Data Science Bootcamp with AI

Learn Data Science, Wrangle Massive Data Sets, & Get Hired as a Data Scientist

Land lucrative offers with an average salary of per year

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Introducing the All-New Data Science Bootcamp with AI

In a world increasingly driven by data, Artificial Intelligence (AI) is the key to unlocking its true potential. upGrad KnowledgeHut is thrilled to announce the launch of our all-new Data Science Bootcamp with AI, a program designed to put you at the forefront of this exciting shift. If you're looking to build solid tech skills and land a job as a skilled Data Scientist at one of the world's best tech companies, or want to kickstart a career as a freelance Data Scientist helping companies harness the predictive power of data, look no further than our Data Science Bootcamp with AI.



This revamped program goes beyond traditional data analysis, equipping you with the skills and knowledge to understand data and leverage AI's power to make groundbreaking predictions and solve complex problems.



Whether you have some background in programming or none at all, our carefully curated bootcamp, designed by industry experts, will equip you with all the real-world tech skills you need to crack interviews and become a sought-after Data Scientist. By the end of the intense 31-week immersive learning program, you will learn to explore, clean, analyze and predict data, and showcase your skill in predictive modeling, pattern recognition, data visualization, and wrangling massive data sets to forecast trends and inform strategy.



Our comprehensive curriculum starts with a solid foundation in Python, SQL, data analysis, and statistical modeling. You'll then delve deep into the world of Machine Learning, exploring a wide range of algorithms from decision trees to natural language processing. But that's not all! This program takes you a step further, unveiling the mysteries of Deep Learning and Neural Networks. By the end, you'll be empowered to build cutting-edge AI models that can truly transform industries.



Our Data Science Bootcamp with AI is designed to prepare you for the real world. You'll gain practical experience through hands-on exercises, industry case studies, and real-world projects mentored by experts. We'll also provide comprehensive career support, including mock interviews, career coaching, and training to help you ace those technical interviews and land your dream data science job.



This program offers all the benefits of our renowned Bootcamp, including 165+ hours of live and engaging instructor-led sessions, 50 hours of live interactive doubt-clearing sessions, and 35 hours of master sessions focused on practical application and interview preparation. In addition, you will also receive complementary access to our self-paced AI Engineer Bootcamp featuring 288 hours of e-learning, 130+ hands-on exercises, 9 capstone projects, and 15 real-world case studies.



You'll also benefit from a stellar average rating of 4.5/5, power-packed project-based learning, industry case studies and guest lectures, hands-on exercises with cloud labs, and the ability to build a strong foundation for various data careers.



Our power-packed project-based immersive learning program features best-in-class training, on-demand learning, plenty of hands-on exercises and assignments with Cloud Labs, industry case studiesreal-world projects, career coaching, mock interviews, hackathons, guest lectures by industry experts as well as training to help you nail tough technical interviews with Data Structures and Algorithms.



As the most comprehensive program available, not only will the KnowledgeHut Data Science Bootcamp with AI make you confident of taking real-world Data Science problems head-on and coming up with the most appropriate solutions, but you will also receive all the career and placement support you need to land a job as a Data Scientist. In addition, you will have built solid skills to take on various job roles in the Data domain including Data Analyst, Data Engineer, Data Architect, Data Storyteller, Machine Learning Scientist, Machine Learning Engineer, Business Intelligence Developer, Database Administrator, and Business Analyst among others.



Join the Data Science Bootcamp with AI and become a highly sought-after data scientist with the power of AI at your fingertips.

Highlights

  • 165+ Hours of Live Instructor-Led Sessions

  • 50 Hours of Live Interactive Doubt-Solving Sessions

  • 35 Hours of Live Master Sessions by Industry Experts

  • 110 Hours On-Demand Self-Paced Videos

  • 250 Hours of Hands-On with Cloud Labs

  • Job-Ready Portfolio of 17 Capstone Projects
  • 133 Auto-Graded Assessments

  • 20+ Industry Case Studies

  • 166 Guided Hands-On Exercises

  • 36 Assignments and Mini Projects

  • 2 Mock Interviews and 1 Hackathon

  • 15 Hours of Mentorship by Industry Experts

  • Complementary AI Engineer Bootcamp

Master the Latest Tools

  • Python
  • Hadoop
  • Spark
  • Tableau
  • SQL
  • no-sql
  • MongoDB
  • MySQL Tools- New
  • AWS Tools- New
  • TensorFlow- New
  • Keras- New
  • NumPy- New
  • Pandas- New

Ride the Wave of High Demand for Data Scientists

benefits of Data Science Bootcamp with AI

Data scientists are among the most in-demand professionals across the spectrum of industries because of their unique ability to make sense of big data, draw insights from it, and helping businesses leverage those insights to drive profitability. Most importantly, data scientists use such insights to solve the everyday problems and make the world a better place.



The KnowledgeHut Data Science Bootcamp has foundational and advanced phases of learning with plenty of projects for you to hone skills that you acquire during classroom lessons. By the end of the Bootcamp, you are well placed to show off your analytics, ML, and applied data science expertise to open doors for opportunities at Tier 1 companies and more!

Engineer a Rewarding Future in Data Science

Talk to your Learning Advisor

The KnowledgeHut Advantage

The most effective project-based immersive learning experience

Immersive Learning

immersive-learning
  • On-demand videos
  • Guided hands-on exercises
  • Auto-graded assessments and recall quizzes
  • Assignments and projects

Learn by Doing

learn-by-doing
  • Learn to code. By actually coding.
  • Get project-ready with work-like experiences.
  • Learn on the job, like devs in tech companies.

Cloud Labs

cloud-labs
  • Access fully provisioned dev environment.
  • Virtual machine spinned up in minutes.
  • Write code right in your browser.

Outcome-Focused

outcome-driven-learning
  • Get advanced learner insights.
  • Measure and track skills progress.
  • Identify areas to improve in.

Blended Learning

blended-learning
  • On-demand, self-paced learning anytime.
  • Code review sessions by experts.
  • Access to discussion forums, community groups.

Pool of Stellar Course Creators and Instructors

Our industry-validated curriculum is designed with inputs from our Software Engineering Advisory Board comprised of industry veterans and renowned experts. The program is delivered by top instructors with several years of experience under their belt.

David Haertzen

Big Data Analytics Leader

Denis Rothman

AI Author, Speaker, and Instructor

Jeffrey Aven

Principal Consultant

Gopikrishnan R

Co-Founder and CTO

Beau - Carnes

Director of Technology, Education

Jignesh Kariya

Sr. Database Consultant

Peter Henstock

Machine Learning & AI Lead

Ashish Gulati

Python & Data Science Consultant

Shobhit Nigam

Program Director

Phillip Kinn

Senior Data Scientist

Emmanuel Segui

Asst. Director, Reporting and Programming

Rahul Tiwari

Data Scientist and Co-Founder

Mark Strefford

Machine Learning Lead

George Mount

Founder

Dr. Vishwakarma J.S.

Enterprise Architect, CTO

Jeremiah Lobo

Data Visualization Lead

Enes Bilgin

Staff Machine Learning Engineer

Harish Masand

Project Manager - Digital Enablement (Data and AI)

Mo Medwani

Sr. Data Scientist

Marie Stephen Leo

Director of Data Science - APAC

Anatoly Zelenin

Freelance Trainer, Author

Bradford Tuckfield

Data Science Instructor and Consultant

Malvik Vaghadia

Principal Consultant - Data and Analytics

Rashmi Banthia

Data Scientist

Avery Smith

Data Scientist

Prince Kumar

Data Scientist

Sudhanshu Saxena

Sr. Data Scientist and Data Science Trainer

Azib Hasan

Freelance Trainer

prerequisites for Data Science Bootcamp with AI

Prerequisites

  • Programming: Knowledge of programming fundamentals is good to have, but not mandatory.
  • Mathematics: Basic knowledge of programming and high-school level math (functions, derivatives, systems of linear equations) is beneficial, though not mandatory.
  • Logical Thinking: The right aptitude, logical thinking, and drive for curiosity are all you need—leave the rest to us!

Who Should Attend the Data Science Bootcamp

Beginner Data Scientists

Database Administrators

Business Analysts

Python Developers

Applications Architects

Data Analysts

Data and Analytics Professionals

Product Managers

Graduates from any discipline

Professionals looking for a career change

Statisticians

Professionals looking to break into tech

Impress Recruiters With a Stellar Project Portfolio

Build professional projects like the top 1% of Data Scientists and create a solid, job-worthy portfolio worthy of Tier 1 companies. Land your dream job as a Data Scientist with ease. Here’s a peek at some of the projects you’ll be able to build:

  • NutriGro Food and Drink

    Build intelligence to predict user’s healthiness based on past grocery orders and recommend healthier options.

  • AnomaData Manufacturing

    Build intelligence to understand the health of machinery using Machine Learning and predict anomaly data/events.

  • tripredictor
    For-Rest from Fires Tools

    Build intelligence using Convolutional Neural Networks to detect fire accidents in buildings and forests.

  • MoodForMusic Music

    Building an application that detects the mood using still images or videos and recommends the music accordingly.

  • VoiceBox Productivity

    Build your own virtual voice assistant using NLP and Python (on similar lines of Alexa and Siri).

  • Propensify Business

    Build a Propensity Model to learn how likely certain target groups customers may act under certain circumstances.

  • SrapIt Business

    Build intelligence using Machine Learning to predict the scrap percentage in manufacturing.

  • bizHealth
    DocAssist Health and Fitness

    Build intelligence to analyse patient data to help doctors decide the best treatment.

  • scrypto
    ByDefault Banking and Finance

    Build a Credit Risk Modelling Application via Machine Learning to predict a customer's creditworthiness.

  • commuticator
    Recommender Shopping

    Build intelligence to help customers discover products they may like and most likely purchase.

What You Will Learn

Programming and Web Essentials

Start from the basics and learn to build advanced data science solutions.

MS Excel Basics

Learn MS Excel fundamentals like cell references, formatting options, applying formulas, and interpreting data visually.

Math and Stats Foundation

Build a foundation with basics of probability, statistics for data science, linear algebra, and calculus.

SQL Basics

Understand database concepts, interact with relational databases, and perform basic SQL queries to retrieve data.

NoSQL Basics

Learn NoSQL databases, perform CRUD operations using MongoDB, and Mongo Query Language (MQL).

Python for Data Science

Perform advanced operations on data and generate statistical inferences using Pandas and NumPy.

Machine Learning with Python

Create ML models to solve problems across domains, and understand regression use-cases.

Deep Learning with Keras and Tensorflow

Understand neural network structures and learn to use Python, TensorFlow, and Keras to implement CNN, RNN.

Natural Language Processing

Learn NLP pipeline with with different libraries such as NLTK, Spacy, TextBlob, Gensim, Pattern, and Stanford CoreNLP.

Deploying Models on Cloud

Build on ML and Deep Learning concepts, create and deploy real-world data science apps on the Cloud.

Data Structures and Algorithms

Get familiar with major algorithms and data structures such as quicksort, mergesort, balanced search trees, hash tables.

Tech Career Launch Prep

Get ready to apply all the skills you learn through the bootcamp to ace technical interviews and land your dream job as a Data Scientist.

Career Planning and Coaching
  • Goal-Setting
  • Personalized Career Planning
  • Career Coaching
Interview Preparation
  • Data Structures and Algorithms
  • Hackathon and Mock Interviews
  • Interview Analysis and Feedback
Dedicated Job Support
  • Target Data Roles
  • Resume, LinkedIn, GitHub Review
  • Comprehensive Placement Assistance

Data Science Bootcamp Curriculum

Download Curriculum
Video preview 1.

Learning Objectives:

Get introduced to the fundamentals of MS Excel along with the formatting concepts and formulas and Statistical Analysis using Excel. 

Topics: Play Video
  • Formatting Concepts 
  • Formulas in Excel 
  • Statistical Analysis 
  • Introduction to Other Features 
Video preview 2.

Learning Objectives:

Understand the first principles of computer programming, learn the meaning and utility of algorithms, loops and flow charts, and how computers, operating systems and the World Wide Web works.

Topics: Play Video
  • Basics of Programming 
  • Programming Concepts 
  • Data Storage and Files 
  • Operating Systems 
  • World of Web
  • Programming Languages
Video preview 3.

Learning Objectives:

Understand the fundamental concept of a database and learn how a Relational Database stores data. Learn to perform advanced data analysis by mastering SQL.

Topics: Play Video
  • What is SQL and Why is it Important?
  • SQL Database Admin Commands
  • The Basics of SQL Query
  • Filtering Data Using WHERE Clause in SQL 
  • Aggregation and Summary Functions in SQL
Video preview 4.

Learning Objectives:

Learn the basics of storing, manipulating, and retrieving data stored in a relational database to advance analysis of data making efficient analysis.

Topics: Play Video
  • Miscellaneous Analysis in SQL
  • Table to Table Relationship in SQL
  • Combining Tables 
  • Advanced SQL Data Analysis 
  • Making Efficient Analysis

Learning Objectives:

Master Python, starting with the fundamentals and go on to understand different data types and data structures. Learn flow control, how to use predefined functions and how to handle errors. Understand basic and advanced visualizations using Matplotlib, Seaborn and Plotly.

Topics:
  • Installation and Set Up
  • Code and Data 
  • Building Blocks
  • Strings
  • Data Structures
  • Flow Control

Learning Objectives:

Understand how to perform outlier analysis, learn Lambda functions and OOPs and how to scrape data. 

Topics:
  • Functions
  • Modules
  • Files
  • Lambda functions, Error, and Exception Handling
  • OOPs

Learning Objectives:

Learn the concepts of probability and statistics including essential concepts like hypothesis and regressions. Master how to process raw data to get it ready for another data processing operation. 

Topics: Play Video
  • Measures of Central Tendencies and Dispersion - (Mean, Median and Mode), (Variance, 
  • Standard Deviation, IQR, Skewness and Kurtosis) 
  • Distributions - Normal Distribution and Standard Normal Distribution, z-Scores
  • Probability Theory - Simple Probability, Rule of Addition and Multiplication, Bayes 
  • Theorem and Law of Large Numbers
  • Central Limit Theorem
  • Binomial and Poisson Distributions

Learning Objectives:

Learn how to test hypotheses and the meaning of Type1 and Type2 errors. Understand the ins and outs of ANOVA and regression analysis. 

Topics:
  • Hypothesis Testing - Intro to Hypothesis, H0, H1, Significance and P-value
  • Type1 and Type 2 errors, Confidence Intervals, Margin of Error
  • z-test and t-test
  • Regressions 
  • ANOVA

Learning Objectives:

Perform exploratory data analysis using NumPy and Pandas and learn all about RegEx and data visualizations.  

Topics:
  • Numpy
  • Pandas
  • Regular Expressions
  • Visualization

Learning Objectives:

Learn how to scrape websites with Python and learn what a clear version of "EDA" means and entails.

Topics:
  • Web Scraping 
  • EDA
Video preview 11.

Learning Objectives:

Get introduced to the CRUD operations of how to create, read, update, and delete documents on MongoDB


Topics: Play Video
  • NoSQL and Document Databases
  • MongoDB Basics
  • Introduction to CRUD Operations in MongoDB
  • MongoDB Drivers and Python

Learning Objectives: 

Get an end-to-end understanding of data visualization using Tableau from the basic to advanced concepts including Tableau calculations. Also learn how to slice and dice data and prepare interactive dashboards.


Topics:
  • Introduction to Data Visualization and Tableau
  • Understanding Tableau Building Blocks
  • Managing Data connections
  • Prepare Data for Use
  • Basic Data Visualization
  • Advanced Data Visualization
  • Slicing and Dicing Data
  • Tableau Basic Calculations
  • Tableau Advanced Calculations
  • Formatting
  • Dashboard Designing
  • Publishing and Sharing

Learning Objectives:

Understand linear algebra and calculus and their applications in Data Science


Topics: Play Video
  • Linear Algebra
  • Calculus

Learning Objectives:

Get a comprehensive understanding of Machine Learning and Linear Regression, one of the simplest algorithms for doing supervised learning. 

Topics:
  • Introduction to Machine Learning, Supervised Vs Unsupervised, Simple Linear Regression - Slope, Intercept and Their Interpretation
  • How to find the Beta Coefficients, OLS Method 
  • Multiple Linear Regression 
  • Model Evaluation and Model Performance Metrics for Regression
  • OLS Assumptions

Learning Objectives: 

Learn how to modify models to avoid the issue of overfitting in Linear Regression. Understand Lasso and Ridge Regression, and Elastic Net - a method of regularized regression.

Topics:
  • What is Regularization?
  • Loss Function in Regularization
  • LASSO REGRESSION
  • RIDGE REGRESSION
  • ELASTIC NET

Learning Objectives: 

Understand how to assign observations to a discrete set of classes using logistic regression along with the different types of classification techniques.

Topics:
  • Introduction to Classification
  • Different Types of Classification Techniques
  • Classification Model Evaluation Metrics 
  • Introduction to Logistic Regression
  • Revisiting Basics - Odds, Odds Ration, Log odds, Likelihood, Sigma and Logit
  • Math Behind Logistic Regression
  • Maximum Likelihood Estimates

Learning Objectives: 

Get a comprehensive understanding of Naïve Bayes and Support Vector Machine and how they are implemented in Python

Topics:
  • KNN Algorithm
  • Naive Bayes
  • Break
  • Support Vector Machines

Learning Objectives: 

Understand the difference between Bagging and Boosting in Machine Learning and learn how they are applied in decision tree methods.

Topics:
  • Decision Tree 
  • Introduction to Ensemble Learning - Bagging
  • Boosting

Learning Objectives: 

Understand how to reduce the dimensionality of input data using the statistical procedure, PCA. Learn how to assign objects to homogenous groups under Clustering.

Topics:
  • Clustering
  • Dimensionality Reduction - PCA
  • Time Series Forecasting
  • The Concepts of Time Series and it's components
  • Stationarity and dealing with Stationarity
  • Stationarity and Lag Identification
  • Basic Time Series Models
  • Performance Measures
  • Advanced Time Series Models
  • Multivariate Time Series Analysis

Learning Objectives:

Understand the various elements of the Hadoop Ecosystem and how they can be used to solve Big Data problems.

Topics:
  • Introduction to Big Data and Hadoop
  • Hadoop Distributed File System
  • Map Reduce Procedure
  • Data Ingestion 
  • Data Processing in Hadoop
  • NoSQL and HBase
  • Apache Oozie
  • Hadoop Cloud on Amazon/Elastic Map Reduce

Learning Objectives:

Get a thorough understanding of Spark SQL, a Spark module for structured data processing. Learn how Relational Data Processing works in Spark.

Topics:
  • Introduction to Spark
  • The Spark Runtime
  • ETL with Spark
  • SparkSQL and DataFrames
  • Introduction to Stream Processing with Spark

Learning Objectives:

Understand how to perform Real-time Stream Processing Using Apache Spark and Spark Streaming with Amazon Kinesis.

Topics: Play Video
  • Stateful Processing with Spark Streaming
  • Sliding Window Operations with Spark Streaming
  • Introduction to Structured Streaming
  • Introduction to Apache Kafka
  • Kafka Integration with Spark Streaming
  • Kafka Integration with Structured Streaming
  • Introduction to Amazon Kinesis
  • Using Spark Streaming with Kinesis
  • Additional Spark Streaming Integrations

Learning Objectives:

Understand how to deploy Machine Learning models to make inferences using Amazon SageMaker

Topics:
  • Model Deployment
  • AWS SageMaker
  • Model Training
  • SageMaker Real Time Inference
  • SageMaker Batch Transform
  • MLOps on SageMaker

Learning Objectives:

Strengthen your fundamentals of Natural Language Processing. Get hands-on experience with TexBlob and then move on to Spacy, a far more advanced library to implement NLP tasks.

Topics:
  • Introduction to NLP 
  • Essentials of NLP 
  • NLP Feature Extraction
  • NLP with Textblob
  • NLP with Spacy
  • Text Classification
  • Text Summarization
  • Topic Modeling

Learning Objectives:

Learn the applications of Natural Language Processing in performing sentiment analysis and creating chatbots. 

Topics:
  • Sentiment Analysis 
  • Chatbots

Learning Objectives:

Gain a solid understanding of Deep Learning, TensorFlow, and how Neural Networks work.

Topics: Play Video
  • Diving deep into Deep Learning
  • Getting Started with TensorFlow
  • Convolutional Neural Networks

Learning Objectives:

Learn about Generative Adversarial Networks and some of its applications. Understand the application of AI in the real world. 

Topics: Play Video
  • Advanced CNNs
  • Natural Language Processing
  • Generative Adversarial Networks (GANs)
  • AI in the Real World

Learning Objectives:

In this complimentary one-week module, you will master Data Structures and Algorithms and get well-poised to crack interviews for Data Science roles at Tier 1 companies.

Topics:
  • Basic Techniques of Algorithm Analysis 
  • Linked Lists and Binary Trees 
  • Advanced Data Structures 
  • Analyze the Asymptotic Performance of Algorithms. 
  • Algorithmic Design Paradigms and Methods of Analysis. 
  • Microservices Architecture and its Implications  

Frequently Asked Questions

Data Science Bootcamp

The Data Science Bootcamp Program is a specialized training course aimed at individuals aspiring to excel in Data Science. It uniquely combines live instructor-led sessions along with live interactive doubt resolving and master sessions for a holistic learning experience in addition to self-paced learning and plenty of hands-on practice and project-based learning. 

By the end of this 31-week immersive learning bootcamp program, you will be able to face real-world Data Science problems and come up with the most appropriate solution with the skills to explore, clean, analyze and predict data. 

In particular, you will build the skills to: 

  • Perform Data Analysis using a wide variety of tools such as Python, SQL, and Tableau
  • Extract data from databases and perform Data Analysis to get meaningful insights
  • Apply quantitative modeling and data analysis techniques to find solutions to business problems
  • Build the ability to effectively present results using data visualization techniques
  • Master statistical  data analysis  techniques  utilized  in  business  decision making
  • Apply Machine Learning Algorithms to help Businesses make predictions
  • Use data mining techniques to get insights from data to solve real-world problems
  • Employ cutting-edge tools and technologies to analyze Big Data
  • Use different Deep Learning frameworks to build real-world AI applications
  • Use  Natural Language  Processing  to  build  Chatbots  and  Sentiment  Analysis  along with many other applications to process text data

Along the way, you’ll put together a compelling professional-grade project portfolio that you can showcase to potential employers and collaborators. Complete the course and acquire job-ready tech skills to land a job as a Data Scientist.

Our program is ideal for those who are looking to build a strong foundation in Data Science, prefer a mix of self-paced and interactive learning, and value high-quality, expert-driven content. If you're seeking comprehensive training with a blend of theory and practical application, this program is a perfect choice.

The Data Science Bootcamp is designed to provide job-ready skills to learners from even non-tech backgrounds. After completing this course, you can become industry-ready and land Data Scientist roles in top organizations. At KnowledgeHut, we take several measures to ensure that you get a job by the end of the Bootcamp: 

Two critical goals of this Bootcamp: 

  • Providing you with comprehensive Data Science Knowledge including the skills to explore, clean, analyze and predict data 
  • Arming you with a complete understanding of Data Structures, Algorithms, and System Design, which is crucial for cracking job interviews

How we ensure that you achieve these critical goals: 

  • Instructor-led sessions with industry experts who will provide demos to ensure concept clarity
  • Detailed content around all the critical concepts and programming languages in the form of videos, hands-on exercises, assessments, reading material, and assignments
  • Sufficient time and effort towards practicing these concepts via Cloud Labs that allows you to code right from your browser
  • Weekly doubt-clearing sessions with experts that can help you close any gaps in understanding of Data Science related knowledge
  • Timely assessments and the ability to track progress with real-time reports that help you stay on track with the Program
  • Dedicated Student Success Managers monitor your progress and guide you toward achieving critical goals
  • You will build Predictive, Deep Learning, and NLP models that help you create a power-packed portfolio you can showcase to potential recruiters
  • Hackathons, coding challenges, and 1-on-1 mock interviews with Data Science experts that will help you ace your interviews
  • Master analyzing problems and writing program solutions to problems with data structures and algorithms

Demonstrable skills are best developed during work-like experiences and building real-world capstone projects through the bootcamp. By the end of the program, you will have job-ready skills and be ready to hit the ground running to take on a variety of job roles in the Data domain.

On completing our Data Science Bootcamp, you will be ready to take on a variety of job roles in the Data domain including:

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Data Architect
  • Data Storyteller
  • Machine Learning Scientist
  • Machine Learning Engineer
  • Business Intelligence Developer
  • Database Administrator
  • Business Analyst

This bootcamp for data science is completely beginner friendly. There are no prerequisites to attend this bootcamp, and all learners with the right aptitude, logical thinking, drive for curiosity, and propensity to learn new skills can ace the Program and emerge with job-ready skills. 

At KnowledgeHut, your tuition is a gateway to a comprehensive set of features within the Data Science Bootcamp, ensuring a holistic and enriching learning experience. Here's a breakdown of what you can expect: 

  • Instructor-led sessions by industry experts
  • Live doubt-clearing sessions on a regular basis
  • On-demand videos to help you learn anytime, anywhere 
  • Cloud Labs enabled practice right from your browser 
  • Comprehensive reading material for concept clarity 
  • Mock Interviews by top industry experts to crack job interviews 
  • Hackathons for practical coding practice 
  • Dedicated Placement Support
  • Access to the KnowledgeHut Community for lifetime support 
  • Connections with a professional network of instructors

Additionally, you will participate in live sessions throughout the bootcamp, including:

  1. Live Interactive Instructor-Led Sessions: 165+ hours
  2. Live Doubt-Resolution Sessions: 50 hours
  3. Live Masterclass Sessions by Industry Experts: 35 hours

These live sessions serve as invaluable opportunities for interactive learning. Our experienced instructors and mentors will not only share theoretical knowledge but also provide practical applications of concepts and skills within a real-world context.

Our instructors and mentors are highly qualified practitioners with decades of valuable industry experience. Instructors lead engaging sessions, and mentors are assigned to individuals, providing personalized one-on-one assistance with assignments, projects, and challenges.

Yes, you can switch the start date of your Bootcamp training with prior notice of at least 24 hours, subject to availability in the desired batch.

A data science bootcamp course can be one of the quickest ways to start your career. If you want to join a data science bootcamp, you definitely need to consider these. Firstly, you need to set your learning goals and then also prepare or understand the basics before starting the bootcamp. You should also consider if you will be able to keep up with a fast-paced curriculum. You should also balance your ambition with realistic goals and check the success rate of the course with the alumni’s data science bootcamp review. These factors will surely help you find the best data science bootcamp easily.

Yes, even without any technical background, a data science bootcamp can definitely help you get into the career field of data science. These programs can help you learn everything you need to start your career. This includes programming languages, data analysis tools, and techniques of machine learning. You will also have to see the reputation of the Bootcamp and the experience of the trainers in it. But the most important thing to do before you choose the best data science bootcamps online is to understand basic programming languages like Python or R.

When you join a data science part-time bootcamp, you can easily learn the skills and stay updated with the trends. You can achieve this by using different strategies. This is because of the constant updates of curriculum and collaborating with experts from the industry. The strong culture of lifelong learning at Bootcamp always provides you with proper resources and opportunities to network with the people in this industry. Bootcamps also help you stay updated by letting you work on hands-on projects. Very often they also make sure to keep developing the members of their faculty.

Usually, online data science bootcamps comparatively cost less than the ones held offline. This happens mostly because data science bootcamps online schools don't pay for things like renting a space, etc. Also, they can teach more students at once, so they can share the data science bootcamp cost among more people. The price can still change depending on how long the program is, what they teach, and the location. Some bootcamps also have facilities for scholarships that can help get into a data science free bootcamp if you have budget issues. So, these things generally make data science bootcamp online cheaper. Make sure you consider everything else when making a decision.

It could be a little difficult to choose between a Data Scientist bootcamp and a master's degree as it entirely depends upon your goals and needs. A Master's degree can be expensive and run on a fixed schedule, but you will be exposed to a wide network and get recognition based on the credential. On the other hand, a bootcamp is cheaper, faster, and more flexible, but it may not hold a strong value like a master's degree. A master's degree can be helpful to you if you are new to the field. If your requirement is to learn and gain experience fast, a bootcamp can be a good choice.

If you are wondering, is data science bootcamp worth it or not?  It depends entirely on your goals and situation. The general perks of enrolling in these courses are that they are cheap and quicker than traditional college programs, which makes them accessible. You will get valuable practical experience and career support. Bootcamps don't cover much of the traditional courses, but they also don't last as long. Make sure to research before you enroll in any course. In the end, it leans towards your needs and budget. Is data science bootcamp worth it or not, you can find out by sorting out your preferences and goals.

To become a data scientist, here's what you can do:

  • Get the Right Education: First, you need to study. Get a bachelor's degree in computer science, statistics, or something related. There are also data science bootcamp part-time or online courses and certifications to explore.
  • Start small with internships or entry-level jobs in data science. This helps you learn and grow your skills.
  • Build a portfolio to show what you can do. Share projects, code samples, or write blog posts about your work.
  • Attend meetups and events to meet fellow data scientists. Learning from their experiences can be super helpful.

In order to join the Data Science Bootcamp with Golden Gate University, you don't need to be a coding expert. All you need is a passion for working with data and solving problems. Data science bootcamp for beginners is designed for people who are new to coding, so not having experience will never affect your learning. But having a little prior knowledge of programming and mathematics sure can uplift you with ease. So, if you are looking for “data science bootcamp near me” or want to start your journey in this field, then this is the best bootcamp for data science to begin your career.

After finishing the course Data Science Bootcamp at KnowledgeHut, you will receive a data science bootcamp certification of completion issued by KnowledgeHut. This certificate holds great significance as it is validated by prominent tech industry employers who actively contribute to the best data scientist bootcamp curriculum and collaborate with KnowledgeHut for the best bootcamp data science for their workforce training needs. Additionally, choosing the blended learning option offers you the opportunity to obtain a certificate from Golden Gate University, California. This credential can greatly enhance your profile during the data science bootcamp job placement.

The KnowledgeHut's online data science bootcamp is 100% online with no in-person classes. This option provides you with the opportunity to learn from the comfort of your own home or office in your own time. The course materials and resources are available to you 24/7, which means you can study at your own pace. You can actively engage in discussion forums and various online tools with instructors and fellow learners through live virtual classrooms. If you are looking for a convenient way to master data science, the KnowledgeHut data science bootcamp is an excellent choice, offering a seamless learning experience in an online bootcamp for data science.

Data science bootcamps have become really popular lately because they offer a quick and affordable way to learn the skills needed for the best data science bootcamp with job guarantee. These bootcamps are flexible, so you can learn when it suits you the best while not forsaking your other duties. They upscale you by teaching programming, stats, and machine learning, plus you get to work on real projects to gain experience. While they are not the same as going to a regular college, an affordable data science bootcamp can be a smart choice if you want to dive into data science fast without piling up a ton of debt.

Data Scientists, Data Analysts, and Machine Learning Engineers all deal with data but in distinct ways. Data Scientists explore data broadly, applying maths, stats, and computer science to uncover insights and build new machine-learning tools. Data Analysts concentrate on collecting and analysing data for trends, aiding decision-making with reports. Machine Learning Engineers construct and implement machine learning models for real-world use. Career-wise, Data Scientists might go into academia, research, or business roles. Machine Learning Engineers often work in tech but can venture into different sectors and senior roles.

It usually takes about 31 weeks to complete data science bootcamp. During this time, you will dive deep into the world of data science by tackling tasks such as going through data and making predictions, along with a few other activities. You will be putting your theoretical knowledge to work with practical projects and coding challenges. You will also receive help in getting ready for job interviews and opportunities for better data science bootcamp with job placement. If you are eager to make data science your career option and don't want to spend years in college, this bootcamp could be a smart choice for you.

Yes, you can become a data scientist through a data science coding bootcamp. These programs teach essential data science skills like coding, machine learning, and problem-solving, along with other important soft skills. It is also crucial to understand that bootcamps are not a substitute for college degrees. These courses lack some theoretical knowledge, which could cause some trouble in landing your first job. But the advantage is that it provides quicker practical and theoretical knowledge which makes it a viable option for those looking to start a data science career, especially if you are willing to put in extra effort to bridge the knowledge gap.

Becoming a data scientist with just a coding bootcamp in data science can be tough. Most data science jobs require coding and tech skills. To become a Data Engineer, you need to have knowledge of SQL, Python, and some Cloud computing knowledge. Business Analysts focus mostly on business and less on coding. Data Scientists need some knowledge of coding. Bootcamps can give you a good start, but it's usually not enough if you want to excel in a Data Scientist role. You will likely need more theoretical and practical knowledge to land such a job. So, consider the specific job you want and plan your learning accordingly.

No, you don't need any specific background to sign up for our best online data science bootcamps. It is open to everyone, including beginners who have never had any experience in coding before. Our courses will teach you all the fundamentals, such as how to code, do machine learning, and understand the core data science concepts. Apart from this, you will learn new skills like solving problems, talking to people, and working in a team. You will also have some quality practical sessions to boost your resume so that after getting certification, you can land on a good opportunity.

Data science and data analytics are closely related fields, but they differ in their scope and objectives. Data science is a broader discipline covering sectors such as data analytics along with data engineering and machine learning. Data scientists use statistical and computational methods to extract deep information and devise novel algorithms. Sometimes, they also work in tackling complex challenges and predicting future trends. On the other hand, data analytics mostly focuses on examining existing data to help in current decision-making. Data analysts use different tools to gather, refine, and segregate data in order to craft reports and dashboards to enhance business decision-making processes.

We cover a range of programming languages and tools in our data science bootcamp curriculum. We have designed it in a way that can provide you with a solid foundation in data science. Two of the main languages that we have in the data science bootcamp syllabus are Python and SQL. We have also added a variety of tools, such as MS Excel, Tableau, Hadoop, Spark, and TensorFlow etc. We also use Python, which is a user-friendly programming language, and SQL for database querying. So, if you are looking for data science bootcamps, you have come to the right place!

You can get a range of job opportunities in the field of data science. Some of the most known roles include Data Analysts, Data Engineers, and Machine Learning Engineers. If you are a Data Analyst, it will be your duty to gather, clean, and analyse data to help in decision-making. Data Engineers mostly construct and maintain data systems for Data Scientists. ML Engineers make machine-learning models that can help with problem-solving. Apart from that, you can also choose to become a Data Administrator, Data Architect, Statistician, Business Analyst, or Data and Analytics Manager.

Yes, learning Python with Data Science can definitely increase your chance of getting a higher salary. Python has a lot of importance in the world of Data Science. This makes it very easy for you to land a job with a higher salary if you have learned Python. According to PayScale, the average salary of Data Scientists is around INR 7.9 LPA. This also increases your data science bootcamp job guarantee. But someone with Python can earn an average salary of around INR 8.8 LPA. If you become an expert in Python, you can easily become an attractive candidate in the job market for Data Science.

Workshop Experience

Every week of the bootcamp is power-packed with learning. Here’s a glimpse at what you’ll be doing on a weekly basis in the Bootcamp: 

  • Learn through comprehensive videos 
  • Practice code using Cloud labs 
  • Strengthen concepts with quizzes 
  • Participate in doubt-solving sessions
  • Complete the weekly assignment and assessment 
  • Get learning support from instructors 
  • Experience skill growth with real-time reports 
  • Dedicated Learning Advisors for every cohort 

In addition to this, you will be building predictive, deep learning, and NLP models that help you create a power-packed portfolio you can showcase to potential recruiters. You will also be participating in Hackathons, Mock Interviews, coding challenges, interview prep sessions, and placement drives throughout your Bootcamp.

Live interactions are a crucial part of our program. They occur in three key formats: 

  • Live Instructor-Led Sessions: Industry experts conduct live interactive training and learners have the opportunity to ask questions and interact with peers in group activities
  • Doubt Resolving Sessions: Where instructors address queries in real-time, ensuring personalized attention. 
  • Master Sessions: Live sessions focusing on practical application, project building, and interview preparation, leveraging the knowledge gained in the instructor-led training sessions. 

Yes, interaction with instructors is a key component. In doubt resolving and master sessions, you'll engage directly with instructors and peers, fostering a collaborative and interactive learning environment.

Master Sessions are designed to deepen your understanding through practical application. These live sessions allow you to apply theoretical knowledge to real-world scenarios, enhance your problem-solving skills, and prepare for industry-specific challenges. 

Nothing to worry about! Every class is recorded and available on PRISM. In addition to this, you will have lifetime access to all the session recordings. However, you are required to catch up with these classes to achieve your milestones in a timely manner.

We understand that scheduling conflicts can occur. To accommodate this, live sessions are recorded and made available for later review, ensuring that you don't miss out on any crucial learning opportunities. 

The Pre-Bootcamp is already a part of the Bootcamp. We start from scratch and cover the course holistically to ensure complete clarity in concepts and skill enforcement. The first few weeks will prepare you for everything that comes later in the Bootcamp.

You have the option to pause the program for 14 days. Before rejoining, you would need to catch up with the Program by watching the recorded instructor-led sessions. You may opt for this option after discussing it with your Student Success Manager. 

You also have the option to defer a program, provided there is a valid reason offered to your Student Success Manager and is approved by the Program Director. Once you are back, you can discuss with your Student Success Manager to know which batch of the Bootcamp you can join. 

Please contact your Learning Advisor for more information about this.

You will be building multiple real-world predictive, deep learning, and NLP models across each milestone of your bootcamp. 

By the end of the Bootcamp, you will have compiled a complete portfolio of projects designed to reinforce all the learnings attained throughout your course. You will gain hands-on experience exploring, cleaning, analyzing and predicting data.

Yes! Upon completing the course and meeting all the requirements, you will receive a certificate of completion issued by KnowledgeHut. Thousands of KnowledgeHut alumni use their course certificates to demonstrate skills to potential employers and across their LinkedIn networks. 

KnowledgeHut’s tech programs are well-regarded by many top employers, who contribute to our curriculum and partner with us to train their teams.

In addition to the structured sessions, you'll have access to a range of support services including online resources, forums, and a dedicated support team. This ensures a continuous learning process and assistance whenever needed. 

Additional FAQs

Additional FAQs on Data Science Bootcamp

Data Science is a fusion of machine learning principles, algorithms, and various tools for identifying, representing, and extracting useful and meaningful information from a pool of data.

Want to learn more? Take a look our easy to easy article on - What is Data Science? Process, Importance, and Examples

With the explosion of information and digital boom, we generate massive terabytes of data daily. Almost every industry, from healthcare and agriculture to automobile industries, invests in Data Scientists for crucial insights, making Data Science one of the highest paying jobs. It is predicted that there will be roughly 11.5 million new jobs in the field by 2026, and the Big Data market size will be an estimated USD 96 billion by then. So, Data Science is a good career option for you. 

Here's a guide to give you visibility on a career in Data Science.

According to a recent study revealed by Indeed, demand for data scientists continues to grow, as the average salary for a Data Scientist is around $100,000. The value of this specialized field is evident in its huge demand and high pay.

To learn more, check out this information rich write up on Data Scientist Salary for 2023 [Freshers & Experienced]

Once you complete the Data Science Bootcamp Certification offered by KnowledgeHut, you can apply for various jobs within the Data Science domain. We've explained the primary ones below: 

  • Business Intelligence Analyst: One of the most important applications of data science is used by a Business Intelligence analyst. A business intelligence analyst's job is to analyze the data to create a clear picture of the direction the business needs to go in and tap in on both business and market trends.
  • Data Mining Engineer: As the name suggests, mining engineers mine the relevant data for an organization. The main job of a data mining engineer is to examine the data for the needs of the business. Other than this, a data mining engineer also needs to keep creating/improving algorithms that would further help improve the data analysis.
  • Data Architect: A data architect has to work together with developers, system designers, and users as well to create blueprints that are used by data management systems for the integration, protection, centralization as well as maintenance of the data sources.
  • Data Scientist: A data scientist's main job is to further a business's interests by analyzing the data given to them. They should drive a business case by researching, developing a hypothesis, and understanding data. This would help explore relationships between the different data points in the data set.
  • Senior Data Scientist: This is a role for someone who is experienced in this field. The responsibility of a senior data scientist is to predict and anticipate what the business needs could be in the future and accordingly fine-tune projects and analysis.

Interested to learn more? Take a look at our write up on - Top 16 Data Science Job Roles To Pursue in 2023

Here's a list of top skills that you must have to be a successful Data Scientist:

1. Python Coding: Python is the language of choice for most when it comes to data science. There are many reasons for its popularity among the data scientists, some of which are - its versatile nature which allows Python to be used for many kinds of applications; simplicity is also a major factor, Python language is easy to read and write; most important of all is the thriving open source community that Python has worldwide which keeps adding to the features of this programming language.

2. R Programming: R programming is preferred by many in the data science field due to the number of tools it offers while programming. Being proficient in at least one of the many analytical tools it provides is essential if data science is your career choice.

3. Hadoop Platform: Although not mandatory, this is an essential skill for a career in data science. According to a study by CrowdFlower on 3490 LinkedIn data science jobs, Hadoop is the second most important skill to become a data scientist.

4. SQL Database and Coding: Learning SQL database is an essential task for any data scientist enthusiast. MySQL offers quick commands that save time while performing operations on the database while decreasing the level of technical expertise required to manage it.

5. Machine Learning and Artificial Intelligence: Machine learning is becoming the next hot prospect in the tech industry, and its applications are endless. It is a field of data science as all Machine learning algorithms are applied to data. If you want to become a successful data scientist, then proficiency in these skills is necessary. A data science enthusiast should have good command over the following:

  • Reinforcement Learning
  • Neural Network
  • Adversarial learning
  • Decision trees
  • Machine Learning algorithms
  • Logistic regression etc.

6. Apache Spark: Apache Spark is a big data computation tool and one of the most used data sharing technologies around the globe. Data scientists prefer Spark over Hadoop due to its speed. Apache Spark is faster because it makes caches of the computations inside system memory, while Hadoop uses the disk for reading/write operations. Easy to use and high-speed computations are what make Apache Spark stand apart. The tool is used to make the algorithms run faster. It significantly helps in the division of large chunks' data processing and in the case of complex and unstructured data sets. Apache Spark prevents any loss of data.

7. Data Visualization: A data scientist is just given a large chunk of data and tasked with analyzing it. To make relations between different data points, a data scientist must have skills in using visualization tools such as d3.js, Tableau, ggplot, and matplotlib. When data scientists create results from the data, these tools help put them in a visual format for everyone to understand better. One of the most important aspects of data visualization is that it significantly helps the organization in a way that brings them closer to the customer's experience and needs by working directly with the data. Data scientists can gain insights from a particular data and use that result to act on a new outcome.

8. Unstructured Data: Data given to data scientists is mainly unstructured, so a data scientist must also be aware of the necessary skills required to manipulate unstructured data. Unstructured data generally means content without any labels and unorganized into database values. For example, videos, social media posts, audio samples, customer reviews, blog posts, etc.

If you like to learn about more relevant skills to master in 2023, take a quick read through our article - Top 30 Data Scientist Skills to Master in 2023.

As quoted in Harvard Business Review, being part of the 'Sexiest Job of the 21st century' has its benefits. These are the top 5 proven benefits of being a data scientist:

  • High Pay: We expect high pay for any job, let alone the data scientist job. And highly qualified professionals such as data scientists naturally get higher pay. Also, due to the high demand in industry and low supply of well-trained data scientists, these jobs are among the highest-paying jobs in the tech world today.
  • Bonuses: Organizations do whatever they can to attract the best data scientists and retain those already performing well. So good rewards are usual if you are a good performer. These bonuses can also be in the form of perks such as signing, equity shares, etc.
  • Education: The qualification bar to become a data scientist is high, so naturally, anyone who is a data scientist would be a scholar. When you search for data scientist jobs, you will probably have a Master's or Ph.D. degree with you. Due to an extensive educational background, sometimes you might also be offered a job as a lecturer or researcher in the field for both governmental and private institutions.
  • Mobility: Data science is used in every field, meaning job opportunities are present around the globe where data is being collected - generally in developed countries. This means that wherever you might be traveling for your data scientist job, you would be getting a hefty salary to go along with an excellent standard of living.
  • Network: Naturally, after investing so much time into education, you would have an educative and useful network of data scientists. Your involvement in international journals generally expands this network through research papers, technical talks at data science conferences, and many more. These networks help in getting better jobs as well through referrals.

Here's an article to help you become a successful Data Scientist - A Successful Data Scientist Career Path – A Full Guide

The Data Science Online Bootcamp is a comprehensive immersive learning program designed to help you land top Data Science roles including Data Scientist, Data Analyst, Data Engineer, Data Architect, Data Storyteller and Machine Learning Engineer among others.

You will have the opportunity to learn hands-on with Cloud Labs, build projects and create a brilliant project portfolio. What distinguishes our program from others is the focus on acing the interview and landing the job. You will receive comprehensive mentor support through the program. In short, it’s everything you’ll need to land a top Data Science role.

Here’s a snapshot of all you need to know about how this bootcamp helps you in your career: 

Duration:31-Week Immersive Learning
Training Options:Blended and Self-Paced
Job Opportunities:6.5 Lakhs+
Salary Range:US: $82,786 to $211,298
India: ₹12 Lakhs to ₹26 Lakhs
Job Roles:Data Scientist, Data Analyst, Data Engineer, Data Architect, Data Storyteller, Machine Learning Engineer

If you want to build a career in Data Science and kick it off as a Data Scientist/Analyst in your dream company, opting for a top Data Science Bootcamp is a good idea. KnowledgeHut’s Data Science Bootcamp is a great fit for aspiring data analysts/scientists, because of multiple reasons: 124 hours of immersive, out-come based practical training from expert instructors, 280 hours of self-paced learning, industry-validated curriculum, 6 capstone projects and 100+ guided, hands-on exercises, and 400 hours of hands-on with Cloud Labs. Therefore, by the end of this course, you will build a portfolio that will catch top recruiters attention.

Anyone who wants to transition to a rewarding career in data science or accelerate their existing data science career can consider enrolling for a Data Science Bootcamp online. However, thanks to KnowledgeHut, anyone can gain strong data science skills on the go. Typical candidate profiles best suited for our Data Science Bootcamp are: 

  • Statisticians 
  • Database Administrators
  • Business Analysts
  • Python Developers   
  • Applications Architects 
  • Data Analysts  
  • Data and Analytics Professionals
  • Product Managers  
  • Graduates from any discipline 
  • Professionals looking for a career change  

One of the reasons this course is one of the best Bootcamps for Data Science is that there’s no need for any prior experience or preparation. There isn’t a qualifying exam or assessment either. All you need is a strong desire to learn, a curiosity for coding, statistics, and Data Science, and motivation to give your best, and you can just enroll with us.  

Our course curriculum, which is delivered by top-notch instructors, reflects the latest trends in data science. It is another reason why our learners call it the best Data Science Bootcamp they’ve attended. Even without a tech background, you will get a well-rounded foundation in data science concepts. Here is our Data Science Bootcamp syllabus in brief: 

  • Programming  
  • Python for Development 
  • Build ML & AI Algorithms 
  • Probability Theory  
  • CRUD Operations 
  • Master NLP 
  • Model Deployment 
  • Data Science Lifecycles 
  • Numpy & Pandas 
  • Relational Databases 

Nowadays, an increasing number of aspiring data scientists and analysts are looking to enroll for the best Data Science Bootcamps instead of a computer science degree. One of the advantages the former has over the latter is its hands-on, immersive nature. For example, KnowledgeHut’s Data Science Bootcamp not only gives you a strong foundation in data science concepts, but also makes you practice with 400 hours of Cloud Labs. At the end of the course, you’ll have worked on 6 capstone projects and also possess a job-ready portfolio.  

Some Data Science Bootcamps are very tough to get into, while others are relatively easier to qualify and enroll for. It all depends on the brand name of the institute, their placement record, among many other factors. However, even with the distinction of having trained 350,000+ candidates across 100+ countries, KnowledgeHut doesn’t have any prerequisites for its Data Science Bootcamp online. Anybody looking to become a skilled data scientist/analyst can apply. 

Most Data Science Bootcamps cost a little under $1,000 on average. How much you eventually pay for an online bootcamp for data science depends on several factors, including the mode of training and the number of hours per week. KnowledgeHut’s Data Science Bootcamp cost is  total value for money. We also offer a flex-track and fast-track training option, keeping the convenience of our candidates in mind. 

We offer a very affordable Data Science Bootcamp with the option of paying the fee in EMIs. For more details about the training cost, click here.

Most Data Science Bootcamps have assessments/interviews to test your existing knowledge of programming, mathematics, and statistics. Depending on the bootcamp in question, the technical interview/skills assessment can be tough. This is another reason why our program is considered the best online Data Science Bootcamp by our learners. There are no prerequisites to attend our course. It is open to candidates without a tech background as well. 

On successful completion of KnowledgeHut’s online bootcamp for data science, the next step is to add it to your resume so that you can land your dream job. You can list the details of the Bootcamp under the “Education” section of your resume, while the details of the projects you’d have done (as a part of the training) can go under the “Projects” section.  

Here's an article on Impressive Data Scientist Resume for 2023 [Tips and Example] to help you further. 

According to a recent study revealed by Indeed, demand for data scientists continues to grow, as the average salary for a Data Scientist is around $100,000. The value of this specialized field is evident in its huge demand and high pay.

To learn more, check out this information rich write up on Data Scientist Salary for 2023 [Freshers & Experienced]

We offer this Data Science Bootcamp certification in various formats, keeping in mind the convenience and busy schedules of our candidates. You can choose from a Self-Paced Learning mode or a Blended Learning mode where you get instructor-led live sessions. You can get more details here.

A Data Scientist collects raw data. Most of the time, the useful data is mixed up with unrequired and unusable or damaged data. The Data Scientist must clean it up, process it, and analyze it to gain valuable insights from the data. Data scientists drive positive outcomes for businesses. Typically, the roles and responsibilities of a data scientist can be summed up as:

  • They should be able to pick features and create and optimize classifiers using machine learning techniques.
  • Data mining.
  • Analyze third-party data sources information and choose useful ones to enlarge the company’s data.
  • Increasing data collection methods to incorporate more appropriate information for the analytic system.

To learn what Data Scientists do in full detail, check out our write up on - What Does a Data Scientist Do in 2023

What Learners are Saying

M
Maria Perez Growth Hacker
4
The curriculum for the Data Science Bootcamp is tough, even difficult, but it shows how thorough and industry-relevant it is. This program is value for money for anyone who wants to enter the Data domain.

Attended Data Science Bootcamp with AI workshop in December

E
Ernest Muller Python Developer
5
This Data Science Bootcamp is a good investment for your career if you want to enter the Data Science field and make your career there. The concepts taught are what you need to know as a Data Scientist in 2022.

Attended Data Science Bootcamp with AI workshop in December

G
Gareth Kingston Product Manager
5
This bootcamp took me through all the important concepts from basic to advanced. The course instructors are terrific because they supplement what they teach with real-life snippets. This is a proper learning experience.

Attended Data Science Bootcamp with AI workshop in December

L
Lucas Evans Data Analyst
5
I encourage all data aspirants to go for this bootcamp. If you want to get started on a career in Data Science, this is the place to go. The instructors have a wealth of experience and answer all your doubts perfectly.

Attended Data Science Bootcamp with AI workshop in December

S
Sumit Chadda Software Engineer
5
What I enjoyed about this Data Science Bootcamp is the kind of projects and assignments we learnt from. They were all real-life and I was able to put into practice whatever was taught easily.

Attended Data Science Bootcamp with AI workshop in December