Post Graduate Program in Data Science and Engineering

5 Quizzes

1 Project

This module will let us get comfortable with the Python programming language used for Data Science. We start with a high-level idea of Object-Oriented Programming and later learn the essential vocabulary(/keywords), grammar(/syntax) and sentence formation(/usable code) of this language.

Python is a widely used high-level programming language and has a simple, easy-to-learn 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 perform various operations based on a boolean condition that evaluates to true or false. In this module, you will learn to implement if-else statements in Python.

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

User-defined functions are functions created by the programmer to perform any application-specific operations. In this module, you will implement user-defined functions in Python using the def keyword.

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 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.

5 Quizzes

1 Project

This module of PG in Data Science courses will teach you all about Exploratory Data Analysis like Pandas, Seaborn, Matplotlib, and Summary Statistics.

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.

In this module, you will learn about various statistical formulas and implement them using Python.

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 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.

5 Quizzes

1 Project

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.

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

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

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.

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

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 T-Test 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 One-Sample T-Test procedure.

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.
Chi-Square 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.

5 Quizzes

1 Project

Here, we will cover everything you need to know about SQL programming, such as DBMS, Normalization, Joins, etc.

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.

An Entity-Relationship (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 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 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.

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.

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

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.

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.

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.

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

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 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.

5 Quizzes

1 Project

This module will get us comfortable with all the techniques used in Linear and Logistic 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.

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 (x-scale) and a response variable (y-scale) so that it can assess whether the model fits your 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.

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.

This module will teach everyone how to work with Misclassification, Probability, AUC, and R-Square.

5 Quizzes

1 Project

In the next module, you will learn all the Supervised Learning techniques used in Machine Learning.

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 or k-Nearest 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 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 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.

5 Quizzes

1 Project

In the next module, you will learn all the Unsupervised Learning techniques used in Machine Learning.

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 K-means clustering and hierarchical clustering.
K-means 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 tree-like structure of clusters. For example, it is used to combine a list of unlabeled datasets into a cluster in the hierarchical structure.

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

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

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

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.

5 Quizzes

1 Project

In this Machine Learning, we discuss supervised standalone models’ shortcomings and learn a few techniques, such as Ensemble techniques, to overcome these shortcomings.

Bagging, also known as Bootstrap Aggregation, is a meta-algorithm 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 meta-algorithm 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 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.

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 is an event usually hosted by a tech organisation, where computer programmers gather for a short period to collaborate on a software project.

1 Quiz

1 Project

This block will teach you all the techniques involved in Time Series.

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

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

This module will teach you how to use this method for univariate data.

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

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

In this module, you will learn about ADF, Random walk, and Auto Arima techniques used in Time Series.

1 Quiz

1 Project

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 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.

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 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.

The word ‘Latent’ indicates that the model discovers the ‘yet-to-be-found’ 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 is a method to create word embeddings by using a two-layer neural network efficiently. It was developed by Tomas Mikolov et al. at Google in 2013 to make the neural-network-based training of the embedding more efficient and since then has become the de facto standard for developing pre-trained word embedding.
In this module, you will learn about the differences between Skip Grams and CBOW (Continuous Bag of Words).

In this module, you will learn even more about Text Classification and Document Vectors using Doc2vec.

2 Quizzes

2 Projects

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.

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

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.