Project-Based Data Science Bootcamp

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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Become a Sought-After Data Scientist

Are you looking to build solid tech skills and land a job as a skilled Data Scientist at one of the world's best tech companies? Do you want to kickstart a career as a freelance Data Scientist helping companies harness the predictive power of data? Are you looking to launch a data service or product as an entrepreneur? Look no further than the KnowledgeHut Data Science Bootcamp.

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Highlights

  • 175 Hours Live Instructor-Led Sessions

  • 220+ Hours On-Demand Self-Paced Learning

  • 400+ Hours Hands-On with Cloud Labs

  • Job-Ready Portfolio of 10 Capstone Projects

  • 32 Industry Case Studies

  • 230 Guided Hands-On Exercises
  • 16 Assignments, 12 Projects

  • Mock Interviews, Hackathons

  • Mentoring by Industry Experts

  • Guest Lectures by Industry Experts

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

data-science-bootcamp-training

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.

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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, InfoGoal LLC

Denis Rothman

AI Author, Speaker, and Instructor

Jeffrey Aven

Principal Consultant Gamma Data

Gopikrishnan R

Co-Founder and CTO, CrosInfo Software Pvt Ltd

Beau - Carnes

Director of Technology Education, freeCodeCamp

Jignesh Kariya

Sr. Database Consultant, HM Revenue & Customs

Peter Henstock

Machine Learning & AI Lead, Pfizer

Ashish Gulati

Python & Data Science Consultant, Tower Research Capita

Shobhit Nigam

Program Director - Data Science & Engineering

Phillip Kinn

Senior Data Scientist, E. & J. Gallo Winery

Emmanuel Segui

Asst. Director, Reporting and Programming, Univ of Alabama

Rahul Tiwari

Data Scientist and Co-Founder, TheScholar

Mark Strefford

Founder and CTO, Timelaps AI Limited

George Mount

Founder, Stringfest Analytics

Dr. Vishwakarma J.S.

Chief Technology Officer, Aeries

Jeremiah Lobo

Data Visualization Lead, Bank of America

Enes Bilgin

Staff Machine Learning Engineer, Argo AI

Harish Masand

Project Manager - Digital Enablement (Data and AI), Hewlett Packard Enterprise

Mo Medwani

Adjunct Faculty - Data Science & Analytics, Mercer University

Marie Stephen Leo

Director of Data Science APAC, Edelman Data & Intelligence

Anatoly Zelenin

Freelance Trainer - Apache Kafka

Bradford Tuckfield

Data Science Instructor and Consultant

Malvik Vaghadia

Principal Consultant - Data and Analytics, NTT DATA

Rashmi Banthi

Data Scientist, AArete

Avery Smith

Data Scientist, Data Career Jumpstart

Prince Kumar

Data Scientist, UnitedHealth Group

Sudhanshu Saxena

Sr. Data Scientist and Data Science Trainer, Accenture

Azib Hasan

Freelance Trainer, Dell

Prerequisites

Prerequisites

  • No prior programming experience or math expertise? No problem! The right aptitude, logical thinking, and drive for curiosity are all you need—leave the rest to us.
  • Basic knowledge of programming and high-school level math (functions, derivatives, systems of linear equations) is beneficial, though not mandatory.

Your Path to Becoming a Skilled Data Scientist

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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 build:

  • Recommender Shopping

    Build intelligence on variables of user behaviour based on transactions and selections to identify and recommend products.

  • Tripredictor Travel and Local

    Build navigational intelligence to predict when a trip would end, and the next trip to be assigned to a particular cab.

  • tripredictor
    For-Rest from Fires Tools

    Build intelligence on the possibility of forest fires in advance to take precautionary measures and avert its occurrence.

  • E-Motion Personal Assistance

    Build intelligence on emotions used in speech to frame more appropriate responses based on the situation at hand.

  • Temp-Rate-Change Weather

    Build intelligence on climate change by generating graphical images with the use of monthly temperature to predict changes.

  • AlgoTrade Finance

    Build intelligence to execute trading orders based on variables like price, time, and volume via automated instructions.

  • RiskMate Business

    Identify and manage the risk linked to a specific business process by using operational metrics and threat intelligence.

  • bizHealth
    BizHealth Business

    Build intelligence on an organization’s health to indicate if it will be profitable or moving towards bankruptcy.

  • scrypto
    Scrypto Finance

    Build predictive intelligence around the movement of volatile crypto currencies to bring stability to their investments,

  • commuticator
    Commuticator Travel and Local

    Build navigational intelligence to predict delays in public transportation that can be communicated to fellow travellers.

  • support
    SupPORT Sports and Betting

    Build intelligence on what players a team would require, helping improve its chances of winning while being cost-effective.

  • D-Tect
    D-Tect Safety

    Build intelligence on crime zones via visual analytics and machine learning algorithms to predict and prevent more crime.

  • Lern
    Lern Education

    Build intelligence on the stickiness and effectiveness of e-learning content in engaging students to learn.

  • Best-In-Class
    BestInClass Education

    Build intelligence on the effectiveness and success rate of classroom training, the teacher, and content being taught.

  • The Next Move
    The Next Move Chess/Board

    Build intelligence around chess movements to predict future moves of players via deep learning and reinforcement models.

  • Detroll
    Detroll Social Media

    Build intelligence in identifying trolls via comments so as to deactivate such accounts using machine learning models.

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
Skill Up Your Teams

Harness the Predictive Power of Data for Your Business

Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.

  • Curated Technical Curriculum 
  • Real-World Product Building Experience with Mentor Guidance
  • Immersive Learning with Cloud Labs
  • Customized training solutions tailored to business needs

500+ Clients

Data Science Bootcamp Curriculum

Download Curriculum

Learning Objectives:

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

Topics:
  • Formatting Concepts 
  • Formulas in Excel 
  • Statistical Analysis 
  • Introduction to Other Features 

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:
  • Basics of Programming 
  • Programming Concepts 
  • Data Storage and Files 
  • Operating Systems 
  • World of Web
  • Programming Languages

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:
  • 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

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:
  • 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:
  • 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
11.

Learning Objectives:

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


Topics:
  • 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:
  • 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

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 (This Module is from Big Data Processing with Hadoop)
  • 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:
  • 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:
  • 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:
  • 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

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.

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 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. 

Your instructors and mentors at the Data Science Bootcamp course are qualified practitioners with decades of relevant industry experience. The instructors will take you through the live sessions. In contrast, mentors will be assigned to a specific person and help the participant one-on-one on various assignments, projects, and challenges. They will also help you to overcome any learning setbacks that you might face.

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.

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
  • Attend live instructor-led 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.

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.

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.

Additional FAQs

Additional FAQs on Data Science Bootcamp

A Data Science Bootcamp is a generic term used to describe any training or workshop that offers to prepare graduates for entry-level or junior roles in data science. This course offered by KnowledgeHut is one of the best Data Science Bootcamps because it offers practical training, offered by expert instructors who teach you the latest curriculum in line with industry standards. 

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 thiscourse, 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.  

The demand for skilled data scientists and engineers isn’t going away anytime soon. The market is still growing, and companies are always on the lookout for machine learning engineers and business intelligence analysts (among other roles). The average salary for Data Science Analysts is $80,265, while the average salary for Senior Data Scientists and Data Engineers is $105,909.  

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.

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. 

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.

Once you complete the Data Science Bootcamp 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.

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.

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.

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.

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 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 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 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 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 workshop in December