Courses

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 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
    Communication

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.

 

Curriculum

  • 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
  • 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

FAQ

  • 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?

  • The key skills a Data Scientist must possess are

    • Problem sorting intuition
    • Statistical Knowledge
    • Programming in an analytic language
    • Data Scientist must be curious to answer the every “why”
  • Q3. Who will be the mentors for this course?

  • To help you attain the best of both educational and industry experience, the classes will be held by both industry specialists and academic experts.

  • Q4. Why Careerera?

  • Careerera’s combine learning form brings a world-class learning experience. It includes instructor-led training, self-paced learning and modified mentoring to offer an immersive learning experience.

    Even our online practice labs, applied projects, and learner social forum makes the deciding on factor.

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

  • We have doubt clearing sessions and interpersonal supervised learning for our candidates. We are committed to supporting you learn.

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

  • As a part of this PG Program, you will get the following:

    • Careerera PG Program certificate
    • Careerera Alumni Status
    • Lifetime access to learning content by Careerera
  • Q7. What is the chief learning from PG Program in Data Science?

  • Skills you acquire from the program are:

    • Predictive Analytics using Python
    • Supervised and unsupervised learning
    • Data Visualization
    • Model building and fine-tuning
    • Big Data Analytics
    • Exploratory Data Analysis
    • Descriptive Statistics
    • Natural Language Processing
  • Q8. What job profiles one may attain upon program accomplishment?

  • The PG Program in Data Science prepares you for the jobs in the field are:

    • Data Architect Data Administrator
    • Data Analyst Business Analyst
    • Data/Analytics Manager
    • Business Intelligence Manager
  • Q9. What is the admission process for this program?

  • The steps for the admission are as mentioned:

    1. Fill the application form online
    2. Application is reviewed by the faculty panels
    3. Shortlisted candidates are interviewed on call
    4. Candidates are then admitted accordingly
  • Q10. Is there any prerequisite for this program?

  • There are no such essential for the program as Careerera aims at delivering the learning to all interested individuals. We welcome both fresher and working professionals to advance their learning and career.

  • Q11. What is the program fee?

  • The fee for the program is Rs 1, 79,000 respectively.

  • Q12. In what mode program is delivered to the candidates?

  • The PG Program in Data Science is delivered in Classroom mode. Sessions of the classroom are held in Noida and Gurugram.

  • Q13. Why Data Science?

  • Increasingly businesses are implementing Data Science to add worth to all facets of their operations. Here are the reasons to pick Data Science as a career:

    • Ranks among the top trending jobs on LinkedIn
    • The market is expected to grow
    • Commendable Salary
  • Q14. Will I be working on projects?

  • Yes, we provide projects and lab as an assignment to enhance the learning experience and also for a better understanding of the program.

  • Q15. What certificate will I receive?

  • The successful participants will be awarded a PG Program in Data Science certificate by Careerera. The certificate will be valid universally.

  • Q16. How will I be assessed during the data science course?

  • The PG Program in Data Science is a tough program and follows a consistent assessment plan. Candidates are assessed in the program they undergo through Case studies Quizzes Assignments Project reports

  • Q17. Is lodging offered?

  • No lodging is offered at our learning center. Candidates are requested to arrange the lodging themselves.

  • Q18. What is the refund procedure for the program?

  • We suggest all candidates have comprehensive knowledge before applying to the program. No refund request will be entertained once the payment has been done. Candidates may choose to defer his/her application to future batch if the seats are available.

  • Q19. Where Careerera delivers the program in Noida?

  • Careerera is located in B-44, Sector-59, Noida 201301, UP, India.

  • Q20. What is the program structure for the particular?

  • The program is a full time three months session including projects. Classes will be conducted on weekends.

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