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Oracleโs Race to Certification is here โ your chance to earn globally recognized certifications for FREE!๐ฅ
๐ก Choose from in-demand certifications in:
โ๏ธ Cloud
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โกBut hurry โ spots are limited, and the clock is ticking!โ ๏ธ
Complete roadmap to learn Python for data analysis
Step 1: Fundamentals of Python
1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)
2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions
3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions
4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)
Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)
2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully
3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation
Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations
2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data
3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn
Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering
2. Exploratory Data Analysis (EDA)
- Descriptive statistics
- Data visualization techniques
- Identifying patterns and outliers
3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions
Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models
2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models
3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)
Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects
2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects
๐จโ๐ป FREE Resources to Learn & Practice Python
1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://t.iss.one/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.iss.one/pythonfreebootcamp/134
7. https://t.iss.one/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://t.iss.one/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://t.iss.one/pythonspecialist/33
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
Step 1: Fundamentals of Python
1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)
2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions
3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions
4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)
Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)
2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully
3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation
Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations
2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data
3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn
Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering
2. Exploratory Data Analysis (EDA)
- Descriptive statistics
- Data visualization techniques
- Identifying patterns and outliers
3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions
Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models
2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models
3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)
Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects
2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects
๐จโ๐ป FREE Resources to Learn & Practice Python
1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://t.iss.one/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.iss.one/pythonfreebootcamp/134
7. https://t.iss.one/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://t.iss.one/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://t.iss.one/pythonspecialist/33
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
โค4
๐ฏ ๐๐ฎ๐บ๐ฒ-๐๐ต๐ฎ๐ป๐ด๐ถ๐ป๐ด ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ฃ๐๐๐ต๐ผ๐ป ๐ณ๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒ๐
Want to break into Data Science or Tech?
Python is the #1 skill you need โ and starting is easier than you think.๐งโ๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JemBIt
Your career upgrade starts today โ no excuses!โ ๏ธ
Want to break into Data Science or Tech?
Python is the #1 skill you need โ and starting is easier than you think.๐งโ๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JemBIt
Your career upgrade starts today โ no excuses!โ ๏ธ
โค1
These are top 5 data structures and algorithms projects, allowing you to dive deep into the world of DSA ๐ช๐ป
โขProject 1: Snakes Game (Arrays)
The Snakes Game project is a classic implementation of the popular game
Snake.
This project allows you to understand the concepts of arrays, loops, and conditional statements. You can further enhance the game by incorporating additional features such as score tracking and power-ups.
โขProject 2: Cash Flow Minimizer (Graphs/ Multisets/Heaps)
The Cash Flow Minimizer project involves solving a cash flow optimization problem using graphs, multisets, and heaps. Given a set of transactions among a group of people, the objective is to minimize the total number of transactions required to settle all debts
โขProject 3: Sudoku Solver (Backtracking)
The Sudoku Solver project aims to solve the popular Sudoku puzzle using backtracking. This project allows you to understand the backtracking algorithm, which is widely used in solving constraint satisfaction problems.
โขProject 4: File Zipper (Greedy Huffman
Encoder)
The File Zipper project focuses on implementing a file compression utility using the Greedy Huffman encoding algorithm. This project provides a practical application of the greedy algorithm and helps you understand the trade-offs between
compression ratio and execution time.
โขProject 5: Map Navigator (Dijkstraโs
Algorithm)
The Map Navigator project aims to develop a navigation system using Dijkstraโs algorithm. It involves finding the shortest path between two locations on a map, considering factors such as distance and traffic.
You can check these amazing resources for DSA Preparation
Join for more: https://t.iss.one/crackingthecodinginterview
All the best ๐๐
โขProject 1: Snakes Game (Arrays)
The Snakes Game project is a classic implementation of the popular game
Snake.
This project allows you to understand the concepts of arrays, loops, and conditional statements. You can further enhance the game by incorporating additional features such as score tracking and power-ups.
โขProject 2: Cash Flow Minimizer (Graphs/ Multisets/Heaps)
The Cash Flow Minimizer project involves solving a cash flow optimization problem using graphs, multisets, and heaps. Given a set of transactions among a group of people, the objective is to minimize the total number of transactions required to settle all debts
โขProject 3: Sudoku Solver (Backtracking)
The Sudoku Solver project aims to solve the popular Sudoku puzzle using backtracking. This project allows you to understand the backtracking algorithm, which is widely used in solving constraint satisfaction problems.
โขProject 4: File Zipper (Greedy Huffman
Encoder)
The File Zipper project focuses on implementing a file compression utility using the Greedy Huffman encoding algorithm. This project provides a practical application of the greedy algorithm and helps you understand the trade-offs between
compression ratio and execution time.
โขProject 5: Map Navigator (Dijkstraโs
Algorithm)
The Map Navigator project aims to develop a navigation system using Dijkstraโs algorithm. It involves finding the shortest path between two locations on a map, considering factors such as distance and traffic.
You can check these amazing resources for DSA Preparation
Join for more: https://t.iss.one/crackingthecodinginterview
All the best ๐๐
โค4
Guys, Big Announcement!
Weโve officially hit 2.5 Million followers โ and itโs time to level up together! โค๏ธ
Iโm launching a Python Projects Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step, hands-on journey โ where youโll build useful Python projects with clear code, explanations, and mini-quizzes!
Hereโs what weโll cover:
๐น Week 1: Python Mini Projects (Daily Practice)
โฆ Calculator
โฆ To-Do List (CLI)
โฆ Number Guessing Game
โฆ Unit Converter
โฆ Digital Clock
๐น Week 2: Data Handling & APIs
โฆ Read/Write CSV & Excel files
โฆ JSON parsing
โฆ API Calls using Requests
โฆ Weather App using OpenWeather API
โฆ Currency Converter using Real-time API
๐น Week 3: Automation with Python
โฆ File Organizer Script
โฆ Email Sender
โฆ WhatsApp Automation
โฆ PDF Merger
โฆ Excel Report Generator
๐น Week 4: Data Analysis with Pandas & Matplotlib
โฆ Load & Clean CSV
โฆ Data Aggregation
โฆ Data Visualization
โฆ Trend Analysis
โฆ Dashboard Basics
๐น Week 5: AI & ML Projects (Beginner Friendly)
โฆ Predict House Prices
โฆ Email Spam Classifier
โฆ Sentiment Analysis
โฆ Image Classification (Intro)
โฆ Basic Chatbot
๐ Each project includes:
โ Problem Statement
โ Code with explanation
โ Sample input/output
โ Learning outcome
โ Mini quiz
๐ฌ React โค๏ธ if you're ready to build some projects together!
You can access it for free here
๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Letโs Build. Letโs Grow. ๐ป๐
Weโve officially hit 2.5 Million followers โ and itโs time to level up together! โค๏ธ
Iโm launching a Python Projects Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step, hands-on journey โ where youโll build useful Python projects with clear code, explanations, and mini-quizzes!
Hereโs what weโll cover:
๐น Week 1: Python Mini Projects (Daily Practice)
โฆ Calculator
โฆ To-Do List (CLI)
โฆ Number Guessing Game
โฆ Unit Converter
โฆ Digital Clock
๐น Week 2: Data Handling & APIs
โฆ Read/Write CSV & Excel files
โฆ JSON parsing
โฆ API Calls using Requests
โฆ Weather App using OpenWeather API
โฆ Currency Converter using Real-time API
๐น Week 3: Automation with Python
โฆ File Organizer Script
โฆ Email Sender
โฆ WhatsApp Automation
โฆ PDF Merger
โฆ Excel Report Generator
๐น Week 4: Data Analysis with Pandas & Matplotlib
โฆ Load & Clean CSV
โฆ Data Aggregation
โฆ Data Visualization
โฆ Trend Analysis
โฆ Dashboard Basics
๐น Week 5: AI & ML Projects (Beginner Friendly)
โฆ Predict House Prices
โฆ Email Spam Classifier
โฆ Sentiment Analysis
โฆ Image Classification (Intro)
โฆ Basic Chatbot
๐ Each project includes:
โ Problem Statement
โ Code with explanation
โ Sample input/output
โ Learning outcome
โ Mini quiz
๐ฌ React โค๏ธ if you're ready to build some projects together!
You can access it for free here
๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Letโs Build. Letโs Grow. ๐ป๐
โค6