Syllabus of Data Science

Course Content:


1. Python and Statistics for Data Science
Basics of Python
Fundamentals of Statistics
Probability
Linear Algebra
Calculus
2. Data Analysis and Visualization with Python
Introduction to NumPy
NumPy Arrays
Mathematical operations in NumPy
NumPy Array manipulation
NumPy Array broadcasting
Data Manipulation with Pandas
Data Structures in Pandas - Series and Data Frames
Data cleaning in Pandas
Data manipulation in Pandas
Data Structures in Pandas - Series and Data Frames
Data cleaning in Pandas
Data manipulation in Pandas
Handling missing values in datasets
Data Visualization
Visualization with Python
Plotting basic charts in Python
Data visualization with Matplotlib
Statistical data visualization with Seaborn
3. Machine Learning
Introduction to Machine Learning (ML)
What is Machine Learning ?
Use Cases of Machine Learning
Types of Machine Learning - Supervised, Unsupervised, Reinforcement Machine Learning workflow
Supervised Learning
Regression
Multi Linear Regression
Introduction to Linear Regression
Use cases of Linear Regression
Fitting a Linear Regression model
Evaluating and interpreting results from Linear Regression models
Classification
Logistic Regression
Introduction to Logistic Regression
Logistic Regression use cases
Understand use of Sigmoid function to perform logistic regression.
Model Evaluation Techniques
Introduction to evaluation metrics and model selection in Machine Learning
Importance of Confusion matrix for predictions
Measures of model evaluation - Sensitivity, specificity, precision, recall & f-score
Use ROC curve to decide best model
Decision trees & Random Forests
Introduction to Decision Trees & Random Forest
Understanding criterion (Entropy & Information Gain) used in Decision Trees
Using Ensemble methods in Decision Trees Applications of Random Forest.
Support vector machines (SVM)
Introduction to SVM
Figure decision boundaries using support vectors
Identify hyperplane in SVM
Applications of SVM in Machine Learning
Unsupervised Learning
Clustering
K-Means
Introduction to K-means clustering
Decide clusters by adjusting centroids
Find optimal 'k value' in kmeans
Applications of clustering in Machine Learning
Recommendation Systems
KNN (K- Nearest neighbors)
Introduction to KNN
Calculate neighbors using distance measures
Find optimal value of K in KNN method
Advantage & disadvantages of KNN
Dimensionality Reduction
Introduction to Curse of Dimensionality
What is dimensionality reduction?
PCA to reduce dimensions
Applications of Principle component Analysis (PCA)
4. Deep Learning Foundation
5. Introduction to Computer Vision
6. Introduction to Natural Language Processing