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Best Data Science Course in United States With Placement Assistance

PG Program in Data Science, Machine Learning & Neural Networks in collaboration with IBM

Become an industry-ready Certified Data Science professional through immersive learning of Data Analysis and Visualization, ML models, Forecasting & Predicting Models, NLP, Deep Learning, and more with this Job Assistance Program

Become an industry-ready Certified Data Science professional through immersive learning of Data Analysis and Visualization, ML models, Forecasting & Predicting Models, NLP, Deep Learning, and more with this Job Guarantee Program

In Collaboration With
  • Silver
    Business
    Partner
  • IBM

19 Aug, 2022

Next Batch
starts on

12 Months

Recommended
20-22 hrs/week

6 Months

Live
Internship

Online

Learning
Format

6000+

Career
Transformed

400+

Hiring
Partners

Program Overview

Best online data science program in United States and across the globe. Get trained with highly in-demand tools, techniques & technologies for Data Science.

Key Highlights

  • 6 Months Internship Part of the Data Science Program6 Months Internship Part of the Program
  • Online best data science program Ideal for both Working and Fresh GraduatesIdeal for both Working Professionals and Fresh Graduates
  • online data science program One-on-One with Industry MentorsOne-on-One with Industry Mentors
  • data science programs with 100% guaranteed Placement 100% Placement Assistance
  • 40+ Projects and Case Studies from the best data science institute40+ Projects and Case Studies
  • Career Success Manager- data science programs near meCareer Program Manager
  • 360 Degree Career Support for online data science program360 Degree Career Support
  • Unique Specialisations for online data science programs with placementUnique Specializations
  • Instant Doubt Resolution by the best data science institute near meInstant Doubt Resolution
  • data science Live Internship programs with placementaLive Internship

IBM Data Science PG course Certification

  1. $ 5,000
    • Best online Investment-Banking course
    • Best online Investment-Banking course
    • Best online Investment-Banking course
    • Best online Investment-Banking course
    8500+ learners
Features
  • 300+ hours of learning
  • Practice Test Included
  • Certificate of completion
  • Enhanced Upskilling

12 Months PG Program in Data Science, Machine Learning & Neural Networks in collaboration with IBM

Get eligible for 4 world-class certifications thus adding that extra edge to your resume.

  • Learning paths and certification from IBM
  • Project Completion Certificate from DataTrained Education
  • Course completion certificate from DataTrained Education
  • Internship Certificate from Partner Companies

Languages and Tools covered

  • Excel in Data Science program - online excel course
  • python in Data Science Program - online python course
  • tableau in Data Science program - online tableau course
  • NLP in Data Science - online NLP course
  • SQL in Data science course - online SQL course

What’s the focus of this course?

Choose from 4 specializations, receive industry mentorship, dedicated
career support, learn 14+ programming tools & languages & much more

4 Unique Specializations- data science programs near me

4 Unique Specializations

Choose from 4 specializations as per your background & career aspirations. Get an Executive Certification In Data Science, Machine Learning & Neural Networks In Collaboration With IBM

Dedicated Career Assistance- data science program institute

Dedicated Career Assistance

Receive 1:1 career counseling sessions & mock interviews with hiring managers. Exhilarate your career with our 400+ hiring partners.

Student Support -  data science online training

Student Support

Chat support for Quick Doubt Resolution is available from 06 AM to 11 PM IST. Program Managers are available on call, chat and ticket during business hours.

Instructors

Join DataTrained – IBM- certified curriculum and learn every skill from the industry’s best thought leaders.

Dr. Deepika Sharma - Training Head, DataTrained

Dr. Deepika Sharma

Training Head, DataTrained

Dr. Deepika Sharma has been associated with academics /corporate education for more than 14 years. She has a deep passion in the field of Artificial Intelligence, Data Science, and Machine Learning.

Shankargouda Tegginmani - Data Scientist, Accenture

Shankargouda Tegginmani

Data Scientist, Accenture

Shankar is a data Scientist with 14 Years of Experience. His current employment is with Accenture and has experience in telecom, healthcare, finance and banking products.

Sanket Maheshwari - Data Scientist, Faasos

Sanket Maheshwari

Data Scientist, Faasos

Experienced Data Scientist with a demonstrated history of working in the information technology and services industry.

Polong Lin - Business Analyst, IBM

Polong Lin

Business Analyst, IBM

Polong Lin is a Data Scientist at IBM in Canada. Under the Emerging Technologies division, Polong is responsible for educating the next generation of data scientists through BDU.

Jay Rajasekharan- Data Scientist, IBM

Jay Rajasekharan

Data Scientist, IBM

Currently, he is driving several productivity programs - using data analytics to drive insights from business operations and implementing optimizations such as streamlining workflows, improving service levels, and ultimately reducing cost.

Mahdi Noorian- Data Scientist, IBM

Mahdi Noorian

Data Scientist, IBM

Mahdi Noorian is a Postdoctoral Fellow at the Laboratory for Systems, Software and Semantics (LS3) of the Ryerson University. He holds a Ph.D degree in Computer Science from University of New Brunswick.

Data Science Course Syllabus

Best-in-class content by leading faculty and industry leaders in the form of live sessions, pre-recorded videos, projects, case studies, industry webinars, and assignments.

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Detailed Syllabus of Data Science Course

  • 300+ Hours of Content - data science program institute
  • 300+

    Hours of Content

  • 80+ Live Sessions - data science online training
  • 80+

    Live Sessions

  • 15 Tools and Software - best tools for data science
  • 15

    Tools and Software

Comprehensive Curriculum

The curriculum has been designed by faculty from IITs, IBM and Expert Industry Professionals.

300+ Hours of Content - data science program institute
300+

Hours of Content

80+ Live Sessions - data science online training
80+

Live Sessions

15 Tools and Software - best tools for data science
15

Tools and Software

Foundations

The Foundations bundle comprises 2 courses where you will learn to tackle Statistics and Coding head-on. These 2 courses create a strong base for us to go through the rest of the tour with ease.

This course will introduce you to the world of Python programming language that is widely used in Artificial Intelligence and Machine Learning. We will start with basic ideas before going on to the language's important vocabulary as search phrases, syntax, or sentence building. This course will take you from the basic principles of AI and ML to the crucial ideas with Python, among the most widely used and effective programming languages in the present market. In simple terms, Python is like the English language.

Python Basics

Python is a popular high-level programming language with a simple, easy-to-understand syntax that focuses on readability. This module will guide you through the whole foundations of Python programming, culminating in the execution of your 1st Python program.

Anaconda Installation - Jupyter notebook operation

Using Jupyter Notebook, you will learn how to use Python for Artificial Intelligence and Machine Learning. We can create and share documents with narrative prose, visualizations, mathematics, and live code using this open-source online tool.

Python functions, packages and other modules

For code reusability and software modularity, functions & packages are used. In this module, you will learn how you can comprehend and use Python functions and packages for AI.

NumPy, Pandas, Visualization tools

In this module, you will learn how to use Pandas, Matplotlib, NumPy, and Seaborn to explore data sets. These are the most frequently used Python libraries. You'll also find out how to present tons of your data in simple graphs with Python libraries as Seaborn and Matplotlib.

Working with various data structures in Python, Pandas, Numpy

Understanding Data Structures is among the core components in Data Science. Additionally, data structure assists AI and ML in voice & image processing. In this module, you will learn about data structures such as Data Frames, Tuples, Lists, and arrays, & precisely how to implement them in Python.

In this module, you will learn about the words and ideas that are important to Exploratory Data Analysis and Machine Learning. You will study a specific set of tools required to assess and extract meaningful insights from data, from a simple average to the advanced process of finding statistical evidence to support or even reject wild guesses & hypotheses.

Descriptive Statistics

Descriptive Statistics is the study of data analysis that involves describing and summarising different data sets. It can be any sample of a world's production or the salaries of employees. This module will teach you how to use Python to learn Descriptive Statistics for Machine Learning.

Inferential Statistics

In this module, you will use Python to study the core ideas of using data for estimating and evaluating hypotheses. You will also learn how you can get the insight of a large population or employees of any company which can't be achieved manually.

Probability & Conditional Probability

Probability is a quantitative tool for examining unpredictability, as the possibility of an event occurring in a random occurrence. The probability of an event occurring because of the occurrence of several other occurrences is recognized as conditional probability. You will learn Probability and Conditional Probability in Python for Machine Learning in this module.

Hypothesis Testing

With this module, you will learn how to use Python for Hypothesis Testing in Machine Learning. In Applied Statistics, hypothesis testing is among the crucial steps for conducting experiments based on the observed data.

Machine Learning

Machine Learning is a part of artificial intelligence that allows software programs to boost their prediction accuracy without simply being expressly designed to do so. You will learn all the Machine Learning methods from fundamental to advanced, and the most frequently used Classical ML algorithms that fall into all of the categories.

With this module, you will learn supervised machine learning algorithms, the way they operate, and what applications they can be used for - Classification and Regression.

Linear Regression - Simple, Multiple regression

Linear Regression is one of the most popular Machine Learning algorithms for predictive studies, leading to the very best benefits. It is an algorithm that assumes the dependent and independent variables have a linear connection.

Logistic regression

Logistic Regression is one of the most popular machine learning algorithms. It is a fundamental classification technique that uses independent variables to predict binary data like 0 or 1, positive or negative , true or false, etc. In this module, you will learn all of the Logistic Regression concepts that are used in Machine Learning.

K-NN classification

k-Nearest Neighbours (Knn) is another widely used Classification algorithm, it is a basic machine learning algorithm for addressing regression and classification problems. With this module, you will learn how to use this algorithm. You will also understand the reason why it is known as the Lazy algorithm. Interesting Right?

Support vector machines

Support Vector Machine (SVM) is another important machine learning technique for regression and classification problems. In this module, you will learn how to apply the algorithm into practice and understand several ways of classifying the data.

We explore beyond the limits of supervised standalone models in this Machine Learning online course and then discover a number of ways to address them, for example Ensemble approaches.

Decision Trees

The Decision Tree algorithm is an important part of the supervised learning algorithms family. The decision tree approach can be used to resolve regression and classification problems unlike others. By learning simple decision rules inferred from previous data, the goal of using a Decision Tree is constructing a training type that will be used to predict the class or value of the target varying.

Random Forests

Random Forest is a common supervised learning technique. It consists of multiple decision trees on the different subsets of the initial dataset. The average is then calculated to enhance the dataset's prediction accuracy.

Bagging and Boosting

When the aim is to decrease the variance of a decision tree classifier, bagging is implemented. The average of all predictions from several trees is used, that is a lot more dependable than a single decision tree classifier.

Boosting is a technique for generating a set of predictions. Learners are taught gradually in this technique, with early learners fitting basic models to the data and consequently analyzing the data for errors.

In this module, you will study what Unsupervised Learning algorithms are, how they operate, and what applications they can be used for - Clustering and Dimensionality Reduction, and so on.

K-means clustering

In Machine Learning or even Data Science, K-means clustering is a common unsupervised learning method for managing clustering problems. In this module, you will learn how the algorithm works and how you can use it.

Hierarchical clustering

Hierarchical Clustering is a machine learning algorithm for creating a bunch hierarchy or tree-like structure. It is used to group a set of unlabeled datasets into a bunch in a hierarchical framework. This module will help you to use this technique.

Principal Component Analysis

PCA is a Dimensional Reduction technique for reducing a model's complexity, like reducing the number of input variables in a predictive model to avoid overfitting. Dimension Reduction PCA is also a well-known ML approach in Python, and this module will cover all that you need to know about this.

DBSCAN

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to identify arbitrary-shaped clusters and clusters with sound. You will learn how this algorithm will help us to identify odd ones out from the group.

Advanced Techniques

EDA - Part1

Exploratory Data Analysis (EDA) is a procedure of analyzing the data using different tools and techniques. You will learn data standardization and represent the data through different graphs to assess and make decisions for several business use cases. You will also learn all the essential encoding techniques.

EDA - Part2

You will also get a opportunity to use null values, dealing with various data and outliers preprocessing techniques to create a machine learning model.

Feature Engineering

Feature Engineering is the process of extracting features from an organization's raw data by using domain expertise. A feature is a property shared by independent units that can be used for prediction or analysis. With this module, you will learn how this works.

Feature Selection

Feature selection is also called attribute selection, variable selection, or variable subset selection. It is the process of selecting a subset of relevant features for use in model development. You can learn many techniques to do the feature selection.

Model building techniques

Here you will learn different model-building techniques using different tools

Model Tuning techniques

In this module, you can learn how to enhance model performance using advanced techniques as GridSearch CV, Randomized Search CV, cross-validation strategies, etc.

Building Pipeline

What is Modeling Pipeline and how does it work? Well, it is a set of data preparation steps, modeling functions, and prediction transform routines organized in a logical order. It allows you to specify, evaluate, and use a series of measures as an atomic unit.

Time Series Analysis

Introduction

A time series is a set of data points that appear in a specific order over a specific time. A time series in investing records the movement of selected data points, like the cost of security, with a set period of time, with data points collected at regular intervals.

Time Series Components

In this module, you will learn about different components that are necessary to analyze and forecast future outcomes.

Stationarity

You will learn what is stationarity and the importance of learning stationarity.

Time Series Models

In this module, you will learn common Time series models as AR, MA, ARIMA, etc.

Model Evaluation

When you build models, you will use different evaluation methods to gauge the product performance or even accuracy. Yes, In this module, you will learn model evaluation methods.

Use Case and Assignment

You will also get a chance to work on assignments and feel at ease while working on the use case scenarios.

Projects

Also, we are providing a few more extra projects for practice, you can assemble and compare your solutions with the ones we provide.

Recommendation Engine

Introduction

In the introduction module, you will learn why recommendation systems are used, their requirement, and their applications.

Understanding the relationship

In this module, you will learn on what basis recommendation engine works and their association rules.

Types of Data in RS

In this module, you will learn all the types of data used in the Recommendation Engine.

Ratings in RS

In this module, you will learn just how the ratings are drawn in the Recommendation Engine.

Similarity and Its Measures

Recommendation systems work on the basis of similarity between the product and the consumers who view it. There are many ways for determining how similar 2 products are. This similarity matrix is used by recommendation systems to recommend the next most comparable product to the customer.

Types of Recommendation Engine

In this module, you will learn different types of Recommendation Engines.

Evaluation Metrics in Recommendation

Once you build the models, you require metrics to evaluate how effective is your model. You will learn various evaluation tools in RE.

Use cases

You will also get an opportunity to focus on additional use cases. Later, you can compare your solution with the SME-provided solution.

Electives

Strong hand-holding with dedicated support to help you master some of the complex processes of Data Science and Artificial Intelligence.

Deep Learning with Computer Vision

  • NLP with ML
  • Deep Learning
  • Computer Vision
  • Business Analytics with Tableau

Deep Learning with NLP

  • NLP with ML
  • Deep Learning
  • Deep NLP
  • Business Analytics with Tableau

Business Analytics with R

  • NLP with ML
  • Business Analytics with "R" programming and R shiny
  • Business Analytics with Tableau
  • Business Analytics with Advanced Excel

Business Analytics with Tableau

  • NLP with ML
  • Deep Learning
  • Business Analytics with Power BI
  • Business Analytics with Tableau

Industry Projects

Learn through real-life industry projects sponsored by top companies across industries

  • Data science program Engage in collaborative projects and learn from peers
  • Data science programMentoring by industry experts to learn and apply better
  • Data science programPersonalized subjective feedback on your submissions to facilitate improvement
Smartphone and Smartwatch Activity - data science training

Smartphone and Smartwatch Activity

The crude accelerometer and whirligig sensor information is gathered from the cell phone and smartwatch at a pace of 20Hz.

Recommendation System-- best online data science programs

Recommendation System

In the connected world, it is imperative that the organizations are using to Recommend their Products & Services to the People.

Air Quality Study--- data science course near me

Air Quality Study

Based on The Data Collected from the Meteorological Department, Predicting The Air Quality Of Different Parts of The country

Why DataTrained for Data Science Program in United States?

The best most exclusive Data Science program in United States is the Post Graduate Program in Data Science, Machine Learning, and Neural Networks. The program is developed with Data scientists from IBM and industry experts working in the data science domain for decades and according to the international industry standards. The course duration is 12 months including a well-balanced curve of practical and theoretical learning’s covering everything from the basics to the advanced levels of Data Science program in United States and across United States.

Enroll now to benefit from the best Data Science program online

6 month practical internship

6 Months internship ensures you graduate as an experienced data science professional rather than a fresher. You can go for an online internship along with your current job.

Resume pepration by DataTrained

Partnered with IIMJobs wherein you get access to their paid resume preparation kit and personal feedback from the industry HR experts. An individual career profile is prepared by our experts so that it suits his/her experience and makes it relevant to a Data Scientist role.

Interview pepration DataTrained

Regular mock HR and Technical interviews by mentors with personal guidance and support. The industry mentor helps students to take projects on Kaggle and move on to the status bar so that their resume looks competitive to the recruiters.

100% granted placement

We generate the Ability Score of every individual which is then sent to our more than 400+ recruitment partner organizations. At last, we organize campus placements every three months to place our students.

Career Impact

DataTrained in collaboration with IBM presents the best online Data Science Program in United States. Over 5000 Careers Transformed.

DataTrained has helped me with the vital knowledge and skills that are needed for a data scientist role. The trainer starts with an example to make us comprehend the concept and then help us build the Algorithms with the real industry datasets. DataTrained brings the power of online learning along with dedicated Mentorship, Counselling, Live Sessions and 6 months Internship.

Aaruni Khare-- Data Scientist
Aruni Khare Data Scientist, RBS

I saw an ad from DataTrained on facebook and I contacted them straight away and enquired about their Data Science online course. Their counselor took me through the complete journey of what they offer and what is data science all about. After continuous conversation for a few weeks, I was pretty sure about the course and now I knew where I need to invest my money and hard work.

Rakshit Jain- Data Scientist, Optum
Rakshit Jain Data Scientist, Optum

The program is a well-balanced mix of pre-recorded classes, live sessions on weekends and printed reading materials they sent to my address. My mentor was Amit Kaushik and he helped me in getting that confidence and completing my assignments on time. I have almost completed the course and have been able to crack Glenmark interview. Thank you so much DataTrained.

Rupam Kumar Chaurasia-- Head Sales, Glenmark
Rupam Kumar Chaurasia Head Sales, Glenmark
66% Average Salary Hike - data science programs with placement 66%

Average Salary Hike

18 LPA Highest Salary - online data science programs and placement $ 2,11,567 PA

Highest Salary

7k+ Jobs  Sourced - data science jobs 7k+

Jobs Sourced

400+ Hiring Partners - online data science training 400+

Hiring Partners

Admission Process

There are 3 simple steps in the Admission Process which is detailed below

Step 1: Fill in a Query Form

Fill up the Query Form and one of our counselors will call you & understand your eligibility.

Step 2: Get Shortlisted & Receive a Call

Our Admissions Committee will review your profile. Upon qualifying, an Email will be sent to you confirming your admission to the Program.

Step 3: Block your Seat & Begin the Prep Course

Block your seat with payment to enroll in the program. Begin with your Prep course and start your Data Science journey!

Data Science Course Fee

$ 5,000

No Cost EMI options are also available. *

best data science institute

I’m interested in this program

What's Included in the Price

Placements

Access to real-life 40 industry projects

6 Months online Internship part of the core curriculum

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