Curriculum

Externship Program - Applied Data Science

Module-1: Introduction to Data Science
  • What is data science
  •  Why is it important
  •  Use Cases of Data Science 
  • The Various Data Science Disciplines
  •  Data Science and Business Buzzwords 
  • What is the difference between Analysis and Analytics 
  • ML In Data Science 
  • Data Science Methodology

Module-2: Python for Data Science
  • Python Basics
  • Python Packages
  • Working with NUMPY
  • Working with Pandas 
  • Introduction to Data Visualization
  • Exploratory Data Analysis with Matplotlib and Seaborn
  • Basic Plotting with Matplotlib and Seaborn

Module-3: Mathematics for Data Science
  • Descriptive Statistics:
  1. Introduction to descriptive Statistics
  2. Mean, Median, Mode
  3. Skewness
  4. Range & IQR
  5. Sample vs. Population
  6. Variance & Standard deviation
  7. Impact of Scaling & Shifting
  • Distributions:
  1. What is a distribution?
  2. Normal distribution
  • Z-Scores
  • Central Limit Theorem
  • Hypothesis Testing
  • Correlation, And Regression
  • Linear Algebra
  • Calculus

Module-4: Data Wrangling Techniques
  • Introduction to Data preprocessing
  • Importing the Dataset
  • Handling Missing data
  • Working with categorical Data
  • Splitting the data into Train and Test set
  • Outlier Analysis
  • Feature Scaling

Module-5: Supervised Learning - Regression
  • Introduction to Regression
  • Linear Regression
  • Multi Linear Regression
  • Polynomial Regression
  • Ridge Regression
  • Lasso Regression

Module-6: Supervised Learning - Classification
  • Introduction to Classification
  • Logistic Regression
  • Decision Tree Classification
  • Random Forest Classification
  • K-nearest Neighbors
  • Naïve-Bayes
  • Support Vector Machine
  • Ensembling Techniques

Module-7: Model Evaluation Metrics
  • Regression Evaluation Metrics
  1. MAE
  2. MSE
  3. R Squared
  4. RMSE
  • Classification metrics
  1. Confusion Metrics
  2. Accuracy
  3. Precision
  4. Recall F1 Score
  5. AUC ROC Curves

Module-8: Model Hyper-parameter Optimization
  • Handling Imbalanced Data
  1. Oversampling
  2. Undersampling
  3. Ensembling Techniques
  4. SMOTE 
  • Hyper-parameter tuning
  1. Grid Search
  2. Randomize Search

Module-9: Unsupervised Learning
  •  Introduction to Clustering
  1. K-Means Clustering
  2. Hierarchical Clustering
  3. Clustering use cases

Module-10: Build & Deploy ML Application
  • Introduction to different modes of deployment
  • Working with Flask Framework
  • Building application with flask framework
  • Integrating Machine Learning model with web application
SmartInternz