Industry Data Science vs Academia Data Science
Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different!
5 big differences between Data Science in academia and in industry 👇:
1️⃣ Model vs Data: Academia focuses on models, industry focuses on data. In academia, it’s all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job.
2️⃣ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case.
3️⃣ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isn’t always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, it’s all about simplicity: trying to find the simplest solution that solves a specific problem.
4️⃣ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless.
5️⃣ Knowledge impact vs $ impact: In academia, it’s all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.
Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different!
5 big differences between Data Science in academia and in industry 👇:
1️⃣ Model vs Data: Academia focuses on models, industry focuses on data. In academia, it’s all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job.
2️⃣ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case.
3️⃣ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isn’t always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, it’s all about simplicity: trying to find the simplest solution that solves a specific problem.
4️⃣ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless.
5️⃣ Knowledge impact vs $ impact: In academia, it’s all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.
👍17❤10
If I were to start my Machine Learning career from scratch (as an engineer), I'd focus here (no specific order):
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
🔥17👍6
For those of you who are new to Data Science and Machine learning algorithms, let me try to give you a brief overview. ML Algorithms can be categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content
ENJOY LEARNING 👍👍
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content
ENJOY LEARNING 👍👍
👍23❤5🔥2👏1🤔1
Time Complexity of 10 Most Popular ML Algorithms
.
.
When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets.
For instance,
1️⃣ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications.
2️⃣ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively.
3️⃣ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure.
4️⃣ K-Nearest Neighbours is simple but can become slow with large datasets due to distance calculations.
5️⃣ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.
.
.
When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets.
For instance,
1️⃣ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications.
2️⃣ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively.
3️⃣ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure.
4️⃣ K-Nearest Neighbours is simple but can become slow with large datasets due to distance calculations.
5️⃣ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.
👍12❤3🔥3
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✔ Certification of participation
✔ Practical datasets for hands-on practice
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Are you looking to become a machine learning engineer? The algorithm brought you to the right place! 📌
I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
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 😊
I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
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 😊
👍10❤6
Here is how you can explain your project in an interview
When you’re in an interview, it’s super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:
➤ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.
➤ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.
➤ 𝗣𝗿𝗼𝗽𝗼𝘀𝗲𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?
➤ 𝗬𝗼𝘂𝗿 𝗥𝗼𝗹𝗲:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure it’s clear whether you were leading the project, a key player, or supporting the team.
➤ 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗼𝗼𝗹𝘀:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.
➤ 𝗜𝗺𝗽𝗮𝗰𝘁 𝗮𝗻𝗱 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got. This helps show the project was a success and highlights your contribution.
➤ 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻:
- If you worked with a team, talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the team’s success?
➤ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁:
- Reflect on what you learned from the project. How did it help you grow professionally? What new skills did you gain, and what would you do differently next time?
➤ 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- Know why you chose the project, what your role was, what decisions you made, and how the results compared to what you expected.
- Be clear on the scope of the project whether it was a long-term effort or a quick task.
- If there’s a pause after you describe the project, don’t hesitate to ask if they’d like more details or if there’s a specific part they’re interested in.
Remember, 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗸𝗲𝘆. You might have done great work, but if you don’t explain it well, it’s hard for the interviewer to understand your impact. So, practice explaining your projects with clarity.
When you’re in an interview, it’s super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:
➤ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.
➤ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.
➤ 𝗣𝗿𝗼𝗽𝗼𝘀𝗲𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?
➤ 𝗬𝗼𝘂𝗿 𝗥𝗼𝗹𝗲:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure it’s clear whether you were leading the project, a key player, or supporting the team.
➤ 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗼𝗼𝗹𝘀:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.
➤ 𝗜𝗺𝗽𝗮𝗰𝘁 𝗮𝗻𝗱 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got. This helps show the project was a success and highlights your contribution.
➤ 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻:
- If you worked with a team, talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the team’s success?
➤ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁:
- Reflect on what you learned from the project. How did it help you grow professionally? What new skills did you gain, and what would you do differently next time?
➤ 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- Know why you chose the project, what your role was, what decisions you made, and how the results compared to what you expected.
- Be clear on the scope of the project whether it was a long-term effort or a quick task.
- If there’s a pause after you describe the project, don’t hesitate to ask if they’d like more details or if there’s a specific part they’re interested in.
Remember, 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗸𝗲𝘆. You might have done great work, but if you don’t explain it well, it’s hard for the interviewer to understand your impact. So, practice explaining your projects with clarity.
👍13❤5
1³+2³+3³+4³+5³+6³+7³+8³+9³
= 2025 😊 Happy New Year 🥳
= 2025 😊 Happy New Year 🥳
❤51👍7
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* 👁 Computer Vision & Image Processing.
* 📚 Exclusive Access to Research Papers.
Why Us?
* Connect with experts and enthusiasts.
* Stay updated, stay ahead.
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Ready for a deep dive? Click here to explore, learn, and grow with
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@MachineLearning_Programming!
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Forwarded from Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert
In every family tree, there is 1 person who breaks out the middle-class chain and works hard to become a millionaire and changes the lives of everyone forever.
May that be you in 2025.
Happy New Year!
May that be you in 2025.
Happy New Year!
❤53
Complete Roadmap to become a data scientist in 5 months
Free Resources to learn Data Science: https://news.1rj.ru/str/datasciencefun
Week 1-2: Fundamentals
- Day 1-3: Introduction to Data Science, its applications, and roles.
- Day 4-7: Brush up on Python programming.
- Day 8-10: Learn basic statistics and probability.
Week 3-4: Data Manipulation and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.
Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.
Work on Data Science Projects: https://news.1rj.ru/str/pythonspecialist/29
Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.
Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.
Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).
Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).
Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).
Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.
Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.
Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.
Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.
Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.
Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.
Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.
Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.
Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.
ENJOY LEARNING 👍👍
Free Resources to learn Data Science: https://news.1rj.ru/str/datasciencefun
Week 1-2: Fundamentals
- Day 1-3: Introduction to Data Science, its applications, and roles.
- Day 4-7: Brush up on Python programming.
- Day 8-10: Learn basic statistics and probability.
Week 3-4: Data Manipulation and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.
Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.
Work on Data Science Projects: https://news.1rj.ru/str/pythonspecialist/29
Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.
Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.
Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).
Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).
Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).
Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.
Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.
Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.
Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.
Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.
Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.
Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.
Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.
Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.
ENJOY LEARNING 👍👍
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🚨30 FREE Dataset Sources for Data Science Projects🔥
Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/
US Government Dataset: https://www.data.gov/
Open Government Data (OGD) Platform India: https://data.gov.in/
The World Bank Open Data: https://data.worldbank.org/
Data World: https://data.world/
BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics
The Humanitarian Data Exchange (HDX): https://data.humdata.org/
Data at World Health Organization (WHO): https://www.who.int/data
FBI’s Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/
AWS Open Data Registry: https://registry.opendata.aws/
FiveThirtyEight: https://data.fivethirtyeight.com/
IMDb Datasets: https://www.imdb.com/interfaces/
Kaggle: https://www.kaggle.com/datasets
UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php
Google Dataset Search: https://datasetsearch.research.google.com/
Nasdaq Data Link: https://data.nasdaq.com/
Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Reddit - Datasets: https://www.reddit.com/r/datasets/
Open Data Network by Socrata: https://www.opendatanetwork.com/
Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/
Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/
IEEE Data Port: https://ieee-dataport.org/
Wikipedia: Database: https://dumps.wikimedia.org/
BuzzFeed News: https://github.com/BuzzFeedNews/everything
Academic Torrents: https://academictorrents.com/
Yelp Open Dataset: https://www.yelp.com/dataset
The NLP Index by Quantum Stat: https://index.quantumstat.com/
Computer Vision Online: http://www.computervisiononline.com/dataset
Visual Data Discovery: https://www.visualdata.io/
Roboflow Public Datasets: https://public.roboflow.com/
Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets
Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/
US Government Dataset: https://www.data.gov/
Open Government Data (OGD) Platform India: https://data.gov.in/
The World Bank Open Data: https://data.worldbank.org/
Data World: https://data.world/
BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics
The Humanitarian Data Exchange (HDX): https://data.humdata.org/
Data at World Health Organization (WHO): https://www.who.int/data
FBI’s Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/
AWS Open Data Registry: https://registry.opendata.aws/
FiveThirtyEight: https://data.fivethirtyeight.com/
IMDb Datasets: https://www.imdb.com/interfaces/
Kaggle: https://www.kaggle.com/datasets
UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php
Google Dataset Search: https://datasetsearch.research.google.com/
Nasdaq Data Link: https://data.nasdaq.com/
Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Reddit - Datasets: https://www.reddit.com/r/datasets/
Open Data Network by Socrata: https://www.opendatanetwork.com/
Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/
Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/
IEEE Data Port: https://ieee-dataport.org/
Wikipedia: Database: https://dumps.wikimedia.org/
BuzzFeed News: https://github.com/BuzzFeedNews/everything
Academic Torrents: https://academictorrents.com/
Yelp Open Dataset: https://www.yelp.com/dataset
The NLP Index by Quantum Stat: https://index.quantumstat.com/
Computer Vision Online: http://www.computervisiononline.com/dataset
Visual Data Discovery: https://www.visualdata.io/
Roboflow Public Datasets: https://public.roboflow.com/
Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets
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