Data Folkz


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400 Hours- Live Classes

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Unlocks 60+ Job Roles

About PG Program in Artificial Intelligence & Big Data Analytics

Divided into 4 units, this is a program of 12-month duration and comprises a total of 13 terms. During the program, the students will be working on more than 40 minor projects and over 15 Capstone projects to gain the competitive skills required to thrive in the industry.

Exploring the PG Program in Artificial Intelligence & Big Data Analytics

The Post Graduation Program in Artificial Intelligence & Big Data Analytics is an online program designed by industry experts and IIT Alumni. It is a 12-month Job Guarantee Program in which students from any technical background can get enrolled. The PG Program in Artificial Intelligence & Big Data Analytics is aimed at equipping students with the technical and management knowledge to drive business transformation. The program will be taught by IIT Alumni trainers and is going to be held on an app-based learning system. This program will open ample career opportunities as it enables over 60 job roles in the field of Data Science, Machine Learning, and Deep Learning. Followed by the completion of the project the students will be working on real-world industry projects and participating in Hackathons and Competitions to gain valuable skills. At Data Folkz’s PG Program in Artificial Intelligence & Big Data Analytics, the student will be learning skills with the presentation of Capstone Projects, Hackathons, and the Kaggle Competition. We provide the student with lifetime access to the classes and mock interviews for students’ preparation to face the real-world challenges related to the field of Big Data. Data Folkz offers the best training and consulting solutions that are effective and reliable. The program is a 100% Job Guarantee Program where you will experience zero risk in ISA and the minimum placement package the student will be getting his/her hands on is 5 LPA. The program comes with a model of Pay after Placement so that the student need not worry about making a decision to get enrolled in the course.

Learning Objectives of this Artificial Intelligence and Big Data Analytics Course

The major objective of this course is to equip the students with the knowledge and skills required to keep up with future technologies such as Artificial Intelligence, Big Data, Cybersecurity, Blockchain, Machine Learning, and the Internet of Things. After the completion of this course, you the students will be able to diagnose analytics in order to make more precise sales forecasts. The online sessions at Data Folkz are covered by IIT Alumni and industry experts and are going to be highly interactive one-to-one sessions. This facilitates a better understanding of the core concepts of the topic and helps in gaining industry-relevant knowledge in the field of Artificial Intelligence and Big Data.

Why Become a Big Data Analyst?

Big Data majorly deals with the extraction of knowledge and actionable insight from raw data. As data is being generated by users worldwide at a rapid rate, data analytics and data modeling are gaining more and more importance. Another important reason to become a Big Data Analytics is that they are able to provide organizations with analyses that are helpful in accessing their overall performance. The PG Program in Artificial Intelligence & Big Data Analytics equips the students with the knowledge to predict upcoming alterations and trends in the market and identify market demographics. Almost every field nowadays requires Big Data Analysts as they carry the potential of producing powerful insights that are of utmost utility in business. Moreover, as per research and data, the Indian Big Data Technology and Service Market has surged significantly. It is expected that from 2021-2026, the CAGR would be 35.1%. Be it the finance or healthcare sector, the science or telecommunications sector, or the government and business sector, Big Data Analyst has got a crucial role to play. Data Folkz offers hands-on industry-based projects and case studies to work on, providing the student with a real-time experience of working as a Big Data Analyst.

Crucial Skills You Will Learn With This Artificial Intelligence and Big Data Course

The course will introduce you to the core foundations of AI with modules in Data Science, Python, Cloud Computing, and much more. After the completion of the course, you will be possessing knowledge about Big Data, the Internet of Things, Machine Learning Algorithms, Neural Networks, and Artificial Intelligence. This course will explore frameworks, tools, and proven strategies in the real world and prepare students for the applications of Big Data and Machine Learning in businesses. In the PG Program for Artificial Intelligence and Big Data Analytics, the student learn both technical and non-technical skills. Talking about the technical skills that the student will learn at Data Folkz during the program, they are: Programming Languages , Linear Algebra and Statistics, Signal Processing Techniques, Neural Network Architectures, Mathematical Knowledge, Deep Learning, Quantitative Analysis, Data Visualization, Predictive Analysis, Data Mining Moving further to the non-technical skills that the student will be learning under this PG Program in Artificial Intelligence & Big Data Analytics, they are: Strong Business Sense, Advanced Communication Skills, Incredible Data Divination, Management Skills, Presentation Skills, Resume Building, Leadership Skills Following the completion of the course, the students will be possessing the knowledge of the following tools: Python, Tableau, Advanced Excel, NumPy, Pandas, Matplotlib, Seaborn, NLTK, OpenCV, SQL, MongoDB, Hadoop, PySpark, MapReduce, Hive

What Projects Students will be Working on in this Artificial Intelligence and Big Data Analytics Course?

During the program, the students will get exposure to over 40 minor projects that will provide them with the required training for more than 60 job roles. Additionally, Data Folkz will be providing the students with the opportunity to work on 15+ Capstone projects. This way, the students will gain experience in dealing with industries in the new-age technology. The Capstone Projects that the students will be working on are: Website Behaviour Analysis, Cardio Vascular Risk Prediction, Customer Segmentation, Customer Behaviour, Credit Card Fraud Detection, Web Scraping, Hotel Booking Analysis, World Bank Global Education Project, Car Price Prediction, Stock Index Price Prediction, Chatbot Capstone, Language Translation, Speech to Text, Object Detection, Barcodes and QR Codes Scanner, Image Segmentation, Customer Churn for a Telecom Company, Customer Complaint Analysis, Sensex Log Data Processing, MLOps with Azure, MLOps with GCP (Google Cloud Platforms)

Who Should do the PG Program in Artificial Intelligence & Big Data Analytics?

The PG Program in Artificial Intelligence & Big Data Analytics is designed by industry experts to equip the students with the knowledge to create potent, meaningful, and valuable reports from raw data. Who can get enrolled in this program? 1)College Student- Grad/Post Grad Student If you are a college student who has just completed his/her Bachelor’s and Master’s degree program, then you can get enrolled in this course. 2)Working Professional- Having less than 5 years of work ex. If you are a working professional with a total experience of fewer than 5 years, then you can pursue this program at Data Folkz. This program is best suited for you if: =>You are a student who doesn’t have any idea where your career is headed. Around 82% of the students go for a Master’s Program following the completion of their Undergraduate Program. Don’t be lost and learn the skills under leading AI and business experts and gain technological and management knowledge to spearhead business upliftment. =>You are from a technical background but due to the lack of job opportunities in the technical field, you are settling yourself with a management position. This category of underserved students amounts to 68%. =>You are a student who did good in your college and was in the top lot but still failed to secure a job through the college placement drive. As per a survey conducted, around 75% of the students on average fail to get placed through the college placement drive. For getting enrolled in this program, you need to be from a technical background. This program by Data Folkz is aimed at providing undergraduate students with high opportunities to get a job. The highly professional and skilled faculty at Data Folkz is dedicated to equipping the students with high-value skills to facilitate a decent demand and supply rate.

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The program is divided into 4 units comprising 13 terms to be covered in a 12-month duration. The total number of learning hours is set to be 200 and you will be trained by IIT Alumni. This program will follow an app-based learning system and for better understanding 1:1 sessions are available. The course also covers a month for Career Center Classes for the enhancement of personal skills.

Unit 1 - Term 1 - Statistics

Introduction to Data

You will be explained the various Data types in this module and learn about variables and their types, sampling techniques, convenience sampling, stratified sampling, simple random sampling, stratified sampling, systematic sampling, and cluster sampling.

Descriptive Statistics

Under this module, you will gain knowledge of Univariate and Bi Variate Analysis, Measures of Central Tendencies, Measures of Dispersion, Skewness and Kurtosis, Outliers and Boxplot, and Covariance and Correlation.

Unit 1 - Term 2 - Advanced Statistics

Inferential Statistics

Learn in detail about the sampling variability and the Central Limit Theorem along with gaining the required information on Confidence Intervals. This module will deal with the teaching of Hypothesis Testing, Parametric Tests, t-Test, Z-Test, and ANOVA. In addition to this, you will also be exposed to Non-Parametric Tests and Chi-Square tests.

Unit 1 - Term 3 - Python Programming

Python Introduction

Learn everything about the Python programming language in detail and gain knowledge of why Python is required in the field of Data Science. Gain an understanding of the installation of Anaconda, understanding Jupyter Notebook, and basic commands in Jupyter Notebook. Additionally, you will be exposed to the basic understanding of Python Syntax and variables and operators.

Data Types

This module will help you understand how to deal with various data types such as strings, lists, sets, tuples, and dictionaries.

Control Flow and Conditional Statements

Learn about the If, Elif, and Else statements in Python along with While Loops, For Loops, Nested Loops, and List and Dictionary Comprehensions.


Gain valuable information about User Defined functions, Lambda Functions, Map, Filter, and Reduce.

Unit 2 - Term 4 - Data Visualization and EDA


This module in Data Visualization and EDA will equip you with the knowledge of Arrays, Basic Operations in Numpy, Indexing, and Array Processing.


Gain information related to Series, DataFrames, Indexing and Slicing, Groupby, Concatenating, Merging Joining, Missing Values, Operations, Data Input and Output, Pivot, and Cross tab.

Visualization with Matplotlib and Seaborn

Introduction to Matplotlib. Learn about Line plots, histograms, box, and Violin Plots, Scatterplot, Heatmaps, and Subplots, and how to use them in Data Science.

Exploratory Data Analysis

Learn in-depth about EDA on a dataset, analysis, and visualization, treating missing values, outlier detection and treatment, and feature engineering.

Web Scraping

Gain knowledge of Web Scraping basics and libraries, web scraping advanced, and what is machine learning. Along with this, learn how to conduct hotel booking analysis.

Unit 2 - Term 5 - Supervised Learning


This module is optional in the PG Program in Artificial Intelligence and Big Data Analytics and deals with the introduction, visualization, joins and calculation, and report making and sharing. The students will be learning how to install Tableau and get a basic understanding of the Tableau Interface. Learn how to connect Tableau to various datasets such as Excel, and CSV files, connecting to the web, and implement data blending and aggregation.

Linear Regression

Gain valuable information related to Simple Linear Regression, Multiple Linear Regression, OLS Assumptions, and Regression Evaluation Metrics.

Case Study

Classification Overview

Get a thorough understanding and overview of classification and gain knowledge related to the importance of Linear Regression.

Logistic Regression

Learn everything about the Logistic Regression, the Logistic Model, Logit and Sigmoid Functions, and setting the threshold and understanding decision boundary.

Evaluation Techniques for Classification

Gain information on the confusion matrix and learn how to attain maximum accuracy while minimizing the error rate. The module will deal with the learning of TPR and FPR, precision and recall, F1 Score, and AUC-ROC.

Case Study

Decision Tree

This module will equip the students with the basic terminology in Decision Tree, Root Node and Terminal Node, Regression Trees, Classification Trees, advantages and disadvantages of Trees, Gini Index, Information Gain/Entropy, and Reduction in Variance, and Overfitting and Pruning.

Bagging-Random Forest

Learn in detail about what is Ensemble Learning, Bagging, and how it functions. Gain valuable information about what exactly is Random Forest and its functioning.


Resampling deals with Cross-Validation, k-Fold Cross-Validation, and Stratified Cross-Validation.

Hyperparameter Tuning

This module deals with Parameters and Hyperparameters and learn how to perform Hyperparameter Tuning, grid search, and randomized search.

Case Study

Boosting Techniques

Learn what is Boosting and how it works. Gain information related to Ada Boosting, Gradient Boosting, and XG Boosting.

Case Study

Support Vector Machine

The Support Vector Machine module deals with Hyperplane, the Maximal Margin Classifier, Support Vector Classifiers, hard and soft Margin classification, classification with non-linear decision boundaries, and kernel trick.

K-Nearest Neighbour

Learn about the KNN algorithm, Linear, Polynomial, and Radial, and how to decide the number of neighbors in KNN.

Naive Bayes Algorithm

Get familiar with the Bayes Theorem and gain exposure to the terminology in Naive Bayes. Become knowledgeable of the types of Naive Bayes Classifier, Multinomial Naive Bayes, Bernoulli Naive Bayes, and Gaussian Naive Bayes.

Unit 2 - Term 6 - Unsupervised Learning

Principal Component Analysis

Get a basic understanding of the concept of Dimensionality Reduction and why there arises a need to conduct it. Learn about the Principal Components and demonstration of 2D PCA and 3D PCA. Gain information about the Eigen Values, Eigen Vectors, and Orthogonality. This module will equip you with the knowledge of transforming the Eigen values into a new data set and will explain the proportion of variance in PCA.

Case Study


This particular module is designed in order to equip the students with the various Clustering methods and give exposure to K-Means Clustering, Centroids, and Medoids. Learn how to decide the optimal value of ‘k’ with the use of the Elbow Method. You will also be learning in detail about Hierarchical Clustering, Divisive and Agglomerative Clustering, and Dendrograms along with their interpretation.

Association Rules

In this module, you will be introduced to the concept of Association Rules Mining where you will learn in detail about the Association rules, market basket analysis, and Apriori/Support/Confidence/Lift.

Case Study

Time Series (Forecasting)

This module will provide you with a thorough understanding of what exactly is Times Series Data. Learn in detail about the Stationarity in Time Series Data and Augmented Dickey-Fuller Test, the Box-Jenkins Approach, the AR Process, and the MA Process. Gain valuable knowledge and skills related to ARIMA, SARIMA, ACF, PACF, and IACF plots. Additionally, the module will cover topics such as the decomposition of Times Series Trend, Seasonality, and Cyclic along with Exponential Smoothing, and EWMA.

Unit 3 - Term 7 - Natural Language Processing (Basic)

Introduction to NLP

This module will take you to the discussion of what is NLP and why is it important. In addition to this, you will get exposure to the applications of NLP, the unstructured data it deals with, and the life cycle of NLP. Gain an understanding of the tools for NLP, Libraries for NLP, and NLTK.

Text Preprocessing

The text Preprocessing module will deal with text normalizing, stop words removal, stemming, and Lemmatization. Along with this, the module will be pouring light on Tokenization, Text standardization, and exploratory data analysis.

Feature Engineering

Feature Engineering will equip the students with the knowledge of One hot encoding, Count Vectorizer, and TFIDF.

Unit 3 - Term 8 - Natural Language Processing (Advanced)

Part of Speech

Gain information about Part of Speech tagging, NER, and learn the core concepts of Topic modeling and Text classification.

NLP and Deep Learning for NLP

Learn what exactly is a sequence-based model. This module deals with concepts such as Vanishing Gradient, Exploding Gradient, Recurrent Neural Networks, LSTM, GRU, Batching Sequence Models, and information retrieval using word embedding. In addition to this, the course module will consist of the learning of text classification using Deep Learning approaches, Natural language generation, transformers and BERT, and text summarization using LSTM encoder and decoder.

Unit 3 - Term 9 - Deep Learning

Introduction to Neural Network

What is Neural Network? Introduction to Neuron and Perceptron along with Primitive Neuron and Sigmoid Neuron. This course module will also explain the types of Activation functions that are used in deep learning networks. Gain an in-depth understanding of Cost Functions, Gradient Descent, Stochastic Gradient Descent, the feedforward model of the neural network, and the disadvantages of the feedforward model. Where applying weights to the feedforward model leads to and backpropagation algorithm.

Artificial Neural Network

This module will deal with the core understanding of Neural Networks, biological inspiration, perceptron learning, and binary classification along with back propagation Learning and Object Recognition.

Tensorflow & Keras

The Tensorflow and Keras module gives students exposure to the Tensorflow, Debugging, and Monitoring, Keras for classification and regression in Typical Data Science Problems, and Setting up KERAS. Learn about the different layers in KERAS, and how to create a Neural Network. Gain valuable information on the training models and monitoring along with Artificial Neural Networks.

Recurrent Neural Network

This module will provide the students with a brief introduction to RNN, its network structure, types, Bidirectional RNN, and its limitations. Additionally, it will deal with training an RNN with a use case, and introduce the students to LSTM and its architecture and variants. The Recurrent Neural Network model will also help in time series forecasting or sequential modeling using LSTM.

Convolutional Neural Network

Introduction to CNN and Convolutional operations are what this module will deal with alongside its features, ReLu, Pooling, and fully connected layer. The students will learn about training a CNN and Image Classification.

Unit 4 - Term 10 - Advanced Deep Learning


This module will give the students an exposure to the introduction to CV and how to use OpenCV to work with image files. Additionally, the students will learn more about image manipulation involving smoothing and blurring, translation, rotation, and cropping. Learn about thresholding and morphological operations and how to open and stream video with OpenCV. Gain valuable information such as creating color histograms with OpenCV, face recognition, and template matching.

Advanced Computer Vision

Learn about transfer learning using Keras and gain information related to VGG, RESNET, object detection, drawing bounding boxes, and Yolo.


This topic will discuss creating pickle and frozen files, cloud deploying machine learning, and Deep Learning model for production.

Unit 4 - Term 11 - Database Management System


This module will deal with what is DBMS, and its advantages over File Systems along with the knowledge of Database Applications, and Database Abstraction.

Data Modelling

Gain information about the types of ER Models, types of Attributes in ER models, and relationships. This module will teach you about the role of keys and their types along with the ER Model to table.

Types of DBMS Languages

In this module, the students will be equipped with the knowledge of Data Definition Language, Data Manipulation Language, Data Control Language, and Transaction Control Language.

Introduction to Relational Database

What exactly is a Relational Database? Get a basic introduction to Relational Algebra and different languages in RDBMS.

Relational Algebra (Basic)

Learn about the basic operators in Relational Algebra such as Projection, Selection, Cross Product, Union, Rename, and Set Difference.

Relational Algebra (Derived)

This module will teach you about the derived operators in Relational Algebra such as Join and Types, Intersection, and Division.

Introduction to Non-Relational Database

What exactly is a Non-Relational Database and why is it important? Gain information about the advantages of a non-relational database and the major differences between relational and non-relational DB.

Types of Data Sources

Learn about the four types of Data Sources named, Document Databases, Key-Value Stores, Column-Oriented Databases, and Graph Databases.

Different Tools

Gain exposure to tools such as MongoDB, ElasticSearch, Apache Cassandra, and HBase.


Learn how to create databases and collections in MongoDB along with the different data types and data insertion. This module will provide you with ample information related to selecting and projecting data. Furthermore, all the queries related to the subject will be discussed.

Unit 4 - Term 12 - Big Data


This module will teach you about what is Big Data and where it comes from. Learn about integrating Diverse Data and applications of Big Data.

Characteristics of Big Data

Here, you will gain the required information about the major characteristics of Volume, Variety, Velocity, Veracity, Valence, and Value.

Hadoop Ecosystem

Learn about the Hadoop ecosystem and what are the benefits that can be reaped from this ecosystem. This module will provide you with the required knowledge to know where to use the Hadoop Ecosystem and the functioning of these tools.


HDFS stands for Hadoop Distributed File Systems and this module will teach you about the three major Hadoop Components and what exactly a Distributed File System is. Gain information about the advantages of HDFS and how it works.

Different Tools

Gain information on the utilization of different tools such as MapReduce, Pig, Hive, Spark, and HBase.

Introduction to MapReduce

What is MapReduce? Learn about the functioning of MapReduce and the three major steps in MapReduce. You will be learning with the help of examples and applications.

MapReduce Queries

Solving basic queries related to the subject.

Introduction to Hive

What is Hive? Learn about the prerequisites of working with Hive and the features of Hive. Gain valuable information about the architecture and working of Hive and its comparison with Pig and SQL.

Hive Queries

This module will deal with the data types in Hive, how to create and drop databases, and how to create, alter, and drop tables. In addition to this, the module will talk about Built-in Operators and Built-in Functions.


The HiveQL module will be discussing concepts such as Grouping, Filtering, Sorting, and Joins.

Hive Advanced

As the name suggests, the advanced version of the module will talk about the Hive structure level optimizations, Hive partitions, Hive Bucketing, Hive Query Optimizations, and Hive Bucketing with Joints. Additionally, the course module will comprise the learning of Hive File Format, Hive Row vs Columns format, ORC file format, and Parquel File Format.

Introduction to Spark

What is Spark and what are the prerequisites to get started with Spark? Learn about the features of Spark and its components along with the Essence of Spark - RDD.

Spark SQL

This module will be an introduction to Spark SQL and will talk about the features of Spark SQL. Additionally, the students will learn about the data sources and running queries in Spark SQL.

Spark Streaming

This module will introduce the students to the concept of Spark Streaming and data sources along with the functioning of Spark Streaming. The experts at Data Folkz will equip the students with the Discretized Streams, input DStreams and Receivers, transformations on DStreams, and checkpointing. Learn how to deploy and monitor applications.


Gain valuable information on Spark Context and Spark Session along with Spark DataFrames, and RDDs. Learn how to create view tables and columns while you become knowledgeable in sorting, filtering, grouping, aggregations, and joins. This module will talk about UDF Functions, and examples of working with Large Volumes of datasets.


A basic introduction to GraphX, property operators, structural operators, and join operators. Learn about the Neighbourhood Aggregation and Vertex and Edge RDDs.

PIG Fundamentals

Get a basic understanding of the core concepts of the PIG language along with the data sets and implementations using Piglatin language. Learn about loading, retrieving, storing, grouping, and joining dataset. This module will also discuss union, filtering, splitting, limiting, sorting, etc.

PIG Advanced

The advanced module based on PIG will talk about the built-in functions and custom functions along with the PIG operators for mathematical, string data handling, macros, parameter, substitution, and UDFs.


Here, the students will be made eligible for analyzing the loan dataset using PIG.

Unit 4 - Term 13 - MLOps (Optional)

Self Paced Learning

The self-paced learning module is all about learning the MLOPs architecture and understanding the various model files that are required for MLops. Learn about Flask Deployment, Kubernetes Architecture, Google Cloud Platform-based MLops architecture, and understand the various components of GCP. In addition to this, this module will help students gain information on how to clone the git repository with the source repository, build Docker image, and Model Deployment using Kubernetes.


During the program, the students will be exposed to a lot of live projects with an aim to equip them with hands-on experience to deal with real-world problems. These projects will help in sharpening the skills of students and make them ready for building a career in the industry. 



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Website Behaviour Analysis

Uncover user trends and gain knowledge on how to track behavior using powerful analytics. Learn how to drive user engagement by collecting, combining, and analyzing quantitative and qualitative user data.

Customer Segmentation

Become knowledgeable in dividing customers into segments on the basis of common characteristics like demographics and behavior to facilitate effective marketing.

Web Scraping

Gain skills in obtaining large amounts of data from a website, collecting the information it possesses, and exporting it to a more structured format.

Hotel Booking Analysis

This project will deal with the real-world data record of hotel bookings consisting of details such as bookings, cancellations, and information related to guests. Gain interesting insights into customers’ behaviors.

Cardio Vascular Risk Prediction

Deal with models that quantify the Cardio Vascular risk over a defined period or over a lifetime along with the age factor. This project is based on a model that is considered a cornerstone of preventive cardiology.

Customer Behaviour

This project is aimed at equipping you with the knowledge of how to study customers and the processes they follow in order to select, use, and dispose of products and services. You will gain valuable insights into using quantitative and qualitative methods for determining customer behavior.

Credit Card Fraud Detection

Become knowledgeable of the tools and techniques that are required in the process of identifying buying attempts that are fraudulent. Get equipped with an understanding of how machine learning can detect credit card fraud.

Customer Complaint Analysis

Digging into the datasets provided by a firm along with customer complaints and other attributes. Learn how to automate your customer complaint root cause analysis to uncover fine reasons for complaints.

Car Price Prediction

Several machine learning and deep learning models predict the price of used cars by utilizing data such as Model Name, Year of Purchase, and various other parameters.

Stock Index Price Prediction

This project deals with the creation of new variables used as an input to the models on the basis of a deep learning approach to predict the movement of stock prices.

Sensex Log Data Processing

Learn how to use Big Data technology to process Sensex Log Data in this project and gain valuable skills in predicting Nifty and Sensex movements.

Chatbot Capstone

Learn the advanced functioning of a Chatbot that simulates human conversation via voice commands or text chats or even both.

Language Translation

Become knowledgeable to provide expert language solutions for any size of business. Work with a deep neural network that excels at functioning as a segment of a machine translation pipeline.

Speech to Text

Learn how to deploy the most accurate speech recognition models in the world at scale while constantly bringing improvements with training and data.

Object Detection

Deal with algorithms that leverage machine learning or deep learning in order to produce meaningful results.

Barcodes and QR Codes Scanner

One of the best machine learning tasks to get started with computer vision. This project deals with the horizontal data bearers and two-dimensional QR codes.

Image Segmentation

Work on image segmentation projects where you will be recognizing and understanding what the image is comprised of at the pixel level.

Customer Churn for a Telecom Company

Working on the Telecom Churn Analysis and doing a complete EDA process to find out the major causes of customer churn in the telecom sector.

MLOps with Azure

Get exposure to a number of asset management and orchestration services to help you manage the lifecycle of model training and deployment workflows.

MLOps with GCP (Google Cloud Platforms)

Work on the automation of AI tasks in order to support an end-to-end lifecycle. Practice MLOps for sharpening your skills in the field of automation and monitoring at all steps of ML system construction.

Cloud Projects

Gain technical job skills by working on real-world cloud projects and learning how to build, deploy, and scale applications, websites, and services on a Google-like infrastructure.

Career Centre

6 Months Placement Drive

Presentation of Capstone Projects

Gain skills to demonstrate your knowledge of the course topics professionally.

Kaggle Competition

Exciting competitions organized by Data Folkz to grow your Data Science.


Put your coding skills to work and solve interesting business problems and real-world challenges.

Resume Building

Make a perfect job-winning resume and jump-start your career in your dream job.

LinkedIn Profile Building

Build your extensive network and track your professional milestones and achievements. Bio Define who you are and what services you have to offer along with your specialty. Summary Write impressive introductory content to showcase your skills and knowledge in this section. Headshot Cover the essentials for making a great portrait and face forward to look into the eyes of those you want to attract.


Professional and globally recognized ISO certificates on Data Folkz to help you get job-ready for an in-demand career field in less than a year.


Data Folkz offers an authorized signature that endorses your skills and knowledge of the course

Presentation Skills

We groom your presentation skills so that you are capable of delivering compelling, engaging, informative, transformative, and engaging presentations.

Communication Skills

Your communication skills are a deciding factor about your personality and it definitely adds to the value of your meetings.

Management Skills

Get equipped with skills that involve things like business planning, decision-making, problem-solving, and coordination.

Leadership Skills

Develop crucial leadership skills such as strategic thinking, planning, people management, and reliability for ensuring success in the workplace.

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Peer Learning

Via Data Folkz PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends – the possibilities are endless and our community has something for everyone!


Career Services

Data Folkz provides a 100% placement guarantee that is set to start immediately following the completion of the course. The Career Centre Classes at Data Folkz will involve skill development, profile development, and personality development to make you ready for placements. 


100% Job Placement

The placement drive at Data Folkz will ensure that you get complete assistance for your placements from the industry experts and veterans. We facilitate a 100% job placement program that assures a minimum package of 5 LPA

Leading Hiring Partners

Data Folkz has partnered with over 20 leading companies that require Data Scientists and are offering jobs to skilled and talented students with an impressive understanding of the core concepts related to Data Science.

3 Guaranteed Interviews

Data Folkz conducts a total of 3 mock interviews to get you prepared for the job interviews by enhancing your skills, profile, and personality.

Exclusive Access to Our Job Portal

You will have exclusive access to our dedicated job portal following the completion of the course. We offer a 100% placement guarantee with the facility of paying for the course fee after placements.

Professional Resume and LinkedIn Profile Building

From building a perfect resume for you and writing a Bio and Summary for LinkedIn to equipping you with the ISO-approved certificates and endorsements, Data Folkz has got you covered.

Become Job-Ready by Gaining Important Skills

Impress the recruiters with your impeccable resume, profile, presentation, communication, management, and leadership skills. Data Folkz grooms you for becoming a professional Data Scientist and securing your career in the field.

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Our course curriculum speaks for itself and shines through the reviews of our students and trainees. Have a look at what our students have to say about the PG Program for Management in Data Science.


I joined 2 months back in Datafolkz. Within these 2 months, we learned so many new topics and a Programming language called Python, and the entire world is moving towards it now. We are excited to learn new things in every other class.

Sandeep - Data Analyst, Accenture

I just start by saying that it's been an absolute pleasure to be a part of Data Folkz. The way of teaching Arvind Sir is really great. He is as nice as his teaching process. He is experienced and professional in teaching. It's very helpful for me to learn much from him. Feeling grateful to him.

Shiv Aditya Mishra
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The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

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Tell us a bit about yourself and why you want to join this program

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An admission panel will shortlist candidates based on their application

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An admission panel will shortlist candidates based on their application

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Selected candidates will be notified within 1–2 weeks


Lakhs per annum (LPA)


Monthly Payable Amount + 18% GST 25000 4500 INR 0
Tenure 3 NA
Total Fees 75000 13500 0
No Cost EMI Starts at

₹ 4,500 + GST

We partnered with financing companies to provide competitive finance option at 0% interest rate with no hidden costs

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Upcoming Application Deadline 10 August 22

Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.

Job Roles

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img Consultant- Decision Science (Python, sql)
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img senior business Analyst
img risk modeling - Analyst
img bi/ data analytics developer
img quant analytics associate
img risk analyst
img tableau developer
img business intelligence engineer
img consultant data quality with SQL
img data analytics - equity research
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img Analyst – Business Development
img BI / Data Visualisation Consultant
img Business Analyst-Risk Modeling
img Data Science Engineer
img Senior Data Scientist
img Lead- Advanced Analytics
img Associate Data Scientist
img Data Scientist : Artificial Intelligence
img Customer Experience strategy Data
img Data Analytics Developer
img Credit Risk Modeling(Machine Learning) - Associate
img Predicitive Analytics
img Data Visualization Analyst
img Machine Learning Engineer
img Quantitative Engineer - Risk - Associate
img Data Scientist - Credit Risk Domain
img Decision Scientist
apply yours


What is an ISA and how does it work?

An Income Share Agreement (ISA) is a way to pay your Masai course fee. A Masai ISA is an agreement between student and Masai under which you agree to pay a fixed monthly payment for 36 months or less. The payment starts only once you’re earn a salary of 5,00,000/- (CTC) or more, after course completion.

Any age restriction for the program?

No age restriction.

Are the classes recorded or live?

The classes are 100% live session

Will I receive a certificate when I complete my course?

. Yes, you will be awarded a globally recognized ISO certificate after the completion of your program.

What is the duration of the Program?

The program will be for seven months with 1-month Career center classes for skill development.

Key Program Highlights

img Complimentary Python Programming Bootcamp
img Only online M.Sc in DS for working professionals
img 60+ Case Studies and Projects
img Daily Doubt Resolution Support
img IIIT Bangalore & LJMU Alumni Status
img No Cost EMI Option
img Career mentorship Sessions(1:1)
img Just-in-Time Interviews
img Personalised Industry Session
img Support available 7 days, 24x7
img Global access to job opportunities
img NASSCOM certificate
img AI Powered Profile Builder
img High Performance Coaching(1:1)
img Career Bootcamp
img Career Essential Soft Skills Program
img 500+ Hours of Learning
img Fortnightly Group Mentorship with Industry Mentors
5 Unique Specializations to choose from
  • Deep Learning
  • Natural Learning Processing
  • Business Intelligence/ Data Analytics
  • Business Analytics
  • Data Engineering


In a duration of 30 weeks, the course requires a commitment from 9am to 9pm for 6 days a week in an intensive and immersive curriculum.

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Week 1-5

Programming Basics in JavaScript, Developer tools and workflow, Introduction to Web, JavaScript on the browser, Data Structures and Algorithms

Week 6-10

Modern Javascript, Basics of Unit Testing, Advanced CSS, Introduction to React and React Ecosystem, Data Structures and Algorithms

Week 11-15

UI Libraries, Reusable Components, Routing and Advanced APIs, Deployment, Unit Testing, and end to end testing, Redux and Hooks, Data Structures and Algorithms

Week 16-20

Node.js and Express.js, Microservices, MongoDB, Data Structures and Algorithms

Week 21-25

System design basics, How do they scale?, All about operating systems, Data Structures and Algorithms

Week 26-30

Projects, Interview Preparation

Career Transformation

The best data science and AI courses are offered by Data Folkz, helping you to advance your technical knowledge and network with industry experts. Contact Data Folkz for the best data science programme with job placement support.

Kristin Watson Python Developer

Fresher to Accenture

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img 48% Average Salary Hike
img 45 LPA Highest Salary
img 97.6% Satisfaction Rate
img 300+ Hiring Partners
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Become an expert in cutting-edge Data Science & Machine Learning tools and techniques with a program designed by Industry Experts from Data Folkz.

Course Duration

12 Months Live Online Sessions