For data analysts working with Python, mastering these top 10 concepts is essential:

1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.

2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.

3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.

4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.

5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.

6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.

7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.

8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.

9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.

10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.

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

ENJOY LEARNING 👍👍

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

ENJOY LEARNING 👍👍

COMMON TERMINOLOGIES IN PYTHON - PART 1

Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?

In this series, we would be looking at the common Terminologies in python.

It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:

IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.

Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately

System Python - This is the version of python that comes with your operating system

Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions

REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)

Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.

Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function

Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.

Note: A return value can be any of these variable types: handle, integer, object, or string

Script - This is a file where you store your python code in a text file and execute all of the code with a single command

Script files - this is a file containing a group of python scripts

import requests

def asteroidOrbits(year, orbitclass):
    base_url = "https://jsonmock.hackerrank.com/api/asteroids/search"
    page = 1
    res = []

    while True:
        response = requests.get(f"{base_url}?orbit_class={orbitclass}&discovery_date={year}&page={page}").json()
        res.extend(response['data'])

        if page >= response['total_pages']:
            break

        page += 1
    res.sort(key=lambda x: (float(x.get('period_yr', 1.00)), x['designation']))
    return [x['designation'] for x in res]

Rest API: Asteroid Orbits ✅

https://topmate.io/coding/898340

If you're a job seeker, these well structured resources will help you to know and learn all the real time Python Interview questions with their exact answer. Folks who are having 0-4 years of experience have cracked the interview using this guide!

Please use the above link to avail them!👆

NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.

Hope this helps in your job search journey... All the best!👍✌️

I kept the minimal price so that everyone can afford but many people are misusing it by selling the same resources at higher cost.
Please stop doing that guys. It's for everyone's benfit 👍❤️

Python Quick Notes Part-1

Thanks for the amazing response on poll ☝️

Let's start the python series on YouTube so that I can explain the concepts with beautiful visuals

Here you can find how to print anything on Python
👇👇
https://youtu.be/3TVm0mzQfXw?si=VKS2xo4aaxbGaKy5

Please watch the complete video to understand it and let me know in comments if you want me to continue it ❤️

I explained it in Hindi Language, let me know in comments if you need similar videos in English as well

Kaafi time lagta hai voiceover and video editing mai, agar thoda sa bhi pasand aata hai to like and comment kardena.
https://youtu.be/3TVm0mzQfXw?si=VKS2xo4aaxbGaKy5

So that I can gradually shift towards more interesting topics 😄❤️

Here are some essential Python Concepts for Data Analyst

Roadmap to learn Python in 50 Days 👇👇
https://www.linkedin.com/posts/sql-analysts_many-people-pay-too-much-to-learn-python-activity-7189472351640576000-8vlW?utm_source=share&utm_medium=member_android

Like this LinkedIn post if you need similar content ❤️

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

⌨️ Benefits of learning Python Programming

1. Web Development: Python frameworks like Django and Flask are popular for building dynamic websites and web applications.

2. Data Analysis: Python has powerful libraries like Pandas and NumPy for data manipulation and analysis, making it widely used in data science and analytic.

3. Machine Learning: Python's libraries such as TensorFlow, Keras, and Scikit-learn are extensively used for implementing machine learning algorithms and building predictive models.

4. Artificial Intelligence: Python is commonly used in AI development due to its simplicity and extensive libraries for tasks like natural language processing, image recognition, and neural network implementation.

5. Cybersecurity: Python is utilized for tasks such as penetration testing, network scanning, and creating security tools due to its versatility and ease of use.

6. Game Development: Python, along with libraries like Pygame, is used for developing games, prototyping game mechanics, and creating game scripts.

7. Automation: Python's simplicity and versatility make it ideal for automating repetitive tasks, such as scripting, data scraping, and process automation.

Python Programming Interview Questions for Entry Level Data Analyst

1. What is Python, and why is it popular in data analysis?

2. Differentiate between Python 2 and Python 3.

3. Explain the importance of libraries like NumPy and Pandas in data analysis.

4. How do you read and write data from/to files using Python?

5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.

6. What are list comprehensions, and how do you use them in Python?

7. Explain the concept of object-oriented programming (OOP) in Python.


8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.

9. How do you handle missing or NaN values in a DataFrame using Pandas?

10. Explain the difference between loc and iloc in Pandas DataFrame indexing.

11. Discuss the purpose and usage of lambda functions in Python.

12. What are Python decorators, and how do they work?

13. How do you handle categorical data in Python using the Pandas library?

14. Explain the concept of data normalization and its importance in data preprocessing.

15. Discuss the role of regular expressions (regex) in data cleaning with Python.

16. What are Python virtual environments, and why are they useful?

17. How do you handle outliers in a dataset using Python?

18. Explain the usage of the map and filter functions in Python.

19. Discuss the concept of recursion in Python programming.

20. How do you perform data analysis and visualization using Jupyter Notebooks?

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

Like for more ❤️

ENJOY LEARNING 👍👍