Month 1: Introduction to Python and Data Manipulation
Introduction to Python programming language
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Python libraries for data manipulation (NumPy, Pandas)
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Handling data structures (arrays, data frames)
00:00
Data cleaning and preprocessing techniques
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Month 2: Exploratory Data Analysis and Visualization
Statistical analysis with Python (descriptive statistics, hypothesis testing)
00:00
Data visualization using Matplotlib and Seaborn
00:00
Exploratory data analysis techniques
00:00
Data storytelling and communication
00:00
Month 3: Machine Learning Fundamentals
Introduction to machine learning concepts and algorithms
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Supervised learning (linear regression, logistic regression, decision trees, random forests)
00:00
Unsupervised learning (clustering, dimensionality reduction)
00:00
Model evaluation and validation techniques
00:00
Month 4: Advanced Machine Learning Techniques
Ensemble methods (bagging, boosting)
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Support vector machines (SVM)
00:00
Neural networks and deep learning
00:00
Model tuning and hyperparameter optimization
00:00
Month 5: Natural Language Processing (NLP)
Introduction to NLP concepts
00:00
Text preprocessing and tokenization
00:00
Sentiment analysis and text classification
00:00
Named Entity Recognition (NER) and text summarization
00:00
Month 6: Big Data Analytics with Python
Introduction to big data and distributed computing frameworks (Hadoop, Spark)
00:00
Working with large datasets using PySpark
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Data preprocessing and feature engineering for big data
00:00
Building machine learning models on big data
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Month 7: Time Series Analysis
Introduction to time series data
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Time series data preprocessing
00:00
Seasonality and trend analysis
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Month 8: Deep Learning and Computer Vision
Convolutional Neural Networks (CNN) for image recognition
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Transfer learning and pre-trained models (e.g., VGG, ResNet)
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Object detection and image segmentation
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Introduction to Generative Adversarial Networks (GANs)
00:00
Month 9: Capstone Project and Deployment
Undertake a data science project from end to end
00:00
Apply knowledge gained throughout the course to solve a real-world problem
00:00
Build a deployable data product or present actionable insights
00:00
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