Data Science Certification Course Training


100+ Learners

Data science is an interdisciplinary field that uses scientific methods, procedures, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data, as well as to apply that knowledge and actionable insights to a variety of application areas.

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    • Learn from industry experts who are passionate about teaching.
    • Instructor-led live sessions
    • Industry-Specific Curriculum
    • Recorded sessions for every class
    • Flexible schedules for working professionals
    • Real-life Case Studies
    • Dedicated Student Success Manager

Main Features

55 Hrs Instructor training

40+Hrs Self-paced Videos

Less theory, more practicals

24 x 7 Lifetime Support & Access

60 Hrs Project Work & Exercises

Certification and Job Assistance

Self learning
Title Price Enroll
Data Science $400
Abilities Covered
Development Applications
Data Structures
Programming And Scripting
  • This course will give you an overview of the most important tools and concepts in the data scientist toolbox.
  • The first is a conceptual overview of the concepts involved in transforming data into usable knowledge. The second section is a hands on introduction to the program tools, such as version control, markdown, git, GitHub, R, and RStudio.
  • The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
  • This course will cover the basic ways that data can be obtained.
  • The course will cover obtaining data from the web, from APIs, databases, and from colleagues in various formats.
  • The course will cover the basics needed for collecting, cleaning, and sharing data.
  • Statistical inference is the process of drawing conclusions about populations or scientific truths from data.
  • Furthermore, there are broad theories frequentists, Bayesian, likelihood, design-based.
  • A practitioner can often be left in a debilitating maze of techniques, philosophies, and nuance.
  • Linear models, as their name implies, relate an outcome to a set of predictors of interest using linear assumptions.
  • Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientists toolkit.
  • This course covers regression analysis, least squares, and inference using regression models.
  • One of the most common tasks performed by data scientists and data analysts is prediction and machine learning.
  • This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications.
  • The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates.
  • The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
  • A data product is the production output from a statistical analysis.
  • This course covers the basics of creating data products using Shiny, R packages, and interactive graphics.
  • The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.
  • The capstone project class will allow students to create a usable public data product that can be used to show their skills to potential employers.
  • Projects will be drawn from real world problems and will be conducted with industry, government, and academic partners.
  • 40
    Hrs of Extensive Training
    8+ 12
    Free Career Guidance
    Data science job demand increased to 80% from previous years
    Data Science are preferred by Global Tech firms
    Data Science prediction will increase by 70% by the end of 2025
    60% of the cloud job postings ask for skills in Data Science
    Average salary of Data scientist $3008,051
    Frequently Asked Questions
  • Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 3 to 6 months.
  • Begin by taking The Data Scientist Toolbox and Introduction to R Programming, in order. The other courses may be taken in any order, and in parallel if desired.
  • Some programming experience in any language is recommended. We also suggest a working knowledge of mathematics up to algebra neither calculus nor linear algebra are required.
  • You’ll have a foundational understanding of the field and be prepared to continue studying data science
  • This course is completely online, so there is no need to show up to a classroom in person. You can access your lectures, readings, and assignments anytime and anywhere via the web or your mobile device.
  • Yes To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
  • Each course in the Specialization is offered monthly.
  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the Enroll button on the left. You all be prompted to complete an application and will be notified if you are approved. You all need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
  • If you subscribed, you get a 7 day free trial during which you can cancel at no penalty. After that, we dont give refunds, but you can cancel your subscription at any time. See our full refund policy.
  • Please Contact us

    +91 6305149934

    Zenith Learning