Apply for Jobs 💼

With your skills and portfolio in place, start applying for data analyst roles. Tailor your resume using keywords from job denoscriptions and prepare to answer SQL and Excel tasks in interviews.

Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

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

Double Tap ♥️ for more

AI/ML roadmap

Topic: Mathematics

- Subtopic: Linear Algebra
- Vectors, Matrices, Eigenvalues and Eigenvectors
- Subtopic: Calculus
- Differentiation, Integration, Partial Derivatives
- Subtopic: Probability and Statistics
- Probability Theory, Random Variables, Statistical Inference

Topic: Programming

- Subtopic: Python
- Python Basics, Libraries like NumPy, Pandas, Matplotlib

Topic: Machine Learning

- Subtopic: Supervised Learning
- Linear Regression, Logistic Regression, Decision Trees
- Subtopic: Unsupervised Learning
- Clustering, Dimensionality Reduction[1](https://i.am.ai/roadmap)
- Subtopic: Neural Networks and Deep Learning
- Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks

Topic: Specializations

- Subtopic: Natural Language Processing
- Text Preprocessing, Topic Modeling, Word Embeddings
- Subtopic: Computer Vision
- Image Processing, Object Detection, Image Segmentation
- Subtopic: Reinforcement Learning
- Markov Decision Processes, Q-Learning, Policy Gradients

Join for more: https://news.1rj.ru/str/machinelearning_deeplearning

Scenario based  Interview Questions & Answers for Data Analyst

1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer.
  Question:
  - Write a SQL query to find the total number of orders placed by each customer.
Expected Answer:
    SELECT CustomerID, COUNT(*) AS TotalOrders
    FROM Orders
    GROUP BY CustomerID;

2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years.
  Question:
  - Write a SQL query to find the names of employees who have been with the company for more than 5 years.
Expected Answer:
    SELECT Name
    FROM Employees
    WHERE DATEDIFF(year, HireDate, GETDATE()) > 5;

Power BI Scenario-Based Questions

1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region.
    Expected Answer:
    - Load the dataset into Power BI.
    - Create relationships if necessary.
    - Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales).
    - Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart).
    - Use the "Filters" pane to filter data as needed.
    - Format the visualization to enhance clarity and readability.

2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API.
  Expected Answer:
    - Use Power BI Desktop to connect to the API.
    - Go to "Get Data" > "Web" and enter the API URL.
    - Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported).
    - Create visualizations using the imported data.
    - Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh.

3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application.
    Expected Answer:
    - Analyze the current performance using Performance Analyzer.
    - Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations.
    - Use aggregated tables to pre-compute results.
    - Simplify DAX calculations.
    - Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals.
    - Ensure proper indexing on the data source.

Free SQL Resources: t.me/mysqldata

Like if you need more similar content

Hope it helps :)

Don't waste your lot of time when learning data analysis.

Here's how you may start your Data analysis journey

1️⃣ - Avoid learning a programming language (e.g., SQL, R, or Python) for as long as possible.

This advice might seem strange coming from a former software engineer, so let me explain.

The vast majority of data analyses conducted each day worldwide are performed in the "solo analyst" scenario.

In this scenario, nobody cares about how the analysis was completed.

Only the results matter.

Also, the analysis methods (e.g., code) are rarely shared in this scenario.

2️⃣ Use Microsoft Excel for as long as possible.

Again, on the surface, strange advice from someone who loves SQL and Python.

When I first started learning data analysis, I ignored Microsoft Excel.

I was a coder, and I looked down on Excel.

I was 100% wrong.

Over the years, Excel has become an exceedingly powerful data analysis tool.

For many professionals, it can be all the analytical tooling they need.

For example, Excel is a wonderful tool for visually analyzing data (e.g., PivotCharts).

You can use Excel to conduct powerful Diagnostic Analytics.

The simple reality is that many professionals will never hit Excel's data limit - especially if they have a decent laptop.

3️⃣ Microsoft Excel might be your hammer, but not every problem is a nail.

Please, please, please use Excel where it makes sense!

If you reach a point where Excel doesn't make sense, know that you can quickly move on to technologies that are better suited for your needs....

#dataanalysis

4️⃣ SQL is your friend.

If you're unfamiliar, SQL is the language used to query databases.

After Microsoft Excel, SQL is the world's most commonly used data technology.

SQL is easily integrated into Excel, allowing you to leverage the power of the database server to acquire and wrangle data.

The results of all this goodness then show up in your workbook.

Also, SQL is straightforward for Excel users to learn.

5️⃣ Python in Excel.

Microsoft is providing you with just what you need to scale beyond Excel limitations.

At first, you use Python in Excel because it's the easiest way to scale and tap into a vast amount of DIY data science goodness.

As 99% of the code you write for Python in Excel translates to any tool, you now have a path to move off of Excel if needed.

For example, Jupyter Notebooks and VS Code.

Hope it helps :)

5 Most Used Excel Functions by Data Analysts

🧵⬇️

1️⃣ VLOOKUP / XLOOKUP:

VLOOKUP is used to look up values in a table or range by row, making it useful for merging datasets or retrieving specific data.

XLOOKUP (newer and more versatile) allows searching both horizontally and vertically and supports approximate matches.

2️⃣ INDEX-MATCH:

The INDEX-MATCH combination is often preferred over VLOOKUP for more flexibility. INDEX retrieves a value from a specified cell range, while MATCH identifies its position. Together, they allow more complex lookups, especially when the lookup column isn’t the leftmost column.

3️⃣ SUMIF / SUMIFS:

SUMIF and SUMIFS allow summing values based on single or multiple conditions, making it easy to analyze specific segments of data, such as summing revenue by region or time period.

4️⃣ COUNTIF / COUNTIFS:

COUNTIF and COUNTIFS are similar to SUMIF but are used for counting cells that meet specific criteria. These functions are helpful for calculating frequencies, such as counting occurrences of a certain value in a dataset.

5️⃣ Pivot Tables:

Pivot Tables aren’t a function but are an essential Excel tool for data analysts. They enable quick summarization, aggregation, and exploration of large datasets, allowing analysts to generate insights without complex formulas.

Like for more ❤️

Top 10 Python functions that are commonly used in data analysis

import pandas as pd: This function is used to import the Pandas library, which is essential for data manipulation and analysis.

read_csv(): This function from Pandas is used to read data from CSV files into a DataFrame, a primary data structure for data analysis.

head(): It allows you to quickly preview the first few rows of a DataFrame to understand its structure.

describe(): This function provides summary statistics of the numeric columns in a DataFrame, such as mean, standard deviation, and percentiles.

groupby(): It's used to group data by one or more columns, enabling aggregation and analysis within those groups.

pivot_table(): This function helps in creating pivot tables, allowing you to summarize and reshape data for analysis.

fillna(): Useful for filling missing values in a DataFrame with a specified value or a calculated one (e.g., mean or median).

apply(): This function is used to apply custom functions to DataFrame columns or rows, which is handy for data transformation.

plot(): It's part of the Matplotlib library and is used for creating various data visualizations, such as line plots, bar charts, and scatter plots.

merge(): This function is used for combining two or more DataFrames based on a common column or index, which is crucial for joining datasets during analysis.

These functions are essential tools for any data analyst working with Python for data analysis tasks.

Hope it helps :)

Stop trying to be extraordinary at every data tool.

- Be ordinary at Power BI.
- Be exceptional at SQL + Excel.
- Be consistent in asking the right questions.

This is how you actually thrive.

Python CheatSheet 📚 ✅

1. Basic Syntax
- Print Statement: print("Hello, World!")
- Comments: # This is a comment

2. Data Types
- Integer: x = 10
- Float: y = 10.5
- String: name = "Alice"
- List: fruits = ["apple", "banana", "cherry"]
- Tuple: coordinates = (10, 20)
- Dictionary: person = {"name": "Alice", "age": 25}

3. Control Structures
- If Statement:

     if x > 10:
         print("x is greater than 10")
     

- For Loop:

     for fruit in fruits:
         print(fruit)
     

- While Loop:

     while x < 5:
         x += 1
     

4. Functions
- Define Function:

     def greet(name):
         return f"Hello, {name}!"
     

- Lambda Function: add = lambda a, b: a + b

5. Exception Handling
- Try-Except Block:

     try:
         result = 10 / 0
     except ZeroDivisionError:
         print("Cannot divide by zero.")
     

6. File I/O
- Read File:

     with open('file.txt', 'r') as file:
         content = file.read()
     

- Write File:

     with open('file.txt', 'w') as file:
         file.write("Hello, World!")
     

7. List Comprehensions
- Basic Example: squared = [x**2 for x in range(10)]
- Conditional Comprehension: even_squares = [x**2 for x in range(10) if x % 2 == 0]

8. Modules and Packages
- Import Module: import math
- Import Specific Function: from math import sqrt

9. Common Libraries
- NumPy: import numpy as np
- Pandas: import pandas as pd
- Matplotlib: import matplotlib.pyplot as plt

10. Object-Oriented Programming
- Define Class:

      class Dog:
          def __init__(self, name):
              self.name = name
          def bark(self):
              return "Woof!"
      

11. Virtual Environments
- Create Environment: python -m venv myenv
- Activate Environment:
- Windows: myenv\Scripts\activate
- macOS/Linux: source myenv/bin/activate

12. Common Commands
- Run Script: python noscript.py
- Install Package: pip install package_name
- List Installed Packages: pip list

This Python checklist serves as a quick reference for essential syntax, functions, and best practices to enhance your coding efficiency!

Checklist for Data Analyst: https://dataanalytics.beehiiv.com/p/data

Here you can find essential Python Interview Resources👇
https://news.1rj.ru/str/DataSimplifier

Like for more resources like this 👍 ♥️

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

Hope it helps :)

Monetizing Your Data Analytics Skills: Side Hustles & Passive Income Streams

Once you've mastered data analytics, you can leverage your expertise to generate income beyond your 9-to-5 job. Here’s how:

1️⃣ Freelancing & Consulting 💼

Offer data analytics, visualization, or SQL expertise on platforms like Upwork, Fiverr, and Toptal.

Provide business intelligence solutions, dashboard building, or data cleaning services.

Work with startups, small businesses, and enterprises remotely.


2️⃣ Creating & Selling Online Courses 🎥

Teach SQL, Power BI, Python, or Data Visualization on platforms like Udemy, Coursera, and Teachable.

Offer exclusive workshops or bootcamps via LinkedIn, Gumroad, or your website.

Monetize your expertise once and earn passive income forever.


3️⃣ Blogging & Technical Writing ✍️

Write data-related articles on Medium, Towards Data Science, or Substack.

Start a newsletter focused on analytics trends and career growth.

Earn through Medium Partner Program, sponsored posts, or affiliate marketing.


4️⃣ YouTube & Social Media Monetization 📹

Create a YouTube channel sharing tutorials on SQL, Power BI, Python, and real-world analytics projects.

Monetize through ads, sponsorships, and memberships.

Grow a LinkedIn, Twitter, or Instagram audience and collaborate with brands.


5️⃣ Affiliate Marketing in Data Analytics 🔗

Promote courses, books, tools (Tableau, Power BI, Python IDEs) and earn commissions.

Join Udemy, Coursera, or DataCamp affiliate programs.

Recommend data tools, laptops, or online learning resources through blogs or YouTube.


6️⃣ Selling Templates & Dashboards 📊

Create Power BI or Tableau templates and sell them on Gumroad or Etsy.

Offer SQL query libraries, Excel automation noscripts, or data storytelling templates.

Provide customized analytics solutions for different industries.


7️⃣ Writing E-books or Guides 📖

Publish an e-book on SQL, Power BI, or breaking into data analytics.

Sell through Amazon Kindle, Gumroad, or your website.

Provide case studies, real-world datasets, and practice problems.


8️⃣ Building a Subnoscription-Based Community 🌍

Create a private Slack, Discord, or Telegram group for data professionals.

Charge for premium access, mentorship, and exclusive content.

Offer live Q&A sessions, job referrals, and networking opportunities.


9️⃣ Developing & Selling AI-Powered Tools 🤖

Build Python noscripts, automation tools, or AI-powered analytics apps.

Sell on GitHub, Gumroad, or AppSumo.

Offer API-based solutions for businesses needing automated insights.


🔟 Landing Paid Speaking Engagements & Workshops 🎤

Speak at data conferences, webinars, and corporate training events.

Offer paid workshops for businesses or universities.

Become a recognized expert in your niche and command high fees.

Start Small, Scale Fast! 🚀

The data analytics field offers endless opportunities to earn beyond a job. Start with freelancing, content creation, or digital products—then scale it into a business!

Hope it helps :)

#dataanalytics