Foundations
The Foundations block comprises four courses where we get our hands dirty with
the introduction to Python, Exploratory
Data Analysis, Statistics, SQL Programming, and some domainspecific knowledge
headon. These courses set our
foundations so that we sail through the rest of the journey with minimal
hindrance.
This module will let us get comfortable with the Python
programming language used for Data Science. We start with a
highlevel idea of ObjectOriented Programming and later
learn the essential vocabulary(/keywords), grammar(/syntax)
and
sentence formation(/usable code) of this language.

Syntax and Semantics of Python programming
Python is a widely used highlevel programming language
and has a simple, easytolearn syntax that highlights
readability. This module will help you drive through all
the fundamentals of programming in Python, such as
syntax and
semantics, and at the end, you will execute your first
Python program.

Conditional statements
Conditional Statements perform various operations based
on a boolean condition that evaluates to true or false.
In this
module, you will learn to implement ifelse statements
in Python.

Loops
A loop is used for executing one or more statements
several times mentioned. It will repeat the sequence of
information
until it meets a specified condition. This module will
teach you how to implement for and while loops.

Iterators
Iterators are objects containing values, where you can
traverse through them. This module will teach you how to
implement Iterator methods like __iter__() and
__next__().

Userdefined functions
Userdefined functions are functions created by the
programmer to perform any applicationspecific
operations. In this
module, you will implement userdefined functions in
Python using the def keyword.

NumPy
This module will give you a deep understanding of
exploring data sets using NumPy. NumPy is one of the
most widely used
Python libraries. NumPy is a package for scientific
computing like working with arrays.

Pandas
Pandas is also one of the most widely used Python
libraries. Pandas is used to analyse and manipulate
data. This module
will give you a deep understanding of exploring data
sets using Pandas.
This module of PG in Data Science courses will teach you all
about Exploratory Data Analysis like Pandas, Seaborn,
Matplotlib, and Summary Statistics.

Pandas
Pandas is one of the most widely used Python libraries.
Pandas is used to analyse and manipulate data. This
module will
give you a deep understanding of exploring data sets
using Pandas.

Summary statistics (mean, median, mode,
variance, standard deviation)
In this module, you will learn about various statistical
formulas and implement them using Python.

Seaborn
Seaborn is also one of the most widely used Python
libraries. Seaborn is a Matplotlib based data
visualisation library
in Python. This module will give you a deep
understanding of exploring data sets using Seaborn.

matplotlib
Matplotlib is another widely used Python libraries.
Matplotlib is a library to create statically animated,
interactive
visualisations. This module will give you a deep
understanding of exploring data sets using Matplotlib.
In the next module, you will learn everything you need to
know about all the statistical methods used for decision
making in this Data Science PG course.

Probability distribution
A statistical function reporting all the probable values
that a random variable takes within a specific range is
known
as a Probability Distribution. This module will teach
you about Probability Distributions and various types
like
Binomial, Poisson, and Normal Distribution in Python.

Normal distribution
Normal Distribution is the most critical Probability
Distribution in Statistics, which describes the
distribution of
values of a variable.

Poisson's distribution
Poisson's Distribution is a Probability Distribution in
Statistics, which determines an event’s occurrence
within a
specified time interval.

Bayes’ theorem
Baye’s Theorem is a mathematical formula named after
Thomas Bayes, which determines conditional probability.
Conditional
Probability is the probability of an outcome occurring
predicated on the previously occurred outcome.

Central limit theorem
This module will teach you how to estimate a normal
distribution using the Central Limit Theorem (CLT).

Hypothesis testing
This module will teach you about Hypothesis Testing in
Statistics. Hypothesis Testing is a necessary procedure
in
Applied Statistics for doing experiments based on the
observed/surveyed data.

One Sample TTest
OneSample TTest is a Hypothesis testing method used in
Statistics. In this module, you will learn to check
whether an
unknown population mean is different from a specific
value using the OneSample TTest procedure.

Anova and ChiSquare
Analysis of Variance, also known as ANOVA, is a
statistical technique used in Data Science, which is
used to split
observed variance data into various components for
additional analysis and tests.
ChiSquare is a Hypothesis testing method used in
Statistics, which is used to measure how a model
compares to actual
observed data.
This module will teach you how to identify the
significant differences between the means of two or more
groups.
Here, we will cover everything you need to know about SQL
programming, such as DBMS, Normalization, Joins, etc.

Introduction to DBMS
Database Management Systems (DBMS) is a software tool
where you can store, edit, and organise data in your
database.
This module will teach you everything you need to know
about DBMS.

ER diagram
An EntityRelationship (ER) diagram is a blueprint that
portrays the relationship among entities and their
attributes.
This module will teach you how to make an ER diagram
using several entities and their attributes.

Schema design
Schema design is a schema diagram that specifies the
name of record type, data type, and other constraints
like primary
key, foreign key, etc. It is a logical view of the
entire database.

Key constraints and basics of normalization
Key Constraints are used for uniquely identifying an
entity within its entity set, in which you have a
primary key,
foreign key, etc. Normalization is one of the essential
concepts in DBMS, which is used for organising data to
avoid
data redundancy.
In this module, you will learn how and where to use all
key constraints and normalization basics.

Joins
As the name implies, a join is an operation that
combines or joins data or rows from other tables based
on the common
fields amongst them.
In this module, you will go through the types of joins
and learn how to combine data.

Subqueries involving joins and aggregations
This module will teach you how to work with
subqueries/commands that involve joins and aggregations.

Sorting
As the name suggests, Sorting is a technique to arrange
the records in a specific order for a clear
understanding of
reported data. This module will teach you how to sort
data in any hierarchy like ascending or descending, etc.

Independent subqueries
The inner query that is independent of the outer query
is known as an independent subquery. This module will
teach you
how to work with independent subqueries.

Correlated subqueries
The inner query that is dependent on the outer query is
known as a correlated subquery. This module will teach
you how
to work with correlated subqueries.

Analytic functions
A function that determines values in a group of rows and
generates a single result for every row is known as an
Analytic
Function.

Set operations
The operation that combines two or more queries into a
single result is called a Set Operation. In this module,
you will
implement various set operators like UNION, INTERSECT,
etc.

Grouping and filtering
Grouping is a feature in SQL that arranges the same
values into groups using some functions like SUM, AVG,
etc.
Filtering is a powerful SQL technique, which is used for
filtering or specifying a subset of data that matches
specific
criteria.
Machine Learning Techniques
The next module is Machine Learning that will teach us all the Machine Learning
techniques from scratch, and the
popularly used Classical ML algorithms that fall in each of the categories.
This module will get us comfortable with all the techniques
used in Linear and Logistic Regression.

Multiple linear regression
Multiple Linear Regression is a supervised machine
learning algorithm involving multiple data variables for
analysis. It
is used for predicting one dependent variable using
various independent variables.
This module will drive you through all the concepts of
Multiple Linear Regression used in Machine Learning.

Fitted regression lines
A fitted regression line is a mathematical regression
equation on a graph for your data. This model can be
used to
identify the relationship between a predictor variable
(xscale) and a response variable (yscale) so that it
can assess
whether the model fits your data.

AIC, BIC, Model Fitting, Training and Test Data
In this module, you will go through everything you need
to know about several models such as AIC, BIC, Model
Fitting,
Training, and Test Data.

Introduction to Logistic regression,
interpretation, odds ratio
Logistic Regression is one of the most popular ML
algorithms, like Linear Regression. It is a simple
classification
algorithm to predict the categorical dependent variables
with the assistance of independent variables.
This module will drive you through all the Logistic
Regression concepts used in Machine Learning, interpret
Machine
Learning models, and find the odds ratio relationship.

Misclassification, Probability, AUC, RSquare
This module will teach everyone how to work with
Misclassification, Probability, AUC, and RSquare.
In the next module, you will learn all the Supervised
Learning techniques used in Machine Learning.

CART
CART, also known as Classification And Regression Tree,
is a predictive machine learning model that describes
the
prediction of outcome variable's values predicated on
other values.
You will learn about the usage of this predictive model
in this module.

KNN (classifier, distance metrics, KNN
regression)
KNN or kNearest Neighbours algorithm is one of the most
straightforward machine learning algorithms for solving
regression and classification problems.
You will learn about using this algorithm like
classification, distance metrics, and KNN regression
through this module.

Decision Trees (hyper parameter, depth, number
of leaves)
Decision Tree is a Supervised Machine Learning algorithm
used for both classification and regression problems. It
is a
hierarchical structure where internal nodes indicate the
dataset features, branches represent the decision rules,
and
each leaf node indicates the result.
You will learn about hyperparameter, depth, and the
number of leaves in this module.

Naive Bayes
Naive Bayes Algorithm is used to solve classification
problems using Baye’s Theorem. This module will teach
you about
the theorem and solving the problems using it.
In the next module, you will learn all the Unsupervised
Learning techniques used in Machine Learning.

Clustering  KMeans & Hierarchical
Clustering  KMeans & Hierarchical
Clustering is an unsupervised learning technique
involving the grouping of data. In this module, you will
learn
everything you need to know about the method and its
types, like Kmeans clustering and hierarchical
clustering.
Kmeans clustering is a popular unsupervised learning
algorithm to resolve the clustering problems in Machine
Learning
or Data Science.
Hierarchical Clustering is an ML technique or algorithm
to build a hierarchy or treelike structure of clusters.
For
example, it is used to combine a list of unlabeled
datasets into a cluster in the hierarchical structure.

Distance methods  Euclidean, Manhattan,
Cosine, Mahalanobis
This module will teach you how to work with all the
distance methods or measures such as Euclidean,
Manhattan, Cosine,
and Mahalanobis.

Features of a Cluster  Labels, Centroids,
Inertia
This module will drive you through all the features of a
Cluster like Labels, Centroids, and Inertia.

Eigen vectors and Eigen values
In this module, you will learn how to implement
Eigenvectors and Eigenvalues in a matrix.

Principal component analysis
Principal Component Analysis is a technique to reduce
the complexity of a model, like eliminating the number
of input
variables for a predictive model to avoid overfitting.
In this Machine Learning, we discuss supervised standalone
models’ shortcomings and learn a few techniques, such as
Ensemble techniques, to overcome these shortcomings.

Bagging & Boosting
Bagging, also known as Bootstrap Aggregation, is a
metaalgorithm in machine learning used for enhancing
the stability
and accuracy of machine learning algorithms, which are
used in statistical classification and regression.
As the name suggests, Boosting is a metaalgorithm in
machine learning that converts robust classifiers from
several
weak classifiers. Boosting can be further classified as
Gradient boosting and ADA boosting or Adaptive boosting.

Random Forest
Random Forest is a popular supervised learning algorithm
in machine learning. As the name indicates, it comprises
several decision trees on the provided dataset’s several
subsets. Then, it calculates the average for enhancing
the
dataset’s predictive accuracy.

AdaBoost & Gradient boosting
Boosting can be further classified as Gradient boosting
and ADA boosting or Adaptive boosting. This module will
teach
you about Gradient boosting and ADA boosting.

Hackathon
Hackathon is an event usually hosted by a tech
organisation, where computer programmers gather for a
short period to
collaborate on a software project.
Applications*
This module concentrates on the applications of Data
Science.
This block will teach you all the techniques involved in
Time Series.

Trend and seasonality
Trend is a systematic linear or nonlinear component in
Time Series metrics, which changes over a while and does
not
repeat.
Seasonality is a systematic linear or nonlinear
component in Time Series metrics, which changes over a
while and
repeats.

Decomposition
This module will teach you how to decompose the time
series data into Trend and Seasonality.

Smoothing (moving average)
This module will teach you how to use this method for
univariate data.

SES, Holt & HoltWinter Model
SES, Holt, and HoltWinter Models are various Smoothing
models, and you will learn everything you need to know
about
these models in this module.

AR, Lag Series, ACF, PACF
In this module, you will learn about AR, Lag Series,
ACF, and PACF models used in Time Series.

ADF, Random walk and Auto Arima
In this module, you will learn about ADF, Random walk,
and Auto Arima techniques used in Time Series.
In this module, you will learn how to convert unstructured
text into a structured text to discover relevant insights.
This method is known as Text Data Mining.

Text cleaning, regular expressions, Stemming,
Lemmatization
Text Cleaning is a necessary procedure to emphasize the
attributes for your machine learning model to choose.
Regular
Expression is a language that states text search
strings. Stemming is a technique used in Natural
Language Processing
(NLP), which plucks out the base form of words by the
removal of affixes from the words. Lemmatization is
another
commonly used NLP technique, which combines the
different inflected word forms to be analysed as a
single item.

Word cloud, Principal Component Analysis,
Bigrams & Trigrams
A word cloud is a data visualization technique, which is
used to represent text data. This module will make you
learn
everything about Word cloud, Principal Component
Analysis, Bigrams, and Trigrams used in Data
Visualization.

Web scrapping, Text summarization, Lex Rank
algorithm
Web Scraping is the process of extracting data from the
web. This module will teach you how to collect and parse
data
using Web Scraping and learn how to implement Text
Summarization and Lex Rank algorithm.

Latent Dirichlet Allocation (LDA) Technique
The word ‘Latent’ indicates that the model
discovers the ‘yettobefound’ or hidden
topics from the documents.
‘Dirichlet’ indicates LDA’s assumption
that the distribution of topics in a document and the
distribution of words in
topics are both Dirichlet distributions.
‘Allocation’ indicates the distribution of
topics in the document.

Word2vec Architecture (Skip Grams vs CBOW)
Word2vec is a method to create word embeddings by using
a twolayer neural network efficiently. It was developed
by
Tomas Mikolov et al. at Google in 2013 to make the
neuralnetworkbased training of the embedding more
efficient and
since then has become the de facto standard for
developing pretrained word embedding.
In this module, you will learn about the differences
between Skip Grams and CBOW (Continuous Bag of Words).

Text classification, Document vectors, Text
classification using Doc2vec
In this module, you will learn even more about Text
Classification and Document Vectors using Doc2vec.
The last topic in this PG in Data Science program is Data
Visualization. The process of the graphical representation
of
data and information is known as Data Visualization. This
module will teach you how to use data visualization tools
for
providing data trends and patterns.

Building interactive dashboards using Tableau
In this module, you will learn how to create an
interactive Tableau dashboard and charts to organise
data.

Data Visualization using Tableau
Tableau is the most widely used data visualization tool
to solve problems. This module will teach you everything
you
need to know about Data Visualization using Tableau.
*Online Instruction
Capstone Project
You will get your hands dirty with realtime projects under industry
experts’ guidance, from Data Science, using Python
to Machine Learning, SQL and Tableau. Successful completion of the project will
earn you a postgraduate certification
in Data Science and Engineering course.
Career Preparation: Aptitude Skill Training and
Development, Resume Review Workshops, Interview
Preparation
This postgraduate certification program in Data Science and Engineering will
guide you through your career path with
Aptitude Skill Training and Development. The program will also guide you in
building your professional resume, attending
mock interviews to boost your confidence and nurture you to nail your
professional interviews.
PG Certificate from Great Lakes Executive Learning
Earn a Postgraduate Certificate in the toprated Data Science and Engineering
course from Great Lakes Executive
Learning.
Its exhaustive Curriculum will foster you into a highlyskilled professional and
help you land a job at the world’s
leading corporations.
Exclusive Campus Hiring Drive
If you are looking for a Data Science course with placement opportunities, this
is the right Data Science and
Engineering course to excel in your career. This course offers you exclusive
campus hiring opportunities with three
months of placement assistance after the program completion.