π Roadmap to Master Data Science in 60 Days! ππ§
π Week 1β2: Foundations
πΉ Day 1β5: Python basics (variables, loops, functions)
πΉ Day 6β10: NumPy Pandas for data handling
π Week 3β4: Data Visualization Statistics
πΉ Day 11β15: Matplotlib, Seaborn, Plotly
πΉ Day 16β20: Descriptive stats, probability, distributions
π Week 5β6: Data Cleaning EDA
πΉ Day 21β25: Missing data, outliers, data types
πΉ Day 26β30: Exploratory Data Analysis (EDA) projects
π Week 7β8: Machine Learning
πΉ Day 31β35: Regression, Classification (Scikit-learn)
πΉ Day 36β40: Model tuning, metrics, cross-validation
π Week 9β10: Advanced Concepts
πΉ Day 41β45: Clustering, PCA, Time Series basics
πΉ Day 46β50: NLP or Deep Learning (basics with TensorFlow/Keras)
π Week 11β12: Projects Deployment
πΉ Day 51β55: Build 2 projects (e.g., Loan Prediction, Sentiment Analysis)
πΉ Day 56β60: Deploy using Streamlit, Flask + GitHub
π§° Tools to Learn:
β’ Jupyter, Google Colab
β’ Git GitHub
β’ Excel, SQL basics
β’ Power BI/Tableau (optional)
π¬ Tap β€οΈ for more!
π Week 1β2: Foundations
πΉ Day 1β5: Python basics (variables, loops, functions)
πΉ Day 6β10: NumPy Pandas for data handling
π Week 3β4: Data Visualization Statistics
πΉ Day 11β15: Matplotlib, Seaborn, Plotly
πΉ Day 16β20: Descriptive stats, probability, distributions
π Week 5β6: Data Cleaning EDA
πΉ Day 21β25: Missing data, outliers, data types
πΉ Day 26β30: Exploratory Data Analysis (EDA) projects
π Week 7β8: Machine Learning
πΉ Day 31β35: Regression, Classification (Scikit-learn)
πΉ Day 36β40: Model tuning, metrics, cross-validation
π Week 9β10: Advanced Concepts
πΉ Day 41β45: Clustering, PCA, Time Series basics
πΉ Day 46β50: NLP or Deep Learning (basics with TensorFlow/Keras)
π Week 11β12: Projects Deployment
πΉ Day 51β55: Build 2 projects (e.g., Loan Prediction, Sentiment Analysis)
πΉ Day 56β60: Deploy using Streamlit, Flask + GitHub
π§° Tools to Learn:
β’ Jupyter, Google Colab
β’ Git GitHub
β’ Excel, SQL basics
β’ Power BI/Tableau (optional)
π¬ Tap β€οΈ for more!
π Data Science Project Ideas for Beginners
1. Exploratory Data Analysis (EDA): Choose a dataset from Kaggle or UCI and perform EDA to uncover insights. Use visualization tools like Matplotlib and Seaborn to showcase your findings.
2. Titanic Survival Prediction: Use the Titanic dataset to build a predictive model using logistic regression. This project will help you understand classification techniques and data preprocessing.
3. Movie Recommendation System: Create a simple recommendation system using collaborative filtering. This project will introduce you to user-based and item-based filtering techniques.
4. Stock Price Predictor: Develop a model to predict stock prices using historical data and time series analysis. Explore techniques like ARIMA or LSTM for this project.
5. Sentiment Analysis on Twitter Data: Scrape Twitter data and analyze sentiments using Natural Language Processing (NLP) techniques. This will help you learn about text processing and sentiment classification.
6. Image Classification with CNNs: Build a convolutional neural network (CNN) to classify images from a dataset like CIFAR-10. This project will give you hands-on experience with deep learning.
7. Customer Segmentation: Use clustering techniques on customer data to segment users based on purchasing behavior. This project will enhance your skills in unsupervised learning.
8. Web Scraping for Data Collection: Build a web scraper to collect data from a website and analyze it. This project will introduce you to libraries like BeautifulSoup and Scrapy.
9. House Price Prediction: Create a regression model to predict house prices based on various features. This project will help you practice regression techniques and feature engineering.
10. Interactive Data Visualization Dashboard: Use libraries like Dash or Streamlit to create a dashboard that visualizes data insights interactively. This will help you learn about data presentation and user interface design.
Start small, and gradually incorporate more complexity as you build your skills. These projects will not only enhance your resume but also deepen your understanding of data science concepts.
Like if you need similar content ππ
ENJOY LEARNING ππ
1. Exploratory Data Analysis (EDA): Choose a dataset from Kaggle or UCI and perform EDA to uncover insights. Use visualization tools like Matplotlib and Seaborn to showcase your findings.
2. Titanic Survival Prediction: Use the Titanic dataset to build a predictive model using logistic regression. This project will help you understand classification techniques and data preprocessing.
3. Movie Recommendation System: Create a simple recommendation system using collaborative filtering. This project will introduce you to user-based and item-based filtering techniques.
4. Stock Price Predictor: Develop a model to predict stock prices using historical data and time series analysis. Explore techniques like ARIMA or LSTM for this project.
5. Sentiment Analysis on Twitter Data: Scrape Twitter data and analyze sentiments using Natural Language Processing (NLP) techniques. This will help you learn about text processing and sentiment classification.
6. Image Classification with CNNs: Build a convolutional neural network (CNN) to classify images from a dataset like CIFAR-10. This project will give you hands-on experience with deep learning.
7. Customer Segmentation: Use clustering techniques on customer data to segment users based on purchasing behavior. This project will enhance your skills in unsupervised learning.
8. Web Scraping for Data Collection: Build a web scraper to collect data from a website and analyze it. This project will introduce you to libraries like BeautifulSoup and Scrapy.
9. House Price Prediction: Create a regression model to predict house prices based on various features. This project will help you practice regression techniques and feature engineering.
10. Interactive Data Visualization Dashboard: Use libraries like Dash or Streamlit to create a dashboard that visualizes data insights interactively. This will help you learn about data presentation and user interface design.
Start small, and gradually incorporate more complexity as you build your skills. These projects will not only enhance your resume but also deepen your understanding of data science concepts.
Like if you need similar content ππ
ENJOY LEARNING ππ
π Admissions Open | Academic Year 2026β27
The Department of Computer Science at Guru Nanak Khalsa College, Matunga, Mumbai (Autonomous) invites applications for its Undergraduate and Postgraduate programs.
π‘ With a strong focus on practical learning, industry exposure, and innovative pedagogy, we prepare students for successful careers in technology and data-driven fields.
π Programs Offered:
β’ B.Sc. Computer Science
β’ BCA (Bachelor of Computer Application)
β’ M.Sc. Computer Science
β’ M.Sc. Big Data Analytics
β¨ Why Choose Us?
β NEP 2020 aligned curriculum
β Dedicated labs & experienced faculty
β Industry-oriented projects & workshops
β Internship & on-the-job training opportunities
π© For enquiries, please email us at:
hod.cs@gnkhalsa.edu.in
π Learn more: https://gnkhalsa.edu.in/
π Nathalal Parekh Marg, Matunga East, Mumbai
The Department of Computer Science at Guru Nanak Khalsa College, Matunga, Mumbai (Autonomous) invites applications for its Undergraduate and Postgraduate programs.
π‘ With a strong focus on practical learning, industry exposure, and innovative pedagogy, we prepare students for successful careers in technology and data-driven fields.
π Programs Offered:
β’ B.Sc. Computer Science
β’ BCA (Bachelor of Computer Application)
β’ M.Sc. Computer Science
β’ M.Sc. Big Data Analytics
β¨ Why Choose Us?
β NEP 2020 aligned curriculum
β Dedicated labs & experienced faculty
β Industry-oriented projects & workshops
β Internship & on-the-job training opportunities
π© For enquiries, please email us at:
hod.cs@gnkhalsa.edu.in
π Learn more: https://gnkhalsa.edu.in/
π Nathalal Parekh Marg, Matunga East, Mumbai
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