Data Science course details

  • Course Duration150 Hrs
  • New Batch StartsEvery Monday
  • Mode of TrainingClassRoom Online

Course content

  • Summary Measures
  • Hypothesis Testing
  • Crosstabs, Correlations & ANOVA
  • Data Types
  • Variables
  • Strings
  • Lists
  • Tuples
  • Dictionaries
  • Sets
  • Conditional Statements
  • Loops
  • Python Comprehension
  • Working with Files
  • Functions
  • Classes
  • Pandas
  • Numpy
Data Preprocessing
  • Data Normalization
  • Handling Skew Data
  • Handling Missing Data
Feature Engineering
  • Variance based filtering
  • Correlation based filtering
  • Features Creation
  • Techniques to create new features
  • Feature Selection
  • Statistical feature selection
  • Model based feature selection
  • Feature Extraction & Transformation
Data Visualizationc
  • Categorical variables vs Continuous Variables
  • Various graphs using Matplotlib, Seaborn
  • Linear Regression
  • Logistic Regression
  • Support Vector Machines
  • CART
  • CHAID
  • Decision Trees
  • Random Forest
  • K-Nearest Neighbors
  • Naïve Bayes
  • Content based learning approaches
  • Collaborative Filtering Approaches
  • Algorithms
    • UBCF
    • IBCF
  • Overfitting Control Techniques
  • Latent Factor Learning Approaches
  • Top-N Recommenders
  • Evaluation Metrics
    • Accuracy
    • Error Rate
  • Rating Prediction
    • RMSE
  • Iterative Algorithm
    • K-Means
    • K-medoids
    • K-Prototype
  • Density based Algorithms
    • DB-SCAN
  • Linear PCA
  • Non-linear PCA
  • t-SNE

FAQS

DataScience FAQs and Prerequisites