What is data warehouse Data warehouse architecture Top down approach Bottom up approach Data Lake Architecture Data Lake and Warehouse Architecture Data Lake vs. Data Warehouse OLTP vs. OLAP Additive Fact Factless Fact Non Additive Fact Semi Additive Fact Role playing dimension Junk dimension Degenerate Dimension Conformed Dimension Slowly Changing Dimension Normal Form 1NF Normal Form 2NF Normal Form 3NF Normal Form 4NF Naming Rules Data Types Default Constraint Primary Key Constraints Foreign Key Constraints Not Null constraint Unique Constriant Data Definition Language Data Query Language Data Manipulation Language Data Control Language Transaction Control Language Concatenation Where Clause Like Clause Between and Is null Order by Character Number Date Conversion General Functions NVL NVL2 COALESCE Case When Decode Avg Count Min Max Stddev Sum Variance Different types of joins Inner Left Outer Join Right Outer Join Full Outer Join Self-Join Non Equi Join Cross Joins Self-Join Detailed understanding of single row subquery Detailed understanding of multiple row subquery In clause Any Clause All Clause Non Pairwise Pairwise Correlated Sub Queries Detail understanding of Exists and With Clauses Union Union All Intersection Minus Insert statements Update Statements Delete Statements Truncate Simple and Complex views Update through views Sequences and its uses Synonyms and its uses Index and its benefits and Drawbacks How to add the constraints Alter statements System tables Unconditional Inserts Conditional Inserts Condition First Insert Pivoting Insert Update using Merge Insert using Merge Connect by Prior REGEXP_LIKE REGEXP_INSTR REGEXP_SUBSTR REGEXP_REPLACE REGEXP_COUNT Row Number Rank Dense Rank Row Number Rank Dense Rank Importance of Materialized Views Complete on Demand Complete on Commit Fast on Demand Explain Plan Understanding of advanced Select Understanding of Joins and advanced joins

Python History and Features Data Types in Python Integer Float String Boolean Complex Numbers Memory Allocation in Python Lists Tuples Range Sets Frozen sets Dictionaries Operators in Python - Part 1 Arithmetic Operators Relational Operators Equality Operators SetsLogical Operators Ternary Operators Operators in Python - Part 2 Bitwise Operators Compound Assignment Operators Special Operators Identity Operators Membership Operators Precedence in Python Math Modules Eval Command Line Arguments Output ways in Python Print and its formats if, elif and else for loops for loops while loops break continue pass Various string related functions and its applications More examples on String operations Functions and its benefits Types of arguments Positional arguments Keyword arguments Default arguments Variable length arguments Types of variables Recursive functions Lambda functions filter map reduce Memory allocation in functions – Function Aliasing Nested functions Exception Handling and its various scenarios Various syntaxes of exception handling Custom exception handling example Modes to open the files Various properties of file object How to write and read data from files With statement seek and tell commands seek and tell commands os module and its functions How to handle the files and folders in Python using os modules pickling and unpickling re module in Python and its functions re module in Python and its functions Object Oriented Programming - Part 1 Constructor in OOPs Self Instance variables Static variables Local variables Static variables Passing members from one class to another class Inner classes Garbage Collector Desctructor Inheritance Single Inheritance Multilevel Inheritance Hierarchical Inheritance Multiple Inheritance Composition and Aggregation Hybrid Inheritance super in OOPs Polymorphism in OOPs Method Overloading Constructor Overloading Method Overriding Understanding of Numpy and its various functions Understanding of arrays and Matrix Discussion on various Numpy functions Discussion on various Numpy functions Pandas - An introduction Understanding of Data Frames Joins between Data Frames Concat Slicing and playing with the csv file and understand various functions on pandas Understanding of various functions in Pandas Understanding of various functions in Pandas Understanding of various functions in Pandas Detailed understanding of dfply Creating various plots using Matplotlib Creating various plots using Seaborn Traditional methods of imputation using mean, median and mode KNN Imputation Outlier Treatment Standardization MinMax Scalar Robust Scaler MaxAbs Scalar Power Transformer Quantile Transformer Normalization Hands on of all the techniques using Python Create and Insert using Python Alter statement, Reading Data from csv and upload in Oracle End to end understanding of how to explore the data before applying Machine Learning End to end understanding of how to explore the data before applying Machine Learning

Understanding Tableau and its products Tableau Public Tableau Desktop Tableau Reader Tableau Online Tableau Server Tableau Prep Tableau Mobile Understanding the Calculated Field concept If and Else statements Left Function Making data ready for visualization using Tableau Prep Dive deep into the concept of Joins and how can we achieve in Tableau Applying Cross database joins using Tableau Prep Dive deep into the concept of Cross database joins Applying Cross database joins using Tableau Prep Understanding of Data Blending Understanding the concept of Primary and Secondary data sources Dive Deep into Data Blending Understanding the usage of Data Joining, Cross Database Join and Data Blending Solving the end-to-end problem using Data Blending Creating the map view and understanding the idea of maps Creating the histogram and dynamic histogram using parameters Creating Donut chart Creating the stackbar chart Creating the interactive dashboard Difference between Tiled and Floating options Adding the buttons to download the dashboard as ppt, image and pdf Creating the story Understanding the difference between Dashboard and Story Understanding the concepts of Data dimension Difference between Discrete and continuous date field What is the relation between aggregation and Granularity? How to apply normal filters or Quick Filters Options in the Normal Filters or Quick Filters Understanding on Context Filters Create a dynamic scatter plot using the concepts of Sets and Parameters Understanding the concept of sets Understanding the concept of Static Sets Understanding the concept of Dynamic Sets How to tag parameters with sets to make it dynamic sets How to tag parameters with reference lines Understanding the concept of Combined sets Formatting the visuals Adding sets as filters Formatting the data as per the requirement using Tableau Prep Formatting the data as per the requirement using Tableau Desktop Advantage and Disadvantage of using Tableau Desktop as data preparation tool Creating the animated scatter plot using Animation concept How to animate the chart using page shelf Which data to choose as Primary data source Formatting the plot Sorting the secondary data source Adding show history option in the animation Understanding the concept of Include Function Creating the visual using Include function in Tableau Implementing the include function on the real time use case Creating the visual using Include function in Tableau Understanding the concept of Exclude and where to use it Implementing the exclude function on the real time use case Creating the visual using exclude function in Tableau Understanding the concept of Fixed and where to use it Implementing the Fixed function on the real time use case Creating the visual using Fixed function in Tableau Customer Order Frequency Cohort Analysis Daily Profit KPI Percentage of Total New Customer Acquisition Average of top deals by city Actual vs. Target Value on the last day of the period Return Purchase by cohort Percentage difference from average across a range Relative Period Filtering User login frequency Proportional Brushing Annual Purchase frequency by customer cohort Comparative sales analysis Preparing the data using Tableau Prep Understanding the datetime dimension and its purpose Preparing the data using Tableau Desktop Pivoting using Tableau Prep Pivoting using Tableau Desktop Running Sum Difference Percentage Difference Understanding Table Across, Table Down Fixing the calculation by dimension Dive deep on the functions Lookup, ZN Dive deep on the functions Lookup, ZN Moving Average

Types of Data Analyzing Categorical Data Reading Pictographs Reading Bar Graphs Reading Pie Chart Two way Frequency Table and Venn Diagram Marginal and Conditional Distribution Displaying Quantitive data with Graphs Frequency Table and Dot plots Creating Histogram Mean Median Mode Range Variance Standard Deviation Interquartile Range Population Sample Difference in formula for Population parameter and Sample statistic Box and Whisker Plot Left Skewed and Right Skewed Outlier detection Mean Absolute Deviation Exploring bivariate numerical data Slope of a line Intercept Covariance Covariance Matrix Karl Pearson Correlation Coefficient Spearman Rank Correlation Coefficient Various scenarios Residuals R-Squared and RMSE Understanding Normal Distribution Z-Score concept Emperical Rule Density Curve More Examples in Z-score and Z-table More Examples in Z-score and Z-table Exploring Gaussian distribution equation Symmetric distribution Skewness Kurtosis KDE Simple Probability Probability - With Counting Outcome Sample space and its subset Intersection and Union of sets Relative complement or Difference between sets Universal Set and Absolute complement Subset, Strict, Subset and Superset Set operations together Set operations together Difference between theoretical and experimental probability Statistical Significance of experiment Probability with venn diagram Addition rule of probability Sample space for compound space Compound probability of independent events Independent events Dependent Probability Conditional Probability Conditional Probability - Tree Diagram Conditional Probability and Independence Permutation Formula Understanding of Zero factorial Factorial and Counting Combinations Continuous and Discrete and its distribution Mean (Expected value) of a discrete random variable Variance and Standard deviation Analyzing distribution of sum of two normally distributed random variables Intuition for why independence matters for variance of sum Analyzing distribution of sum of two normally distributed random variables Binomial Variables Recognizing Binomial variables 10% Rule of assuming independence Binomial Distribution Binomial Probability Binompdf and Binomcdf functions Mean and variance of Bernoulli distribution Expected value and variance of Binomial distribution Geometric random variables – Introduction Probability of a geometric random variable Cumulative geometric probability Cumulative geometric probability Proof of expected value of geometric random variables Detailed understanding of Poisson Distribution Sampling Distributions Sampling distribution of sample proportion Normal conditions for sampling distributions of sample proportions Probability of sample proportions Inferring population mean from sample mean Central Limit Theorem Standard Error of the mean Confidence intervals and margin of error Interpretation of Confidence interval Margin of Error Condition for valid confidence interval for a proportion Introduction to T-Statistics Conditions for valid t intervals Find critical t value Confidence interval for a mean with paired data Sample size for a given margin of error for a mean T-statistic confidence interval Small sample size confidence intervals Simple Hypothesis Testing Idea Behind Hypothesis Testing null and alternative hypothesis P-values and significance tests Comparing P-values to different significance levels Estimating a P-value from a simulation Type 1 and Type 2 Errors Introduction to Power in significance tests Power in significance test Constructing hypothesis for a significance test about a proportion Conditions for a z test about a proportion Calculating a z statistic in a test about a proportion Calculating a P-value given a z statistic Making conclusions in a test about a proportion Hypothesis testing and p-values Small sample hypothesis test Large Sample proportion hypothesis testing Detailed understanding of Comparing Two Proportions Statistical Significance of experiment Difference of Sample mean distribution Confidence interval of difference of means Chi-Square distribution introduction Pearson’s Chi-Square test (Goodness of fit) Chi-Square statistic for Hypothesis testing Chi-square goodness of fit example Filling out frequency table for independent events Contingency table chi-square test Chi-Squared test for Homogeneity Chi-Squared test for association (Independence) Introduction to inference about slope in linear regression Conditions for Inference on slope Confidence interval for the slope of a regression line Using a P-value to make conclusions in a test about slope Using a confidence interval to test slope Anova – Calculating SST, SSW and SSB Hypothesis testing with F-statistic

PG Certification In Data Science

In our data-driven and technological age, businesses are looking to utilize these technological advancements
in the day to day work and looking for individuals with a robust understanding
of data science as they are more valuable than ever.

Apply Now

Course description

About the Course

Masters to PG in Data Science from The Scholar is designed for students with all levels of experience and for Freshers, equipping learners for a wide variety of interesting and in-demand careers. Gain specific, marketable skills in the top coding languages, along with critical knowledge in Data Science standard programming using Python, mathematics, statistics, data analysis, and machine learning.

This program is designed by Industry professionals and give learners end to end understanding of the real world scenarios which will help the learners to gain the best knowledge add they can apply in the job while working.

The Complete Program will Have 150+ Hrs. of Live Training + 150+ Hrs. of Working on Assignments & Case Study .

Course Overview at a Glance

Delivery Type

Online

Learning Hours

300+ Hrs (12 Months)

Program Fee

1,65,000/ +GST

Batch Days

Weekends + 1 Weekday

Eligibility

Who Can Join & What to Expect in Class

Person from any domain who are keen on learning Data Science for professional development.
Software developers/ IT Professionals who are willing to move into Data Science.
Professors from Institutes who are willing to upgrade their skills into Data Science.
Middle and senior level executives from profit and not-for-profit organization across industries/sectors can attend the program.

Eligibility

Our Selection Process

Aptitude Test

Based on Individual Profile

Personal Interview with Academic Expert

For Creating an Impressive Profile

Admission Letter & Enrollment

and How to Get it on top 5 Pages

Assignments, Videos & Case Studies

Course Roadmap – Built Just for You

Module 1: SQL

Introduction to Data Warehouse

What is data warehouse
Data warehouse architecture
Top down approach
Bottom up approach
Data Lake Architecture
Data Lake and Warehouse Architecture
Data Lake vs. Data Warehouse
OLTP vs. OLAP

Facts and Dimensions

Additive Fact
Factless Fact
Non Additive Fact
Semi Additive Fact
Role playing dimension
Junk dimension
Degenerate Dimension
Conformed Dimension
Slowly Changing Dimension

Normal Forms

Introduction to constraints – Part 1

Introduction to constraints – Part 2

Types of SQL Commands

Operators and Clauses

Single Row Function – Part 1

Single Row Function – Part 2

Multi Row Function

Joins in SQL – Part 1

Joins in SQL – Part 2

Joins in SQL – Part 3

Single Row SubQuery

Multiple Rows SubQuery

Multiple Column Sub Queries

Exists and With Clauses

Set Operators in SQL

Data Manipulation

Views in SQL

Sequences, Synonyms and Index

Alter Statements in SQL

Multi table inserts

Merge in SQL

Hierarchical Data fetching

Regular Expressions

Analytics Functions – Part 1

Analytics Functions – Part 2

Materialized Views

SQL Interview Prep – Advanced Select

SQL Interview Prep – Joins

Module 2: Python

Basics of Python (Python History and Features)

Basics of Python (Data Types Part 1)

Basics of Python (Data Types Part 2)

Basics of Python (Lists in Python)

Detailed explanation of Lists and its functions

Basics of Python (Tuples, Dictionaries and Sets)

Detailed explanation of Tuples, Dictionaries and Sets

Operators in Python - Part 1
Arithmetic Operators
Relational Operators
Equality Operators
SetsLogical Operators
Ternary Operators
Operators in Python - Part 2
Bitwise Operators
Compound Assignment Operators
Special Operators
Identity Operators
Membership Operators
Precedence in Python
Math Modules

Input ways in Python

Input statement

Flow Control – Part 1

Flow Control – Part 2

String Operations in Python

String Operations in Python – More Examples

Functions in Python – Part 1

Functions in Python – Part 2

Functions in Python – Part 3

Exception Handling – Part 1

Exception Handling – Part 2

Exception Handling – Example

File Handling – Part 1

File Handling – Part 2

Binary files

Regular Expressions – Part 1

Introduction to OOPs

Object Oriented Programming – Part 2

Object Oriented Programming – Part 3

Object Oriented Programming – Part 5

Object Oriented Programming – Part 6

Object Oriented Programming – Part 7

Object Oriented Programming – Part 8

Object Oriented Programming – Part 9

Object Oriented Programming – Part 10

Object Oriented Programming – Part 11

Numpy – Part 1

Numpy – Part 2

Numpy – Part 3

Pandas – Part 1

Pandas – Part 2

Pandas – Part 3

Pandas – Part 4

Pandas – Part 5

dfply understanding

Data Visualization of Matplotlib

Data Visualization of Seaborn

Data Preprocessing using SKLearn- Missing Value Imputation

Data Preprocessing using SKLearn – Outlier Treatment

Data Preprocessing using SKLearn – Feature Scaling – Theory

Standardization
MinMax Scalar
Robust Scaler
MaxAbs Scalar
Power Transformer
Quantile Transformer
Normalization

Data Preprocessing using SKLearn – Feature Scaling – Handson

Python connection with Oracle – Part 1

Python connection with Oracle – Part 2

Exploratory Data Analysis – Part 1

Exploratory Data Analysis – Part 2

Module 3: Tableau

Tableau – An introduction

Understanding Tableau and its products
Tableau Public
Tableau Desktop
Tableau Reader
Tableau Online
Tableau Server
Tableau Prep
Tableau Mobile

Tableau – Creating first chart

Tableau – Data from Heterogeneous data sources

Dive deep into the concept of Joins and how can we achieve in Tableau
Applying Cross database joins using Tableau Prep
Dive deep into the concept of Cross database joins
Applying Cross database joins using Tableau Prep
Understanding of Data Blending
Understanding the concept of Primary and Secondary data sources

Tableau – Data Blending in Tableausources

Tableau – Bins, Parameters and creating our first dashboard

Tableau – Dashboard and Story

Tableau – Time series analysis

Understanding the concepts of Data dimension
Difference between Discrete and continuous date field
What is the relation between aggregation and Granularity?
How to apply normal filters or Quick Filters
Options in the Normal Filters or Quick Filters
Understanding on Context Filters

Tableau – Sets and Parameters for more dynamic visualization

Create a dynamic scatter plot using the concepts of Sets and Parameters
Understanding the concept of sets
Understanding the concept of Static Sets
Understanding the concept of Dynamic Sets
How to tag parameters with sets to make it dynamic sets
How to tag parameters with reference lines

Tableau – Combined Sets and Formatting of the visuals

Tableau – Animation in Tableau – Data Prep

Tableau – Animation in Tableau – Visualization

Creating the animated scatter plot using Animation concept
How to animate the chart using page shelf
Which data to choose as Primary data source
Formatting the plot
Sorting the secondary data source
Adding show history option in the animation

Tableau – Level of Details (Include)

Tableau – Level of Details (Include and Exclude)

Implementing the include function on the real time use case
Creating the visual using Include function in Tableau
Understanding the concept of Exclude and where to use it
Implementing the exclude function on the real time use case
Creating the visual using exclude function in Tableau

Tableau – Level of Details (Fixed)

Tableau – Level of Details (Real time use cases)

Customer Order Frequency
Cohort Analysis
Daily Profit KPI
Percentage of Total
New Customer Acquisition
Average of top deals by city
Actual vs. Target
Value on the last day of the period
Return Purchase by cohort
Percentage difference from average across a range
Relative Period Filtering
User login frequency
Proportional Brushing
Annual Purchase frequency by customer cohort
Comparative sales analysis

Tableau – Table Calculation – Data Prep Part 1

Tableau – Table Calculation – Data Prep Part 2

Tableau – Table Calculation – Functions Part 1

Running Sum
Difference
Percentage Difference
Understanding Table Across, Table Down
Fixing the calculation by dimension
Dive deep on the functions Lookup, ZN
Dive deep on the functions Lookup, ZN

Tableau – Table Calculation – Functions Part 2

Creating the logic for Quality assurance in the output

Module 4: Statistics

Statistics – An Introduction

Types of Data
Analyzing Categorical Data
Reading Pictographs
Reading Bar Graphs
Reading Pie Chart
Two way Frequency Table and Venn Diagram
Marginal and Conditional Distribution
Displaying Quantitive data with Graphs
Frequency Table and Dot plots
Creating Histogram

Measure of Central Tendency

Measure of Spread – Part 1

Measure of Spread – Part 2

Population
Sample
Difference in formula for Population parameter and
Sample statistic
Box and Whisker Plot
Left Skewed and Right Skewed
Outlier detection
Mean Absolute Deviation

Regression – Part 1

Regression – Part 2.1

Regression – Part 2.2

Regression – Part 2.3

Regression – Part 3

Regression – Part 4

Normal Distribution – Part 1

Normal Distribution – Part 2

Normal Distribution – Part 3

Symmetric Distribution, Skewness, Kurtosis and KDE

Probability – An Introduction

Simple Probability
Probability - With Counting Outcome
Sample space and its subset
Intersection and Union of sets
Relative complement or Difference between sets
Universal Set and Absolute complement
Subset, Strict, Subset and Superset
Set operations together
Set operations together
Difference between theoretical and experimental probability

Probability – Part 2

Statistical Significance of experiment
Probability with venn diagram
Addition rule of probability
Sample space for compound space
Compound probability of independent events
Independent events

Probability – Part 3.1

Probability – Part 3.2

Permutations and Combinations

Random Variables

Random Variables Variance

Binomial Distribution – Part 1

Binomial Distribution – Part 2

Geometric Random Variables – Part 1

Geometric Random Variables – Part 2

Poisson Distribution

Sampling Distributions

Central Limit Theorem

Confidence Interval

Margin of error

T-statistic

Introduction to T-Statistics
Conditions for valid t intervals
Find critical t value
Confidence interval for a mean with paired data
Sample size for a given margin of error for a mean
T-statistic confidence interval
Small sample size confidence intervals

Significance Tests – An Introduction

Type 1 and Type 2 Erros

Constructing Hypothesis for a significance test about a proportion

Constructing hypothesis for a significance test about a proportion
Conditions for a z test about a proportion
Calculating a z statistic in a test about a proportion
Calculating a P-value given a z statistic
Making conclusions in a test about a proportion

More on Significance Testing

Comparing two proportions

Comparing two means

Introduction to Chi Squared Distribution

Chi-Square distribution introduction
Pearson’s Chi-Square test (Goodness of fit)
Chi-Square statistic for Hypothesis testing
Chi-square goodness of fit example
Filling out frequency table for independent events

Chi Square Test for Homogeneity and Association

Advanced Regression

Introduction to inference about slope in linear regression
Conditions for Inference on slope
Confidence interval for the slope of a regression line
Using a P-value to make conclusions in a test about slope
Using a confidence interval to test slope

Anova and Fstatistic

Module 5 : Machine Learning

Introduction to Python Introduction to Logistic Regression

How “Classification” and “Regression”works?
Data matrixnotation.
Classification vs Regression(examples)
K-Nearest Neighbors Geometric intuition with a toyexample.
Failure cases ofK-NN
Distancemeasures:Euclidean(L2),Manhattan(L1),Minkowski, Hamming
Cosine Distance & CosineSimilarity
How to measure the effectiveness ofk-NN?
Test/Evaluation time and spacecomplexity.
k-NNLimitations.
Decision surface for K-NN as Kchanges.
Overfitting andUnderfitting.
Need for Crossvalidation.
K-fold crossvalidation.
Visualizing train, validation and testdatasets
How to determine overfitting andunderfitting?
Time basedsplitting
k-NN forregression.
Weightedk-NN
Voronoidiagram.
Binary searchtree
How to build akd-tree.
Find nearest neighbors usingkd-tree
Limitations ofkd-tree
Extensions.
Hashing vsLSH.
LSH for cosinesimilarity
LSH for euclideandistance.
Probabilistic classlabel
Code Sample: Decisionboundary.
Code Samples:Cross-Validation

Classification algorithms in varioussituations

Introduction
Imbalanced vs balanceddataset.
Multi-classclassification.
k-NN, given a distance or similaritymatrix
Train and test setdifferences.
Impact ofOutliers
Local Outlier Factor(Simple solution: mean distance tok-NN).
k-distance (A),N(A)
reachability-distance(A,B)
Local-reachability-density(A)
Local OutlierFactor(A)
Impact of Scale & Columnstandardization.
Interpretability
Feature importance & Forward FeatureSelection
Handling categorical and numericalfeatures.
Handling missing values byimputation.
Curse ofdimensionality.
Bias-Variancetradeoff.
Intuitive understanding ofbias-variance.
RevisionQuestions.
Best and worst case of analgorithm

Performance measurement ofmodels

Accuracy
Confusion matrix, TPR, FPR, FNR,TNR
Precision & recall,F1-score.
Receiver Operating Characteristic Curve (ROC) curve andAUC.
Log-loss.
R-Squared/ Coefficient ofdetermination.
Median absolute deviation(MAD)
Distribution oferrors.

Supervised Machine Learning – Classification – Naive Bayes

Conditionalprobability.
Independent vsMutually exclusiveevents.
ayes Theorem withexamples.
Exercise problems on BayesTheorem
Naive Bayesalgorithm.
Toy example: Train and teststages.
Naive Bayes on Textdata.
Laplace/AdditiveSmoothing.
Log-probabilities for numericalstability.
Bias and Variancetradeoff.
Feature importance andinterpretability.
Imbalanceddata
Outliers.
Missingvalues.
Handling Numerical features (GaussianNB)
Multiclassclassification.
Similarity or Distancematrix.
Largedimensionality.
Best and worstcases

Supervised Machine Learning – Classification – Logistic Regression

Geometric intuition of logisticregression
Sigmoid function:Squashing
Mathematical formulation of objectivefunction.
WeightVector.
L2 Regularization: Overfitting andUnderfitting.
L1 regularization andsparsity.
Probabilistic Interpretation: Gaussian NaiveBayes
Loss minimizationinterpretation
Hyperparameter search: Grid Search and RandomSearch
ColumnStandardization.
Feature importance and modelinterpretability.
Collinearity offeatures.
Train & Run time space and timecomplexity.
Real worldcases.
Non-linearly separable data & featureengineering.
Code sample: Logistic regression, GridSearchCV,RandomSearchCV
Extensions to Logistic Regression: Generalized linear models(GLM)

Supervised Machine Learning – Regression – Linear Regression

Solving optimizationproblems

Differentiation.
Online differentiationtools
Maxima andMinima
Vector calculus:Grad
Gradient descent: geometricintuition.
Learningrate.
Gradient descent for linearregression.
SGDalgorithm
Constrained optimization &PCA
Logistic regression formulationrevisited.
Why L1 regularization createssparsity?

Supervised Machine Learning – Classification and Regression – Support Vector Machines (SVM)

Geometricintuition.
Mathematicalderivation.
why we take values +1 and -1 for support vectorplanes
Loss function (Hinge Loss) basedinterpretation.
Dual form of SVMformulation.
Kerneltrick.
Polynomialkernel.
RBF-Kernel.
Domain specificKernels.
Train and run timecomplexities.
nu-SVM: control errors and supportvectors.
SVMRegression.
Cases.

Supervised Machine Learning – Classification and Regression – Decision Trees

Geometric Intuition of decision tree: Axis parallelhyperplanes.
Sample Decisiontree.
Building a decision Tree: Entropy(Intuition behindentropy)
Building a decision Tree: InformationGain
Building a decision Tree: GiniImpurity.
Building a decision Tree: Constructing aDT.
Building a decision Tree: Splitting numericalfeatures.
Featurestandardization.
Categorical features with many possiblevalues.
Overfitting andUnderfitting.
Train and Run timecomplexity.
Regression using DecisionTrees
Cases
Supervised Machine Learning - Classification and Regression - Ensemble Models
What areensembles?
Bootstrapped Aggregation (Bagging)Intuition
Random Forest and theirconstruction.
Bias-Variancetradeoff.
Train and Run-timeComplexity.
Bagging: codeSample.
Extremely randomizedtrees.
Random Forest:Cases.
BoostingIntuition
Residuals, Loss functions, andgradients.
GradientBoosting
Regularization byShrinkage.
Train and Run timecomplexity.
XGBoost: Boosting +Randomization
AdaBoost: geometricintuition.
Stackingmodels.
Cascadingclassifiers.

Unsupervised learning – Clustering – Kmeans

What isClustering?
Unsupervisedlearning
Applications.
K-Means: Geometric intuition,Centroids.
K-Means: Mathematical formulation: Objectivefunction
K-MeansAlgorithm.
How to initialize:K-Means++
Failurecases/Limitations.
K-Medoids
CodeSamples.
Time and Spacecomplexity

Unsupervised learning – Clustering – Hierarchical clustering Technique

Agglomerative & Divisive,Dendrograms
AgglomerativeClustering.
Proximity methods: Advantages andLimitations.
Time and SpaceComplexity.
Limitations of HierarchicalClustering.
Codesample.

Unsupervised learning – Clustering – DBSCAN (Density based clustering)

Density basedclustering
MinPts and Eps:Density
Core, Border and Noisepoints.
Density edge and Density connectedpoints.
DBSCANAlgorithm.
Hyper Parameters: MinPts andEps.
Advantages and Limitations ofDBSCAN.
Time and SpaceComplexity.

Association Rule Mining (Apriori)

Recommender Systems and Matrix Factorization

Problem formulation: Moviereviews.
Content based vs CollaborativeFiltering.
Similarity basedAlgorithms.
Matrix Factorization: PCA,SVD.
Matrix Factorization:NMF.
Matrix Factorization for Collaborativefiltering
Matrix Factorization for featureengineering.
Clustering asMF.
Hyperparametertuning.
Matrix Factorization for recommender systems: Netflix PrizeSolution.
Cold Startproblem.
Word Vectors asMF.
Eigen-Faces.

Dimensionality reduction andVisualization

What is dimensionalityreduction?
Row vector, and Columnvector.
How to represent adataset?
How to represent a dataset as aMatrix.
Data preprocessing: ColumnStandardization
Co-variance of a DataMatrix.
MNIST dataset (784dimensional)
Code to load MNIST dataset.

Principal ComponentAnalysis.

Geometricintuition.
Mathematical objectivefunction.
Alternative formulation of PCA: distanceminimization
Eigenvalues andeigenvectors.
Compound probability of independent events
PCA for dimensionality reduction andvisualization.
Visualize MNISTdataset.
Limitations ofPCA
PCA for dimensionality reduction(not-visualization)

T-distributed stochastic neighborhood embedding(t-SNE)

What ist-SNE?
Neighborhood of a point,Embedding.
Geometricintuition.
Crowdingproblem.
How to apply t-SNE and interpret its output(distill.pub)
t-SNE onMNIST.

Module 6 : Deep Learning & 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 andRMSProp
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 Paceed )

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

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