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✅ Programming Important Terms You Should Know 💻🚀

Programming is the backbone of tech, and knowing the right terms can boost your learning and career.

🧠 Core Programming Concepts

• Programming: Writing instructions for a computer to perform tasks.

• Algorithm: Step-by-step procedure to solve a problem.

• Flowchart: Visual representation of a program’s logic.

• Syntax: Rules that define how code must be written.

• Compilation: Converting source code into machine code.

• Interpretation: Executing code line-by-line without compiling first.

⚙️ Basic Programming Elements

• Variable: Storage location for data.

• Constant: Fixed value that cannot change.

• Data Type: Type of data (int, float, string, boolean).

• Operator: Symbol performing operations (+, -, **, /, ==).

• Expression: Combination of variables, operators, and values.

• Statement: A single line of instruction in a program.

🔄 Control Flow Concepts

• Conditional Statements: Execute code based on conditions (if, else).

• Loops: Repeat a block of code (for, while).

• Break Statement: Exit a loop early.

• Continue Statement: Skip the current loop iteration.

• Switch Case: Multi-condition decision structure.

📦 Functions & Modular Programming

• Function: Reusable block of code performing a task.

• Parameter: Input passed to a function.

• Return Value: Output returned by a function.

• Module: File containing reusable functions or classes.

• Library: Collection of pre-written code.

🧩 Object-Oriented Programming (OOP)

• Class: Blueprint for creating objects.

• Object: Instance of a class.

• Encapsulation: Bundling data and methods together.

• Inheritance: One class acquiring properties of another.

• Polymorphism: Same function behaving differently in different contexts.

• Abstraction: Hiding complex implementation details.

📊 Data Structures

• Array: Collection of elements stored sequentially.

• List: Ordered collection that can change size.

• Stack: Last In First Out (LIFO) structure.

• Queue: First In First Out (FIFO) structure.

• Hash Table / Dictionary: Key-value data storage.

• Tree: Hierarchical data structure.

• Graph: Network of connected nodes.

⚡ Advanced Programming Concepts

• Recursion: Function calling itself.

• Concurrency: Multiple tasks running simultaneously.

• Multithreading: Multiple threads within a program.

• Memory Management: Allocation and deallocation of memory.

• Garbage Collection: Automatic memory cleanup.

• Exception Handling: Handling runtime errors using try, catch, except.

🌐 Software Development Concepts

• Framework: Pre-built structure for building applications.

• API: Interface allowing different software to communicate.

• Version Control: Tracking code changes using tools like Git.

• Debugging: Finding and fixing code errors.

• Testing: Verifying that code works correctly.

Double Tap ♥️ For Detailed Explanation of Each Topic

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✅ FREE Tools to Learn Coding & Practice Skills 💻🚀 

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Important Topics You Should Know to Learn Python 👇

Lists, Strings, Tuples, Dictionaries, Sets – Learn the core data structures in Python.

Boolean, Arithmetic, and Comparison Operators – Understand how Python evaluates conditions.

Operations on Data Structures – Append, delete, insert, reverse, sort, and manipulate collections efficiently.

Reading and Extracting Data – Learn how to access, modify, and extract values from lists and dictionaries.

Conditions and Loops – Master if, elif, else, for, while, break, and continue statements.

Range and Enumerate – Efficiently loop through sequences with indexing.

Functions – Create functions with and without parameters, and understand *args and **kwargs.

Classes & Object-Oriented Programming – Work with init methods, global/local variables, and concepts like inheritance and encapsulation.

File Handling – Read, write, and manipulate files in Python.


Free Resources to learn Python👇👇

👉 Free Python course by Google

https://developers.google.com/edu/python

👉 Freecodecamp Python course

https://www.freecodecamp.org/learn/data-analysis-with-python/#

👉 Udacity Intro to Python course

https://bit.ly/3FOOQHh

👉Python Cheatsheet

https://t.me/pythondevelopersindia/262?single

👉 Practice Python

http://www.pythonchallenge.com/

👉 Kaggle

https://kaggle.com/learn/intro-to-programming
https://kaggle.com/learn/python

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SQL (Structured Query Language) is a standard programming language used to manage and manipulate relational databases. Here are some key concepts to understand the basics of SQL:

1. Database: A database is a structured collection of data organized in tables, which consist of rows and columns.

2. Table: A table is a collection of related data organized in rows and columns. Each row represents a record, and each column represents a specific attribute or field.

3. Query: A SQL query is a request for data or information from a database. Queries are used to retrieve, insert, update, or delete data in a database.

4. CRUD Operations: CRUD stands for Create, Read, Update, and Delete. These are the basic operations performed on data in a database using SQL:
   - Create (INSERT): Adds new records to a table.
   - Read (SELECT): Retrieves data from one or more tables.
   - Update (UPDATE): Modifies existing records in a table.
   - Delete (DELETE): Removes records from a table.

5. Data Types: SQL supports various data types to define the type of data that can be stored in each column of a table, such as integer, text, date, and decimal.

6. Constraints: Constraints are rules enforced on data columns to ensure data integrity and consistency. Common constraints include:
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   - Foreign Key: Establishes a relationship between two tables.
   - Unique: Ensures that all values in a column are unique.
   - Not Null: Specifies that a column cannot contain NULL values.

7. Joins: Joins are used to combine rows from two or more tables based on a related column between them. Common types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN).

8. Aggregate Functions: SQL provides aggregate functions to perform calculations on sets of values. Common aggregate functions include SUM, AVG, COUNT, MIN, and MAX.

9. Group By: The GROUP BY clause is used to group rows that have the same values into summary rows. It is often used with aggregate functions to perform calculations on grouped data.

10. Order By: The ORDER BY clause is used to sort the result set of a query based on one or more columns in ascending or descending order.

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🔥 A-Z Data Science Road Map

1. 📊 Math and Statistics
- Descriptive statistics
- Probability
- Distributions
- Hypothesis testing
- Correlation
- Regression basics

2. 🐍 Python Basics
- Variables
- Data types
- Loops
- Conditionals
- Functions
- Modules

3. 🐼 Core Python for Data Science
- NumPy
- Pandas
- DataFrames
- Missing values
- Merging
- GroupBy
- Visualization

4. 📈 Data Visualization
- Matplotlib
- Seaborn
- Plotly
- Histograms, boxplots, heatmaps
- Dashboards

5. 🧹 Data Wrangling
- Cleaning
- Outlier detection
- Feature engineering
- Encoding
- Scaling

6. 🔍 Exploratory Data Analysis (EDA)
- Univariate analysis
- Bivariate analysis
- Stats summary
- Correlation analysis

7. 💾 SQL for Data Science
- SELECT
- WHERE
- GROUP BY
- JOINS
- CTEs
- Window functions

8. 🤖 Machine Learning Basics
- Supervised vs unsupervised
- Train test split
- Cross validation
- Metrics

9. 🎯 Supervised Learning
- Linear regression
- Logistic regression
- Decision trees
- Random forest
- Gradient boosting
- SVM
- KNN

10. 💡 Unsupervised Learning
- K-Means
- Hierarchical clustering
- PCA
- Dimensionality reduction

11. ⭐ Model Evaluation
- Accuracy
- Precision
- Recall
- F1
- ROC AUC
- MSE, RMSE, MAE

12. 🛠️ Feature Engineering
- One hot encoding
- Binning
- Scaling
- Interaction terms

13. ⏳ Time Series
- Trends
- Seasonality
- ARIMA
- Prophet
- Forecasting steps

14. 🧠 Deep Learning Basics
- Neural networks
- Activation functions
- Loss functions
- Backprop basics

15. 🚀 Deep Learning Libraries
- TensorFlow
- Keras
- PyTorch

16. 💬 NLP
- Tokenization
- Stemming
- Lemmatization
- TF-IDF
- Word embeddings

17. 🌐 Big Data Tools
- Hadoop
- Spark
- PySpark

18. ⚙️ Data Engineering Basics
- ETL
- Pipelines
- Scheduling
- Cloud concepts

19. ☁️ Cloud Platforms
- AWS (S3, Lambda, SageMaker)
- GCP (BigQuery)
- Azure ML

20. 📦 MLOps
- Model deployment
- CI/CD
- Monitoring
- Docker
- APIs (FastAPI, Flask)

21. 📊 Dashboards
- Power BI
- Tableau
- Streamlit

22. 🏗️ Real-World Projects
- Classification
- Regression
- Time series
- NLP
- Recommendation systems

23. 🧑‍💻 Version Control
- Git
- GitHub
- Branching
- Pull requests

24. 🗣️ Soft Skills
- Problem framing
- Business communication
- Storytelling

25. 📝 Interview Prep
- SQL practice
- Python challenges
- ML theory
- Case studies

------------------- END -------------------

✅ Good Resources To Learn Data Science

1. 📚 Documentation
- Pandas docs: pandas.pydata.org
- NumPy docs: numpy.org
- Scikit-learn docs: scikit-learn.org
- PyTorch: pytorch.org

2. 📺 Free Learning Channels
- FreeCodeCamp: youtube.com/c/FreeCodeCamp
- Data School: youtube.com/dataschool
- Krish Naik: YouTube
- WhatsApp channel
- StatQuest: YouTube

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​Infosys Digital Specialist Engineer :- https://pdlink.in/4yJ98gb
​
​The best way to prepare is to learn from candidates who've already been through the process.
​

Best YouTube Channels To Learn

• Cybersecurity - John Hammond
• Networking - David Bombal
• Python - Code With Harry
• UI/UX - GFXMentol
• React - Codevolution
• JavaScript - Traversy Media
• Java - Kunal Kushwaha
• DevOps - Techworld With Nana
• Blockchain - Telusko
• Al/ML- Krish Naik
• Web Development - Traversy Media
• AWS - Code With Harry
• SQL - Programming With Mosh
• DBMS -Edureka
• Ruby-The Ruby Way
• Scala - Scala Love
• SAP -Intellipaat
• C- FeecodeCamp
• R- Krish Naik