©How fresher can get a job as a data scientist?©
India as a job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
India as a job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
❤3
𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱 😍
Learn Fundamental Skills with Free Online Courses & Earn Certificates
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Learn Fundamental Skills with Free Online Courses & Earn Certificates
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- GenAI
- Data Science,
- BigData
- Python
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- Machine Learning
- Cyber Security
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❤2
A-Z of essential data science concepts
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
❤2
Here’s a solid 𝗕𝗘𝗛𝗔𝗩𝗜𝗢𝗥𝗔𝗟 𝗥𝗢𝗨𝗡𝗗 𝗧𝗜𝗣 to boost your chances to nail that job offer!
Technical skills might get you through initial rounds, but behavioral rounds are where many stumble — especially with senior managers who really want to know if you fit the team.
Here’s how to ace it:
1️⃣ When HR shares your interviewer's name, hunt for their LinkedIn profile.
2️⃣ Check out their work history and interests to find common ground.
3️⃣ Mention something relevant during the chat — it shows you’ve done your homework and builds rapport.
4️⃣ Remember, this round is two-way: they’re checking if you suit their culture, and you’re seeing if they suit your career goals.
5️⃣ So, ask smart questions about the role and company culture — it proves you’re genuinely interested.
💡 𝗣𝗿𝗼 𝘁𝗶𝗽: Stay polite but confident; senior leaders love that mix!
Technical skills might get you through initial rounds, but behavioral rounds are where many stumble — especially with senior managers who really want to know if you fit the team.
Here’s how to ace it:
1️⃣ When HR shares your interviewer's name, hunt for their LinkedIn profile.
2️⃣ Check out their work history and interests to find common ground.
3️⃣ Mention something relevant during the chat — it shows you’ve done your homework and builds rapport.
4️⃣ Remember, this round is two-way: they’re checking if you suit their culture, and you’re seeing if they suit your career goals.
5️⃣ So, ask smart questions about the role and company culture — it proves you’re genuinely interested.
💡 𝗣𝗿𝗼 𝘁𝗶𝗽: Stay polite but confident; senior leaders love that mix!
❤1
Creating a data science and machine learning project involves several steps, from defining the problem to deploying the model. Here is a general outline of how you can create a data science and ML project:
1. Define the Problem: Start by clearly defining the problem you want to solve. Understand the business context, the goals of the project, and what insights or predictions you aim to derive from the data.
2. Collect Data: Gather relevant data that will help you address the problem. This could involve collecting data from various sources, such as databases, APIs, CSV files, or web scraping.
3. Data Preprocessing: Clean and preprocess the data to make it suitable for analysis and modeling. This may involve handling missing values, encoding categorical variables, scaling features, and other data cleaning tasks.
4. Exploratory Data Analysis (EDA): Perform exploratory data analysis to understand the data better. Visualize the data, identify patterns, correlations, and outliers that may impact your analysis.
5. Feature Engineering: Create new features or transform existing features to improve the performance of your machine learning model. Feature engineering is crucial for building a successful ML model.
6. Model Selection: Choose the appropriate machine learning algorithm based on the problem you are trying to solve (classification, regression, clustering, etc.). Experiment with different models and hyperparameters to find the best-performing one.
7. Model Training: Split your data into training and testing sets and train your machine learning model on the training data. Evaluate the model's performance on the testing data using appropriate metrics.
8. Model Evaluation: Evaluate the performance of your model using metrics like accuracy, precision, recall, F1-score, ROC-AUC, etc. Make sure to analyze the results and iterate on your model if needed.
9. Deployment: Once you have a satisfactory model, deploy it into production. This could involve creating an API for real-time predictions, integrating it into a web application, or any other method of making your model accessible.
10. Monitoring and Maintenance: Monitor the performance of your deployed model and ensure that it continues to perform well over time. Update the model as needed based on new data or changes in the problem domain.
1. Define the Problem: Start by clearly defining the problem you want to solve. Understand the business context, the goals of the project, and what insights or predictions you aim to derive from the data.
2. Collect Data: Gather relevant data that will help you address the problem. This could involve collecting data from various sources, such as databases, APIs, CSV files, or web scraping.
3. Data Preprocessing: Clean and preprocess the data to make it suitable for analysis and modeling. This may involve handling missing values, encoding categorical variables, scaling features, and other data cleaning tasks.
4. Exploratory Data Analysis (EDA): Perform exploratory data analysis to understand the data better. Visualize the data, identify patterns, correlations, and outliers that may impact your analysis.
5. Feature Engineering: Create new features or transform existing features to improve the performance of your machine learning model. Feature engineering is crucial for building a successful ML model.
6. Model Selection: Choose the appropriate machine learning algorithm based on the problem you are trying to solve (classification, regression, clustering, etc.). Experiment with different models and hyperparameters to find the best-performing one.
7. Model Training: Split your data into training and testing sets and train your machine learning model on the training data. Evaluate the model's performance on the testing data using appropriate metrics.
8. Model Evaluation: Evaluate the performance of your model using metrics like accuracy, precision, recall, F1-score, ROC-AUC, etc. Make sure to analyze the results and iterate on your model if needed.
9. Deployment: Once you have a satisfactory model, deploy it into production. This could involve creating an API for real-time predictions, integrating it into a web application, or any other method of making your model accessible.
10. Monitoring and Maintenance: Monitor the performance of your deployed model and ensure that it continues to perform well over time. Update the model as needed based on new data or changes in the problem domain.
❤2
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Get Certified & Stay Ahead🎓
❤2
React.js 30 Days Roadmap & Free Learning Resource 📍👇
👨🏻💻Days 1-7: Introduction and Fundamentals
📍Day 1: Introduction to React.js
What is React.js?
Setting up a development environment
Creating a basic React app
📍Day 2: JSX and Components
Understanding JSX
Creating functional components
Using props to pass data
📍Day 3: State and Lifecycle
Component state
Lifecycle methods (componentDidMount, componentDidUpdate, etc.)
Updating and rendering based on state changes
📍Day 4: Handling Events
Adding event handlers
Updating state with events
Conditional rendering
📍Day 5: Lists and Keys
Rendering lists of components
Adding unique keys to components
Handling list updates efficiently
📍Day 6: Forms and Controlled Components
Creating forms in React
Handling form input and validation
Controlled components
📍Day 7: Conditional Rendering
Conditional rendering with if statements
Using the && operator and ternary operator
Conditional rendering with logical AND (&&) and logical OR (||)
👨🏻💻Days 8-14: Advanced React Concepts
📍Day 8: Styling in React
Inline styles in React
Using CSS classes and libraries
CSS-in-JS solutions
📍Day 9: React Router
Setting up React Router
Navigating between routes
Passing data through routes
📍Day 10: Context API and State Management
Introduction to the Context API
Creating and consuming context
Global state management with context
📍Day 11: Redux for State Management
What is Redux?
Actions, reducers, and the store
Integrating Redux into a React application
📍Day 12: React Hooks (useState, useEffect, etc.)
Introduction to React Hooks
useState, useEffect, and other commonly used hooks
Refactoring class components to functional components with hooks
📍Day 13: Error Handling and Debugging
Error boundaries
Debugging React applications
Error handling best practices
📍Day 14: Building and Optimizing for Production
Production builds and optimizations
Code splitting
Performance best practices
👨🏻💻Days 15-21: Working with External Data and APIs
📍Day 15: Fetching Data from an API
Making API requests in React
Handling API responses
Async/await in React
📍Day 16: Forms and Form Libraries
Working with form libraries like Formik or React Hook Form
Form validation and error handling
📍Day 17: Authentication and User Sessions
Implementing user authentication
Handling user sessions and tokens
Securing routes
📍Day 18: State Management with Redux Toolkit
Introduction to Redux Toolkit
Creating slices
Simplified Redux configuration
📍Day 19: Routing in Depth
Nested routing with React Router
Route guards and authentication
Advanced route configuration
📍Day 20: Performance Optimization
Memoization and useMemo
React.memo for optimizing components
Virtualization and large lists
📍Day 21: Real-time Data with WebSockets
WebSockets for real-time communication
Implementing chat or notifications
👨🏻💻Days 22-30: Building and Deployment
📍Day 22: Building a Full-Stack App
Integrating React with a backend (e.g., Node.js, Express, or a serverless platform)
Implementing RESTful or GraphQL APIs
📍Day 23: Testing in React
Testing React components using tools like Jest and React Testing Library
Writing unit tests and integration tests
📍Day 24: Deployment and Hosting
Preparing your React app for production
Deploying to platforms like Netlify, Vercel, or AWS
📍Day 25-30: Final Project
*_Plan, design, and build a complete React project of your choice, incorporating various concepts and tools you've learned during the previous days.
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING 👍👍
👨🏻💻Days 1-7: Introduction and Fundamentals
📍Day 1: Introduction to React.js
What is React.js?
Setting up a development environment
Creating a basic React app
📍Day 2: JSX and Components
Understanding JSX
Creating functional components
Using props to pass data
📍Day 3: State and Lifecycle
Component state
Lifecycle methods (componentDidMount, componentDidUpdate, etc.)
Updating and rendering based on state changes
📍Day 4: Handling Events
Adding event handlers
Updating state with events
Conditional rendering
📍Day 5: Lists and Keys
Rendering lists of components
Adding unique keys to components
Handling list updates efficiently
📍Day 6: Forms and Controlled Components
Creating forms in React
Handling form input and validation
Controlled components
📍Day 7: Conditional Rendering
Conditional rendering with if statements
Using the && operator and ternary operator
Conditional rendering with logical AND (&&) and logical OR (||)
👨🏻💻Days 8-14: Advanced React Concepts
📍Day 8: Styling in React
Inline styles in React
Using CSS classes and libraries
CSS-in-JS solutions
📍Day 9: React Router
Setting up React Router
Navigating between routes
Passing data through routes
📍Day 10: Context API and State Management
Introduction to the Context API
Creating and consuming context
Global state management with context
📍Day 11: Redux for State Management
What is Redux?
Actions, reducers, and the store
Integrating Redux into a React application
📍Day 12: React Hooks (useState, useEffect, etc.)
Introduction to React Hooks
useState, useEffect, and other commonly used hooks
Refactoring class components to functional components with hooks
📍Day 13: Error Handling and Debugging
Error boundaries
Debugging React applications
Error handling best practices
📍Day 14: Building and Optimizing for Production
Production builds and optimizations
Code splitting
Performance best practices
👨🏻💻Days 15-21: Working with External Data and APIs
📍Day 15: Fetching Data from an API
Making API requests in React
Handling API responses
Async/await in React
📍Day 16: Forms and Form Libraries
Working with form libraries like Formik or React Hook Form
Form validation and error handling
📍Day 17: Authentication and User Sessions
Implementing user authentication
Handling user sessions and tokens
Securing routes
📍Day 18: State Management with Redux Toolkit
Introduction to Redux Toolkit
Creating slices
Simplified Redux configuration
📍Day 19: Routing in Depth
Nested routing with React Router
Route guards and authentication
Advanced route configuration
📍Day 20: Performance Optimization
Memoization and useMemo
React.memo for optimizing components
Virtualization and large lists
📍Day 21: Real-time Data with WebSockets
WebSockets for real-time communication
Implementing chat or notifications
👨🏻💻Days 22-30: Building and Deployment
📍Day 22: Building a Full-Stack App
Integrating React with a backend (e.g., Node.js, Express, or a serverless platform)
Implementing RESTful or GraphQL APIs
📍Day 23: Testing in React
Testing React components using tools like Jest and React Testing Library
Writing unit tests and integration tests
📍Day 24: Deployment and Hosting
Preparing your React app for production
Deploying to platforms like Netlify, Vercel, or AWS
📍Day 25-30: Final Project
*_Plan, design, and build a complete React project of your choice, incorporating various concepts and tools you've learned during the previous days.
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING 👍👍
❤6
Data Analyst vs Data Engineer vs Data Scientist ✅
Skills required to become a Data Analyst 👇
- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic noscripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.
Skills required to become a Data Engineer: 👇
- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.
Skills required to become a Data Scientist: 👇
- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.
Bonus Skills Across All Roles:
- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
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Share with credits: https://news.1rj.ru/str/sqlspecialist
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Skills required to become a Data Analyst 👇
- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic noscripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.
Skills required to become a Data Engineer: 👇
- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.
Skills required to become a Data Scientist: 👇
- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.
Bonus Skills Across All Roles:
- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤5
Forwarded from Artificial Intelligence
The key to starting your AI career:
❌It's not your academic background
❌It's not previous experience
It's how you apply these principles:
1. Learn by building real AI models
2. Create a project portfolio
3. Make yourself visible in the AI community
No one starts off as an AI expert — but everyone can become one.
If you're aiming for a career in AI, start by:
⟶ Watching AI and ML tutorials
⟶ Reading research papers and expert insights
⟶ Doing internships or Kaggle competitions
⟶ Building and sharing AI projects
⟶ Learning from experienced ML/AI engineers
You'll be amazed how quickly you pick things up once you start doing.
So, start today and let your AI journey begin!
React ❤️ for more helpful tips
❌It's not your academic background
❌It's not previous experience
It's how you apply these principles:
1. Learn by building real AI models
2. Create a project portfolio
3. Make yourself visible in the AI community
No one starts off as an AI expert — but everyone can become one.
If you're aiming for a career in AI, start by:
⟶ Watching AI and ML tutorials
⟶ Reading research papers and expert insights
⟶ Doing internships or Kaggle competitions
⟶ Building and sharing AI projects
⟶ Learning from experienced ML/AI engineers
You'll be amazed how quickly you pick things up once you start doing.
So, start today and let your AI journey begin!
React ❤️ for more helpful tips
❤3
A–Z list of programming languages
A – Assembly
Low-level language used to communicate directly with hardware.
B – BASIC
Beginner’s All-purpose Symbolic Instruction Code – great for early learning.
C – C
Powerful systems programming language used in OS, embedded systems.
D – Dart
Used primarily for Flutter to build cross-platform mobile apps.
E – Elixir
Functional language for scalable, maintainable applications.
F – Fortran
One of the oldest languages, still used in scientific computing.
G – Go (Golang)
Developed by Google, known for its simplicity and performance.
H – Haskell
Purely functional language used in academia and finance.
I – Io
Minimalist prototype-based language with a small syntax.
J – Java
Versatile, object-oriented, used in enterprise, Android, and web apps.
K – Kotlin
Modern JVM language, official for Android development.
L – Lua
Lightweight noscripting language often used in game development.
M – MATLAB
Designed for numerical computing and simulations.
N – Nim
Statically typed compiled language that is fast and expressive.
O – Objective-C
Used mainly for macOS and iOS development (pre-Swift era).
P – Python
Beginner-friendly, widely used in data science, web, AI, automation.
Q – Q#
Quantum programming language developed by Microsoft.
R – Ruby
Elegant syntax, used in web development (especially Rails framework).
S – Swift
Apple’s modern language for iOS, macOS development.
T – TypeScript
Superset of JavaScript adding static types, improving large-scale JS apps.
U – Unicon
Language combining goal-directed evaluation with object-oriented features.
V – V
Simple, fast language designed for safety and readability.
W – Wolfram Language
Used in Mathematica, powerful for symbolic computation and math.
X – Xojo
Cross-platform app development language with a VB-like syntax.
Y – Yorick
Used in scientific simulations and numerical computation.
Z – Zig
Low-level, safe language for systems programming, alternative to C.
React ❤️ for more
A – Assembly
Low-level language used to communicate directly with hardware.
B – BASIC
Beginner’s All-purpose Symbolic Instruction Code – great for early learning.
C – C
Powerful systems programming language used in OS, embedded systems.
D – Dart
Used primarily for Flutter to build cross-platform mobile apps.
E – Elixir
Functional language for scalable, maintainable applications.
F – Fortran
One of the oldest languages, still used in scientific computing.
G – Go (Golang)
Developed by Google, known for its simplicity and performance.
H – Haskell
Purely functional language used in academia and finance.
I – Io
Minimalist prototype-based language with a small syntax.
J – Java
Versatile, object-oriented, used in enterprise, Android, and web apps.
K – Kotlin
Modern JVM language, official for Android development.
L – Lua
Lightweight noscripting language often used in game development.
M – MATLAB
Designed for numerical computing and simulations.
N – Nim
Statically typed compiled language that is fast and expressive.
O – Objective-C
Used mainly for macOS and iOS development (pre-Swift era).
P – Python
Beginner-friendly, widely used in data science, web, AI, automation.
Q – Q#
Quantum programming language developed by Microsoft.
R – Ruby
Elegant syntax, used in web development (especially Rails framework).
S – Swift
Apple’s modern language for iOS, macOS development.
T – TypeScript
Superset of JavaScript adding static types, improving large-scale JS apps.
U – Unicon
Language combining goal-directed evaluation with object-oriented features.
V – V
Simple, fast language designed for safety and readability.
W – Wolfram Language
Used in Mathematica, powerful for symbolic computation and math.
X – Xojo
Cross-platform app development language with a VB-like syntax.
Y – Yorick
Used in scientific simulations and numerical computation.
Z – Zig
Low-level, safe language for systems programming, alternative to C.
React ❤️ for more
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✅ Data Analytics Roadmap for Freshers in 2025 🚀📊
1️⃣ Understand What a Data Analyst Does
🔍 Analyze data, find insights, create dashboards, support business decisions.
2️⃣ Start with Excel
📈 Learn:
– Basic formulas
– Charts & Pivot Tables
– Data cleaning
💡 Excel is still the #1 tool in many companies.
3️⃣ Learn SQL
🧩 SQL helps you pull and analyze data from databases.
Start with:
– SELECT, WHERE, JOIN, GROUP BY
🛠️ Practice on platforms like W3Schools or Mode Analytics.
4️⃣ Pick a Programming Language
🐍 Start with Python (easier) or R
– Learn pandas, matplotlib, numpy
– Do small projects (e.g. analyze sales data)
5️⃣ Data Visualization Tools
📊 Learn:
– Power BI or Tableau
– Build simple dashboards
💡 Start with free versions or YouTube tutorials.
6️⃣ Practice with Real Data
🔍 Use sites like Kaggle or Data.gov
– Clean, analyze, visualize
– Try small case studies (sales report, customer trends)
7️⃣ Create a Portfolio
💻 Share projects on:
– GitHub
– Notion or a simple website
📌 Add visuals + brief explanations of your insights.
8️⃣ Improve Soft Skills
🗣️ Focus on:
– Presenting data in simple words
– Asking good questions
– Thinking critically about patterns
9️⃣ Certifications to Stand Out
🎓 Try:
– Google Data Analytics (Coursera)
– IBM Data Analyst
– LinkedIn Learning basics
🔟 Apply for Internships & Entry Jobs
🎯 Titles to look for:
– Data Analyst (Intern)
– Junior Analyst
– Business Analyst
💬 React ❤️ for more!
1️⃣ Understand What a Data Analyst Does
🔍 Analyze data, find insights, create dashboards, support business decisions.
2️⃣ Start with Excel
📈 Learn:
– Basic formulas
– Charts & Pivot Tables
– Data cleaning
💡 Excel is still the #1 tool in many companies.
3️⃣ Learn SQL
🧩 SQL helps you pull and analyze data from databases.
Start with:
– SELECT, WHERE, JOIN, GROUP BY
🛠️ Practice on platforms like W3Schools or Mode Analytics.
4️⃣ Pick a Programming Language
🐍 Start with Python (easier) or R
– Learn pandas, matplotlib, numpy
– Do small projects (e.g. analyze sales data)
5️⃣ Data Visualization Tools
📊 Learn:
– Power BI or Tableau
– Build simple dashboards
💡 Start with free versions or YouTube tutorials.
6️⃣ Practice with Real Data
🔍 Use sites like Kaggle or Data.gov
– Clean, analyze, visualize
– Try small case studies (sales report, customer trends)
7️⃣ Create a Portfolio
💻 Share projects on:
– GitHub
– Notion or a simple website
📌 Add visuals + brief explanations of your insights.
8️⃣ Improve Soft Skills
🗣️ Focus on:
– Presenting data in simple words
– Asking good questions
– Thinking critically about patterns
9️⃣ Certifications to Stand Out
🎓 Try:
– Google Data Analytics (Coursera)
– IBM Data Analyst
– LinkedIn Learning basics
🔟 Apply for Internships & Entry Jobs
🎯 Titles to look for:
– Data Analyst (Intern)
– Junior Analyst
– Business Analyst
💬 React ❤️ for more!
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