Overview

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.

Masters Program 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 aad 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 .

Highlights

	Designed for Fresher’s & Working Professional		Dedicated Customer Support
	150 Hours of In Depth Live Online Learning		Career Support
	Covering Most Demanding Tools		LinkedIn Profiling By Industry Expert
	25+ Case Studies & Capstone Projects		Building Project Portfolio
	One to One with Industry Mentors		AI based Resume Building
	24/7 LMS Acces		Mock Interviews Industry Tech Experts

Program Details

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

Normal Form 1NF
Normal Form 2NF
Normal Form 3NF
Normal Form 4NF

Introduction to constraints - Part 1

Naming Rules
Data Types
Default Constraint
Primary Key Constraints
Foreign Key Constraints

Introduction to constraints - Part 2

Not Null constraint
Unique Constriant

Types of SQL Commands

Data Definition Language
Data Query Language
Data Manipulation Language
Data Control Language
Transaction Control Language

Operators and Clauses

Concatenation
Where Clause
Like Clause
Between and
Is null
Order by

Single Row Function - Part 1

Character
Number
Date

Single Row Function - Part 2

Conversion
General Functions
NVL
NVL2
COALESCE
Case When
Decode

Multi Row Function

Avg
Count
Min
Max
Stddev
Sum
Variance

Joins in SQL - Part 1

Different types of joins
Inner
Left Outer Join
Right Outer Join
Full Outer Join

Joins in SQL - Part 2

Self-Join
Non Equi Join
Cross Joins

Joins in SQL - Part 3

Self-Join

Single Row SubQuery

Detailed understanding of single row subquery

Multiple Rows SubQuery

Detailed understanding of multiple row subquery
In clause
Any Clause
All Clause

Multiple Column Sub Queries

Non Pairwise
Pairwise
Correlated Sub Queries

Exists and With Clauses

Detail understanding of Exists and With Clauses

Set Operators in SQL

Union
Union All
Intersection
Minus

Data Manipulation

Insert statements
Update Statements
Delete Statements
Truncate

Views in SQL

Simple and Complex views
Update through views

Sequences, Synonyms and Index

Sequences and its uses
Synonyms and its uses
Index and its benefits and Drawbacks

Alter Statements in SQL

How to add the constraints
Alter statements
System tables

Multi table inserts

Unconditional Inserts
Conditional Inserts
Condition First Insert
Pivoting Insert

Merge in SQL

Update using Merge
Insert using Merge

Hierarchical Data fetching

Connect by Prior

Regular Expressions

REGEXP_LIKE
REGEXP_INSTR
REGEXP_SUBSTR
REGEXP_REPLACE
REGEXP_COUNT

Analytics Functions - Part 1

Row Number
Rank
Dense Rank

Analytics Functions - Part 2

Row Number
Rank
Dense Rank

Materialized Views

Importance of Materialized Views
Complete on Demand
Complete on Commit
Fast on Demand
Explain Plan

SQL Interview Prep - Advanced Select

Understanding of advanced Select

SQL Interview Prep - Advanced Select

Understanding of advanced Select

SQL Interview Prep - Advanced Select

Understanding of advanced Select

SQL Interview Prep – Joins

Understanding of Joins and advanced joins

SQL Interview Prep – Joins

Understanding of Joins and advanced joins

SQL Interview Prep – Joins

Understanding of Joins and advanced joins

SQL Interview Prep – Joins

Understanding of Joins and advanced joins

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

Understanding the Calculated Field concept
If and Else statements
Left Function
Making data ready for visualization using Tableau Prep

Tableau - Mode of connection

Understanding the concept of Live and Extract connections
Data Source filters
Union and its usage

Tableau - Data from Heterogeneous data sources

Dive deep into the concept of Joins and how can we achieve in Tableau
Applying the data joining 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 Tableau

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

Tableau - Bins, Parameters and creating our first dashboard

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

Tableau - Dashboard and Story

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

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

Understanding the concept of Combined sets
Formatting the visuals
Adding sets as filters

Tableau - Animation in Tableau - Data Prep

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

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)

Understanding the concept of Include Function
Creating the visual using Include function in Tableau

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)

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

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

Preparing the data using Tableau Prep
Understanding the datetime dimension and its purpose

Tableau - Table Calculation - Data Prep Part 2

Preparing the data using Tableau Desktop
Pivoting using Tableau Prep
Pivoting using Tableau Desktop

Tableau - Table Calculation - Functions Part 1

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

Tableau - Table Calculation - Functions Part 2

Moving Average

Creating the logic for Quality assurance in the output

Basics of Python (Python History and Features)

Python History and Features

Basics of Python (Data Types Part 1)

Data Types in Python
Integer
Float
String
Boolean
Complex Numbers

Basics of Python (Data Types Part 2)

Memory Allocation in Python
Lists
Tuples
Range
Sets
Frozen sets
Dictionaries

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
Logical 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

Eval
Command Line Arguments
Output ways in Python
Print and its formats

Flow Control - Part 1

if, elif and else
for loops

Flow Control - Part 2

for loops
while loops
break
continue
pass

String Operations in Python

Various string related functions and its applications

String Operations in Python - More Examples

More examples on String operations

Functions in Python - Part 1

Functions and its benefits
Types of arguments
Positional arguments
Keyword arguments
Default arguments
Variable length arguments

Functions in Python - Part 2

Types of variables
Recursive functions
Lambda functions
filter

Functions in Python - Part 3

map
reduce
Memory allocation in functions – Function
Aliasing
Nested functions

Exception Handling - Part 1

Exception Handling and its various scenarios

Exception Handling - Part 2

Various syntaxes of exception handling

Exception Handling – Example

Custom exception handling example

File Handling - Part 1

Modes to open the files
Various properties of file object
How to write and read data from files With statement
seek and tell commands

File Handling - Part 2

seek and tell commands
os module and its functions

Binary files

Write data in csv files
How to handle the files and folders in Python using os modules
pickling and unpickling

Regular Expressions - Part 1

re module in Python and its functions
re module in Python and its functions
Object Oriented Programming - Part 1

Introduction to OOPs

Constructor in OOPs

Object Oriented Programming - Part 2

Self
Instance variables
Static variables

Object Oriented Programming - Part 3

Static variables
Local variables

Object Oriented Programming - Part 5

Passing members from one class to another class
Inner classes

Object Oriented Programming - Part 6

Garbage Collector
Desctructor

Object Oriented Programming - Part 7

Inheritance
Single Inheritance
Multilevel Inheritance
Hierarchical Inheritance
Multiple Inheritance

Object Oriented Programming - Part 8

Composition and Aggregation
Hybrid Inheritance

Object Oriented Programming - Part 9

super in OOPs

Object Oriented Programming - Part 10

Polymorphism in OOPs

Object Oriented Programming - Part 11

Method Overloading
Constructor Overloading
Method Overriding

Numpy - Part 1

Understanding of Numpy and its various functions
Understanding of arrays and Matrix

Numpy - Part 2

Discussion on various Numpy functions

Numpy - Part 3

Discussion on various Numpy functions

Pandas - Part 1

Pandas - An introduction
Understanding of Data Frames
Joins between Data Frames
Concat

Pandas - Part 2

Slicing and playing with the csv file and understand various functions on pandas

Pandas - Part 3

Understanding of various functions in Pandas
Pandas - Part 4
Understanding of various functions in Pandas

Pandas - Part 5

Understanding of various functions in Pandas

dfply understanding

Detailed understanding of dfply

Data Visualization of Matplotlib

Creating various plots using Matplotlib

Data Visualization of Seaborn

Creating various plots using Seaborn

Data Preprocessing using SKLearn- Missing Value Imputation

Traditional methods of imputation using mean, median and mode
KNN Imputation

Data Preprocessing using SKLearn - Outlier Treatment

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

Hands on of all the techniques using Python

Python connection with Oracle - Part 1

Create and Insert using Python

Python connection with Oracle - Part 2

Alter statement, Reading Data from csv and upload in Oracle

Exploratory Data Analysis - Part 1

End to end understanding of how to explore the data before applying Machine Learning

Exploratory Data Analysis - Part 2

End to end understanding of how to explore the data before applying Machine Learning

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

Mean
Median
Mode

Measure of Spread - Part 1

Range
Variance
Standard Deviation
Interquartile Range

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

Exploring bivariate numerical data
Slope of a line
Intercept

Regression - Part 2.1

Covariance
Covariance Matrix

Regression - Part 2.2

Karl Pearson Correlation Coefficient

Regression - Part 2.3

Spearman Rank Correlation Coefficient
Various scenarios

Regression - Part 3

Residuals

Regression - Part 4

R-Squared and RMSE

Normal Distribution - Part 1

Understanding Normal Distribution
Z-Score concept
Emperical Rule
Density Curve

Normal Distribution - Part 2

More Examples in Z-score and Z-table

Normal Distribution - Part 3

More Examples in Z-score and Z-table
Exploring Gaussian distribution equation

Symmetric Distribution, Skewness, Kurtosis and KDE

Symmetric distribution
Skewness
Kurtosis
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
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

Dependent Probability
Conditional Probability

Probability - Part 3.2

Conditional Probability - Tree Diagram
Conditional Probability and Independence

Permutations and Combinations

Permutation Formula
Understanding of Zero factorial
Factorial and Counting
Combinations

Combinatorics and Probability

Probability using combinations
General form

Random Variables

Continuous and Discrete and its distribution
Mean (Expected value) of a discrete random variable
Variance and Standard deviation

Random Variables Variance

Intuition for why independence matters for variance of sum
Analyzing distribution of sum of two normally distributed random variables
Analyzing distribution of sum of two normally distributed random variables

Binomial Distribution - Part 1

Binomial Variables
Recognizing Binomial variables
10% Rule of assuming independence
Binomial Distribution

Binomial Distribution - Part 2

Binomial Probability
Binompdf and Binomcdf functions
Mean and variance of Bernoulli distribution
Expected value and variance of Binomial distribution

Geometric Random Variables - Part 1

Geometric random variables – Introduction
Probability of a geometric random variable
Cumulative geometric probability

Geometric Random Variables - Part 2

Cumulative geometric probability
Proof of expected value of geometric random variables

Poisson Distribution

Detailed understanding of Poisson Distribution

Sampling Distribution

Sampling Distributions
Sampling distribution of sample proportion
Normal conditions for sampling distributions of sample proportions
Probability of sample proportions

Central Limit Theorem

Inferring population mean from sample mean
Central Limit Theorem
Standard Error of the mean

Confidence Interval

Confidence intervals and margin of error
Interpretation of Confidence interval

Margin of error

Margin of Error
Condition for valid confidence interval for a proportion

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

Simple Hypothesis Testing
Idea Behind Hypothesis Testing
null and alternative hypothesis
P-values and significance tests

Type 1 and Type 2 Erros

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

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

Constructing Hypothesis for a signficance test about a mean

Writing Hypothesis for a significance test about a mean
Condition for a t test about a mean
When to use z or t statistics in significance tests
Calculating t statistic for a test about a mean
Calculate p-value from t statistic
Comparing P-value from t statistic to significance level
Significance Test for Mean

More on Significance Testing

Hypothesis testing and p-values
Small sample hypothesis test
Large Sample proportion hypothesis testing

Comparing two proportions

Detailed understanding of Comparing Two Proportions

Comparing two means

Statistical Significance of experiment
Difference of Sample mean distribution
Confidence interval of difference of 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

Contingency table chi-square test
Chi-Squared test for Homogeneity
Chi-Squared test for association (Independence)

Advanced Regression

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

Anova and Fstatistic

Anova – Calculating SST, SSW and SSB
Hypothesis testing with F-statistic

Supervised Machine Learning - Classification and Regression - KNN

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.
Bayes 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

Geometric intuition of LinearRegression.
Mathematicalformulation.
Real worldCases.
Code sample for LinearRegression

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.
Metrics forClustering.
K-Means: Geometric intuition,Centroids.
K-Means: Mathematical formulation: Objectivefunction
K-MeansAlgorithm.
How to initialize:K-Means++
Failurecases/Limitations.
K-Medoids
Determining the rightK.
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)

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

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: FeatureNormalization
Mean of a datamatrix.
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.
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.

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

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

Eligibility

Eligibilty		Selection Process
	Any Graduate with Min 60% and Above marks		Aptitude Test
	No Experience is Mandatory		Personal Interview with Academic Expert
	No Programming Background Required		Admission Letter & Enrollment

Certificate

Our Salary Graph

Program Fee

Tenure	Deadline	Amount ( In INR )
1st	On Selection	30000 +GST
2nd	1st March 2023	27000 +GST
3rd	1st April 2023	27000 +GST
4th	1st May 2023	27000 +GST
5th	1st June 2023	27000 +GST
6th	1st July 2023	27000 +GST
7th	After Successful Placement	60000 +GST

Note : 7% Discount On Lumpsum Payment .

No Cost Emi also available of 18 / 24 Months (Subject to Approval of Docs By Financial Partner )

Placement Support

1:1 Industry Mentorship

Based on Individual Profile

Assignments and Projects

To Justify Real Time Scenario Experience

LinkedIn Profile Building

For Creating an Impressive Profile

Mock Interviews By Experts

and Then Final Placements

Resume Building By Experts

and How to Get it on top 5 Pages

115+Potential Recruiters
3700+ Hrs Training Delivered
1 Lac + Job Opportunities in 2021

Capstone Projects

Domain: Car Rental

Fare Prediction for Uber

Uber wants to improve the accuracy of its fare prediction model. Help Uber by choosing the best data and AI technologies and building its next-generation model.

Domain: Automobiles

Test Bench Time Reduction for MercedesBenz

Mercedes-Benz wants to reduce the time it spends on the test bench. Faster testing will reduce a cars time to market. Build and optimize the ML algorithm to achieve this objective.

Domain: E-Commerce

Product Rating Prediction for Amazon

Amazon offers product recommendations based on customer activity and the buying habits of other shoppers. Help Amazon improve the recommendation engine for revenue per customer.

Domain: Transport

Predict Taxi Fares with Random Forests

Use regression trees and random forests to find places where New York taxi drivers earn the most.

Domain: Life Sciences and Health Care

Clustering Heart Disease Patient Data

Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.

Domain: Banking and Finance

Predicting Credit Card Approvals

Build a machine learning model to predict if a credit card application will get approved.

Domain: Digital Media

Movie Recommendation

Learn to build recommendation systems to model individual user preference on Over-The- Top (OTT) media service platforms such as Netflix.

Domain: Web analytics

Question Pair Similarity

Learn to identify question pairs having same intent and detect duplicate questions by using Natural Language Processing (NLP) and advanced Machine Learning Techniques.

Domain: Ecommerce

Amazon Fashion Discovery Engine

Learn to build a recommendation engine which suggests similar apparel products to a given apparel product on any e-commerce websites such as Amazon.

Domain: Web analytics

Tag predictions for questions

Learn to develop a high-performance automated tagging system to aid topic discovery in Stack Overflow, a popular online community to learn and share knowledge.

Domain: Life Sciences and Health care

Cancer Prediction

Learn to model the progression and treatment of cancerous conditions using patients medical records using supervised Machine learning Algorithms

Domain: HR Analytics

HR Analytics Employee Attrition & Performance

Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’

Domain: Ecommerce

Machine Learning for dynamic pricing

Machine Learning can be very helpful in case of dynamic pricing and can improve your KPI’s. This helpfulness comes from ML algorithm ability of learning new patterns from data. Those algorithms continuously learn from new information and detect new demands and trends

Domain: Campaign management

Sales conversion optimization

Social Media Ad Campaign marketing is a leading source of Sales Conversion and in this project learners will have to work on the optimization of the sales conversions

Domain: Banking and Financials

Claim Prediction

The insurance companies are extremely interested in the prediction of the future. Accurate prediction gives a chance to reduce financial loss for the company. The insurers use rather complex methodologies for this purpose

Domain: Banking and Financials

Home Credit Default Risk

Can you predict how capable each applicant is of repaying a loan?

Domain: Automotive

Mercedes-Benz Greener Manufacturing

Can you cut the time a Mercedes-Benz spends on the test bench?

Domain: Supply chain

Grupo Bimbo Inventory Demand

Maximize sales and minimize returns of bakery goods

Domain: Retail

Warranty Claims

To predict an item when sold, what is the probability that customer would file for warranty

Domain: Banking and Financials

Predicting Churn for Bank Customers

When a business loses customers, it needs to bring new customers in to replace the loss in revenue. And that can get very expensive, because the costs of new customer acquisition is usually much more expensive than existing customer retention

Domain: Ecommerce

Brazilian E-Commerce Public Dataset by Olist

In this project learners will have to predict the forecasted sales

Domain: Banking and Financials

Credit Scoring

The consumer credit department of a bank wants to automate the decision making process for approval of home equity lines of credit

Domain: Life Sciences and Health Care

Breast Cancer Prediction

Predict the cancer type is malignant or benign

Domain: HR Analytics

Data Science Nigeria Staff Promotion

Predicting staff that are likely to be promoted based on defined personal and performance parameters

Domain: Life Sciences and Health Care

Medical Appointment No Shows

Why do 30% of patients miss their scheduled appointments?

Domain: Education

Student Feedback Dataset

Data Science in Education makes it easy for administrators to keep an eye on the activities and teaching methods of the teachers. This helps them in identifying the most effective teaching methodologies

Domain: Oil and Natural Gas

Brent Oil Prices

Forecast the brent oil price for next 1 year

Domain: Hospitality and Travel

Hotel booking demand

Predict whether the booking will be cancelled or not

Domain: Manufacturing

Bosch Production Line Performance

The data for this project represents measurements of parts as they move through Bosch's production lines. Each part has a unique Id. The goal is to predict which parts will fail quality control

Domain: Crime Analytics

Hengker Kalimalang nih boss

Predict the category of crimes that occurred in the city by the bay

Domain: Ecommerce

Otto Group Product Classification Challenge

Classify products into the correct category

Tools Covered

POTENTIAL RECRUITERS

Our Mentors

Mr Rahul Tiwary

14+ Yr Exp. Professionals | Ex Deloitte | Ex Accenture | Ex Tech Mahindra

Mr Rakesh Sharma

14+ Yr Experience Professionals

Mr Rajesh Kumar

AVP, HDFC Ergo , 13+ yr Experience Professional

Deepak Arora

AVP @ Barclays | 11+Yr Experience Professional

Ritu Shukla

Sr. Data Analyst ( 8 + Yr Experienced Professional)

Mr. Muzzammil

2+ Yr Exp. in Python, ML Few names in Industry who got 100% as appraisal & many recognition by his Employer.

Alumni Speaks

Samira Gholizadeh - Entrepreneur & Founder at Mind Mover Academy

Rahul is an amazing educator. After passing many courses about data science and python , I finally found my confidence when Rahul was my instructor. His powerful personality, skills and motivational ..read more

Vishal Lote

The Scholar is a great platform where the mentors have a lot of knowledge in data science field. I started my data science journey with the guidance of The Scholar. They taught me how to solve the t ..read more

Geetika Puri - Associate Consultant (Senior Data Analysts) at Capgemini

â€œI rarely come across real talents who stand out like Rahul. I had the pleasure of working with Rahul on Machine Learning algorithm specifically. Rahulâ€™s ability to handle multiple projec ..read more

Swadesh Das

I was into non technical background and looking to change the career into Data Science. I have attended Data science course conducted by The Scholar. The course was very informative, and the mentor' ..read more

Dinkar Ji - Consultant

Rahul was my instructor for data science learning using python. His command over the subject is outstanding and he makes sure he is able to impart his knowledge to the students in the best way possibl ..read more

Shubham Puri

Its real life experience to learn Data Science, Tableau, Python with The Scholar. The teaching standards and content quality is really amazing with real time use cases. Also I got an opportunity to ..read more

Preetika Sindhwani - Business Analyst at SAP

Rahul Tiwari has in-depth knowledge about the tools he's been using and teaching and the best part is, he is always updated about the new features and updates which comes along with the every new vers ..read more

Don Nachtwey - Data Warehouse Analyst at GDIT ( Washington)

Rahul was my course instructor for Tableau. I am pleased to gain knowledge and skills in such a powerful and flexible tool. Rahul demonstrated a thorough understanding of this product which he clearly ..read more

Shikhar Agarwal

I had the opportunity to work closely with Rahul on a BI project for a US based retail chain. Right from initial interaction with him, I was impressed with his in depth understanding of business analy ..read more

Anamika Mishra

If data Analysis in your mind, no need to go anywhere else, join The scholar, it is fine if you are not from Technical background or data background like me. They can clear all your doubts from scrat ..read more

Sanjay Yadav - IT Analyst at TCS

I personally recommend data scientists or BI enthusiast to for this course. Rahul is very good at explaining each and every topic with so ease that learning becomes fun ..read more

Rakshit Pophali

I started my data science journey with the guidance of The Scholar. The mentors from The Scholar taught me tableau, python and data science. The mentors from The Scholar are with full of knowledge an ..read more

Sandeep Malik

Rahul is a Fabulous Trainer....I have learned a lot from Rahul and must say that he is a very Sincere ,honest and a very Hardworking Trainer also his knowledge in Data Science is Awesome......I can sa ..read more

Harpreet Singh - Sr BI Analyst at Symantec (NLOCK)

I have attended Data science course conducted by The Scholar ( Rahul) . The course was very informative, His concepts on Data science are very clear which made the sessions simple and very interactive ..read more

Akanchha Yadav - Financial Services at Evalueserve

Rahul way of instructing is very good and easy to understand for all. I learnt tableau under him and I must say he has the ability to express complicated and technical information clearly and concisel ..read more

Abhineet Saxena

Rahul is a great mentor. His style of teaching is absolutely great. He is very strong in Python from data Science area and has a very clear understanding of basics. I would recommend him to the people ..read more

Mayur Korde

It was a very nice and quality experience to learn data science with The Scholar . The course is well designed and our trainer was experienced and good at presenting the concept clearly by presenting ..read more

Kushal Pawar - Scrum Master at Infosys

It gives me an immense pleasure to express my sincere gratitude to Rahul , for widening my knowledge acumen in Data visualisation. If somebody is seeking for any assistance in Data science, Rahul is o ..read more

Shimelis Kitancho

The Scholar is a Data Science Experts : They are an excellent teacher, guide, expert, or master in the field. ..read more

Jagjit Hanspal - Sr BI/ETL Consultant at Intact

Rahul was my Mentor for Tableau Certification Training. He has in depth knowledge in Tableau and is a brilliant teacher. I appreciate his help. ..read more

Krishna Nath - Project Chief Executive

Rahul is an excellent instructor, educator. I took Python Programming Certification Course from him. He has very deep understanding on Python, Data Science and related subjects. I am a beginner in thi ..read more

Mahima K.

Rahul has played a major role in my journey of learning data science. His approach towards teaching makes it very conducive for the students to quickly understand the complex concepts. Interactive ses ..read more

Ashwini K.

I have attended Data Science for Beginners in The Scholar, as a person coming from a different background, it was difficult for me to understand many concepts in Machine learning. That course with T ..read more

Rabindra Sah - Chief Engineer - Strategic Projects at Tata Technologies

Interacted very closely with Rahul & attended his sessions at The Scholar . He is very strong on Python, Maths, data science & Machine Learning. Very strong fundamentals and nice way to understand bus ..read more

Aastha Chandok

I was always keen to know what Data Science is in this world of big data. Rahul Sir is a great teacher and mentor. He helped me alot in having a clear vision of what Data Science is. It is always fun ..read more

SB Subhaprakash - Entrepreneur In Residence - Wakefit Gift Cards | IIM Rohtak

I have attended Rahul's Tableau and Python sessions. I have to say, those sessions were real gem. Rahul's sessions were full of interactions and insightful. I would recommend everyone to attend his se ..read more

Umang Pandya - Project Manager

One thing is for sure that Rahul's Transfer of skills as per his best knowledge is to keep things simple and igniting to his students at the same time. Complex theories explained easily. I am glad th ..read more

Ajinkya Bhalerao - Data Engineer at Balfour Beatty plc

Rahul is very result oriented and proficient in Data Analysis as well as Statistics behind it. Also the way he teaches complex techniques using simple examples is really noteworthy, it was really a go ..read more

Anamika Pandey

The mentors at The Scholar have been exemplary and visionary mentors, great educators who guided me on my journey of understanding Visualization based performance monitoring dashboards and on Data Sci ..read more

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PG Certification In Data Science

Take an interdisciplinary approach to processing, modelling, analyzing and visualizing data

PROGRAM AT A GLANCE

Overview

Highlights

Program Details

Eligibility

Certificate

Our Salary Graph

Program Fee

Placement Support

1:1 Industry Mentorship

Assignments and Projects

LinkedIn Profile Building

Mock Interviews By Experts

Resume Building By Experts

Capstone Projects

Domain: Car Rental

Fare Prediction for Uber

Domain: Automobiles

Test Bench Time Reduction for MercedesBenz

Domain: E-Commerce

Product Rating Prediction for Amazon

Domain: Transport

Predict Taxi Fares with Random Forests

Domain: Life Sciences and Health Care

Clustering Heart Disease Patient Data

Domain: Banking and Finance

Predicting Credit Card Approvals

Domain: Digital Media

Movie Recommendation

Domain: Web analytics

Question Pair Similarity

Domain: Ecommerce

Amazon Fashion Discovery Engine

Domain: Web analytics

Tag predictions for questions

Domain: Life Sciences and Health care

Cancer Prediction

Domain: HR Analytics

HR Analytics Employee Attrition & Performance

Domain: Ecommerce

Machine Learning for dynamic pricing

Domain: Campaign management

Sales conversion optimization

Domain: Banking and Financials

Claim Prediction

Domain: Banking and Financials

Home Credit Default Risk

Domain: Automotive

Mercedes-Benz Greener Manufacturing

Domain: Supply chain

Grupo Bimbo Inventory Demand

Domain: Retail

Warranty Claims

Domain: Banking and Financials

Predicting Churn for Bank Customers

Domain: Ecommerce

Brazilian E-Commerce Public Dataset by Olist

Domain: Banking and Financials

Credit Scoring

Domain: Life Sciences and Health Care

Breast Cancer Prediction

Domain: HR Analytics

Data Science Nigeria Staff Promotion

Domain: Life Sciences and Health Care

Medical Appointment No Shows

Domain: Education

Student Feedback Dataset

Domain: Oil and Natural Gas

Brent Oil Prices

Domain: Hospitality and Travel

Hotel booking demand

Domain: Manufacturing

Bosch Production Line Performance

Domain: Crime Analytics

Hengker Kalimalang nih boss

Domain: Ecommerce

Otto Group Product Classification Challenge

Tools Covered