37 Lessons( 10 week )

• Month 1-2: Foundations of Data Science
• 1. Overview of Data Science 0 minute
• 2. Python Basics for Data Science 0 minute
• 3. Jupyter Notebooks 0 minute
• Month 3-4: Data Wrangling and Feature Engineering
• 1. Handling Large Datasets with Pandas 0 minute
• 2. Time Series Data in Pandas 0 minute
• 3. Data Cleaning and Best Practices 0 minute
• Month 5: Advanced Topics in Data Science
• 1. Ensemble Methods (Random Forests, Gradient Boosting) 0 minute
• 2. Support Vector Machines (SVM) 0 minute
• 3. Neural Networks and Deep Learning 0 minute
• Month 6: Real-world Applications and Projects and Deployment.
• 1. Build a model to predict stock prices using historical data 0 minute
• 2. Create a weather prediction model based on meteorological data. 0 minute
• 3. Predict the outcome of a sports event using historical game statistics. 0 minute
• Month 6: Real-world Applications and Projects and Deployment.
• 1. Sentiment analysis on product reviews or social media comments. 0 minute
• 2. Text summarization tool using NLP techniques. 0 minute
• 3. Build a chatbot that can understand and respond to user queries. 0 minute
• Month 6: Real-world Applications and Projects and Deployment.
• 1. Create an image classification model for recognizing objects in images. 0 minute
• 2. Build a facial recognition system. 0 minute
• Month 6: Real-world Applications and Projects and Deployment.
• 1. Build a movie or music recommendation system 0 minute
• 2. Develop a personalized news recommendation engine. 0 minute
• 3. Create a recommendation system for an e-commerce platform. 0 minute
• Week 3-4: Advanced Python for Data Science
• 1. Advanced Python Programming Techniques 0 minute
• 2. NumPy and Advanced Array Manipulation 0 minute
• 3. Pandas for Data Manipulation 0 minute
• Week 5-6: Data Visualization and Exploration
• 1. Data Visualization with Matplotlib and Seaborn 0 minute
• 2. Interactive Visualizations with Plotly 0 minute
• 3. Exploratory Data Analysis (EDA) Techniques 0 minute
• Week 3-4: Feature Engineering and Dimensionality Reduction
• 1. Feature Engineering Techniques 0 minute
• 2. Handling Categorical Data in Machine Learning 0 minute
• 3. Dimensionality Reduction with PCA and t-SNE 0 minute
• Week 5-6: Model Building and Evaluation
• 1. Introduction to Machine Learning Algorithms 0 minute
• 2. • Model Training and Evaluation 0 minute
• Week 3-4: Natural Language Processing (NLP) and Text Mining
• 1. Introduction to NLP 0 minute
• 2. Text Preprocessing and Feature Extraction 0 minute
• 3. Sentiment Analysis and Named Entity Recognition 0 minute
• Week 5-6: Big Data and Cloud Computing
• 1. Introduction to Big Data (Hadoop, Spark) 0 minute
• 2. Cloud Platforms for Data Science (AWS, Google Cloud, Azure) 0 minute
• 3. Data Pipelining 0 minute