The Data Science Course: Complete Data Science Bootcamp 2025
Complete Data Science Training: Math, Statistics, Python, Advanced Statistics in Python, Machine and Deep Learning
4.7 Stars
448,389 ratings
Duration: Choose Your Course Length —
1, 2, 3, 4, 5, or 6 Months (Online & Remote)
📌 BATCH START DATE:
⏰ LAST DATE TO APPLY:
⏳ Batch Starts In:
₹3999/- ₹1999/- 🎯
📢 Enroll Now & Save Big!
REGISTRATION FORM
Fill up the Below form to get registered in the program
OUR STUDENTS ARE SELECTED IN
About This Course
Data Science is at the heart of today’s tech revolution. This course is designed to give you hands-on experience with real-world datasets and tools to build intelligent, data-driven solutions. You’ll master key technologies like Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, SQL, Machine Learning, and Deep Learning.
Whether you’re a student, working professional, or someone curious about data, this bootcamp is your gateway to understanding the full lifecycle of data science projects—from data cleaning to model deployment—preparing you for high-impact roles in tech, business, and research.
✅ No prior technical experience required. We start from scratch and take you step-by-step through all the core concepts, tools, and real-world projects you’ll need to succeed in the world of data science.
What You Will Learn
- The course provides the entire toolbox you need to become a data scientist
- Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
- Impress interviewers by showing an understanding of the data science field
- Learn how to pre-process data
- Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
- Start coding in Python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in Python
- Carry out cluster and factor analysis
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Apply your skills to real-life business cases
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
- Unfold the power of deep neural networks
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
- Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations.
🔷 Foundations of Data Science
Introduction to Data Science & Industry Applications
Understanding the Data Science Lifecycle
Overview of tools: Python, Jupyter, Pandas, NumPy, Matplotlib, SQL
🔶 Data Analysis & Visualization Mastery
Data wrangling and cleaning with Pandas & NumPy
Visualizing data with Matplotlib, Seaborn, Plotly
Exploratory Data Analysis (EDA) techniques
Working with real-world datasets
🔷 Statistics, Math & ML Essentials
Descriptive & Inferential Statistics for Data Science
Probability, Distributions & Hypothesis Testing
Linear Algebra & Calculus for Machine Learning
Introduction to Machine Learning algorithms (Regression, Classification, Clustering)
🔶 Real-World Machine Learning Projects
Building ML models using Scikit-learn
Evaluation metrics & model tuning
Feature engineering & preprocessing
Case studies from healthcare, finance, and retail
🔷 Databases, SQL & Big Data Tools
Writing complex SQL queries
Connecting Python with databases
Basics of Big Data: Hadoop, Spark overview
Working with cloud data tools (Google Colab, AWS, BigQuery basics)
🔶 Capstone Projects & Deployment
End-to-end data science project lifecycle
Presenting results with dashboards and storytelling
Model deployment using Flask/Streamlit
Hosting projects on GitHub & portfolio creation
Course Content Overview
66 sections • 525 lectures • 32h 8m total length
A Practical Example: What You Will Learn in This Course (Preview) – 02:32
How Does This Course Work – 02:04
Download All Resources and Important FAQs – 00:33
Data Science and Business (Buzzword: Why are there so Many?) (Preview) – 05:01
Data Science vs Data Analytics – 03:38
What is the difference: Analytics vs Analysis – 01:09
Understanding Data, Analytics, and Data Science: An Introduction – 01:01
Combining BI, Data, and Analysis with Data Science: An Introduction – 02:01
Curiosity check (1 question)
Traditional Data vs Big Data – 04:04
The Economics of Data – 01:01
AI vs Data Science vs ML – 04:04
Tools used in the various disciplines – 00:40
A breakdown of our Data Science Infographic – 01:09
Quiz – 1 question
Applying Traditional Data, Big Data, to Traditional Data Science and AI – 07:09
The Reason Behind These Disciplines – 06:04
Quiz – 1 question
Techniques for Working with Traditional Data – 02:15
Techniques for Working with Traditional Data – 03:01
Techniques for Working with Big Data – 03:06
Techniques for Working with Big Data – 04:29
Techniques for Working with AI Data – 02:06
Techniques for Working with AI Data – 03:15
Quiz (4 questions)
Techniques for Working with Traditional Methods – 02:01
Techniques for Working with Traditional Methods – 03:00
Techniques for Working with Big Data – 03:00
Techniques for Working with AI Methods – 03:02
Machine Learning (ML) – 03:05
Types of Machine Learning – 02:04
A Deeper Understanding of ML – 02:08
Topics of Machine Learning – 02:33
Topics and Types of Artificial Intelligence (AI) – 02:09
Quiz (4 questions)
Necessary Programming Languages & Software to Build a Data Science Toolkit – 06:06
Quiz – 4 questions
Finding the Job – What to Expect and What to Look for – 02:09
Quiz – 1 question
Debunking Common Misconceptions – 04:10
Quiz – 1 question
The Basic Probability Formula – 07:08
Computing Expected Values – 04:16
Computing Expected Values – 03:28
Frequency – 04:54
Events and Their Complements – 06:05
Quiz – 3 questions
Fundamentals of Combinatorics – 07:04
Permutations and How to Use Them – 04:15
Permutations and How to Use Them – 03:00
Simple Operations with Factorials – 02:59
Solving Questions with Factorials – 03:00
Solving Variations without Repetition – 03:05
Solving Variations with Repetition – 03:02
Quiz – 4 questions
Solving Combinations – 04:06
Solving Combinations – 04:00
Solving Questions with Separate Sample Spaces – 03:05
Solving Questions with Separate Sample Spaces – 03:01
Combinatorics in Real Life – The Lottery – 04:00
Quiz – 4 questions
A Practical Example of Combinatorics – 03:55
This course includes:
- 31.5 hours on-demand video
- 131 coding exercises
- 93 articles
- 542 downloadable resources
- Access on mobile and TV
- Closed captions
- Certificate of completion
About The Instructor
👨🏫 Instructor: Saurabh Mehta
Lead Data Science Trainer | AI Consultant | Industry Mentor
Hello, I’m Saurabh Mehta, your instructor for this journey into the world of Data Science.
With over 8 years of hands-on experience in the fields of data analytics, machine learning, and AI-driven solutions, I’ve had the opportunity to work with leading IT firms, mentor engineering students, and design industry-relevant curriculum for aspiring data professionals.
My goal in this course is simple:
To help you build a strong foundation in Data Science, step by step — from theory to real-world application.
Major Project:
AI-ENABLED DATA SCIENCE ANALYTICS SYSTEM
🧠 Project Title: "InsightX – Analyze, Predict & Visualize Real-World Data with AI"
Project Description:
In this capstone project, learners will develop a powerful data science analytics system capable of processing and deriving insights from real-world datasets. The project involves end-to-end data handling — including data collection, cleaning, analysis, modeling, and visualization.
The highlight of the system is its integration with AI-powered prediction and recommendation engines, using OpenAI’s API, machine learning models, and advanced data visualization libraries.
Trusted Collaborators
Description
*Update 2025: Intro to Data Science module updated for recent AI developments*
The Problem
Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.
However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.
And how can you do that?
Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)
Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture
The Solution
Data science is a multidisciplinary field. It encompasses a wide range of topics.
Understanding of the data science field and the type of analysis carried out
Mathematics
Statistics
Python
Applying advanced statistical techniques in Python
Data Visualization
Machine Learning
Deep Learning
Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.
So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2024.
We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.
Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).
The Skills
1. Intro to Data and Data Science
Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?
Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.
2. Mathematics
Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.
We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.
Why learn it?
Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.
3. Statistics
You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.
Why learn it?
This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.
4. Python
Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.
Why learn it?
When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.
5. Tableau
Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.
Why learn it?
A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.
6. Advanced Statistics
Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.
Why learn it?
Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.
7. Machine Learning
The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.
Requirements
- No prior experience is required. We will start from the very basics
- You’ll need to install Anaconda. We will show you how to do that step by step
- Microsoft Excel 2003, 2010, 2013, 2016, or 365
Who this course is for:
- You should take this course if you want to become a Data Scientist or if you want to learn about the field
- This course is for you if you want a great career
- The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
"Get Industry level Certification!"
4.7 Stars
448,389 ratings
Duration: Choose Your Course Length —
1, 2, 3, 4, 5, or 6 Months (Online & Remote)
📌 BATCH START DATE:
⏰ LAST DATE TO APPLY:
⏳ Batch Starts In:
₹3999/- ₹1999/- 🎯
📢 Enroll Now & Save Big!
REGISTRATION FORM
Fill up the Below form to get registered in the program
Meet our Ex-Interns
Profiles are from linkedin, Click to Cross-Check
Shivesh Singh – Software Engineering Student | AI/ML Enthusiast
Junaid Aftab – Full Stack Developer | Recruiter
Ayush Kumar – Computer Science Student | Learner | Generative AI
Sheebamol TG-Web developer/WordPress Developer
Uday Dalvi-Java/ web dev/ SQL developer
Saipranav Sapare-Front-End intern at Solar Secure Solutions (A part of CWS)
Ayushi mallawat – Full Stack Web developer Intern at Solar Secure Solutions (Part of CWS)
Mayank Parmar-Intern at Solar Secure Solutions | Web developer| SVNIT (Part of CWS)
Ritesh Shukla- Full stack developer Intern at Soalr secure solutions(Part of CWS)
Mohit Kapil- IIT Roorkee’23 | Frontend Developer
Mritunjay Mishra-Software Developer | Java | Python
Rachin-Associate QA Engineer at Empyra software solutions
PRATIBHA KUMARI-RAMS Engineer @ Alstom
Ramavath Harish-NIT MEGHALAYA
Rahul Bansal – Product Manager at HP India
krishnavamshi Thadooru-Mern Full Stack Developer
Shubhanshu roy-MVJ College of Engineering, Bangalore, India
Deepthi Muthineni-Project Engineer at Wipro
Ayush Parasha- Student at Arya College of Engineering and IT
Pranav Joshi- Full Stack Developer
RITU SINGH-FullStack Developer
Jahruddeen Ansari-DevOps Engineer | AWS Practitioner Certification – Associate
Syed Kashif- New Delhi
Jonal Suthar – TCS | Systems Engineer
Testimonals
Some Top recruiters...
FAQs
To register, simply fill up the registration form by entering your name, email ID, and mobile number. Mention if you’re a student or working professional, along with your college or company details. You can also select a preferred start date as per the website, and we’ll try to match it.
After you register, you’ll get an acknowledgment email by within 5 mins after the successful registration and course portal link via mail on your course start date by 1.00 PM (as mentioned on the website). This email includes all course details and login instructions.
Yes, we can help with necessary documents if your college requires internship proof or training completion letters.
Yes! The course is flexible. You can learn at your own pace and resume anytime during the access period.
Yes, you’ll need a computer or laptop to complete assignments, projects, and follow along with course videos.
This course is pre-recorded, but live Q&A sessions or mentor support may be provided occasionally.
Don’t worry! We have mentor support to help you with coding issues via email, chat, or dedicated support platforms.
You’ll learn web development step-by-step with practical assignments, build real-world projects, and receive a certificate upon completion. The course is beginner-friendly and structured to simulate a real internship experience.
You’ll build full websites, including e-commerce, portfolios, and blog platforms. You’ll also do a capstone project to showcase your skills.
It’s divided into modules – HTML, CSS, JavaScript, React, Node.js, MongoDB, plus project work. After each module, you’ll apply what you’ve learned in mini-projects.
You’ll work on over 20 projects to build real-world experience.
👉 Paid Project Opportunity: Based on your performance (quality, speed, and accuracy), you may be eligible for paid tasks offered by our USA team, subject to availability.
Anyone interested can join! Basic knowledge of HTML, CSS, or JavaScript is a bonus but not compulsory. Graduates from any stream are welcome. There are no restrictions based on nationality, age, or background.
You can learn anytime – 100% flexible. Learn at your own speed, any time of the day.
Once enrolled, you’ll have lifetime access to all training content, unless there’s a policy change in the future.
No prior experience is needed! Just basic computer knowledge and willingness to learn.
You can learn anytime – 100% flexible. Learn at your own speed, any time of the day.
You get lifetime access to the course content and downloadable resources.
Yes, detailed training is provided before each assignment, along with guidance and examples.
This is a training internship, so there is no stipend. But it will add great value to your resume and skills.
Yes, we offer placement support, resume building help, and interview tips after successful course completion.
4.5 Stars
SkillSecureX is a forward-thinking EdTech company dedicated to transforming how students and aspiring professionals build careers in the IT sector. Founded with a vision to make high-quality, industry-relevant learning accessible to all, SkillSecureX specializes in online internships and certified training programs in Web Development, Data Science, Artificial Intelligence, and other cutting-edge technologies.
Our Offices
🇮🇳 India Office
SkillSecureX Technologies
56 A-2 Kanakadasa Layout, Seegehalli, Magadi road,Bangalore 560091
- +91-7618025090
- +91-7618025090
- projects@skillsecurex.com
General Enquiries:
- info@skillsecurex.com
Student Support & Internship Queries:
- support.india@skillsecurex.com
Careers (India Office):
- careers.india@skillsecurex.com
🇺🇸 USA Office – HQ
1128 International Parkway, Fredericksburg, VA 22406, United States
- hr@skillsecurex.com
Careers/Job Applications (US Office):
- careers.usa@skillsecurex.com
Press & Media Relations:
- media@skillsecurex.com
Send us a messege
Subscribe Us
Copyright 2025 SkillSecureX Technologies