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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Makeover Your Career with Data Science

Build analytical skills and programming knowledge as you become a confident data scientist with expert guidance in the KnowledgeHut Data Science Bootcamp. Get the blended learning advantage with the right mix of live, instructor-led sessions and self-paced modules and work on real projects that get you opportunities with the world’s top companies.

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Highlights

  • 124 Hours of Live Instructor-Led Sessions

  • 400 Hours of Hands-On with Cloud Labs

  • 280 Hours of On-Demand Self-Paced Learning

  • Auto-Graded Assessments and Recall Quizzes

  • Capstone Projects and Assignments

  • Lifetime Access to Courseware

Data Scientists are In Demand

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, help businesses leverage those insights to drive profitability, and most importantly, use such insights to solve the everyday problems and make the world a better place.

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Engineer a great future for yourself

Sign up for the DS Bootcamp!

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.
Prerequisites

Prerequisites

There are no prerequisites for attending this bootcamp.

For details on system requirements, please refer to the FAQs

Who Should Sign-up for the Data Science Bootcamp

Statisticians

Beginner Data Scientists

Beginner ML Engineers

Python Developers

Applications Architects

Data Analysts

AI Engineers

Product Managers

Graduates from any discipline

Professionals looking for a career change

The KnowledgeHut Edge

Build a Professional-Grade Portfolio

Leverage newly acquired front-end skills and showcase your job-ready portfolio to potential employers

Made With and For Top Employers

Industry-validated curriculum developed with guidance from our Data Science Advisory Board

Same Rigor, Different Pace

Full-time learning not a fit for you? Get the same tried-and-trusted full-time experience on a flexible schedule

Go Beyond Technology

Learn critical techno-managerial processes and ways to efficiently churn out professional projects

Get Personalized Career Guidance

Make the best of 1-on-1 mentorship and build confidence with mock interviews, resumé guidance, career coaching

Continual Learning Support

Webinars, e-books, tutorials, articles, interview questions - Get all the resources to help fuel a lifetime of learning

What You Will Learn in the Data Science Bootcamp

Programming

Go from zero or minimal coding experience to building end to end Data Science solutions

Python for Development

Become an efficient Python developer and build convolution neural networks

Build ML & AI Algorithms

Learn to train and use algorithms to solve complex real-world problems

Probability Theory

Understand the basics of Statistics and Probability Theory for Data Science

CRUD Operations

Learn to use MongoDB to perform CRUD operations

Master NLP

Confidently use the NLP pipeline (Stop-words, Whitespace, punctation & number removal, Stemming, Lemmatization)

Model Deployment

Deploy models on cloud (AWS, Azure ang Google Cloud)

Data Science Lifecycles

Understand the structure of Neural networks and lifecycle of applied Data Sciences

Numpy & Pandas

Generate statistical inferences using pandas & Numpy, use Numpy for numerical & mathematical computations

10 Relational Databases

Understand how a Relational Database stores data, the main objects used, and using a DBMS such as MySQL workbench

Your Path to Becoming a Skilled Data Scientist

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Transform Your Workforce

Scale up your technology talent

KnowledgeHut Bootcamps are comprehensive technical learning programs designed to generate maximum outcomes for talent to get productive in a product development environment.

  • Curated technical curriculum for entry-level developers
  • Work-like product building experience with mentor guidance
  • Immersive learning with Cloud Labs
  • Customized Training Solutions tailored to business needs

500+ Clients

Data Science Bootcamp Curriculum

Learning Objectives

Master the fundamentals to go from zero or minimal coding experience to building end to end Data science solutions.

Topics

1. Basics of programming

  • Introduction to Basics of Programming
  • What are Computers?
  • What is Computer Programming?
  • What is Data?
  • What is Code?
  • Structure of a Program

2. Programming Concepts

  • What is a Variable?
  • What are Data Types?
  • Basic Data Structure: The Array
  • What are Algorithms?
  • Conditionals: if-then Statements • for and while Loop
  • What are Flowcharts? 
  • Functions

3. Data Storage and Files

  • Introduction to Data Storage 
  • Data Storage 
  • Text Files 
  • Excel Files 
  • Other File Formats 
  • Databases

4. Operating Systems

  • Introduction to Operating Systems 
  • What is an Operating System? 
  • Windows, macOS, and Linux 
  • Mobile Operating System 
  • Command Line Interface

5. World of Web

  • Introduction to World of Web
  • World Wide Web
  • Websites and Webpages 
  • The Cloud

6. Programming Languages

  • Introduction to Programming Languages 
  • Types of Programming Languages
  • Various Languages

Learning Objectives

Gain an understanding of MS Excel and how to build formulae in order to comprehend data.

Topics
 1. MS Excel Basics
  • What are Rows, Cells, and Columns? 
  • Different Types of Data Types 
  • Use of Name Box and Range 
  • Use of Cell Reference and Cell Edits
    • Use of Function Box 
      • Understand the Menu and Toolbars 
        • Moving the Cells to Different Position
        2. Formatting Concepts
        • Understand the Formatting Techniques
        • Data Type Formatting
        • Formatting Techniques on Rows and Column
        • Conditional Formatting

        3. Formulas in Excel

        • Text Functions
        • Date Functions
        • Basic Excel Formulas

         4. Introduction to Other Features 

        • Dependent Drop-down Lists
        • Filters in Excel
        • Pivot Tables

        Learning Objectives

        Understand what relational databases are and how to write simple queries to extract data from them.

        Topics

        1.Introduction to Relational Databases

        • Course introduction
        • What is a Relational Database
        • The Relational Data Model
        • Tables, Data Types and Constraints
        • What is SQL
        • Schemas
        • Database Objects
        • Introduction to MySQL Workbench

        2.Introduction to SQL Operations, Clauses and Functions

        • Overview of Employees Data Model
        • SQL Commands
        • SELECT Statement
        • SQL Operations and Clauses
        • Filtering Records Using the WHERE clause
        • Introduction to SQL Functions

        Learning Objectives

        Understand the differences between relational and NoSQL databases and perform CRUD operations using MongodB

        Topics

        1.NoSQL and Document Databases

        • Introduction and welcome to the course
        • What is a NoSQL database
        • Types of NoSQL databases
        • What is a Document Database
        • Advantages of Document Databases

        2.MongoDB basics

        • MongoDB architecture
        • MongoDB terminology
        • Modelling in MongoDB
        • Distributed Database
        • Databases, Collections and Documents in MongoDB
        • Data Types

        3.MongoDB Atlas set up

        • Atlas account set up + data install
        • Atlas overview
        • Using command line or terminal with Atlas
        • Importing data
        • Exporting data

        4.Introduction to CRUD operations in MongoDB

        • Creating a database
        • Creating a collection
        • Inserting a document
        • Updating a document
        • The _id field in MongoDB
        • Using the findOne() method to return a document
        • Using the find() method to query documents
        • Using the $gt and $lt comparison operators
        • Working with arrays
        • Using the find() method to project fields

        5.MongoDB drivers + Python demo

        • MongoDB drivers and ecosystem
        • Installing python
        • Basic CRUD operations with PyMongo

        Learning Objectives

        Understand the basics of statistics, the probability theory, and learn to use linear algebra and calculus to create python data structures.

        Topics

        1.Probability

        • Introduction
        • What is Probability Theory?
        • What is Probability?
        • What are Random Variables?
        • What is Conditional Probability?
        • What is Expectation and Variance?
        • What are Distributions?
        • What is the Law of Large Numbers?
        • What is the Central Limit Theorem?

        2.Statistics

        • Descriptive Statistics
        • Measures of Central Tendency Mean, Median, Mode
        • Measures of Dispersion
        • Standard Deviation
        • Variance
        • Range
        • Quartiles
        • IQR
        • Shapes of the Data Skewness and Kurtosis Correlation
        • Inferential Statistics
        • Hypothesis Testing and Statistical Significance
        • p-value and its Limitations
        • Confidence Intervals
        • T-tests and One-way ANOVA
        • Regression

        3.Linear Algebra

        • Introduction
        • Vectors and Vector Spaces
        • Operations and Manipulations on Vectors
        • Matrices
        • Matrix Algebra
        • Linear Transformations
        • Eigenvalues and Eigenvectors
        • Principal Components Analysis (PCA)

        4.Calculus

        • Introduction to Calculus
        • Linear Functions, Slope and Intercept
        • What are Limits?
        • What are Derivatives?
        • What are Integrals?
        • What is Optimization?

        Learning Objectives

        Develop an understanding of how data works with code and use powerful tools like Pandas and NumPy, and techniques to visualize data. End this module with an interesting capstone project.

        Topics

        1.Introduction to Python

        • Python for Data Science
        • Installation and Setup
        • Hello World

        2.Code & Data

        • What is Code and Data?
        • Creating Data
        • Using Data with Code
        • Syntax and Naming Conventions

        3.Building Blocks

        • Data Types
        • Arithmetic Operators
        • Lists Basics
        • Important Functions and Features

        4.Strings

        • Creating and Indexing
        • Negative Indexing, Slicing and Stepping
        • Immutability Concept
        • Important String Functions

        5.Data Structures

        • Data Structures
        • Lists
        • List Functions - Part 1
        • List Functions - Part 2
        • Tuples
        • Sets
        • Dictionaries

        6.Flow Control

        • Conditional Statements
        • if elif else
        • other operators
        • While statement
        • for with sequences
        • for with range

        7.Functions

        • User Defined Functions
        • Passing Parameters
        • Return Values

        8.Modules

        • Introduction to Modules
        • Installing Modules
        • Fetching Modules
        • Important Modules and Packages

        9.Files

        • What are Files?
        • Opening and Reading Text Files
        • Different Modes of Opening Files
        • Reading & Writing into Text Files

        10.NumPy

        • What is NumPy?
        • Creating & using NumPy Arrays
        • NumPy Array Attributes
        • Array Indexing and Slicing
        • Array Operations - Part 1
        • Array Operations - Part 2
        • Different Ways of Creating Arrays
        • Random Number Generation

        11.Pandas

        • Introduction to Pandas
        • Reading various file formats
        • Creating Pandas Series and DataFrames
        • Series Operations
        • DataFrame Operations part 1
        • DataFrame Operations part 2

        12.Regular Expression

        • Introduction to RegEx
        • Meta characters (part 1)
        • Meta characters (part 2)
        • Special sequences (part 1)
        • Special sequences (part 2)

        13.Visualization

        • Introduction to visualization
        • Basic plots
        • Sub plots
        • Bar, Pie, Histogram and Scatter
        • Plots using Pandas

        Capstone

        • Analysis of football games in FIFA World cup
        • Birth country analysis of major actors/actresses worldwide

        Learning Objectives

        Understand the data and ML ecosystem in Python and create appropriate ML models for different problem domains. Tune your models for better performance and analyze their performance.

        Topics

        1.Introduction

        • ML: Domain Overview
        • Python: Quick Preview
        • Data and ML Ecosystem in Python
        • Course IS/ISNOT

        2.Python Basics

        • Working with Jupyter Notebooks
        • Python Basics
        • Writing Better Python Code
        • numpy: vectorized operations, multicast, filtering

        3.Data in Python

        • Pandas: working with 2-D data
        • Data Cleaning
        • Sources of data

        4.Data Visualization

        • "Seeing" data with pandas, matplotlib

        5.Statistics

        • Basic Statistics concepts with numpy and pandas
        • Statistical visualization using seaborn
        • Understanding Skew and Kurtosis
        • Common probability distributions

        6.Advanced Data Analytics

        • Reshaping and combining
        • Indexing and locators
        • Group By
        • Working with Time Series
        • Advanced Data Analytics Exercise

        7.Machine Learning Basics

        • Machine Learning Concepts
        • Scikit learn API
        • First Model: classification with KNN
        • Classification evaluation terms and metrics
        • Model parameters and performance score

        8.Feature Extraction

        • The need for numeric features
        • Vectorization: Converting non-numeric features to numeric
        •  Label Encoding
        • Caveats and Pitfalls
        • Feature Extraction from Natural Language

        9.Support Vector Machines and Regression

        • SVM classfier and the concept of Support Vectors
        • SVM kernels methods
        • SVM Parameters and Model Selection
        • SVM in regression: SVR

        12. Unsupervised Learning

        •  What is Unsupervised Learning
        • Clustering and the K-Means model
        • Application of K-Means Clustering
        • Other unsupervised learning techniques

        13. Dimensionality Reduction

        • Introduction to Dimensionality Reduction
        • Principal Component Analysis (PCA)

        Capstone: Titanic Classification

        Learning Objective:

        Learn to use TensorFlow & Keras to implement CNN, RNN

        Topics

        1. Diving into Deep Learning

        •  Introduction to Deep Learning
        • Basics of Deep Learning
        • Importance of Deep Learning

        2. Getting started with TensorFlow

        • TensorFlow and Keras
        • Boston House Price Prediction using Google Colab 
        • Training a Model
        • Evaluating Deep Learning Models

        3. Convolutional Neural Networks

        • Introduction to CNNs
        • How do CNNs Work?
        • Image Classification
        • Improving our model

        4. Advanced Computer Vision

        • Classifying dogs
        • Image segmentation

        5. Natural Language Processing 

        • Introduction to Natural Language Processing (NLP)
        • Recurrent Neural Networks (RNNs)
        • Text Classification

        6. Generating Images

        • Introduction to GANs
        • Implementing a GAN in Tensorflow 

        7. AI in the Real World

        • Introduction to AI in the Real World
        • Getting Started with AI in the Real World
        • AI in Production
        Capstone: Dog breed classification  

        Learning Objectives:

        Learn Natural Language Processing with different libraries such as NLTK, Spacy, TextBlob, Gensim, Pattern, and Stanford CoreNLP, create end to end applications using vertex AI

        Topics

        1.Introduction to Natural Language Processing

        • What Is Natural Language Processing?
        • What Makes Natural Language Processing Difficult?
        • Common Terms Associated with Language Processing
        • Natural Language Processing Libraries
          • NLTK
          • TextBlob
          • SpaCy
          • Gensim
          • Pattern
          • Stanford CoreNLP

        2.Practical Understanding of a Corpus and DataSet

        • What is a corpus?
        • Why do we need a corpus?
        • Understanding corpus analysis
        • Preparing a dataset for NLP applications
        • Web scraping

        3.Understanding the Structure of Sentences

        • Understanding components of NLP
        • Natural Language Understanding Vs. Natural Language Generation
        • Branches of NLP
        • Defining context-free grammar
        • Morphological analysis
        • What is morphology? | morphemes?  | a stem? | morphological analysis?  | a word? | a token?
        • What is part of speech tags?
        • Difference between stemming and lemmatization
        • What is syntactic analysis? | What is semantic analysis?
        • What is the difference between polysemy and homonymy?
        • Syntactic ambiguity
        • Approach to handle syntactic ambiguity
        • Semantic ambiguity | Pragmatic ambiguity | Discourse integration | Pragmatic analysis

        4.Essentials of NLP

        • Text Search Using Regular Expressions
        • Text to List
        • Preprocessing the Text
        • Text normalization
        • Modeling normalized data
        • Tokenization
        • Stop word removal
        • Part-of-speech tagging
        • Stemming and lemmatization

        5.Feature Engineering, Text Vectorization and Transformation Pipelines

        • Understanding feature engineering
        • Understanding the basics of parsers
        • Understanding the concept of parsing
        • Developing parsers step-by-step
        • Extracting and understanding the features
        • Understanding the concept of POS tagging and POS taggers
        • Name entity recognition
        • Understanding n-gram using a practice example
        • Understanding BOW
        • Basic statistical features for NLP
        • Understanding TF-IDF
        • Vectorization
        • Encoders and decoders
        • One-hot encoding

        6.Natural Language Processing (NLP) with Libraries - Part 1

        • NLP with TextBlob
        • TextBlob -- a Python framework built on top of NLTK
        • Tokenization
        • Text classification
        • Part-of-speech tagging
        • Comparing the similarity of words
        • Generating n-grams
        • Spell checking
        • Sentiment analysis
        • NLP with SpaCy
        • spaCy’s Statistical Models
        • spaCy’s Processing Pipeline
        • spaCy in Action
          • Part-of-Speech (POS) Tagging using spaCy
          • Dependency Parsing using spaCy
          • Named Entity Recognition using spaCy
          • Rule-Based Matching using spaCy

        7.Natural Language Processing (NLP) with Libraries - Part 2

        • NLP with Gensim
        • Create a Corpus from a given Dataset
        • Create a TFIDF matrix in Gensim
        • Create Bigrams and Trigrams with Gensim
        • Create Word2Vec model using Gensim
        • Create Doc2Vec model using Gensim
        • Create Topic Model with LDA
        • Create Topic Model with LSI
        • Compute Similarity Matrices
        • Summarize text documents
        • NLP with Pattern
        • Tokenization
        • Text classification
        • Part-of-speech tagging
        • Comparing the similarity of words
        • Generating n-grams
        • Spell checking
        • Sentiment analysis
        • NLP with Stanford CoreNLP
        • Tokenization
        • Text classification
        • Part-of-speech tagging
        • Comparing the similarity of words
        • Generating n-grams
        • Spell checking
        • Sentiment analysis

        8.Machine Learning for NLP Problems

        • Understanding the basics of machine learning
        • Types of ML
        • Supervised learning
        • Unsupervised learning
        • Reinforcement learning
        • Classification for Text Analysis
        • Text Classification
        • Identifying Classification Problems
        • Classifier Models
        • Building a Text Classification Application
        • Cross-Validation 86
        • Model Construction
        • Model Evaluation
        • Model Operationalization
        • Clustering for Text Similarity
        • Unsupervised Learning on Text
        • Clustering by Document Similarity
        • Distance Metrics
        • Partitive Clustering
        • Hierarchical Clustering

        9.Topic Modeling and Word Embeddings

        • Topic Model and Latent Dirichlet Allocation (LDA)
        • Topic Modeling with LDA on Movie Review Data
        • Non-Negative Matrix Factorization (NMF)
        • Word2Vec
        • Continuous Bag-of-Words (CBoW)
        • Global Vectors for Word Representation (GloVe)
        • Paragraph2Vec: Distributed Memory of Paragraph Vectors (PV-DM)

        10.Sentiment Analysis

        • Unsupervised Lexicon-Based Models
        • Bing Liu’s Lexicon
        • MPQA Subjectivity Lexicon
        • Pattern Lexicon
        • TextBlob Lexicon
        • AFINN Lexicon
        • SentiWordNet Lexicon
        • VADER Lexicon

        Learning Objectives:

        End the program as an accomplished Data Scientist

        Frequently Asked Questions

        Data Science Bootcamp

        The KnowledgeHut Data Science Bootcamp has been developed in a way that people with zero or minimal coding experience can also learn the essential skills on the go. There are no educational prerequisites to sign up for the bootcamp. It will be advantageous if you understand basic mathematics and statistics and have any experience with a general-purpose programming language like Python, R, Java, C++.

        These are the system requirements for the Data Science Bootcamp:

        • Any workstation or laptop with Internet access, with at least 8Gb of RAM

        The following is required but the learner will be guided on how to use the same:

        • A Kaggle and GitHub account
        • Anaconda installation

        No, you do not need any specific software to be pre-installed on your system. We recommend having the following before your cohort starts:

        A web browser such as Google Chrome, Microsoft Edge or Firefox

        Anaconda with Jupyter (learner will be guided during the course on installation steps)    

        The Data Science Bootcamps conducted online are interactive in nature and fun to learn as a substantial amount of time is spent on hands-on practical training, use-case discussions, and quizzes. The Bootcamp classes are Online, instructor-led and can be taken part from anywhere in the world, suiting your ease.

        The Bootcamp is divided into three stages:

        Pre-Bootcamp

        During/Actual Bootcamp

        Post-Bootcamp

        The instructors and mentors at the Data Science Bootcamp course are qualified practitioners with several years of relevant industry experience.

        The instructors will take you through the live sessions whereas Mentors will be assigned to a specific person and will help the participant one-on-one on various assignments, projects, and challenges. Moreover, they will help you to overcome the challenges that you might face.

        Yes, you can sign up for the data science bootcamp even with non-technical background. You need to have a passion for solving problems, be curious about coding and mathematics, and an open mind to learn new things.

        Well, spotting out the mistakes and correcting them is how we learn and grow. We follow Agile practices and pair programming during project development. You don’t need to worry, as you will always be getting support from our trainers and mentors to take you out of the problems.

        The training conducted is interactive in nature and easy to learn, focusing on hands-on practical training, use case discussions, and quizzes. To improve your online training experience, our trainers use an extensive set of collaborative tools and techniques.

        You can attend the online bootcamp data science training and learn from anywhere in the world through the more preferred, virtual live and interactive training. 

        It is live and interactive training led by an instructor in a virtual classroom.

        You will receive a registration link to your email id from our training delivery team. Just log in from your PC or other devices.

        If it happens that you miss a class, then you can opt for any of the following two options:

        Watch the online recording of the session

        Attend another live batch

        Data Science Career FAQs

        Data science is a fusion of machine learning principles, algorithms, and various tools for the identification, representation, and extraction of useful and meaningful information from a pool of data.

        Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row. It is the most in-demand career paths for skilled professionals, however, is being challenged by the dire shortage of talent. In August 2018, LinkedIn reported that there's a shortage of 151,717 people with 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.

        Once you complete the data science bootcamp offered by KnowledgeHut, you can apply for a variety of jobs within the Data Science domain. We’ve explained the primary ones below:  

        Data Science Career Path: A mathematician, a computer scientist, and even a trend spotter - all these are the characteristics of a data scientist. Their job is to decode and make sense of large amounts of data. This data is then efficiently analyzed with inferences made and presented to all the stakeholders who can be both, technical as well as non-technical. There are multiple career paths in data science which are explained below:

        Business Intelligence Analyst: One of the most important applications of data science is used by a Business Intelligence analyst. It is the job of a business intelligence analyst to analyze the data to create a clear picture of the direction the business needs to go in and tap-in on both, business as well as market trends.

        Data Mining Engineer: data mining engineer, as the name suggests mines 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 on creating/improving algorithms that would further help improve the analysis of the data itself.

        Data Architect: A data architect has to work together with developers, system designers, and users as well to create blueprints which are used by data management systems for the integration, protection, centralization as well as maintenance of the data sources.

        Data Scientist: The main job of a data scientist is to further the interests of a business through the analysis of data given to them. They should drive a business case by analysis, development of a hypothesis, and the development of an understanding of data. This would help in exploring 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.

        We have compiled a list of top skills one needs to be a successful data scientist:

        Python Coding

        R Programming

        Hadoop Platform

        SQL database and coding

        Machine Learning and Artificial Intelligence

        Apache Spark

        Data Visualization

        Unstructured data

        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.

        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 offers is important if data science is going to be your choice of career.

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

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

        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.

        Apache Spark: Apache Spark is a big data computation tool and is also 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 read/write operations. Easy to use and high-speed computations are what makes Apache Spark stand apart. The tool is used to make the algorithms run faster. It significantly helps in the division of data processing of large chunks as well as in the case of complex and unstructured data sets. Apache Spark prevents any loss of data.

        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, it is imperative that a data scientist has skills to use visualization tools such as d3.js, Tableau, ggplot, and matplotlib. When data scientists create results from the data, these tools help to put these results in a visual format for everyone to understand it 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.

        Unstructured data: Data given to data scientists is largely unstructured, so it is essential that a data scientist is aware of the necessary skills required to manipulate unstructured data as well. 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.

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

        High Pay: For any job, let alone the data scientist job, we expect high pay. 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 one of the highest paying jobs in the tech world today.

        Bonuses: Organizations do whatever they can to attract best data scientists as well as retain who are already performing well. So good bonuses are usual if you are a good performer. These bonuses can also be in the form of perks such as signing perks, or equity shares, etc.

        Education: The qualification bar to become a data scientist is really high so naturally anyone who is a data scientist would be a scholar. You would probably have a Masters or a Ph.D. degree with you by the time you are searching for data scientist jobs. Due to an extensive educational background, sometimes you might also be offered a job as a lecturer or a researcher in the field for both, governmental as well as private institutions.

        Mobility: Data science is used in every field which means 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 a great standard of living as well.

        Network: Naturally, after investing so much time into education you would be having an educative and useful network of data scientists. This network is generally expanded by your involvement in international journals through research papers, technical talks at data science conferences and many more. These networks help in getting better jobs as well through referrals.

        A data scientist collects raw data. Most of the times the actually useful data is mixed up with unrequired and unusable or damaged data. It is the data scientist who must clean it up, process and analyze to gain useful 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:

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

        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. This course offered by KnowledgeHut is a great fit for aspiring data analysts/scientists, because of several reasons – the immersive, out-come based practical training, expert instructors, industry-validated curriculum, and Cloud Labs. At the end of the course, you’ll also have built a portfolio worthy of top recruiters. 

        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 
        • Beginner Data Scientists  
        • Beginner ML Engineers 
        • Python Developers   
        • Applications Architects 
        • Data Analysts  
        • AI Engineers 
        • 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 het 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 Cloud Labs. At the end of the day, you’ll have worked on 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. 

        What learners are saying

        N
        Neil Radia Project Manager
        4

        5 stars What a totally awesome Data Science bootcamp! I tried learning on my own through text books and online material, but it was such a struggle as I had no one to clear my doubts. Knowledgehut has brought out a totally different and interactive, comprehensive, logical systematic approach to the subject that made it super fun to learn. Love all your courses(This is my fifth!).

        Attended Data Science Career Track Bootcamp workshop in July 2021

        D
        Dave Murphy Web Developer
        5

        Best quality in the market today In today’s world, Data science is among the best career options for an IT professional. Having already done a bunch of courses from KnowledgeHut, I was already sure of the quality of the training. And I was not disappointed. Their Data Science Bootcamp was an intensive yet refreshing course that has made me very confident to look for a job as an analyst. Thank you KnowledgeHut!

        Attended Data Science Career Track Bootcamp workshop in July 2021

        K
        Kausik Malakar Data Architect
        5

        Absolutely worth it The Data Science curriculum was very challenging and rigorous, but the trainer hand-held us through the whole learning journey, answered all our doubts and gave us illustrations from his own industry experience. One of the best investments I have ever made.

        Attended Data Science Career Track Bootcamp workshop in July 2021

        E
        Eden Knight Data Analyst
        4

        I successfully transitioned my career I am a SDE who was unhappy with my job. I took a giant leap of faith and transitioned to a Data Science career after completing KnowledgeHut’s Data Science bootcamp. I love the challenges and the paycheck! Thank you Knowledgehut for giving me the confidence that I could do it. All of you who are not too happy with your present role- there's a whole world of opportunity out there. Take the first step.

        Attended Data Science Career Track Bootcamp workshop in July 2021

        P
        Peter Cozyn Data Engineer
        5

        I now have a job offer! The hands-on learning really helped. For someone like me who is completely new to this field, it was easy to learn all the Data Science and Machine Learning tools, especially Time series forecasting, machine learning and recommender engines. I have a job offer from Uber and am so grateful!

        Attended Data Science Career Track Bootcamp workshop in July 2021