What are the available training programs for this online PG certification in Data science course?

This training program is highly industry-focused and will assist you in passing interviews on your first or second attempt. The expert team will also provide you with round-the-clock assistance and 100% placement assistance.

What skills are you gaining for interviews as a result of taking this online course?

It will assist you in developing an impressive resume and LinkedIn profile, as well as a GitHub profile that recruiters will find difficult to reject, as well as interview tips for getting placed with high-paying jobs.

What is the highest possible salary for this Data Science certification program?

The maximum annual salary for this data science position is Rs 28 lakhs.

What is the average salary for this position after the completion of this PG certification in Data Science online course?

Those who complete this online degree earn an average annual salary of $9.25 lakhs.

Who issues the completion certificate for this online PG certification in Data Analytics?

A certificate of completion for this online course is provided by "The Scholar."

Are there any installments for the fee payments?

Yes, there are five installment methods, including GST

Who explains this online course on the Nulearn platform?

The highly experienced professional mentors from the nulearn platform explain this online course.

PG Certification in Data Science by The Scholar via Nulearn: Fee, Duration, How to Apply

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based	Frequency of Classes Weekdays, Weekends

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

Normal Forms 1NF
Normal Forms 2NF
Normal Forms 3NF
Normal Forms 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 Constraint

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 U+2013 Joins

Understanding of Joins and advanced joins

Module 2: Python

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

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

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

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

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 Errors

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

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

Module 5: Machine Learning

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 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

Classification algorithms in various situations

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

Performance measurement of models

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.

Supervised Machine Learning - Classification - Naive Bayes

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.

Supervised Machine Learning - Classification - Logistic Regression

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)

Supervised Machine Learning - Regression - Linear Regression

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

Solving Optimization Problems

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?

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

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.

Supervised Machine Learning - Classification and Regression - Decision Trees

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.

Unsupervised learning - Clustering – K means

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

Unsupervised learning - Clustering - Hierarchical clustering Technique

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

Unsupervised learning - Clustering - DBSCAN (Density based clustering)

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.

Association Rule Mining (Apriori)

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

Recommender Systems and Matrix Factorization

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.

Dimensionality reduction and Visualization

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.

Principal Component Analysis.

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)

T-distributed stochastic neighborhood embedding(t-SNE)

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.

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 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?

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

Description	Amount
Course Exam Fee	Rs.1,40,000

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PG Certification in Data Science

Online

10 Months

₹ 140000

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