Course Description
QUICK FACTS
Introduction to the Data Science Lifecycle and Environment Setup (Anaconda, Jupyter).
Python Basics: Data Types, Variables, Control Flow, and Loops.
Advanced Python: Functions, Modules, and Exception Handling.
Introduction to NumPy for Numerical Data Processing.
Data Manipulation with Pandas: DataFrames, Cleaning, and Transformation.
Descriptive Statistics: Mean, Median, Mode, Variance, and Standard Deviation.
Inferential Statistics: Probability Distributions, Hypothesis Testing, and P-values.
Data Visualization with Matplotlib and Seaborn.
Identifying Patterns, Outliers, and Correlations in Large Datasets.
Feature Engineering and Data Pre-processing Techniques.
Linear and Logistic Regression for Predictive Modeling.
Decision Trees, Random Forests, and Ensemble Learning (Boosting/Bagging).
Support Vector Machines (SVM) and K-Nearest Neighbors (KNN).
Unsupervised Learning: K-Means Clustering and Principal Component Analysis (PCA).
Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, and ROC-AUC.
Introduction to Artificial Neural Networks (ANN) and Backpropagation.
Building Models with TensorFlow and Keras.
Convolutional Neural Networks (CNN) for Image Recognition.
Recurrent Neural Networks (RNN) and LSTMs for Time-Series Forecasting.
Hyperparameter Tuning and Optimization Algorithms.
Text Processing: Tokenization, Stemming, Lemmatization, and Stop-word Removal.
Sentiment Analysis and Text Classification using NLP.
Introduction to Large Language Models (LLMs) and GPT Architecture.
Prompt Engineering and Building Applications with LangChain.
Ethical AI: Bias Detection and Responsible AI Practices.
CAREER GROWTH
Climb the ladder of success with structured role progression.
The most effective project-based immersive learning experience The most effective project-based immersive learning experience The most effective project-based immersive learning experience
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