PG Program in Data Science, Machine
Learning & Neural Networks with IBM

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Career Transformed
6 Months
Live Internship
10 Months
Recommnded 20-22 hrs/week
19 July 2020
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Program Overview

Key Highlights

  • 6 Months Internship Part of the Program
  • Ideal for both Working and Fresh Graduates
  • One-on-One with Industry Mentors
  • 100% guaranteed Placement
  • 40+ Projects and Case Studies
  • Career Success Manager
  • 360 Degree Career Support
  • Unique Specialisations
  • Instant Doubt Resolution
  • Live Internship

Programming Languages and Tolls Covered

10 Months PG Program in Data Science, Machine Learning & Artificial Intelligence in collaboration with IBM

Get eligible for 4 world-class certifications thus adding that extra edge to your resume.
  • Learning paths and certification from IBM
  • Course completion certificate from DataTrained Education
  • Project completion certificate from DataTrained Education
  • Internship Certificate from Partner Companies


Learn from India’s leading Software Engineering faculty and Industry leaders


Strong hand-holding with dedicated support to help you master Software Development

Machine Learning

  • Will cover all the Machine Learning Libraries.
  • Will be able to do projects from scratch till production.
  • Projects will be recognized in the industry after course completion.
  • Real-Time Projects will be assigned to you by our partner organisation to become an Expert from novice.
  • Will get experience certificate from the Organisation after internship.

Deep Learning

  • Will cover all the Deep Learning libraries like Tensor flow, Keras etc.
  • Will be able to do advance level projects.
  • Real-Time data will be shared with you during internship with partner organisation.
  • Will be part of any product development team.
  • Experience certificate will be provided to you as a team member of product development.

Artificial Intelligence

  • Advance libraries & new technologies will be introduced like computer vision, Neural Network etc.
  • Will develop product based on artificial Intelligence with partner Organisation.
  • Will be able to develop projects by own.


Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects, assignments and live sessions
. Introduction to Data Science
. Data Science Era
. Data Science involvement in Industries
. Business Intelligence vs. Data Science
. Data Science Life Cycle
. Tools of Data Science
. Introduction to Python
. Introduction to Machine Learning
Module 2
. Introduction to Python Programming
. Introduction to Python
. Basic Operations in Python
. Variable Assignment
. Functions: in-built functions, user defined functions
. Condition: if, if-else, nested if-else, else-if
Module 3
. Data Structure - Introduction
. List: Different Data Types in a List, List in a List
. Operations on a list: Slicing, Splicing, Sub-setting
. Condition (true/false) on a List
. Applying functions on a List
. Dictionary: Index, Value
. Operation on a Dictionary: Slicing, Splicing, Sub-setting
. Condition (true/false) on a Dictionary
. Applying functions on a Dictionary
. Modules and Packages
. Regex operations
. Introduction to SQL (Structured Query Language)
. Basic SQL statement
. Advanced SQL (Searching, sorting, grouping)
. Accessing databases using python
. Data Types in an Array, Dimensions of an Array
. Operations on Array: Indexing, Slicing, Splicing, Sub-setting
. Conditional (T/F) on an Array
. Loops: For, While
. Shorthand for For
. Conditions in shorthand for For
. Control statements
. Shape Manipulation
. Linear Algebra
. Python Pandas - Home
. Python Pandas - Introduction
. Python Pandas - Environment Setup
. Introduction to Data Structures
. Python Pandas - Series
. Python Pandas - DataFrame
. Python Pandas - Panel
. Python Pandas - Basic Functionality
. Function Application
. Python Pandas - Reindexing
. Python Pandas - Iteration
. Python Pandas - Sorting
. Working with Text Data
. Options & Customization
. Indexing & Selecting Data
. Python Pandas - Missing Data
. Python Pandas - GroupBy
. Python Pandas - Merging/Joining
. Python Pandas - Concatenation
. Python Pandas - Date Functionality
. Python Pandas - Categorical Data
. Python Pandas - Visualization
. Intro to Statistics
. Statistical Inference
. Terminologies of Statistics
. Descriptive statistics
. Statistical functions
. Measures of Centers
. Mean
. Median
. Mode
. Measures of Spread
. Variance
. Standard Deviation
. Histogram
. Probability
. Normal Distribution
. Binary Distribution
. Poisson distribution
. Skewness
. Bell curve
. Hypothesis Building and Testing
. Chi-Square Test
. Correlation Matrix
. SciPy and its Characteristics
. SciPy sub-packages
. SciPy sub-packages – Integration
. SciPy sub-packages – Optimize
. Linear Algebra
. SciPy sub-packages - Statistics
. Data Analysis Pipeline
. What is Data Extraction
. Types of Data
. Raw and Processed Data
. Data Wrangling
. Exploratory Data Analysis
.Introduction to Machine Learning
.Machine Learning Use-Cases
.Machine Learning Process Flow
.Machine Learning Categories
.Data Preprocessing
.Data preparation
.Intro to Scikit Learn
.Linear Regression
.R2 score
.Logistic Regression
.Introduction to Dimensionality
.Why Dimensionality Reduction
.Factor Analysis
.Scaling dimensional model
.Intro to Kaggle and UCI repository
.K-nearest neighbours
.Confusion Matrix
.Classification report
.Support Vector Machines
.Working of SVM
.Naive Bayes
.Hyperparameter Optimization
.Decision Tree Classifier
.Random Forest Classifier
.Ensemble Techniques and SVM tuning
.Underfitting & Overfitting
.AUC-ROC Curve
.Cross –validation
.Unsupervised learning
.Clustering Algorithms
.K-Means Clustering
.Hierarchical Clustering
.Recommendation Engine
.Time Series
. MatplotLib
. Bar Plot
. Histogram Plot
. Box Plot
. Area Plot

. Scatter Plot
. Pie Plot
. Seaborn
. Computer Vision or
. Natural Language Processing (NLP)
.Live Projects in an Internship Company with access to Virtual Cloud based Linux System
Hours of Content
Case Study and Projects
Live Sessions
Coding Assignments
Tools and Software

Industry Projects

Learn through real-life industry projects sponsored by top companies across industries
Smartphone and Smartwatch Activity

The crude accelerometer and whirligig sensor information is gathered from the cell phone and smartwatch at a pace of 20Hz.

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Recommendation System

In the connected world, it is imperative that the organizations are using to Recommend their Products & Services to the People. Based on their Purchase History & Visiting the store , helps us in Recommendation.

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Sales prediction

Implementing various Algorithims to ensure about the revenue generation from the Sales team based on thie Customer base & Their past Purchase Order.

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Air Quality Study

Based on The Data Collected from the Meterological Department, Predicting The Air Quality Of Diffrent Parts of The country

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Why Datatrained

Why Join PGP - Data Science, ML & Neural Networks?


  • 6 Months internship ensures you graduate as an experienced data science professional rather than a fresher. You can go for an online internship along with your current job.

Resume Feedback

  • Partnered with Analytics Jobs wherein you get access to their paid resume preparation kit and personal feedback from the industry HR experts. An individual career profile is prepared by our experts so that it suits his/her experience and makes it relevant to a Data Scientist role.

Interview Preparation

  • Regular mock HR and Technical interviews by mentors with personal guidance and support. The industry mentor helps students to take projects on Kaggle and move on to the status bar so that their resume looks competitive to the recruiters.


  • We generate the Ability Score of every individual which is then sent to our more than 250 recruitment partner organizations. At last, we organize campus placements every three months in Noida, Gurgaon, Ahmedabad, Bangalore, and Chennai to place our students.

Career Impact

Over 500 Careers Transformed
Average Salary Hike
Highest Salary
Jobs Sourced
Hiring Partners

Our Students Work At

Admission Process

There are 3 simple steps in the Admission Process which is detailed below
Step 1: Fill in a Query Form
Fill up the Query Form and one of our counselor will call you & understand your eligibility.
Step 2: Get Shortlisted & Receive a Call
Our Admissions Committee will review your profile. Upon qualifying, an Email will be sent to you confirming your admission to the Program.
Step 3: Block your Seat & Begin the Prep Course
Block your seat with a payment of INR 10,000 to enroll into the program. Begin with your Prep course and start your Data Science journey!

Program Fee

₹1,20,000 ($2,000)

No Cost EMI options are also available. *

What's Included in the Price

  • Industry recognized certificate from IBM
  • Access to real-life 40 industry projects
  • 6 Months online Internship part of the core curriculum

I’m interested in this program

Frequently Ask Questions

Amid our preparation, you will get a great deal of project work and an \"Ability Score\" (figured based on your execution all through different stages). We at that point forward your project work and ability Score to organizations, your projects fill in as a proof (portfolio) of your range of abilities which when joined with our ability Score gives them a far-reaching examination of your insight identified with your activity profile. Organizations don't get this sort of investigation or straightforwardness anywhere else, and subsequently, they get you hired. Additionally, they get a confirmation that they are not employing a new kid on the block but rather a trained professional who will be productive from day one.
Projects are adjusted to what is educated to you in different aptitude levels. The trouble level is simple, and activities are there to guarantee a greater amount of hands-on training. Just your last task can be tolerably troublesome, yet that shouldn't be an issue since you will get support at every level from your Data Scientist mentor. These projects are like what the Data Scientists undertake in there day to day work, so think about this as a replication of the same.
Yes. You will get two certificates - one for the training and another for your project work
Although we believe that skills are enough to get you hired, however, some companies hiring for DATA Scientist profile in the industry will expect following out of you. FRESH GRAD OR A COLLEGE STUDENT A degree in B.Tech/M.Tech (Any Trade), BCA, MCA or B.Sc (Statistics or Mathematics), BA (Maths or Economics or Stats), B.Com. WORKING PROFESSIONAL Professional experience of 1+ years in Python, R, SAS, Business intelligence, Data warehousing, SQL. If your professional experience is not related to data analytics, you can still make a switch to Data scientist provided that you hold any of the degrees specified above.
Employment/internship position meet shortlisting through is simply reliant on your Ability Score. You need to acquire an Ability Score above the required benchmark in order to be shortlisted by organizations. In case you don't get this Ability Score, we continue giving you projects until you achieve that optimum level of Ability Score. When you have scored on the benchmark in no less than 2 out of 3 projects, it is adequate proof alongside your task portfolio for a company to hire you. Keep in mind that we can just ensure to impart in you what it takes to be a Data Scientist, yet you need to ace your destinies yourself.
Please be assured, we were able to place our last 2 batches with a minimum package of 4.5 lakh, an average package of 5.2 lakh and the highest package of 14.5 lakh.
Although it will not likely to happen to see our past success rate. We will try every inch of our efforts to place you. However, in case if we fail to do so, we will refund the fee directly into your bank account within 6 months of your course completion date. No questions asked .Data Preprocessing .Data preparation .Intro to Scikit Learn Module3 .Regression .Types .Algorithms .Linear Regression .RMSE .R2 score .Logistic Regression .Introduction to Dimensionality .Why Dimensionality Reduction .PCA .Factor Analysis .Scaling dimensional model .Encoding .Intro to Kaggle and UCI repository Module4 .K-nearest neighbours .Metrics .Confusion Matrix .Classification report .Support Vector Machines .Kernel .Working of SVM .Naive Bayes .Hyperparameter Optimization .Decision Tree Classifier .Random Forest Classifier .Ensemble Techniques and SVM tuning .Underfitting & Overfitting .Entropy .AUC-ROC Curve .Cross –validation Module4 .K-nearest neighbours .Metrics .Confusion Matrix .Classification report .Support Vector Machines .Kernel .Working of SVM .Naive Bayes .Hyperparameter Optimization .Decision Tree Classifier .Random Forest Classifier .Ensemble Techniques and SVM tuning .Underfitting & Overfitting .Entropy .AUC-ROC Curve .Cross –validation
Data science doesn’t need any previous technical or programming experience. We will teach you maths and stats at a very beginner level.
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