Executive Global Certificate Program in Data Science
& Big Data Management in collaboration with
FORE School of Management

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Career Transformed
11 Months
Recommnded 10-12 hrs/week
22nd May 2021
Next Batch starts on
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About DataTrained

Programming Languages and Tools Covered

Executive Global Certificate Program in Data Science & Big Data Management in collaboration with FORE School of Management

Get 2 world-class certifications thus adding that extra edge to your resume.
  • 1. Learning paths and "Certificate in Big Data Analytics for Business and Management" by FORE and UCRx
  • 2. Course completion certificate from DataTrained Education


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


Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects, assignments and live sessions

Module 1

  • 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
  • Measures of Central Tendency and Dispersion
  • Probability Theory (Different Approaches, Rules of Probability, Conditional probability and Bayes’ Theorem)
  • Random Variables and Probability Distributions Discrete Probability Distributions - Binomial and Poisson Distribution
  • Continuous Probability Distributions – Normal Distribution
  • Correlation and Regression Analysis: Simple & Multiple Regression
  • Concept of Hypotheses Testing, Type I & Type II Errors, Power of The Test, Hypothesis Testing of Mean and Proportion, Two Sample Tests, Tests for Difference in Means and Proportions.
  • Chi-Square Goodness-of-Fit Test, Test of Independence

Module: Machine Learning Algorithms (using R and Python*)

  • Developing familiarity with R; Data structures; Summarizing data; Data Exploration and transformation; integrating datasets; data & dates wrangling
  • Data Visualization and story-telling. Developing relationships between various features and plotting distributions
  • Data Mining: Measures of Proximity; Cluster Analysis: Curse of Dimensionality;
  • K-means clustering and Model based clustering
  • Text clustering and Agglomeration clustering;
  • Evaluation of clusters; Cluster Validation; Clustering tendency
  • Classification Analysis: Decision tree Induction; Cross-validation, parameter tuning & grid search
  • Techniques of Dimensionality Reduction: PCA and SVD (Singular Value Decomposition)
  • Neural Network
  • Random Forest and Regression Trees; Determining feature importance with Boruta
  • Gradient Boosting Technique for Machine Learning & grid search of its parameters
  • Evaluating Classification: ROC, AUC, Precision, Recall, Specificity, Sensitivity; kappa metric; Overfitting; Bias-variance trade-off; L1 & L2 regularization
  • Ensemble modeling: A review of variety of techniques; Balancing datasets
  • eXtreme Gradient Boosting (XGBoost)
  • LightGBM: Light Gradient Boosting Machine

Module: Hadoop and Kafka Eco System; Processing streaming data and analysis

  • Introduction to Hadoop and its ecosystem; Hadoop file storage formats
  • Linux and Hadoop shell commands
  • Hadoop streaming
  • Hive on Tez and hadoop
  • Pig on Tez and hadoop
  • Pyspark and SparkSQL: Data storage and Extraction with SQL; Executing ML algorithms (including grid-search)) using MLlib and ML libraries
  • Recommender Engine using Mahout on hadoop
  • Installation of Hadoop ecosystem
  • Apache Kafka: Stream data processing

Module: NoSQL and Graph Databases

  • Introduction to NoSQL Databases and CAP theorem; Comparison with RDBMS
  • Redis in-memory data structure store
  • MongoDB Document Database
  • Hbase column family database on hadoop
  • Neo4j Graph Database

Module: Deep learning, NLP & AI

  • Autoencoders and anomaly detection
  • Deep Learning with Convolution Neural Network
  • Using very Deep Convolution networks and Data Augmentation
  • Transfer Learning
  • Generative-Adversarial Networks (GAN)
  • Recurrent Neural Networks & LSTM
  • Natural Language Processing & Word2Vec transformation
  • Introduction to python; Using iPython; Basic data types and data structures in python and pandas; Loops a Conditionals in python;
  • Exploring data with pandas—Quick Start
  • Numpy: Arrays; Basic arrays operations; Comparison operators and value testing for arrays; Array item selection and manipulation;
  • Data Visualization in python; Data Visualization using t-distributed stochastic neighbor embedding (t-sne)
  • k-means clustering with scikit-learn
  • Decision trees classifier
  • Ensemble Modeling
  • Logistic Regression (along with Dimensionality Reduction, PCA)
  • Support Vector Machines
  • Introduction to Keras on Tensorflow
  • Basics of Web analytics
  • Analytic techniques
  • Tools: Google trends, Google Website optimizer, Google Analytics, Google Tag manager
  • Data Analysis and Data Visualization
  • Data Visualization and story-telling
  • K-means clustering
  • Model based clustering
  • Dimensionality reduction and t-sne visualization
  • Decision trees Induction
  • K-Nearest Neighbour
  • Neural Network
  • Naïve Bayes Modeling
  • Random Forest
  • Feature plotting
  • eXtreme Gradient Boosting (XGBoost)
  • Support Vector Machines
  • Regression trees
  • Apache Pig Exercises
  • Analyse data on Spark/PySpark
  • mongoDB Exercises
  • Deep-Learning: Autoencoder
  • Deep Learning
  • Introduction to ChatBots
  • ChatBot use cases
  • Model Architecture Designing
  • Backend Development
  • Frontend Development
  • API Development and Integration
  • Deploy to local server
  • Deploy on Cloud
  • IBM Cloud Deployment
    • Introduction to IBM Cloud
    • Creating Services
    • Model Building
    • Understanding pipelines
    • Model deployment
    • Frontend development using Flask
    • API integration
    • Testing
  • GCP (Google Cloud Platform) Deployment
    • Introduction to GCP
    • Model building
    • Model deployment
    • Testing
  • AWS (Amazon Web Services) Deployment
    • Introduction to AWS
    • Model building
    • Model deployment
    • Testing
  • Azure Deployment
    • Introduction to Azure
    • Model building
    • Model deployment
    • Testing
  • Resume building
  • Interview skill development
  • Mock interview sessions
  • Placement Assistance

Who should join?

Data being ubiquitous, the program cuts across job or academic profiles


Ambitious Executives (from Private/Public sectors) looking forward to sharpening their skills and making sense of data in order to innovate and add more value to their organization and to society.


Lecturers and Professors for extending the horizon of their knowledge through deepening their research skills.

Data Scientists/ Developers

Techniques taught to them will have applications in a broad range of disciplines.

Students/Research Scholars

IInd year students currently enrolled in Engineering / PGDM/ MBA or any graduate or post-graduate program who have had an introductory course in statistics. These students can look forward to better placement opportunities with added skill set.


Why Datatrained

Why Join Executive Global Certificate Program in Data Science & Big Data Management?

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

₹2,50,000 ($3,500) + 18% GST

No Cost EMI options are also available. *

What's Included in the Price

  • Industry recognized certificate from FORE & UCRx
  • Industry recognized certificate from DataTrained Education
  • Placement Assistance

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 from the DataTrained and another for FORE & UCLA
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.

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.

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.
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.
Placement assistance will be provided by DataTrained only. FORE School of Management does not have any role in placements.
Data science doesn’t need any previous technical or programming experience. We will teach you maths and stats at a very beginner level.
For Queries and Suggestions

Call Datatrained Now

Email us for Enrolment Queries at admissions@datatrained.com
Email us for Payment and Other Queries at support@datatrained.com