 ## Foundation + Core +Tools

Statistics
Probability
Linear Algebra
Calculus

#### Core

Data Analytics
Machine Learning
Deep Learning
Data Visualization

Python
SQL
Tablleau

## 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.
• 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
• 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