Is Python Really Essential for Data Analysis as a Fresher?

Starting out in data analysis can be overwhelming, especially when everyone seems to say Python is a must-have. But here’s a fresher’s reality check: Python is not always required at the start!

💡 Why You Don’t Need to Worry About Python Right Away:
1️⃣ Excel, Power BI and SQL First! - Many entry-level roles prioritize skills in Excel and SQL. These tools alone can handle a lot of data tasks like cleaning, aggregating, and visualizing data.
2️⃣ Gradual Learning Path 📈 - Once you’re comfortable with the basics, Python is a powerful next step, especially for handling larger datasets or automating processes.
3️⃣ Value in Flexibility - Python’s libraries like Pandas and Matplotlib allow for more complex analysis, but they’re skills you can learn over time as you grow in your role.

🔑 Takeaway? Start with what’s essential—Excel, Power BI and SQL—and build your Python skills as you gain more experience.

Pandas basics to advanced.pdf

Python for Web3 and Smart Contracts Roadmap

Stage 1 – Python Basics (Syntax, OOP)
Stage 2 – Blockchain Fundamentals (Transactions, Ledgers)
Stage 3 – Web3(.)py and Ethereum Basics
Stage 4 – Smart Contracts with Solidity
Stage 5 – Decentralized Storage (IPFS)
Stage 6 – Integrate Wallets and MetaMask
Stage 7 – Decentralized Application (DApp) Development
Stage 8 – Deploy and Test Smart Contracts

🏆 – Python Web3 Developer

TOP 10 Python Concepts for Job Interview

1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming

2 Years of Python Experience in one article 👇👇
https://medium.com/@data_analyst/2-years-of-python-experience-in-one-article-71e855c143f3?sk=16e4684a4caea308f6cd08bca0a7dfde

⌨️ Data Types In NumPy

Many people pay too much to learn Python, but my mission is to break down barriers. I have shared complete learning series to learn Python from scratch.

Here are the links to the Python series

Complete Python Topics for Data Analyst: https://t.me/sqlspecialist/548

Part-1: https://t.me/sqlspecialist/562

Part-2: https://t.me/sqlspecialist/564

Part-3: https://t.me/sqlspecialist/565

Part-4: https://t.me/sqlspecialist/566

Part-5: https://t.me/sqlspecialist/568

Part-6: https://t.me/sqlspecialist/570

Part-7: https://t.me/sqlspecialist/571

Part-8: https://t.me/sqlspecialist/572

Part-9: https://t.me/sqlspecialist/578

Part-10: https://t.me/sqlspecialist/577

Part-11: https://t.me/sqlspecialist/578

Part-12:
https://t.me/sqlspecialist/581

Part-13: https://t.me/sqlspecialist/583

Part-14: https://t.me/sqlspecialist/584

Part-15: https://t.me/sqlspecialist/585

I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.

But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.

You can refer these amazing resources for Python Interview Preparation.

Complete SQL Topics for Data Analysts: https://t.me/sqlspecialist/523

Complete Power BI Topics for Data Analysts: https://t.me/sqlspecialist/588

I'll continue with learning series on Excel & Tableau.

Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.

Hope it helps :)

Don't Confuse to learn Python.

Learn This Concept to be proficient in Python.

𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:
- Python Syntax
- Data Types
- Variables
- Operators
- Control Structures:
if-elif-else
Loops
Break and Continue
try-except block
- Functions
- Modules and Packages

𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Classes and Objects
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction

𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀:
- Pandas
- Numpy

𝗣𝗮𝗻𝗱𝗮𝘀:
- What is Pandas?
- Installing Pandas
- Importing Pandas
- Pandas Data Structures (Series, DataFrame, Index)

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀:
- Creating DataFrames
- Accessing Data in DataFrames
- Filtering and Selecting Data
- Adding and Removing Columns
- Merging and Joining DataFrames
- Grouping and Aggregating Data
- Pivot Tables

𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Handling Missing Values
- Handling Duplicates
- Data Formatting
- Data Transformation
- Data Normalization

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀:
- Handling Large Datasets with Dask
- Handling Categorical Data with Pandas
- Handling Text Data with Pandas
- Using Pandas with Scikit-learn
- Performance Optimization with Pandas

𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Lists
- Tuples
- Dictionaries
- Sets

𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Reading and Writing Text Files
- Reading and Writing Binary Files
- Working with CSV Files
- Working with JSON Files

𝗡𝘂𝗺𝗽𝘆:
- What is NumPy?
- Installing NumPy
- Importing NumPy
- NumPy Arrays

𝗡𝘂𝗺𝗣𝘆 𝗔𝗿𝗿𝗮𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Creating Arrays
- Accessing Array Elements
- Slicing and Indexing
- Reshaping Arrays
- Combining Arrays
- Splitting Arrays
- Arithmetic Operations
- Broadcasting

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗡𝘂𝗺𝗣𝘆:
- Reading and Writing Data with NumPy
- Filtering and Sorting Data
- Data Manipulation with NumPy
- Interpolation
- Fourier Transforms
- Window Functions

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆:
- Vectorization
- Memory Management
- Multithreading and Multiprocessing
- Parallel Computing