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Foundation Program in Data Science

Get started with Data Science and Big Data Analytics by getting a firm grip of the fundamentals. Land a dream job as Big Data Engineer or Big Data Analyst!
  • 40 Hours Classroom & Online Sessions
  • 60 Hours Assignments & Real-Time Projects
  • Complimentary Python and R Programming Beginners Course
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data science foundation course review in USA - 360digitmg
3152 Learners
Academic Partners & International Accreditations
  • Data Science using Python and R Program with Microsoft
  • Data Science using Python and R Program course with nasscomm
  • Data Science using Python and R Program course
  • Data Science using Python and R Program course with SUNY
  • Data Science using Python and R Program course with NEF

"The job requirements for data science is projected to boom from 365,000 openings to 2,721,000 in the coming years." - (Source). The USA has long been the home of technological innovation with Silicon Valley representing the pinnacle of the high tech industry. The US Department of Labor estimates (which usually tend to be highly conservative) that the employment for computer and research scientists (read Data Science professionals) will grow by 19% by the year 2026. The USA is also a highly diverse country where a lot of cultures and subcultures merge to form a beautiful and inviting community. This offers a wide variety of cuisines to satisfy every technical palette. The US is truly a dream destination for tech aspirants and the domain of data science brings with itself one of the most exciting career paths of all times. Join the best Data Science training institute in the USA and be ready for the data revolution.

Data Science

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Total Duration

3 Months

data science foundation course pre-requisite - 360digitmg


  • Computer Skills.
  • Basic Programming Knowledge.
  • Analytical Mindset.

Data Science Foundation Program Overview

The purpose of this course is to enable the students to understand the basic concepts of Data Science and how enterprises can extract insights from data. You will be introduced to the different types of data including Big Data and how to deal with each of them using Python and R. You will use data mining techniques on both structured and unstructured data. One of the highlights of this course is that it teaches you the use of machine learning algorithms to analyze big data.

What is Data Science?

Organizations collect tons of data which is most of the time underutilized and contains meaningful information. The information needs to be extracted to drive actionable insights that can be used to make critical decisions. This is where Data Science comes to our rescue and extracts actionable insights from raw data. Every experience our senses perceive is data and we see and feel its impact around us in the form of incalculable benefits in business, research, and our everyday lives. Countries today are racing to digitize all information to sift through massive lakes of data, trying to look for connections and patterns that can deliver breakthrough insights.

Data Science Course Learning Outcomes

This course in Data Science aims at laying down a strong foundation in all the concepts of data science which are domain-specific or technical. They will develop the capability to handle a deep set of core competencies in the area of programming, statistics, data analytics, machine learning, data wrangling, and data visualization. Students will be able to make use of quantitative and qualitative methods and strategies to acquire, manage, analyze, and generate insights from data. They will be able to develop the skills and techniques needed to run classification and predictive analytics over various types of data. They will gain experience in techniques used to recognize patterns in data and establish data relationships. They will also become familiar with modern open-source programming languages and will demonstrate the ability to communicate their findings using a high level of tools to visualize data. In today’s data-rich environment, they will be able to appreciate the various data analytics tasks which include extraction, cleaning, prediction, and interpretation of data. This course focuses on both practical and technical perspectives where students will get an opportunity to gain hands-on experience of working with real data using R and Python. So, if you have a passion for discovering answers through data analysis then join this foundation course in Data Science and earn a certification that will help you to accelerate your career growth. You will also

Gain understanding about commonly used Data Science Packages of R and Python
Churn the Data using Mathematical computations
Develop Predictive analytical solutions to enable data driven decisions
Peek into the Project life cycle for Data Analytics projects
Develop Models to Predict a variable of interest using Supervised Learning techniques
Derive insights from the business Data using Unsupervised Learning techniques

Block Your Time

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40 hours

Classroom Sessions

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60 hours


data science course - 360digitmg

60 hours

2 Live Projects

Who Should Sign Up?

  • IT Engineers
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • Banking and Finance Analysts
  • Marketing Managers
  • Supply Chain Professionals
  • HR Managers
  • Math, Science and Commerce Graduates

Modules for Fundamentals of Data Science Course

This module covers all aspects of data science including data manipulation, visualization, foundational knowledge in probability and statistics, preprocessing using modern tools along with the theoretical and practical aspects of predictive analytics algorithms. The modules have been designed to convey the skills required for using open-source tools to run data analytics. They will also run you through the basic principles needed to study and interpret data and teach you to put across your findings effectively through written and oral reports. Students will be able to identify and develop the ability to evaluate, integrate, and apply the appropriate skills required to design models for prediction using ML algorithms.

This module introduces the popular programming languages- R and Python. They are used in Statistics, Machine Learning and Data Science. We will see the programming paradigms, similarities, and differences between both languages. It will also familiarize the users with some of the important data structures of each language.

This module introduces the participants to the basic statistical foundations that every analyst is expected to know. Learners will be introduced to the concepts of Random Variables, Probability Distributions, First, Second, Third and Fourth moments of a Probability Distribution Function and other summary and Descriptive Statistics.

This module deals with various concepts of the Predictive Analytics Domain and introduces some of the fundamental heuristics. We learn about Confidence Intervals, Predictive Power, and Power of a test.

This module will talk about hypothesis testing which is a very common technique in deciding whether a process needs to be changed or not. This will be done using various tests for two or more distributions (ANOVA).

This module briefly describes the overall life cycle of a Data Analytics project. The framework relies heavily on the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology which was developed while dealing with such tasks. All the lifecycle stages from business problem formulation to model deployment are discussed in detail.

This module introduces a major paradigm of Machine Learning - Supervised Learning. As the name suggests, the goal of this particular approach is to use labeled historical data and learn patterns and make predictions on future data.

This module describes the other major paradigm of Machine Learning - Unsupervised Learning. This is different from the approach previously discussed as there is no labeled data in here. The approaches try to learn from the data and group them into Clusters or Segments or reduce the dimensions based on some heuristic.

How We Prepare You
  • data science foundation course in USA
    Additional Assignments of over 60-80 hours
  • data science foundation course in USA
    Live Free Webinars
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    Resume and LinkedIn Review Sessions
  • data science foundation course in USA
    3 Month Access to LMS
  • data science foundation course in USA
    24/7 Support
  • data science foundation course in USA
    Job Assistance in Data Science Fields
  • data science foundation course in USA
    Complimentary Courses
  • data science foundation course in USA
    Unlimited Mock Interview and Quiz Session
  • data science foundation course in USA
    Hands-on Experience in a Live Project
  • data science foundation course in USA
    Life Time Free access to Industry Webinars

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