Masters in Data Science Engineering

Know your way around the Masters in Data Science Engineering.

Master the study of data, where information comes from, what it signifies and how it can be turned into a helpful resource in the making of business and IT strategies. This helps organizations to know current market opportunities and raise the organization’s competitive benefit.The data science field employs arithmetic, figures and computer science regulations, and integrates techniques like machine learning, cluster analysis, data mining, and visualization.

The Master in Data Science and Engineering gives you wide coverage to main ideas and techniques from Python, R to Machine Learning and more. Practical labs and assignment work bring these ideas to life with our instructors and assistants to supervise you with the path.

Equip your career with this commended Master in Data Science and Engineering with Careerera and the team.

Why Data Science?

Increasingly businesses are implementing Data Science to add worth to all facets of their operations. This has led to a great call for Data Scientists, experts who are talented in technology, math, and business, but the delivery hasn’t kept up. This demand for Data Scientists has turned up a large number of well-paid job chances for Data Scientists

  • Ranks among the top trending jobs on LinkedIn
  • The market is expected to grow
  • Commendable Salary

How Data Science Works?

The particular benefits of Master in Data Science and Engineering differ depending on the organization’s and the industry’s goals.

The chief objectives of Data Scientist are:

  • Collecting a large amount of data or figure and analyzing it.
  • Using data-driven techniques for resolving business issues.
  • Communicating the outcome to business and IT leaders.
  • Spotting trends, outlines, and relations within data.
  • Converting data into persuasive visualizations.
  • Deploying text analytics and data preparation.

The technologies and proficiency that a Data Scientist works with:

  • Programming skills in  Python, R, and SQL
  • Reporting and data visualization techniques
  • Big Data Hadoop and its diverse tools
  • Data mining for knowledge discovery and exploration

Audience Profile:

The Master in Data Science and Engineering is ideal for the job roles are:

  • Engineers,
  • Software and IT Professionals
  • Marketing and Sales Professionals
  • Managers

Course Essentials:

The people and professionals who are viewing for this Master in Data Science must possess the minimum eligibility i.e.

  • Bachelor’s Degree
  • Basic understanding of Data Science

Program Learning:

Skills you will Acquire from the program

  • Statistics
  • Predictive Analytics using Python
  • Machine Learning
  • Data Visualization
  • Big Data Analytics
  • Exploratory Data Analysis
  • Descriptive Statistics
  • Inferential Statistics
  • Model building and fine-tuning
  • Supervised and unsupervised learning
  • Natural Language Processing
  • Ensemble Learning

Course Highlights:

  • Quiz as a course segments
  • PG Program Certificate
  • Flexible Learning
  • Projects to advance your skill
  • Alumni Status
  • Practical Projects on Integrated Labs
  • 24*7 Customer Support

Course Delivery:

  • Advance Learning: We at Careerera believe in innovating advanced and new techniques for the industry. Our research-based education is very advanced comparatively.
  • Teaching Methodology: We conduct  an online program to deliver knowledge in every possible way.
  • Practical and Innovative: Careerera is known for its case-based education.
  • Certification: Once our candidates positively complete the course we award them with the Careerera Certificate for the particular program.


Program Curriculum

See which topics you will have to assimilate.

  • Basics of Maths/Computer Programming
  • Statistics
  • Introduction to R
  • Introduction to Python (Basic Python, data science related libraries, like NumPy, Scikit etc.
  • Introduction to SQL/DataBase (RDBMS, Oracle, MySQL etc.)
  • Introduction to Big Data (Introduction to NoSQL/Spark/Hadoop/exadata etc.)
  • Data Mining
  • Visualisation (tableau, powerBI etc.)
  • Advance statistics
  • Machine Learning Algorithm Overview
  • Supervised Learning Algorithms
  • Regression: Polynomial
  • Classification: Binary
  • Classification: KNN
  • Classification: Multiclass
  • Classification: SVM
  • Classification: Decision Trees
  • Classification: Random Forest
  • Concepts: Gradient Descent, Train_Test_Split, K Fold Cross Validation, Feature selection,Feature Scaling etc.
  • Concepts: Overfiting, Confusion Matrix, ROC curve etc.
  • Classification: Neural Networks
  • Unsupervised Learning
  • KMA/Clustering
  • Recommendation Engine/Movie Ratings etc.
  • Reinforcement Learning
  • Capstone Project
  • ML Hackathon
  • Building blocks
  • Advanced Python Libraries (Tensorflow)
  • Data Science on Cloud (Google Colab, Microsoft Azure ML Studio)
  • NLP
  • Basics of NLP, cosine algorithm etc.
  • Chatbot
  • Image/Video Processing
  • CNN, RCNN, Faster-RCNN, Yolo
  • Face Recognition
  • Object Recognition
  • Hackathon
  • Time Series Forecasting
  • Web & Social Media Analytics (Industry Case study)
  • Finance & Risk Analytics (Industry Case study)
  • Market & Retail Analytics (Industry Case study)
  • Supply Chain & Logistics Analytics (Industry Case study)
  • Group Presentation
  • 50:50 on Theory/Lab.. As for every 4 hours block, 2 hour would be theory + 2 hour lab/handson
  • Home Assignments
  • MCQ quizes as part of Theory part
  • Two hackathons of 8 hours each
  • For each Algorithm, coaching flow would be as under:
    • 1. Related Mathematics
    • 2. Concept/Algorithm
    • 3. Intro to Python or R library, and actual implementation

Why Careerera

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Get the answers to your questions here.

Q1 : What is the need of Data Science?

Data Science holds the key to the future. Data Science is essential for better marketing. Organizations are applying data to examine their marketing tactics and make better commercials.

Q2 : What skills does a Data Scientist require?

Q3 : Who will be the mentors for this course?

Q4 : Why Careerera?

Q5 : Will I be provided support if I find it hard to understand the ideas?

Q6 : What should I expect from Careerera PG Program in Data Science?

Q7 : What is the chief learning from PG Program in Data Science?

Q8 : What job profiles one may attain upon program accomplishment?

Q9 : What is the admission process for this program?

Q10 : Is there any prerequisite for this program?

Q11 : What is the program fee?

Q12 : In what mode program is delivered to the candidates?


Masters in Data Science Engineering

This Certificate is Proudly Presented to

John Smith

For Successful Completion of the Masters in Data Science Engineering with all the Mandatory Course Requirements
and Capstone Projects with Distinction.





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