1: Introduction to Data Science: Definition, Roles, and Skill sets 
2: Introduction to various Data Science Tools: Python, R, SQL, Jupyter Notebooks, etc.
3: Installation and Setup of Tools
4: Introduction to Python Programming: Variables, Types, Lists, and Dictionaries

1. Python Conditional and Looping Structures
2. Python Functions, Modules, and Libraries

3. Introduction to NumPy 

1. Introduction to Pandas: Series and DataFrames
2. Data Cleaning and Preparation with Pandas

1. Descriptive Statistics and Visualizations with Matplotlib and Seaborn
2. EDA Case Study 

1. Correlation and Regression Analysis
2. Introduction to Bayesian Statistics 

1. Supervised Learning: Linear Regression
2. Supervised Learning: Logistic Regression
3. Introduction to Classification: k-NN, Decision Trees 

1. Unsupervised Learning: Clustering, k-Means, Hierarchical
2. Unsupervised Learning: Dimension Reduction, PCA
3. Model Evaluation and Validation: Confusion Matrix, ROC Curve, Cross-Validation (2 hours)

1. Ensemble Methods: Random Forests, Bagging, Boosting
2. Support Vector Machines 
3. Introduction to Neural Networks 

1. Introduction to Natural Language Processing
2. Introduction to Large Language Models (LLMs) 

1. Introduction to Convolution Neural Networks (CNN)
2. Introduction to Recurrent Neural Networks (RNN) 

1. Introduction to Hadoop, Spark
2. Introduction to Visualisation: Tableau and Power BI 

1. End-to-end Data Science Project Walkthrough (6 hours)

We prepare you for real-world interviews by providing one-on-one career coaching, mock interviews, and resume/LinkedIn editing.

You will work with senior Data Scientists/analysts who make hiring decisions.