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Data Science Course

93,702 Ratings

  • Master Python, SQL, Machine Learning, Power BI, Generative AI & more
  • Live Interactive sessions from IIT faculty & top industry experts
  • 24X7 Support for doubt resolutions & technical queries
  • Guaranteed Placement Assistance to land into your dream job
  • Advanced Certification from iHub IIT Roorkee (An Innovation Hub of IIT Roorkee)

Ranked #1 Data Science Course by India TV



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Key Highlights

50+ Live sessions across 7 months
218 Hrs Self-paced Videos
200 Hrs Project & Exercises
Learn from IIT Roorkee Faculty and Industry Practitioners
1:1 with Industry Mentors and 24*7 Support
Resume Preparation and LinkedIn Profile Review
Campus Immersion at IIT Roorkee
No-cost EMI Option
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Trustpilot 3109
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About Online Data Science Course Overview

What courses will this Data Science Program offer?

In this advanced Data Science Course Certification, you will learn 12 Data Science Courses along with multiple case studies and project work.

Online Instructor-led:

  • Course 1: Linux and Python
  • Course 2: Data wrangling with SQL
  • Course 3: Python with Data Science Course includes Data Visualization, Data Mining, Data Interpretation
  • Course 4: Linear Algebra and Advanced Statistics
  • Course 5: Machine Learning and Prediction Algorithms
  • Course 6: Supervised and Unsupervised Learning in ML
  • Course 7: Data Science tools: Power BI, Tensorflow, and SciPy
  • Course 8: Deep Learning
  • Course 9: Deploying Machine Learning Models With Cloud
  • Course 10: Data Analysis with MS-Excel (Self-Paced)

Additional modules for the Data Science Capstone Project, and Business Case Studies will also be covered.

With the increase in the need for data-driven solutions for business organizations, there is a rapid increase in the requirement for data scientists with appropriate Data Science skills.

This Data Science Training Online will help you master skills like SQL, and Python for Data Science, machine learning, data structures, storytelling, AI, Power BI, etc. This certification can quickly accelerate your career in this evolving domain and take it to the next level.

  • Data Scientist is the best job in the 21st Century — Harvard Business Review
  • The number of jobs for all data professionals in the United States will increase to 2.7 million — IBM
  • Global Big Data market achieves US$122 billion in sales in 6 years — Frost & Sullivan

In India, PayScale reports an average data scientist salary of INR 8,74,528 in 2023. According to Glassdoor and Indeed, the average annual salary for data scientists in the United States in 2023 is around US$125,660 and US$122,801, respectively.

Data Science is important as it helps us derive valuable insights from vast amounts of data. By analyzing patterns and trends, businesses can make informed decisions that enhance efficiency and improve customer experiences. It allows organizations to predict future trends, identify opportunities, and mitigate risks. Data Science plays an important role in advanced research and innovation in various fields, such as healthcare, finance, and technology. The importance of Data Science lies in its ability to turn raw data into actionable intelligence, driving progress and success across diverse industries.

Some of the core responsibilities of data scientists are:

  1. Understand the Problem: Data scientists should be aware of the business pain points and ask the right questions.
  2. Collect Data: They need to collect enough data to understand the problem at hand and better solve it in terms of time, money, and resources.
  3. Process the Raw Data: We rarely use data in its original form, and it must be processed. There are several ways to convert it into a usable format.
  4. Explore the Data: After processing data and converting it into a usable form, data scientists must examine it to determine its characteristics and find evident trends, correlations, and more.
  5. Analyze the Data: To understand the data, they use various tool libraries, such as machine learning, statistics and probability, time series analysis, and more.
  6. Communicate Results: At last, results must be communicated to the right stakeholders, laying the groundwork for all identified issues.

Data science is a great career choice for anyone interested in using their skills to solve real-world problems and make a difference. As per the US Bureau of Labor Statistics, the number of jobs in the field of Data Science is going to grow by 27.9% each year. This means data scientists are in high demand across all industries, and they have the opportunity to work on a variety of interesting and challenging projects. Data scientists also have good job security, opportunities for advancement, and competitive salaries.

Yes, this Data Science Course is designed to develop strong foundational skills with advanced concepts delivered to you as capstone projects that present a great option to test your learning.

Many top companies hire data scientists. A few of them are Amazon, Google, IBM, Facebook, Microsoft, Walmart, Target, Visa, Bank of America, Accenture, Fractal Analytics, etc. Enrolling in a Data Science Course would open doors to opportunities available in all such companies.

Typically, the data scientist certification course completion varies, but learners can expect a swift transition to Data Science after fulfilling course requirements successfully.

These are the Data Science projects covered in this Data Science Course:

  • Face Detection

The Face Detection project in this Data Science Course uses machine learning and image processing to explore computer vision. It develops models to find and recognize faces in pictures.

  • AI Chatbot

In this AI Chatbot project that teaches you about natural language processing and machine learning you will make chatbots that are smart and can understand and respond to users, which will make the user experience better and increase automation.

  • Restaurant Revenue Prediction

The Restaurant Revenue Prediction uses historical data and machine learning to forecast restaurant revenues, and helps with financial planning.

  • Build the Book Recommender Application

This Book Recommender Application uses data filtering and machine learning. This app suggests books that match your interests. It uses Data Science to enhance your digital reading.

  • Census Project

The Census Project in this Data Science Course helps you analyze and visualize demographic data to find trends and insights. It shows how Data Science can inform policy-making and social research.

  • Housing Price Prediction

In the Housing Price Prediction project, you will study regression analysis and predictive modeling. You will create algorithms to forecast housing prices using different factors. This project shows how Data Science affects real estate and investment strategies.

  • HR Analytics

In the HR Analytics project, you’ll use data analysis and predictive modeling to find patterns and predict employee behavior and turnover. This shows how Data Science can help with human resource strategies and organizational management.

  • Joke Rating Prediction

The Joke Rating Prediction project lets you analyze user responses and predict joke ratings. You can use Data Science to understand consumer preferences and optimize content.

There are numerous job opportunities available for data scientists after enrolling in this Data Science Course, like:

  • Data Analyst
  • Big Data Engineer
  • Data Scientist
  • Statistician
  • Data Engineer
  • Machine Learning Engineer
  • Data Architect
Criteria Data Analyst Business Analyst Data Scientist
Skillset Analyzing business needs Analyzing historical data Making data-driven decisions
Who is eligible? Anyone can learn Anyone can learn Anyone can learn
What do they do? Full life cycle analysis, including business needs, activities, and designing Implementing technology solutions and analyzing and reporting business capabilities Statistical analysis and the development of Machine Learning systems
Average salaries US$68,000 US$75,000 US$112,000
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With data collection, ‘the sooner, the better’ is the best answer. - The CEO of Yahoo
Everything is going to be connected with data and mediated by software - The CEO of Microsoft
The world is now awash in data, and we can see consumers more cleanly - The Co-founder of PayPal

Data Science Program Career Transitions

55% Average Salary Hike

$1,20,000 The Highest Salary

12000+ Career Transitions

500+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Meet the Data Science Mentors

Who can apply for the Data Scientist Training?

  • Information Architects and Statisticians
  • Developers looking to master Machine Learning and Predictive Analytics
  • Big Data, Business Analysis, Business Intelligence, and Software Engineering Professionals
  • Aspirants who are looking to work as Machine Learning Experts, Data Scientists, etc.
  • Anyone who wants to learn machine learning, artificial intelligence, data visualization, data analytics, data structures, and algorithms (DSA).

What role does a Data Scientist play?

Data Scientist

Design and implement scalable codes alongside effectively developing high-quality applications.

Analytics and Insights Analyst

Develop solutions for fixing quality issues in the data upon investigating the reported errors in the data.

AI & ML Engineer

Use Lambda functions and API Gateway to integrate machine learning models into web apps and deploy models in SageMaker.

Data Engineer & Data Analyst

Perform data cleansing, and data transformation, analyze the outcomes, and present the insights in reports and dashboards.

Junior Data Scientist

Analyze the operating behavior using advanced statistical techniques and tools. Also, create algorithms with prescriptive and descriptive methods.

Applied Scientist

Derive intelligence for business products through designing and developing machine learning models.

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Skills Covered under this Data Scientist Course



Mathematical Modelling

Data Science

Descriptive Statistics

Inferential Statistics

Data Analysis

Machine Learning

Generative AI

Prompt Engineering


Artificial Intelligence

Large Learning Models

Supervised & Unsupervised Learning


Story Telling

Data Visualization

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Tools Covered

pyspark python jupyter Scipy numpy pandas matplotlib tensorflow SQL tableau excel git SparkSQL
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Data Scientist Course Fees

Online Classroom Preferred

  • Live Classes from IIT Faculty & Industry Experts
  • Certification from iHUB IIT Roorkee
  • Career Services (Mock Interviews, Resume Preparation)
  • Placement Assistance upon clearing PRT
  • Dedicated Learning Manager
16 Jul


07:00 AM TO 09:00 AM IST (GMT +5:30)

20 Jul


10:00 AM TO 01:00 PM IST (GMT +5:30)

20 Jul


08:00 PM TO 11:00 PM IST (GMT +5:30)

$1,492 10% OFF Expires in

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  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

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Data Science Course Syllabus

Live Course Self Paced Industry Expert Academic Faculty

Module 1 – Preparatory Session - Linux and Python



  • Introduction to Python and IDEs – The basics of the Python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding. 


  • Introduction to Linux  – Establishing the fundamental knowledge of how Linux works and how you can begin with Linux OS. 
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux. 
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Module 2 – Data Wrangling with SQL


SQL Basics – 

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables 

Advanced SQL –  

  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

  • Types of UDFs, Inline table value, multi-statement table. 
  • Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance

  • Record grouping, searching, sorting, etc. 
  • Clustered indexes, common table expressions.

Hands-on exercise: 

Writing comparison data between the past year and the present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

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Extract Transform Load

  • Interacting with APIs

Data Handling with NumPy

  • NumPy Arrays, CRUD Operations, etc.
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

  • Loading the data, data frames, series, CRUD operations, splitting the data, etc.

Data Preprocessing

  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.

Data Visualization

  • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc, with Python seaborn.
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Descriptive Statistics – 

  • Measure of central tendency, the measure of spread, five points summary, etc. 


  • Probability Distributions, Bayes’ theorem, central limit theorem. 

Inferential Statistics –  

  • Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.
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Introduction to Machine Learning 

  • Supervised, Unsupervised Learning.
  • Introduction to scikit-learn, Keras, etc.


  • Introduction classification problems, Identification of a regression problem, dependent and independent variables.
  • How to train the model in a regression problem.
  • How to evaluate the model for a regression problem.
  • How to optimize the efficiency of the regression model.


  • Introduction to classification problems, Identification of a classification problem, and dependent and independent variables.
  • How to train the model in a classification problem.
  • How to evaluate the model for a classification problem.
  • How to optimize the efficiency of the classification model.


  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
  • How to train the model in a clustering problem.
  • How to evaluate the model for a clustering problem.
  • How to optimize the efficiency of the clustering model.
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  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
  • Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach.
  • Support Vector Machine – SVM or support vector machines for regression and classification problems.
  • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding the local minimum and maximum of a given function.
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.
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  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  • Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
  • Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
  • Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

Performance Metrics

  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
  • Confusion matrix – To evaluate the true positive/negative, and false positive/negative outcomes in the model.
  • r2, adjusted r2, mean squared error, etc.
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Artificial Intelligence Basics 

  • Introduction to keras API and TensorFlow

Neural Networks

  • Neural networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks 

Deep Learning 

  • Introduction to Deep Learning (by Academic Faculty)
  • Deep neural networks
  • Convolutional Neural Networks 
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, restricted boltzmann machine 
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The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

  • Extracting, loading and transforming data into usable format to gather insights. 
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements. 
  • Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
  • Assessment and monitoring of the model created using the machine learning models.
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  • Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
  • Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
  • Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
  • Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
  • Object Detection – A much more advanced yet simple case study that will guide you toward making a machine learning model that can detect objects in real-time.
  • Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you with some really helpful and actionable insights for specific stocks.
  • Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
  • AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.
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  • LSTM – What is LSTM?, How does LSTM work, Applications of LSTM, etc.
  • Transformers – What are transformers, how does a transformer work in deep learning, applications of transformers, types of transformers, encoder-decoded, self-attention, etc.
  • BERT – Language Models, What is BERT, How does BERT work, how is BERT different from LSTM, applications of BERT, etc.
  • GPT – What are generative pre-trained models (GPT), how does a GPT work?, real life examples of GPT, etc.
  • LLM – NLP and Language models, what are LLMs, how does a LLM work, applications of LLM, etc.
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Power BI Basics

  • Introduction to Power BI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.


  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics  

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity
  • Embedded Power BI API and Power BI Mobile
  • Power BI Advance and Power BI Premium

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data.

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Introduction to MLOps 

  • MLOps lifecycle
  • MLOps pipeline 
  • MLOps Components, Processes, etc

Deploying Machine Learning Models 

  • Introduction to Azure Machine Learning 
  • Deploying Machine Learning Models using Azure
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Version Control 

  • What is version control, types, SVN.


  • Git Lifecycle, Common Git commands, Working with branches in Git
  • Github collaboration (pull request), Github Authentication (ssh and Http)
  • Merging branches, Resolving merge conflicts, Git workflow
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  • Job Search Strategy
  • Resume Building
  • Linkedin Profile Creation
  • Interview Preparation Sessions by Industry Experts
  • Mock Interviews
  • Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.
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Excel Fundamentals 

  • Reading the Data, Referencing in formulae , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 
  • Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 
  • VBA Macros, Ranges and Worksheet in VBA 
  • IF conditions, loops, Debugging, etc.

Excel For Data Analytics 

  • Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.  

Data Visualization with Excel

  • Charts, Pie charts, Scatter and bubble charts
  • Bar charts, Column charts, Line charts, Maps
  • Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Excel Power Tools 

  • Power Pivot, Power Query and Power View

Classification Problems using Excel

  • Binary Classification Problems, Confusion Matrix, AUC and ROC curve 
  • Multiple Classification Problems  

Information Measure in Excel

  • Probability, Entropy, Dependence 
  • Mutual Information 

Regression Problems Using Excel

  • Standardization, Normalization, Probability Distributions 
  • Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
  • Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

Hands-on Exercise:

Classification problem using excel on sales data, and statistical tests on various samples from the population.

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Data Science Projects

Career Services

Career Services

Career Oriented Sessions

Throughout the course

Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions that will help you stay on track with your up upskilling.


Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & LinkedIn profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage


Mock Interview Preparation

After 80% of the course completion.

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.


1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learner’s educational background, past experience, and future career aspirations.


Placement Assistance

Upon movement to the Placement Pool

Placement opportunities are provided once the learner is moved to the placement pool upon clearing Placement Readiness Test (PRT)


Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Data Science Certification

Advanced-Certification-in-Data-Science-and-AI Click to Zoom

What should I do to unlock my Data Science Certification?

You can unlock your Data science certification issued by us in three simple steps:

  1. Complete the Data Science Online Course along with the given assignments
  2. Work on several industry-based projects and execute the same successfully
  3. Pass the Data Scientist certification exam

You will receive and can download the certificate of this Data Science Course via our Learning Management System. Share your certificate on your LinkedIn or resume.

The Data Science Certification you receive from us is valid for your entire lifetime, and top organizations worldwide recognize it.

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Data Science Training FAQs

Why should I enroll in this Data Science Certification course online?

This is the best Data Science Course for professionals who want to expand their knowledge base and start a career in the Data Science field. There are many reasons for choosing this course from Intellipaat:

  • A personal mentor to track your progress
  • Immersive online instructor-led sessions conducted by SMEs
  • Extensive LMS, allowing you to view recorded sessions within 3 hours
  • Real-time exercises, assignments, and projects
  • 24/7 learning support
  • A large community of like-minded learners
  • Industry-recognized Intellipaat badge
  • Personalized job support

Along with the Data Science Course, Intellipaat offers other courses such as machine learning, artificial intelligence, Python, Python for Data Science, data analytics, and business analytics.

If you are looking for free resources on Data Scientist online courses, then read our blogs on Data Science Tutorials and Data Science Interview Questions.

Data Science, a branch of computer science, encompasses a wide range of algorithms, tools, scientific methods, and machine learning techniques, as well as a basic understanding of data structures. This field is dedicated to identifying hidden trends and patterns from both structured and unstructured data.

Data scientists are experts in Data Science responsible for collecting and analyzing vast chunks of structured and unstructured data from a range of data sources. These professionals combine their knowledge and skills in mathematics, statistics, and computer science to enable organizations to analyze and process business data and interpret it to help the company make improved business decisions. This Data Science Course can help you become capable of handling all these responsibilities

There are several ways to become a data scientist. Data scientists use numerous Data Science tools/technologies, such as R and Python programming languages and SAS analysis tools. As a budding data scientist, you should be familiar with data analysis, statistical software packages, data visualization, and handling large datasets.

Python is the most popular and preferred language in Data Science because it is an easy-to-use, easy-to-learn, open-source programming language. Moreover, it is a dynamic language that supports multiple paradigms. Apart from this, some other languages used in Data Science include R and SQL.

This Data Science Course is for beginners who are new to Data Science and experienced professionals who wish to upskill themselves in this domain.

You will gain access to our job portal once you complete the entire Data Science Training program and execute the assignments and projects part of the program.

To be eligible to get into the placement pool, the learner has to complete the Data Science Course along with the submission of all projects and assignments. After this, he/she has to clear the Placement Readiness Test (PRT) to get into the placement pool and get access to our job portal as well as the career mentoring sessions.

In a Data Science Course, newcomers and those at an intermediate level can expect to gain knowledge in various areas. Industry professionals often choose this Data Science Training, which lasts six to twelve months, to gain a strong foundation in the discipline. Along with the theory, our Data Science Course provides virtual labs, real-world projects, fun interactive quizzes, and practice tests to make your learning even better.

Intellipaat helps fresh college graduates with its placement assistance programs. These Data Scientist Courses can help a fresh college graduate seeking employment.

Yes, Data Science can be very easy to learn for those with a passion and the right resources. With numerous online courses, tutorials, and communities dedicated to Data Science, learners can grasp the basics quickly. Motivation, combined with the right tools, makes the journey engaging and accessible to many.

To become a data scientist, one needs a relevant degree, a strong mathematical understanding, and proficiency in languages like Python. Furthermore, mastery of data visualization tools like Power BI or Tableau, keeping yourself updated on the latest algorithms used for ML and AI application development, and hands-on experience via projects are vital. In this Data Science Course, soft skills training is also included, as it plays an important role to become a successful data scientist.

Yes, this Data Science Course covers all the foundational knowledge required to learn the basics of Data Science. Also, students are provided with basic preparatory modules that they can start even before the commencement of his/her classes. So, even if you are from a non-tech background with zero knowledge of programming, this Data Science Course is made to transition you to a tech career in a comfortable way.

The decision between Data Science and data analytics depends on your goals. Data Science is broader and focuses on gaining insights, creating models, and solving complex problems using various techniques. Data Science is best suited for those interested in research and innovation. On the other hand, data analysis is more about interpreting existing data to make data-driven decisions. If you enjoy working with structured data to gain useful insights and contribute to business strategies, data analytics may be a better fit. Consider your preferences and career aspirations to make an informed decision between Data Science and data analytics.

Statistics is the language of Data Science. It forms the basis for effective decision-making as it helps us understand complex data and recognize patterns, trends, and relationships. Statistical knowledge can be used to gain meaningful insights, make informed predictions, and validate results. It enables data scientists to accurately interpret results and make data-driven decisions that improve the overall reliability of the analysis. A deep understanding of statistics helps data scientists extract valuable information from data and ensure that their conclusions are useful in decision-making.

Yes, coding is essential in Data Science. It serves as the foundation for analyzing and interpreting data. Proficiency in languages like Python or R programming allows data scientists to manipulate and process data efficiently, build statistical models, and extract valuable insights. While some tasks can be accomplished with basic coding skills, advanced projects often demand a deeper understanding of programming. Acquiring coding skills allows individuals to navigate the complexities of Data Science, enhancing their ability to draw meaningful conclusions from data sets.

Yes, Data Science requires math, but the level of math skills depends on the specific tasks. Basic math skills, such as statistics and algebra, are essential for analyzing and interpreting data. Advanced topics such as calculus and linear algebra may be required for more complex modeling. A solid understanding of mathematics improves the ability to draw meaningful insights from data.

AI and Data Science are interconnected but serve different purposes. Data Science focuses on extracting insights from data, while AI involves creating systems that can perform tasks without explicit programming. Both are valuable in the tech landscape.

Absolutely! A commerce student can pursue Data Science. Data Science involves analyzing and interpreting data to gain insights and make informed decisions. While a background in computer science or statistics can be helpful, it’s not mandatory.

As a commerce student, you may need to acquire some additional skills in programming languages like Python or R and familiarize yourself with statistical methods and data analysis tools. There are many online courses and resources available to help you learn these skills.

Remember, Data Science is a multidisciplinary field, and individuals from various educational backgrounds can excel in it. So, if you’re interested, go ahead and explore the world of Data Science!

Intellipaat offers query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail yourself of email support for all your queries. We can also arrange one-on-one sessions with our support team If your query does not get resolved through email. However, 1:1 session support is given for 6 months from the start date of your course.

Intellipaat provides placement assistance to all learners who have completed the training and moved to the placement pool after clearing the PRT (Placement Readiness Test). More than 500+ top MNCs and startups hire Intellipaat learners. Our alumni work with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.

Apparently, no. Our job assistance is aimed at helping you land your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final hiring decision will always be based on your performance in the interview and the requirements of the recruiter.

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