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Course Description:

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 PGP 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 PGP 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 PGP 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 Java, 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 PGP 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 PGP 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 classroom programs as well as 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.



  • Sl. No.
  • Chapter
  • Chapter - 1
  • Introduction to Data Science
  • Chapter - 2
  • Introduction to programming in Python
  • Chapter - 3
  • Introduction to SQL programming
  • Chapter - 4
  • Working On MY SQL
  • Chapter - 5
  • Exploratory Data Analysis,Data Cleaning,Data Manipulation
  • Chapter - 6
  • Statistical Methods Of Decision Making
  • Chapter - 7
  • Machine Learning-Supervised Learning:Regression
  • Chapter - 8
  • Machine Learning:Supervised Learning:classification
  • Chapter - 9
  • Unsupervised Learning:Clustering
  • Chapter - 10
  • Unsupervised Learning:PCA
  • Chapter - 11
  • Ensemble Techniques:Bagging,Boosting
  • Chapter - 12
  • Data Visualization
  • Chapter - 13
  • Data Science Applications
  • Chapter - 14
  • Time series Forcasting
  • Chapter - 15
  • Text Mining and Sentimental Analysis
  • Chapter - 16
  • Capstone project