PG Certification in Data Science BY The Scholar via Nulearn, Program, Fees, Eligibility, Admission, Syllabus

• How “ Classification” and “Regression”works?
• Data matrixnotation.
• Classification vs Regression(examples)
• K-Nearest Neighbors Geometric intuition with a toy example.
• Failure cases of K-NN
• Distance measures: Euclidean(L2),Manhattan(L1),Minkowski, Hamming
• Cosine Distance & Cosine Similarity
• How to measure the effectiveness of K-NN?
• Test/Evaluation time and space complexity.
• k-NN Limitations.
• Decision surface for K-NN as K changes.
• Overfitting and Underfitting.
• Need for Cross validation.
• K-fold cross validation.
• Visualizing train, validation and test data sets
• How to determine overfitting and underfitting?
• Time based splitting
• k-NN for regression.
• Weighted k-NN
• Voronoi diagram.
• Binary search tree
• How to build akd-tree.
• Find nearest neighbors using kd-tree
• Limitations of kd-tree
• Extensions.
• Hashing vs LSH.
• LSH for cosine similarity
• LSH for euclidean distance.
• Probabilistic class label
• Code Sample: Decision boundary.
• Code Samples: Cross-Validation

• Introduction
• Imbalanced vs balanced data set.
• Multi-class classification.
• k-NN, given a distance or similarity matrix
• Train and test set differences.
• Impact of Outliers
• Local Outlier Factor(Simple solution: mean distance to k-NN).
• k-distance (A),N(A)
• reachability-distance(A,B)
• Local-reachability-density(A)
• Local Outlier Factor(A)
• Impact of Scale & Column standardization.
• Interpretability
• Feature importance & Forward Feature Selection
• Handling categorical and numerical features.
• Handling missing values by imputation.
• Curse of dimensionality.
• Bias-Variance tradeoff.
• Intuitive understanding of bias-variance.
• Revision Questions.
• Best and worst case of an algorithm

• Accuracy
• Confusion matrix, TPR, FPR, FNR,TNR
• Precision & recall,F1-score.
• Receiver Operating Characteristic Curve (ROC) curve and AUC.
• Log-loss.
• R-Squared/ Coefficient of determination.
• Median absolute deviation(MAD)
• Distribution of errors.

• Conditional probability.
• Independent vs Mutually exclusive events.
• Bayes Theorem with examples.
• Exercise problems on Bayes Theorem
• Naive Bayes algorithm.
• Toy example: Train and test stages.
• Naive Bayes on Text data.
• Laplace/Additive Smoothing.
• Log-probabilities for numerical stability.
• Bias and Variance tradeoff.
• Feature importance and interpretability.
• Imbalanced data
• Outliers.
• Missing values.
• Handling Numerical features (Gaussian NB)
• Multiclass classification.
• Similarity or Distance matrix.
• Large dimensionality.
• Best and worst cases.

• Geometric intuition of logistic regression
• Sigmoid function: Squashing
• Mathematical formulation of objective function.
• Weight Vector.
• L2 Regularization: Overfitting and Underfitting.
• L1 regularization and sparsity.
• Probabilistic Interpretation: Gaussian Naive Bayes
• Loss minimization interpretation
• Hyperparameter search: Grid Search and Random Search
• Column Standardization.
• Feature importance and model interpretability.
• Collinearity of features.
• Train & Run time space and time complexity.
• Real world cases.
• Non-linearly separable data & feature engineering.
• Code sample: Logistic regression, Grid Search CV, Random Search CV
• Extensions to Logistic Regression: Generalized linear models(GLM)

• Geometric intuition of Linear Regression.
• Mathematical formulation.
• Real world Cases.
• Code sample for Linear Regression

• Differentiation.
• Online differentiation tools
• Maxima and Minima
• Vector calculus: Grad
• Gradient descent: geometric intuition.
• Learning rate.
• Gradient descent for linear regression.
• SGD algorithm
• Constrained optimization &PCA
• Logistic regression formulation revisited.
• Why L1 regularization creates sparsity?

• Geometric intuition.
• Mathematical derivation.
• why we take values +1 and -1 for support vector planes
• Loss function (Hinge Loss) based interpretation.
• Dual form of SVM formulation.
• Kernel trick.
• Polynomial kernel.
• RBF-Kernel.
• Domain specific Kernels.
• Train and run time complexities.
• nu-SVM: control errors and support vectors.
• SVM Regression.
• Cases.

• Geometric Intuition of decision tree: Axis parallel hyperplanes.
• Sample Decision tree.
• Building a decision Tree: Entropy(Intuition behind entropy)
• Building a decision Tree: Information Gain
• Building a decision Tree: Gini Impurity.
• Building a decision Tree: Constructing a DT.
• Building a decision Tree: Splitting numerical features.
• Feature standardization.
• Categorical features with many possible values.
• Overfitting and Underfitting.
• Train and Run time complexity.
• Regression using Decision Trees.
• Cases
• Supervised Machine Learning – Classification and Regression – Ensemble Models
• What are ensembles?
• Bootstrapped Aggregation (Bagging)Intuition.
• Random Forest and their construction.
• Bias-Variance tradeoff.
• Train and Run-time Complexity.
• Bagging: code Sample.
• Extremely randomized trees.
• Random Forest: Cases.
• Boosting Intuition
• Residuals, Loss functions, and gradients.
• Gradient Boosting
• Regularization by Shrinkage.
• Train and Run time complexity.
• XG Boost: Boosting +Randomization
• Ada Boost: geometric intuition.
• Stacking models.
• Cascading classifiers.

• What is Clustering?
• Unsupervised learning
• Applications.
• Metrics for Clustering.
• K-Means: Geometric intuition, Centroids.
• K-Means: Mathematical formulation: Objective function
• K-Means Algorithm.
• How to initialize: K-Means++
• Failure cases/Limitations.
• K-Medoids
• Determining the right K.
• Code Samples.
• Time and Space complexity

• Agglomerative & Divisive, Dendrograms
• Agglomerative Clustering.
• Proximity methods: Advantages and Limitations.
• Time and Space Complexity.
• Limitations of Hierarchical Clustering.
• Code sample.

• Density based clustering
• MinPts and Eps:Density
• Core, Border and Noise points.
• Density edge and Density connected points.
• DBSCAN Algorithm.
• Hyper Parameters: MinPts and Eps.
• Advantages and Limitations of DBSCAN.
• Time and Space Complexity.

• Understanding of Support
• Understanding of Confidence
• Understanding of Lift
• Time and Space complexity

• Problem formulation: Movie reviews.
• Content based vs Collaborative Filtering.
• Similarity based Algorithms.
• Matrix Factorization: PCA,SVD.
• Matrix Factorization: NMF.
• Matrix Factorization for Collaborative filtering
• Matrix Factorization for feature engineering.
• Clustering as MF.
• Hyperparameter tuning.
• Matrix Factorization for recommender systems: Netflix Prize Solution.
• Cold Start problem.
• Word Vectors as MF.
• Eigen-Faces.

• What is dimensionality reduction?
• Row vector, and Column vector.
• How to represent a dataset?
• How to represent a dataset as a Matrix.
• Data preprocessing: Feature Normalization
• Mean of a datamatrix.
• Data preprocessing: Column Standardization
• Co-variance of a Data Matrix.
• MNIST dataset (784 dimensional)
• Code to load MNIST dataset.

• Geometric intuition.
• Mathematical objective function.
• Alternative formulation of PCA: distance minimization
• Eigenvalues and eigenvectors.
• PCA for dimensionality reduction and visualization.
• Visualize MNIST dataset.
• Limitations of PCA
• PCA for dimensionality reduction (not-visualization)

• What is -SNE?
• Neighborhood of a point, Embedding.
• Geometric intuition.
• Crowding problem.
• How to apply t-SNE and interpret its output(distill.pub)
• t-SNE on MNIST.

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Module 6: Deep Learning and AI (Self Paced)

• Introduction to Python
• Introduction to Logistic Regression

Introduction to Artificial Neural Network

• History of Neural networks and Deep Learning.
• How Biological Neurons work?
• Growth of biological neural networks.
• Diagrammatic representation: Logistic Regression and Perceptron.
• Multi-Layered Perceptron (MLP).
• Notation.
• Training a single-neuron model.
• Training an MLP: Chain Rule.
• Training an MLP:Memoization.
• Backpropagation.
• Activation functions.
• Vanishing Gradient problem.
• Bias-Variance tradeoff.

Deep Multi-layer Perceptrons

• Deep Multi-layer perceptrons:1980s to 2010s
• Dropout layers & Regularization.
• Rectified Linear Units (ReLU).
• Weight initialization.
• Batch Normalization.
• Optimizers: Hill-descent analogy in 2D
• Optimizers: Hill descent in 3D and contours.
• SGD Recap
• Batch SGD with momentum.
• Nesterov Accelerated Gradient (NAG)
• Optimizers: AdaGrad
• Optimizers : Adadelta and RMSProp
• Adam
• Which algorithm to choose when?
• Gradient Checking and clipping
• Softmax and Cross-entropy for multi-class classification.
• How to train a Deep MLP?

Convolutional Neural Network

• Biological inspiration: Visual Cortex
• Convolution: Edge Detection on images.
• Convolution: Padding and strides
• Convolution over RGB images.
• Convolutional layer.
• Max-pooling.
• CNN Training: Optimization
• Receptive Fields and Effective Receptive Fields
• ImageNet dataset.
• Data Augmentation.
• Convolution Layers in Keras
• AlexNet
• VGGNet

Recurrent Neural Network

• Why RNNs?
• Recurrent Neural Network
• Training RNNs: Backprop
• Types of RNNs
• Need for LSTM/GRU
• LSTM
• GRUs

Module 7: Power BI (Self Paced)

• Introduction to PowerBI and its architecture
• Import data from CSV files
• Import data from Excel files
• Import data from Web 1
• Import data from Web 2
• Import Real-time Streaming Data
• Import data from Oracle
• Import Data from Folder
• Dataflows – Introduction
• Dataflows – Create Gateway from Scratch
• Dataflows – Create Entities from CSV file
• Dataflows – Create Entities Using SQL Server
• Remove Rows
• Remove Columns
• Make first row as headers
• How to create calculate columns
• How to remove duplicates
• Unpivot columns and split columns
• Change Data type, Replace Values and Rearrange the columns
• Append Queries
• Merge Queries
• Visuals Intro
• Visuals-Bar Charts
• Visuals-Line Charts
• Visuals-Pie Chart
• Stacked bar Chart
• Clustered Column Chart
• Visuals-Area Chart and Analytics Tab Explained-0
• Visuals-Area Chart and Analytics Tab Explained-1
• Visuals-Combo Chart
• Visuals-Scatter Chart
• Visuals-Treemap Chart
• Visuals-funnel Chart
• Visuals-Card and Multi-Row Card
• Visuals-Gauge Card
• Visuals-KPIs
• Visuals-Matrix
• Visuals-Table
• Visuals-Text boxes – Shapes – Images
• Visuals-Slicers
• Visuals-Maps
• Custom Visuals – Word Cloud
• Visualization interactions
• Modeling and Relationships
• Other ways to create Relationship
• OLTP vs OLAP
• Star Schema vs Snowflake Schema
• DAX101 – Importing Data for Dax Learning
• DAX101 – Resources for Dax Learning
• DAX101 – What is Dax
• DAX101 – Dax Data Types
• DAX101 – Dax Operators and Syntax
• DAX101 – M vs Dax
• DAX101 – Create a Column
• DAX101 – Rules to Create Measures
• DAX101 – Calculated Columns vs Calculated Measures-0
• DAX101 – Calculated Columns vs Calculated Measures-1
• DAX101 – Sum()
• DAX101 – AVERAGE()-MIN()-MAX()
• DAX101 – SUMX()
• DAX101 – DIVIDE()
• DAX101 – COUNT()-COUNTROWS()
• DAX101 – CALCULATE()-0
• DAX101 – CALCULATE()-1
• DAX101 – FILTER()
• DAX101 – ALL()
• DAX101 – Time Intelligence – Create Date Table in M (important)
• DAX101 – Time Intelligence – Create Date Table in DAX
• DAX101 – Time Intelligence – SAMEPERIODLASTYEAR()
• DAX101 – Time Intelligence – TOTALYTD()
• DAX101 – Display Last Refresh Date
• DAX101 – Time Intelligence – PREVIOUSMONTH()
• DAX101 – Time Intelligence – DATEADD()
• DAX101 – Quick Measures
• PowerBI Reports
• PowerBI Workspaces
• PowerBI Datasets
• What are Dashboards
• How to create Workspace and Publish Report
• Favorite dashboards, reports, and apps in Power BI
• Subscribe to a Report or Dashboard
• Rename Workspace or Report or Dashboard
• Display Reports or Dashboards in Full screen mode
• Delete Reports or Dashboards
• Dashboard Menus
• File and View Options
• Printing Dashboard and Reports
• PATH Function
• PATHCONTAINS Function
• PATHITEM Function
• PATHITEMREVERSE Function
• PATHLENGTH Function
• RLS – Static Row Level Security
• RLS – Dynamic Row Level Security
• RLS – Organizational Hierarchy
• Sharing and Collaboration
• Sharing Dashboard
• Sharing Workspaces
• Sharing App
• Publish To Web

Evaluation Process

Exam certification for this PG certification in Data Science online course consists of two steps: an aptitude test, a personal interview with an academic expert, an admission letter, and enrollment.

How It Helps

This online PG Certification in Data Science benefits everyone who wants to become a trained data scientist. It is designed for individuals with a variety of backgrounds as well as freshmen, and it prepares students for a wide range of interesting and in-demand careers. Gain knowledge and employable skills in the most popular coding languages, as well as data science fundamentals such as Python standard programming, mathematics, statistics, data analysis, and machine learning.

This program was developed by industry professionals to give learners a thorough understanding of real-world scenarios, allowing them to learn the most and apply it on the job. Python, SQL, Statistics, Machine Learning, PowerBI, Tableau, Deep Learning, and AI (Self paced) are part of the syllabus. After completing this course they will get the course completion certificate.

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PG Certification in Data Science FAQ’S

Who is eligible for PG certification in Data Science?

PG Diploma In Data Science eligibility requires candidates to have a bachelor’s degree with at least 50% overall or an equivalent grade, preferably in science or computer science from an accredited university. For some colleges candidates may need relevant work experience to be eligible for the particular course.

Can I do PG in Data Science?

PG in data science can be done by an individual who wants to take a career path in Computer Science, Engineering, Management, or Business Analysis. The Eligibility differs depending on the type of course you want to do (Master’s degree, Certificate course, MBA, PG diploma).

Is PG Diploma in Data Science worth it?

Higher salary: Data Science is one of the highest-paying fields in the world. Completing a PG Diploma in Data Science can help you command a higher salary than your peers in other fields.

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