Data Science Course

• Variables and Datatypes
• Data Structures
• Conditional and Looping Statements
• Functions

• NumPy arrays and functions 
• Accessing and modifying NumPy arrays 
• Saving and loading NumPy arrays 
• Pandas Series (Creating, Accessing, and Modifying Series) 
• Pandas DataFrames (Creating, Accessing, Modifying, and Combining DataFrames) 
• Pandas Functions 
• Saving and loading datasets using Pandas

• Data overview
• Univariate analysis (Histogram, Boxplots, and Bar graphs)
• Bivariate/Multivariate analysis (Line Plot, Scatterplot, Lmplot, Jointplot, Violin Plot, Striplot, Swarmplot, Catplot, Pairplot, Heatmap)
• Customizing of Plots
• Missing value treatment
• Outlier detection and treatment

• Experiments, Events, and Definition of Probability
• Introduction to Inferential Statistics
• Introduction to Probability Distributions (Random Variable, Discrete and Continuous Random Variables, Probability Distributions)
• Binomial Distribution
• Normal Distribution

• Sampling
• Central Limit Theorem
• Estimation
• Introduction to Hypothesis Testing
• Hypothesis Formulation and Performing a Hypothesis Test
• One-tailed and Two-tailed Tests
• Confidence Intervals and Hypothesis Testing

• Test for one mean
• Test for equality of means
• Chi-square Test of independence
• One-way ANOVA

• Introduction to learning from data
• Simple and Multiple Linear Regression
• Evaluating a regression model
• Pros and Cons of Linear Regression

• Statistician vs ML Practitioner
• Linear Regression Assumptions
• Statistical Inferences from a Linear Regression Model

• Introduction to Logistic Regression
• Interpretation from a Logistic Regression model
• Changing the threshold of a Logistic Regression model
• Evaluation of a classification model, Pros and Cons

• Introduction to Decision Tree
• Different impurity measures
• Splitting criteria in a Decision Tree 
• Methods of Pruning a Decision Tree
• Regression Trees, Pros and Cons

• Introduction to Ensemble Techniques
• Introduction to Bagging
• Sampling with Replacement
• Introduction to Random Forest

• Introduction to Boosting
• Boosting Algorithms (Adaboost, Gradient Boost, XGBoost)
• Stacking

• Feature Engineering
• Cross-validation
• Oversampling and Undersampling
• Model Tuning and Performance
• Hyperparameter Tuning
• Grid Search
• Random Search
• Regularization

• Introduction to Clustering
• Types of Clustering
• K-means Clustering
• Importance of Scaling
• Silhouette Score
• Visual Analysis of Clustering

• Hierarchical Clustering
• Cophenetic Correlation
• Introduction to Dimensionality Reduction
• Principal Component Analysis

• Supervised vs Unsupervised Machine Learning
• Generative AI vs Discriminative AI
• Brief Timeline of Generative AI
• Overview of Generative Models
• Generative AI Business Applications

• Introduction to Prompts
• The Need for Prompt Engineering
• Different Types of Prompts (Conditional, Few-shot, Chain-of-thought, Returning Structured Output)
• Limitations of Prompt Engineering

• Introduction to Databases and SQL
• Fetching data
• Filtering data
• Aggregating data

• In-built functions (Numeric, Datetime, Strings)
• Joins
• Window functions

• Subqueries
• Order of query execution

This course provides you with a fundamental understanding of the basics of Python programming and builds a strong foundation of the basics of coding to build Data Science applications

• Introduction to Python Programming
• Data Science Application Case Study

In this course, you will learn how to read, explore, and effectively visualize data using Tableau and tell stories by analyzing data using Tableau dashboards

• Storytelling with Data
• Creating Interactive Dashboards

In this course, you will learn the role of model deployment in realizing the value of an ML model and how to build and deploy an application using Python.

• Introduction to Model Deployment
• Serialization and Containerization