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Certification Program in Data Science

Data Science Using Python and R Programming offers the best in industry data science program which is a potent culmination of best in class instructors, groundbreaking course material and an AI-powered LMS platform – AISPRY.
  • 130 Hours Classroom & Online Sessions
  • 140 Hours Assignments & Real-Time Projects
  • Complimentary Python and R Programming Beginners Course
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Academic Partners & International Accreditations
  • Data Science course career ex
  • Data Science course with nasscom
  • Data Science course with microsoft certificate
  • Certification Program in Data Science with SUNY
  • Certification Program in Data Science with NEF

"With hundreds of companies hiring for the role of Data Scientist, 12 million new jobs will be created in the field of Data Science by the year 2026. " - (Source). From the last four years, the Data Scientist job is ranked as number one in U.S by Glassdoor. As per the reports of U.S. Bureau of Labor Statistics, the demand for data science skills will bring a 27.8percent rise in employment by 2026. The demand for Data Scientists is astonishing and greater, but there is a lack of professional Data Scientists. Data science, Research, and analytics are now available to businesses with the aid of automation and education. Many training programs are being conducted to provide Data Scientists to the business. Data Science is widening and is being adopted by many companies to gain a competitive edge and generate revenue by improving production. To be at the forefront in this data-driven world, industries require professional Data Scientists with strong technical skills.

Data Science

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Total Duration

4 Months

data science online course pre-requisites- 360digitmg


  • Computer Skills
  • Basic Mathematical Concepts
  • Analytical Mindset

Data Science Training Program Overview

The Data Science Using Python and R is a truly transformative program which takes the students from novices to job-ready candidates in this competitive US market. The defining feature of this program that it aims to blend scientifically rigorous and multidisciplinary knowledge into easily understandable concepts. This course aims to be a primer and will attract learners from diverse domains such as Business Professionals, Programmers, Researchers, Academia, Industry practitioners among others. Students will begin by learning the basics of statistics and slowly progress to learning more complex algorithms in the data science toolkit such as regression, tree-based methods, supervised and unsupervised algorithms.

Data Science Courses Learning Outcomes

Data Science course using R and Python training in the USA is designed to build the workforce what the current market needs. This course helps students and professionals to acquire the knowledge of Data Science and all the technical skills required. 360DigiTMG offers a Data Science course in USA which focusses to train the aspirants with industry use cases so that the students can gain in-depth knowledge of the applications of tools. Data Scientist job is considered as the sexiest job of this century, this course enables students to grab lucrative jobs and achieve their goals. The course includes basics and advanced versions of Data Science. The training is delivered by industry experts who have exceptional experience and a team of dedicated mentors who guide students throughout their learning journey.

Work with various data generation sources
Perform Text Mining to generate Customer Sentiment Analysis
Analyse structured and unstructured data using different tools and techniques
Develop an understanding of Descriptive and Predictive Analytics
Apply Data-driven, Machine Learning approaches for business decisions
Build models for day-to-day applicability
Perform Forecasting to take proactive business decisions
Use Data Concepts to represent data for easy understanding

Block Your Time

industrial revolution

130 hours

Classroom Sessions

industrial revolution

140 hours


industrial revolution

140 hours

Live Projects

Who Should Sign Up?

  • Candidates aspiring to be IR 4.0 consultants
  • CEOs and other senior executives
  • Plant heads, Operations heads, Line managers
  • Employees of organisations that are planning to implement smart factories
  • Research and Development heads and Industrial engineering heads
  • Professionals who want to be a part of an organisation’s change management

Modules for Data Science Course

The modules of the Data Science course are designed meticulously as per the business trends. Much emphasis is placed on algorithms, concepts, and statistical tools. Python is considered to be the most important programming language and the data scientists have to be pro in using Python. The module introduces descriptive analytics, Data mining, Data visualization, Linear regression, and Multiple Linear regression. Students will learn about Lasso, Ridge, and logistic regression. Learn predictive modeling which is very important and useful. Learn various concepts like Machine Learning algorithms - the K-nearest neighbor algorithm which could be used both for classification and regression. The decision tree algorithm is a popular non-linear tree-based algorithm. Furthermore, the module introduces the concept of Bagging which is a type of ensemble technique and Random Forest algorithm which is a type of bagging algorithm. Students will learn the Naive Bayes model which is based on the Bayesian Probability Technique. This algorithm has been successfully deployed to detect spam with great accuracy. The other modules explain the difference between ANN and Deep Learning is that the network in deep neural networks consists of multiple hidden layers vs just a single layer in the ANN. Learn the concept of a time series and techniques to deal with time-series data such as AR, ARMA, and ARIMA models. Students will also learn the latest techniques called Black box and Support vector machines. This course is delivered with real-time projects, students gain hands-on experience and will able to perform with confidence. This type of training helps to build technical knowledge among the students and prepare them to face real business challenges.

CRISP-DM stands for Cross-industry standard process for data mining and is the bedrock framework of any data science project. This module will explain in detail the cyclical process and all the phases of this methodology. The phases are:

  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation
  • Deployment

Exploratory Data Analysis is usually where the data scientists tend to spend most of their time in the project. This often involves understanding the dataset, summarizing and describing the data at hand.

This is the phase where the statistical techniques are applied to draw inferences from the data. Typically, there are a lot of statistical techniques that are leveraged during this module and applied to the data.

This module introduces the various visual plots such as the bar plot, histogram, scatter plot, box and whiskers plot, etc., and how they can be leveraged to identify patterns and correlations in the data.

This module introduces the basic concepts of probability distributions and how that knowledge is extensively applied in data science projects. The various types of distributions like Gaussian (Normal), Bernoulli, Poisson, Binomial, Multinomial, etc.

This module introduces the concepts of Hypothesis testing and defines in detail about Null and Alternative Hypothesis and the scenarios where each of them could be proved. It discusses in detail the concepts like parametric tests like 1 sample t-test, 2 sample t-test, ANOVA and nonparametric tests like Chi-Square tests.

This module marks the beginning of predictive analytics and lays the foundation for the rest of the course modules of Machine Learning. The module introduces one of the oldest and simplest supervised learning techniques - the linear regression (ordinary least squares).

As can be easily understood, this is an extension of the OLS Linear Regression, by expanding the input space into multiple linear outputs. We discuss this using one of the most commonly used datasets and learn how it can be implemented in Python and R programming languages.

This module discusses the other advanced and useful types of regression models - Lasso and Ridge regression. We discuss the pros and cons of each of these models with practical examples using Python and R.

Also called the Logit model, this algorithm predicts the probability of the event falling into a certain category - Pass/Fail, Fraud/Not Fraud, Yes/No etc. We also get introduced to the concept of maximum likelihood estimation techniques which form the regression coefficients.

This is an extension of the Logistic Regression - the only difference being the output class could have more than 2 classes.

Count data is a data type in which the observations can only consist of non-negative numbers (0,1,2, 3 …). This module introduces regression models for such data such as Poisson Regression and Negative Binomial Regression and their application in survival analytics.

This module provides a general overview of unsupervised learning before launching into one of the most famous of its techniques - Clustering. Multiple algorithms such as connectivity based (Hierarchical), centroid based (k-Means), Distribution based, etc are discussed and implemented along with real- life scenarios.

This module discusses another great technique called the Principal Component Analysis. It is one of the dimensionality reduction algorithms which answers the question - how do we reduce the feature space without losing a lot of information.

This is a rules based machine learning algorithm where the algorithm learns interesting patterns in data and attempts to answer the question - what goes with what. Different algorithms such as Apriori, FP-growth are discussed and implemented.

This module builds on the concepts of association rule learning and implements a recommendation engine. A great example could be the recommendation engine as seen in Amazon or any other e-commerce platform that suggests items based on your current or previous selections.

This module builds on the fundamentals of Graph Theory and delves into the architecture and implementation of any network with emphasis on Social Networks. The module extensively covers the modelling and visualization of networks and some practical implementations.

This module introduces machine learning algorithms by discussing one of the most popular algorithms - the K-nearest neighbor algorithm which could be used both for classification and regression.

This module introduces the decision tree algorithm which is a popular non-linear tree based algorithm. Furthermore, the discussion then introduces the concept of Bagging which is a type of ensemble technique and Random Forest algorithm which is a type of bagging algorithm.

This module builds on the Bagging algorithm introduced earlier and also adds another ensemble technique called Boosting. The theory behind both the ensemble techniques is discussed in detail.

This module introduces Gradient Descent and explains how it is combined with boosting to achieve gradient boosting. Then we discuss the advanced implementations of gradient descent such as AdaBoost and Extreme Gradient Boosting.

All the prior modules have been dealing with structured data (data that can be stored as rows and columns and that follow relational requirements). This module introduces the unstructured data in the form of text data and provides some tools and techniques to analyze it.

This module discusses another important algorithm - Naive Bayes model which is based on the Bayesian Probability Technique. This algorithm has been successfully deployed to detect spam with great accuracy.

This module discusses the perceptron, which is the fundamental concept of an artificial neural network. The basic version of an ANN called the multi layer perceptron (MLP) is also discussed.

This module discusses the various building blocks of neural networks - perceptron, backpropagation, activation functions, dropout, dense vs sparse layers ,etc.

The difference between ANN and Deep Learning is that the network in deep neural networks consists of multiple hidden layers vs just a single layer in the ANN. The architecture is explored and some guidelines are laid out.

This module discusses another Black Box technique - Support Vector Machines.

This module builds on the concepts from Module 12 and discusses how survival analysis is performed in practical and real-life scenarios.

This module introduces the concept of a time series. All the previous modules (except text data) were longitudinal or cross-sectional datasets. There is no temporal component in the data (or it is ignored for the purposes of algorithm). This module introduces techniques to deal with time-series data such as AR, ARMA and ARIMA models.

This module builds on the concepts previously discussed and delves into some of the data-driven algorithms available to address the time-series problems such as MA, EMA and Econometric Models.

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How We Prepare You
  • data science foundation course in USA
    Additional Assignments of over 140 hours
  • data science foundation course in USA
    Live Free Webinars
  • data science foundation course in USA
    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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Data Science Certification Panel of Coaches

data scientist trainers

Bharani Kumar Depuru

  • Areas of expertise: Data Analytics, Digital Transformation, Industrial Revolution 4.0
  • Over 18+ years of professional experience
  • Trained over 2,500 professionals from eight countries
  • Corporate clients include Deloitte, Hewlett Packard Enterprise, Amazon, Tech Mahindra, Cummins, Accenture, IBM
  • Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, (DSDM Atern)
  • Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business
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data scientist trainers

Sharat Chandra Kumar

  • Areas of expertise: Data sciences, Machine learning, Business intelligence and Data Visualization
  • Trained over 1,500 professional across 12 countries
  • Worked as a Data scientist for 18+ years across several industry domains
  • Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology Infrastructure Library
  • Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, Python, Tableau, Cognos
  • Corporate clients include DuPont, All-Scripts, Girnarsoft (College-, Car-) and many more
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data scientist trainers

Bhargavi Kandukuri

  • Areas of expertise: Business analytics, Quality management, Data visualisation with Tableau, COBOL, CICS, DB2 and JCL
  • Electronics and communications engineer with over 19+ years of industry experience
  • Senior Tableau developer, with experience in analytics solutions development in domains such as retail, clinical and manufacturing
  • Trained over 750+ professionals across the globe in three years
  • Worked with Infosys Technologies, iGate, Patni Global Solutions as technology analyst
Read More >
data science online course certification - 360digitmg


Earn a certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognised at the workplace and boost your confidence. The Data Science Certificate is your passport to an accelerated career path.

Alumni Speak

Nur Fatin

"Coming from a psychology background, I was looking for a Data Science certification that can add value to my degree. The 360DigiTMG program has such depth, comprehensiveness, and thoroughness in preparing students that also looks into the applied side of Data Science."

"I'm happy to inform you that after 4 months of enrolling in a Professional Diploma in Full Stack Data Science, I have been offered a position that looks into applied aspects of Data Science and psychology."

Nur Fatin

Associate Data Scientist

Thanujah Muniandy

"360DigiTMG has an outstanding team of educators; who supported and inspired me throughout my Data Science course. Though I came from a statistical background, they've helped me master the programming skills necessary for a Data Science job. The career services team supported my job search and, I received two excellent job offers. This program pushes you to the next level. It is the most rewarding time and money investment I've made-absolutely worth it.”

Thanujah Muniandy

Ann Nee, Wong

"360DigiTMG’s Full Stack Data Science programme equips its graduates with the latest skillset and technology in becoming an industry-ready Data Scientist. Thanks to this programme, I have made a successful transition from a non-IT background into a career in Data Science and Analytics. For those who are still considering, be bold and take the first step into a domain that is filled with growth and opportunities.”

Ann Nee, Wong

Mohd Basri

"360DigiTMG is such a great place to enhance IR 4.0 related skills. The best instructor, online study platform with keen attention to all the details. As a non-IT background student, I am happy to have a helpful team to assist me through the course until I have completed it.”

Mohd Basri

Ashner Novilla

"I think the Full Stack Data Science Course overall was great. It helped me formalize and think more deeply about ways to tackle the projects from a Data Science perspective. Also, I was remarkably impressed with the instructors, specifically their ability to make complicated concepts seem very simple."

"The instructors from 360DigiTMG were great and it showed how they engaged with all the students even in a virtual setting. Additionally, all of them are willing to help students even if they are falling behind. Overall, a great class with great instructors. I will recommend this to upcoming deal professionals going forward.”

Ashner Novilla


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FAQs for Data Science Course Training

There are numerous jobs available for data science professionals. Once you finish the training, assignments and the live projects successfully, we will circulate your resume to the organizations with whom we have formal agreements on job placements. We also conduct regular webinars to help you with your resume and job interviews. We cover all aspects of post-training activities that are required to get a successful placement.

There is a huge disparity in how these terms are used, sometimes DS, DA and BA are used interchangeably. Although, the gap is narrowing now, BA is strictly dealing with advanced analytics but DS is more about bringing predictive power using machine learning techniques. One thing is clear, Data Modelling typically means designing the scheman etc. Though there are no hard rules that distinguish one from another, you should get the role descriptions clarified before you join an organization.

The US market is currently going through an unprecedented economy and the job growth has also been the best in recent times. Multiple reputed sources are documenting the acute shortage of data science professionals. Our program aims to address this by preparing the candidates not only by providing theoretical concepts, but helping them learn by doing. You will also greatly benefit from doing a Live project through Innodatatics, a leading Data Analytics company which will prepare you in dealing with implementing a data science project end-to-end.

It has been well documented that there is a startling shortage of data science professionals worldwide and for the US market in particular. Now the onus is on you, the candidate and if you can demonstrate strong knowledge of Data Science concepts and algorithms, then there is a high chance for you to be able to make a career in this profession.


To help you achieve that 360DigiTMG provides internship opportunities through Innodatatics, our USA-based consulting partner, for deserving participants to help them gain real-life experience. You will be involved in executing a project end to end and this will help you with gaining the job training to help you in this career path.

The data science profession has given rise to a multitude of sub-domains although most of the responsibilities overlap there are subtle and pertinent differences in each of the roles. See below for a short description of what each of the roles represents. Please be wary that depending on the organizational structure and the industry, the roles may have different meaning but this should serve as a basic guideline.


A Data Analyst is tasked with Data Cleansing, Exploratory Data Analysis, and Data Visualization, among other functions. These responsibilities pertain more to the use and analysis of historical data for understanding the current state. So simply put, a Data Analyst can answer the question ‘what happened?’


A Data Scientist on the other hand will go beyond a traditional analyst and build models and algorithms to solve business problems using statistical tools such as Python, R, Spark, Cloud technologies, Tableau etc. The data scientist has an understanding of ‘what happened’ but will typically go a bit further to answer ‘how we can prevent/predict that from happening?’


A Data Engineer is the messenger that carries or moves data around. He is responsible for the data ingestion process, building data pipelines to make it flow seamlessly across source, target systems and also responsible for building the CI/CD (continuous integration, continuous development) pipelines.


A Data Architect has a much broader role that involves establishing the hardware and software infrastructure needed for an organization to perform Data Analysis. They help in selecting the right database, servers, network architecture, GPUs, cores, memory, hard disk etc.

After every classroom session, you will receive assignments through the online Learning Management System. Our LMS is a state-of-the-art system which facilitates learning at your convenience. We do impose a strict condition – you will need need to complete the assignments in order to obtain your data scientist certificate.

Since this course is a blended program, you will be exposed to a total of 80 hours of instructor-led live training. On top of that you will also be given assignments which could have a total duration running into 60-80 hours. In addition to this, you will be working on a live project for a month. All of our assignments are carried out online and the datasets, code, recorded videos are all accessed via our LMS.

We understand that despite our best efforts, sometimes life happens. In such scenarios you can access all of the course videos in the LMS.

Each student is assigned a mentor during the course of this program. If the mentor determines additional support is needed to help the student, we may refer you to another trainer or mentor.

Each student is assigned a mentor during the course of this program. If the mentor determines additional support is needed to help the student, we may refer you to another trainer or mentor.

Jobs in the field of Data Science in USA

Jobs in the Field of Data Science in USA

The job profiles of Data Science include Data Scientist, Senior Data Scientist, Data Analyst, Python Developer, Data Engineer, Data Scientist- Machine Learning, etc.

Salaries in USA for Data Science

Salaries for Data Science Professionals in USA

The average salary for a Data Scientist in USA at early-Career is $94,534, at mid-career, it is $107,651, and for an experienced Data Scientist, the average salary is $120,530.

Data Science course Projects in USA

Data Science Projects in USA

Data Science with AI and Machine Learning projects are being carried out for forecasting climate changes, Breast cancer detection, fraud detection, etc.

Role of Open Source Tools in Data Science in USA

Role of Open Source Tools in Data Science

Python is a very eminent programming language for Data Science. Along with Python, Knowledge of R and R studio statistical tools is a must.

Modes of Training for Data Science with Python in USA

Modes of Training for Data Science

360DigiTMG offers both classrooms as well as online training for students. It also provides individual mentorship to the students.

Industry Application of Data Science in USA

Industry Applications of Data Science

The industrial applications of Data Science are vast and are extensively used in industries that include Automation, Manufacturing, Airlines, Food, Pharmaceutical, Finance, Healthcare, Education, Oil and gas industry, etc.

Companies That Trust Us

360DigiTMG offers customised corporate training programmes that suit the industry-specific needs of each company. Engage with us to design continuous learning programmes and skill development roadmaps for your employees. Together, let’s create a future-ready workforce that will enhance the competitiveness of your business.


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