Pandas for Data Science

Complete Roadmap to become a data scientist in 5 months

Free Resources to learn Data Science: https://t.me/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://t.me/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 👍👍

🚨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 Science Resolution for 2025

Data Science Interview Cheat Sheet! 🧠

1️⃣ Key Concepts
Master statistics, machine learning, and programming basics. They’re always top priorities!

2️⃣ Essential Tools
Know your way around Python, SQL, and data visualization platforms like Tableau or Power BI.

3️⃣ Real-World Projects
Be ready to explain your projects—what problem you solved, how you did it, and the results you achieved! 🌟

4️⃣ Problem-Solving Skills
Practice coding challenges and case studies.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

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.

Machine Learning Roadmap

Who is Data Scientist?

He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.

A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:

Determines correct datasets and variables.

Identifies the most challenging data-analytics problems.

Collects large sets of data- structured and unstructured, from different sources.

Cleans and validates data ensuring accuracy, completeness, and uniformity.

Builds and applies models and algorithms to mine stores of big data.

Analyzes data to recognize patterns and trends.

Interprets data to find solutions.

Communicates findings to stakeholders using tools like visualization.

𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 (𝗡𝗼 𝗦𝘁𝗿𝗶𝗻𝗴𝘀 𝗔𝘁𝘁𝗮𝗰𝗵𝗲𝗱)

𝗡𝗼 𝗳𝗮𝗻𝗰𝘆 𝗰𝗼𝘂𝗿𝘀𝗲𝘀, 𝗻𝗼 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀, 𝗷𝘂𝘀𝘁 𝗽𝘂𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.

𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘:

1️⃣ Python Programming for Data Science → Harvard’s CS50P
The best intro to Python for absolute beginners:
↬ Covers loops, data structures, and practical exercises.
↬ Designed to help you build foundational coding skills.

Link: https://cs50.harvard.edu/python/

https://t.me/datasciencefun

2️⃣ Statistics & Probability → Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
↬ Clear, beginner-friendly videos.
↬ Exercises to test your skills.

Link: https://www.khanacademy.org/math/statistics-probability

https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

3️⃣ Linear Algebra for Data Science → 3Blue1Brown
↬ Learn about matrices, vectors, and transformations.
↬ Essential for machine learning models.

Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr

4️⃣ SQL Basics → Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
↬ Writing queries, joins, and filtering data.
↬ Real-world datasets to practice.

Link: https://mode.com/sql-tutorial

https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

5️⃣ Data Visualization → freeCodeCamp
Learn to create stunning visualizations using Python libraries:
↬ Covers Matplotlib, Seaborn, and Plotly.
↬ Step-by-step projects included.

Link: https://www.youtube.com/watch?v=JLzTJhC2DZg

https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34

6️⃣ Machine Learning Basics → Google’s Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
↬ Learn supervised and unsupervised learning.
↬ Hands-on coding with TensorFlow.

Link: https://developers.google.com/machine-learning/crash-course

7️⃣ Deep Learning → Fast.ai’s Free Course
Fast.ai makes deep learning easy and accessible:
↬ Build neural networks with PyTorch.
↬ Learn by coding real projects.

Link: https://course.fast.ai/

8️⃣ Data Science Projects → Kaggle
↬ Compete in challenges to practice your skills.
↬ Great way to build your portfolio.

Link: https://www.kaggle.com/

Machine Learning Algorithms and Frameworks

Top 10 important data science concepts

1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.

2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.

3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.

4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.

5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.

6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.

7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.

8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.

9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.

10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.me/datasciencefun

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🔹 Supervised Learning - Key Algorithms 🔹

1️⃣ Linear Regression – Predicts continuous values by fitting a straight line. (📈 House prices)
2️⃣ Logistic Regression – Classifies data into categories (yes/no). (📩 Spam detection)
3️⃣ SVM (Support Vector Machine) – Finds the best boundary to separate classes. (🚀 Image classification)
4️⃣ Decision Tree – Splits data based on conditions to classify. (🌳 Diagnosing diseases)
5️⃣ Random Forest – Multiple decision trees combined for accuracy. (🏦 Loan predictions)
6️⃣ k-NN (k-Nearest Neighbors) – Classifies based on the nearest neighbors. (🛒 Product recommendations)
7️⃣ Naive Bayes – Uses probability to classify data. (📨 Spam filter)
8️⃣ Gradient Boosting – Combines weak models to build a strong one. (📊 Customer churn prediction)
9️⃣ XGBoost – Faster and more efficient gradient boosting. (🏆 Machine learning competitions)

✨ Key Tip: Choose algorithms based on data type (classification/regression)

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.me/datasciencefun

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Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining

Ai terms you should know

The most popular programming languages:

1. Python
2. TypeScript
3. JavaScript
4. C#
5. HTML
6. Rust
7. C++
8. C
9. Go
10. Lua
11. Kotlin
12. Java
13. Swift
14. Jupyter Notebook
15. Shell
16. CSS
17. GDScript
18. Solidity
19. Vue
20. PHP
21. Dart
22. Ruby
23. Objective-C
24. PowerShell
25. Scala

According to the Latest GitHub Repositories

Here are 10 project ideas to work on for Data Analytics

1. Customer Churn Prediction: Predict customer churn for subscription-based services. Skills: EDA, classification models. Tools: Python, Scikit-Learn.
2. Retail Sales Forecasting: Forecast sales using historical data. Skills: Time series analysis. Tools: Python, Statsmodels.
3. Sentiment Analysis: Analyze sentiments in product reviews or tweets. Skills: Text processing, NLP. Tools: Python, NLTK.
4. Loan Approval Prediction: Predict loan approvals based on credit risk. Skills: Classification models. Tools: Python, Scikit-Learn.
5. COVID-19 Data Analysis: Explore and visualize COVID-19 trends. Skills: EDA, visualization. Tools: Python, Tableau.
6. Traffic Accident Analysis: Discover patterns in traffic accidents. Skills: Clustering, heatmaps. Tools: Python, Folium.
7. Movie Recommendation System: Build a recommendation system using user ratings. Skills: Collaborative filtering. Tools: Python, Scikit-Learn.
8. E-commerce Analysis: Analyze top-performing products in e-commerce. Skills: EDA, association rules. Tools: Python, Apriori.
9. Stock Market Analysis: Analyze stock trends using historical data. Skills: Moving averages, sentiment analysis. Tools: Python, Matplotlib.
10. Employee Attrition Analysis: Predict employee turnover. Skills: Classification models, HR analytics. Tools: Python, Scikit-Learn.

And this is how you can work on

Here’s a compact list of free resources for working on data analytics projects:

1. Datasets
• Kaggle Datasets: Wide range of datasets and community discussions.
• UCI Machine Learning Repository: Great for educational datasets.
• Data.gov: U.S. government datasets (e.g., traffic, COVID-19).
2. Learning Platforms
• YouTube: Channels like Data School and freeCodeCamp for tutorials.
• 365DataScience: Data Science & AI Related Courses
3. Tools
• Google Colab: Free Jupyter Notebooks for Python coding.
• Tableau Public & Power BI Desktop: Free data visualization tools.
4. Project Resources
• Kaggle Notebooks & GitHub: Code examples and project walk-throughs.
• Data Analytics on Medium: Project guides and tutorials.

ENJOY LEARNING ✅️✅️

#datascienceprojects

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://t.me/datasciencefun

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An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

Basically, there are 3 different layers in a neural network :

Input Layer (All the inputs are fed in the model through this layer)

Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)

Output Layer (The data after processing is made available at the output layer)

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

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