Foundation + Core +Tools

Foundation

Statistics
Probability
Linear Algebra
Calculus

100 Hrs

Core

Data Analytics
Machine Learning
Deep Learning
Data Visualization

100 Hrs

Tools

Python
SQL
Tablleau
 

100 Hrs

5 Modules + Capstone Project

Foundations + Tools + Data Analytics
  • Python programming -basics .
  • Descriptive, Predictive & Prescriptive analytics .
  • Variable types.
  • Measures of central tendency & dispersion.
  • Key visualization charts and tables .
  • Sampling.
  • Central limit theorem .
  • Law of large numbers.
  • Laws of probability and their applications.
  • Hypothesis testing.
  • 4 key statistical distributions.
    • Normal distribution.
    • T -distribution
    • Chi Square Distribution
    • F – Distribution
  • Estimation
    • P-value.
    • Confidence interval
    • Test of significance
  • Introducing Linear Regression.
    • Correlation.
    • Concept of best fit line.
      • R squared and Adjusted R squared.
    • Outliers and their effects
    • Regression vs Causation.
    • Error Metrics.-RMSE,MAPE,
Foundations + Tools + Machine Learning 1
  • Statistical learning vs Machine learning.
  • Models and modelling.
  • Linear Algebra basics.
    • Principal Component Analysis (PCA).
  • Vectors and Calculus.
  • Gradient descent vs Ordinary Least Square Method.
  • Python programming for Machine learning -Key libraries.
    • Pandas
    • Numpy
    • Scikit learn
    • Matplotlib
    • Seaborn
    • Scipy
  • Data Cleaning
    • Null value and Outlier identification and imputation techniques.
  • Data Transformations.
    • Common transformation techniques for categorical variables.
    • Feature scaling techniques.
    • PCA
  • Data visualization using python libraries
  • Solving real life problems through linearity and non linearity cases.
    • Multiple Linear Regression
    • Ridge Regression
    • Lasso regression
    • Elastic net
  • Feature Engineering :
    • Curse of dimensionality
    • Variable selection strategy
    • One hot encoding for categorical variables
    • Label encoding for categorical variables
  • Bias -Variance trade offs :
    • Overfitting vs Underfitting
    • Regression Errors and diagnostics
  • Parametric vs Non parametric models :
    • Explain ability vs Black box
    • Industry applications
  • Data split techniques
    • Cross validation vs train_test splits .
  • Improve model accuracy through Hyper-parameter tuning
    • Grid search.
    • Random search .
Foundations + Tools + Machine Learning 2
  • ·SQL basics
    • Data types & Operators
    • Maths
    • Tables
    • Data extraction and Transformations
    • Strings
  • Classification vs Regression
    • Basic concepts
    • Used cases and industry applications
  • 7 -Classification models
  • Decision boundary and errors .
  • Logistic Regression
    • Difference wrt Linear regression.
    • Concept of Sigmoid function.
    • Intuitive and Mathematical understanding
    • Hyper pameter tuning.
    • Used cases and Industry application.
  • Support vector Machines
    • Concept of hyper plane
    • Intuitive and mathematical understanding
    • Hyper parameter tuning.
    • Used cases and Industry applications
  • Naïve Bayes
    • Concept of Naïve bayes theorm .
  • Intuitive and mathematical understanding.
  • Used cases and Industry applications.
  • K NN
    • Concept of Distance metrics.
    • Intuitive and mathematical understanding.
    • Hyper parameter tuning.
    • Used cases and industry applications.
  • Decision trees.
    • Concept of Entropy and Information gain.
    • Intuitive and mathematical understanding.
    • Hyper parameter tuning.
    • Used cases and industry applications.
  • Ensembling.
    • Concept of ensemble.
    • Intuitive and mathematical understanding.
    • Used cases and industry applications.
  • Ensemble -Bagging and Boosting.
    • Concept of bootstap.
    • Key differences between bagging and boosting.
  • Random forest -Bagging
    • Intuitive and mathematical understanding.
    • Hyper parameter tuning.
    • Used cases and industry applications.
  • Ada boost -Boosting.
    • Intuitive and mathematical understanding.
    • Hyper parameter tuning.
    • Used cases and industry applications.
  • Classification Evaluation metrics.
    • Classification accuracy.
    • Confusion metrics.
    • ROC curve.
    • Classification score -FI score.
  • Model Selection strategy.
  • Machine learning pipelines.
  • Unsupervised learning.
    • Key differences between Supervised and unsupervised learning.
    • Concept of clustering.
    • Clustering with K Means.
    • Intuitive and mathematical understanding.
    • Used cases and industry applications.
  • Clustering with Dendrograms.
    • Intuitive and graphical display.
    • Used cases and industry applications.
Foundations + Tools + Deep Learning
  • Calculus-Partial Differentiation.
  • Python for deep learning.
    • Python libraries –
      • Tensor flow
      • Keras
  • Deep learning -Used cases and Industry applications.
  • Multi layer perceptions.
  • Key Activation functions.
  • Deep and wide architectures.
  • Training networks.
  • Backward propagation.
  • Hyper parameters.
  • Regularization using drop outs.
  • Artificial Intelligence.
    • Structured and Unstructured data.
    • NLP basics.
    • Computer vision basics.
Tools + Data Visualization + Business Intelligence
  • Business story telling basics.
    • Data , Narratives & Infographics .
    • Importance of narratives backed by key infographics.
    • Understanding 2 compelling styles.
    • Gestalt principles of visual perception.
  • Business story telling using Tableau.
    • Dimensions & Measures.
    • Marks card .
    • Filters .
    • Key analytics .
    • Groups & Sets.
    • Parameters .
    • Fields .
    • Joints.
    • Regex operations.
    • LOD (Level of details) .
    • Conditional statements .
    • Key charts and tables.
    • Geographical Maps , Pareto Charts, Word cloud.
  • 20 Industry case studies.
  • 10 Class assignments. 
  • 5 Module exams.
  • 1 Certification Exam