Facebook Linkedin Instagram
Facebook Linkedin Instagram

Data Science

5/5
| 486 Learners | 03+ Experts | 18+ Hours – By CLOUDREACH TECHNOLOGY
With so much data at our fingertips, it is more crucial than ever to draw context and meaningful connections from data. Data scientists can help with it. Data scientists, who are a mix of mathematicians, computer scientists, and trend-spotters, transform their organization's information stockpiles into pertinent and useful insights that can help businesses stay one step ahead of their rivals.

Program Details

With the help of this certificate program's three courses, you'll learn how to use common tools and procedures to gather correlations from data. To examine data sets and assist in the making of decisions, use statistical techniques and machine learning algorithms. Take your profession and your data analytics skills to the next level.
Call Us

+1 469-754-0554
+1 469-983-0350

Ready to upskill?

Data Science Course Curriculum

The Lessons

  • Basic R programming knowledge.
  • How to use statistical ideas like probability, inference, and modeling in the real world.
  • Learn how to use the tidyverse, including ggplot2 for data visualization and dplyr for data manipulation.
  • Get to know fundamental tools for working as a data scientist, such as RStudio, git and GitHub, and Unix/Linux.
  • Apply machine learning techniques.
  • A thorough understanding of the core ideas in data science through inspiring real-world case studies.
Join our free webinar

Free Career Counselling

About your Data Science Course

Program Summary

The need for knowledgeable data scientists is expanding quickly in business, academia, and government. You are equipped with the intellectual background and practical abilities you need to take on real-world data analysis difficulties thanks to the HarvardX Data Science program. In addition to teaching you R programming, data manipulation with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document creation with RStudio, the program also covers concepts like probability, inference, regression, and machine learning.

We pose specific questions in each session, use relevant case studies, and learn by analyzing data to find the answers. Case studies include Forming a Baseball Team (inspired by Moneyball), Movie Recommendation Systems, US Criminal Rates, The Financial Crisis of 2007–2008, and Trends in World Health and Economics.

The R software framework will be used throughout the course. R, statistical ideas, and data analysis methods will all be covered at the same time. We think that learning how to address a particular problem helps you remember R knowledge.

What separates data science from big data and data analytics?
A variety of techniques and algorithms are used in the discipline of data science to extract meaningful insights from unstructured data. It uses a variety of techniques for data modeling and other data-related tasks like data erasure, preprocessing, and analysis. Big Data refers to the massive volume of organized, unstructured, and semi-structured data that is produced through numerous organizations and channels. Giving operational insights into complicated business scenarios is one of the duties of data analytics. This foretells future chances that the organization can take advantage of.
What statistics are used by data scientists?
Data science heavily relies on statistics. It is one of the most crucial disciplines to offer tools and techniques to uncover structure in data and to provide a deeper understanding of it. It greatly influences the collection, exploration, analysis, validation, etc. of data.
The distinction between extrapolation and interpolation?
A variable's hypothetical values can be estimated using extrapolation or interpolation based on other observations. In a sequence of values, interpolation is the estimation of a value between two known values, and extrapolation is the estimation of a value by extending a known sequence of values or data beyond the region that is unquestionably known.
Which one of R and Python would you pick for text analysis?
Python would be the better option between R and other programming languages because it features the Pandas module, which offers fast data analysis tools and simple data structures. Nevertheless, depending on the intricacy of the data being examined, you can use either of these languages.
A recommender system: What is it?
One of the most extensively used uses of machine learning in organizations is recommendation systems. A user can interact with a variety of products thanks to this system. Although contemporary recommenders include both strategies, the machine learning techniques used in recommender systems are often divided into two categories: content-based and collaborative filtering methods. While collaborative approaches derive similarity via interactions, content-based techniques are based on similarities in item properties.

Archives

Categories

Cart

No products in the cart.

REQUEST A CALLBACK!

Drop your details and we will call back you.

Drop Your Details

Upload your latest resume and we will surely get back to you!