What kind of placement support does StarAgile provide with its Data Science certification?

StarAgile assists learners by sharing job opportunities that we receive through various sources, including company job portals, HR networks, and industry contacts. These opportunities are shared based on the requirements. Placement assistance support will be provided for a period of up to 6 months from the date of course completion. We only focus on job opportunities within the US, Canada, Europe and Remote opportunities. Remote opportunities shared may still require candidates to be legally authorized to work in specific regions, states, or provinces as defined by the employer. StarAgile does not provide visa sponsorship, work permits, immigration support, or employer sponsorship. Learners are responsible for ensuring they have valid work authorization for the country and role they are applying to. StarAgile does not offer or guarantee placements or interview opportunities. We at StarAgile, including all employees, do not provide job referrals, job opportunities, or direct recommendations. Compensation, salary ranges, and employment benefits are solely determined by the hiring employer and vary based on experience, skills, location, and market conditions. StarAgile does not influence or guarantee compensation outcomes. Learners are responsible for actively applying to job opportunities shared and participating in the job search process.

What is included in StarAgile's Data Science with Gen AI course?

StarAgile's data science certification program includes fundamental to advanced concepts in a structured learning path. The duration of data science course is 4 months of intensive online instruction. The best data science courses feature 3 Capstone live projects that simulate real-world industry challenges, certification recognized globally. The comprehensive curriculum encompasses Excel mastery from basic formulas to advanced PivotTables and data visualization techniques, statistical analysis methods, programming fundamentals in Python and R, and practical applications across various domains. This data scientist certification course includes practice tests to assess your progress, live interactive sessions with industry experts, and recorded content available for lifetime access, ensuring flexible learning that accommodates your schedule and pace.

What is the duration of the course?

The data science duration is 4 months of structured, intensive training designed to provide comprehensive coverage of all essential topics while allowing students to balance learning with their professional and personal commitments. The data science online classes are carefully structured to ensure a thorough understanding of both foundational concepts and advanced techniques, with significant flexibility for self-paced learning alongside scheduled live sessions with instructors. This duration has been optimized based on industry feedback and learner success rates to ensure you gain job-ready skills without rushing through critical concepts.

What are the prerequisites to join this program?

To enroll in this data science course for beginners and professionals, candidates should have an undergraduate or postgraduate degree in Mathematics, Statistics, Computer Science, Economics, or related quantitative fields. While the course is accessible to beginners with the right educational foundation, it's recommended that students have basic familiarity with programming languages such as Python, R, or MATLAB to maximize their learning experience. A foundational understanding of mathematical and statistical concepts, including algebra, probability, and basic calculus, will help you grasp advanced topics more effectively. The data science classes are structured to accommodate learners with varying levels of expertise, provided they meet these basic educational requirements and demonstrate commitment to learning.

Do you offer a money-back guarantee for the course?

Yes. We offer a 100% money-back guarantee within the first 7 days of enrollment. If you feel the course is not right for you, you can request a full refund within 7 days from the date you enroll, and we will refund 100% of your payment with no questions asked.

Is this course suitable for beginners or only professionals?

This data science online course is strategically designed for both beginners entering the field and professionals looking to upskill or transition careers. While a basic understanding of quantitative concepts is required, the curriculum starts with fundamental principles before progressively advancing to sophisticated topics. The data science course for beginners includes comprehensive Excel training, starting from basic interface navigation and formula creation to advanced PivotTables, data analysis, and complex visualization techniques. Students from mathematics, statistics, computer science, and economics backgrounds can successfully complete this program, making it an ideal starting point for those new to the field while still offering substantial depth and advanced concepts for experienced practitioners seeking to formalize their skills or learn new technologies.

Does the course cover practical projects and real-world case studies?

Yes, the best data scientist course online program includes 3 Capstone live projects as core components that are essential for your portfolio and job readiness. These projects provide extensive hands-on experience applying theoretical knowledge to real-world scenarios that mirror actual industry challenges. The projects are carefully designed to simulate actual industry challenges across different sectors, ensuring graduates are genuinely job-ready upon completion with tangible proof of their skills and problem-solving abilities.

What tools and technologies will I learn in this course?

This AI and data science course covers a comprehensive toolkit essential for modern data professionals working in today's technology-driven business environment. The curriculum includes extensive data science online training covering formulas, PivotTables, advanced charts, What-If Analysis, and dashboard creation, programming languages like Python, R, and MATLAB for data manipulation and data analysis, statistical analysis tools for hypothesis testing and modeling, and data visualization platforms including Power BI and Tableau. Students in these data science online classes learn to work efficiently with multiple sheets and workbooks, import and export data from various sources, including CSV, TXT, and PDF formats, and utilize advanced functions such as VLOOKUP, XLOOKUP, and INDEX-MATCH for complex data operations. The course also comprehensively covers conditional formatting for visual insights, sparklines for trend analysis, slicers and timelines for interactive filtering, and collaborative tools essential for professional data science work in team environments.

Will I get hands-on experience with Gen AI tools and applications?

Yes, as part of this cutting-edge AI and data science course, students gain valuable exposure to Generative AI applications integrated seamlessly within the data science workflow. The curriculum is specifically designed to prepare students for the evolving landscape where artificial intelligence and traditional data science converge to create powerful analytical solutions. Through the 3 Capstone projects included in this data scientist certification course, learners apply these advanced tools to solve complex real-world problems, ensuring they're equipped with the latest industry-relevant skills that employers are actively seeking in 2026 and beyond.

What is the course format — online, hybrid, or classroom-based?

StarAgile's course is delivered entirely online through a sophisticated learning management system, providing maximum flexibility for working professionals, students, and career changers globally. These data science online classes allow you to access content anytime and from anywhere, enabling you to learn at your own pace from the comfort of your home or office anywhere in the world. The format strategically combines live interactive sessions with experienced instructors for real-time learning and doubt clarification, recorded lectures available for review and reinforcement whenever needed, and self-paced learning modules for a deep dive into topics at your convenience, ensuring comprehensive coverage without geographical constraints or schedule conflicts.

Data Science Course Online With AI Training

Data Science Course offers 148 hours of training covering Gen AI, Agentic AI, AIOps, and MLOps with hands-on mini projects and practice assignments.
International Placement Support
7 days Refund | 100% Money Back Guarantee*
Includes a real-world capstone project and a 4 month internship certificate.
Choose any 2 specialization domains and enjoy VIP access to Data Science Course content for 3 years.

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Key Features
Course Content
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Overview
FAQS

Key Features of the Data Science Course

148 Hours of live instructor-led training
8 Capstone Projects
6+ Tools Covered
4 Months Internship Certificate
Microsoft Certification

Gen AI Integrated Curriculum
15+ Years Experienced Industry Trainer
International Placement Support
100% Money Back Guarantee*

Let's walk you through the journey at StarAgile

Course Curriculum

Our course is designed by industry experts for excellent academic and industrial experience. We have a balanced combination of theoretical, technical, and practical knowledge for you to get the best training experience for everyone regardless of their background.

6 Months

Module 1

Python

Module 2

SQL

Module 3

Excel

Module 4

Data Analysis

Module 5

Machine Learning

Module 6

Deep Learning and Computer Vision

Module 7

Statistics and Probability

Module 8

Applied Artificial Intelligence for Data Science

Module 9

Capstone Projects

Module 10

Power BI

Module 11

Tableau

Module 1
Module 2
Module 3
Module 4
Module 5
Module 6
Module 7
Module 8
Module 9
Module 10
Module 11

Python

➔ An Introduction to Python

➔ Introductory Remark about Python

- A Brief History of Python

- How Python differs from other languages

- Python Version

➔ Getting Help

- How to execute a Python program

- Writing your first program: Google Colab, VS Code, and Anaconda

➔ Python Basics

- Python keywords, variables, and Identifiers

➔ Decision making & Loops

- Introduction

- Control flow and syntax

- The if statement

- Python operators

- The while Loop

- Break and continue

- The for Loop

- Pass statement

- Exercise

➔ Data Structures

- Introduction

- List

- Tuple

- String

- Dictionary

- Set

- Exercise

➔ Functions in Python

- Introduction

- Calling a function

- Function arguments

- Built-in function

- Scope of variables

- Decorators

- Passing a function to a function

- Lambda

- Closures

- Exercise

➔ Exception Handling

- Errors

- Run Time Errors

- Handling IO Exception

- Try…except statement

- Raise

- Assert

➔ Mini Project

Title: Simple Calculator Application

Description: Create a console-based calculator that takes user input for two numbers and performs mathematical operations (add, subtract, multiply, divide)

Tools Covered: Python basics (Control structures, basic I/O)

Machine Learning

➔ Introduction to Machine Learning

- What is Machine Learning?

- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

- Applications of Machine Learning in the Real World

➔ Supervised Learning: Regression

- Introduction to Regression

- Simple Linear Regression

- Multiple Linear Regression

- Polynomial Regression

- Regularization: Ridge, Lasso

➔ Supervised Learning: Classification

- Introduction to Classification

- Logistic Regression

- K-Nearest Neighbors (KNN)

- Support Vector Machines (SVM)

- Naive Bayes Classifier

➔ Model Evaluation and Tuning

- Confusion Matrix, Precision, Recall, F1-Score

- ROC Curve and AUC

- Cross-Validation

- Grid Search and Random Search

➔ Decision Trees and Random Forests

- Introduction to Decision Trees

- Decision Tree Algorithm: Classification and Regression

- Random Forests and Bagging

➔ Unsupervised Learning: Clustering

- Introduction to Clustering

- K-Means Clustering

- Hierarchical Clustering

- DBSCAN Algorithm

➔ Ensemble Learning Techniques

- Introduction to Ensemble Methods

- Bagging: Random Forests

- Boosting: AdaBoost, Gradient Boosting, XGBoost

➔ Time Series Analysis

- Introduction to Time Series

- Time Series Forecasting with ARIMA

- Exponential Smoothing and Moving Averages

➔ Case Studies

- Case Study 1: Regression

- Case Study 2: Classification

- Case Study 3: Clustering

➔ What is Natural Language Processing (NLP) and Why It Matters?

- Definition of NLP and its role in Machine Learning

- Real-world applications: chatbots, sentiment analysis, translation, spam detection

➔ Text Preprocessing Basics

- Tokenization, stop-word removal, stemming & lemmatization

- Converting text to numbers: Bag of Words, TF-IDF

- Quick Python demo (e.g., using NLTK or spaCy)

➔ Feature Representation & Models

- Word embeddings: Word2Vec, BERT (conceptual overview)

- How embeddings capture meaning and context

- Example: Similarity between words using vectors

➔ Mini Project

Title: Customer Churn Prediction with Logistic Regression

Description: Predict whether customers will leave a service using logistic regression

Tools Covered: Data encoding (OneHot, Label Encoding), handling missing values (MCAR, MAR, MNAR), outlier treatment (IQR, Z-score methods), feature scaling, logistic regression with scikit-learn, and evaluation metrics

Deep Learning and Computer Vision

➔ Introduction to Deep Learning

- What is Deep Learning?

- Neural Networks Overview

- Key Concepts: Neurons, Weights, Activation Functions

- Feedforward Neural Networks (FNNs)

- Forward and Backpropagation (conceptual)

- Gradient Descent and Optimization (intuition only)

➔ Deep Learning Frameworks

- Introduction to TensorFlow and Keras

- Building a Basic Neural Network with Keras

- Training and Evaluating the Model

➔ Convolutional Neural Networks (CNNs)

- Introduction to Convolutional Layers

- Pooling Layers and Fully Connected Layers

- Image Preprocessing

- Image Rescaling and Normalization

- Basic Data Augmentation Techniques

- Building a CNN for Image Classification

➔ Transfer Learning

- Concept of Transfer Learning

- Using Pre-trained Models (VGG, ResNet, etc.)

- Fine-Tuning Pre-trained Models

➔ Recurrent Neural Networks (RNNs)

- Introduction to Sequence Modeling

- Basic RNN Architecture and Limitations

- Need for LSTMs vs Traditional RNNs

- LSTM Architecture Overview

- Input Gate, Forget Gate, Output Gate (conceptual)

- Applications: text, speech, time-series

- When to use RNN vs LSTM

➔ Case Studies

- Case Study 1: Image Classification with CNN

- Case Study 2: Object Detection using YOLO

➔ Mini Project

- Title: Smart Image Classifier for Everyday Objects

- Description: Build a deep learning model that classifies everyday objects (e.g., fruits, vehicles, or household items) using an image dataset, apply preprocessing techniques, train a CNN, and evaluate performance with visualizations

- Tools Covered: Neural Networks (Feedforward & CNNs), Image Processing, TensorFlow/PyTorch, Object Detection Concepts

Applied Artificial Intelligence for Data Science

➔ Introduction to AI & Generative AI Foundations

- Definition and evolution of Artificial Intelligence

- Difference between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)

- Common AI applications in daily life

o Recommendation systems

o Voice assistants

o Chatbots

- Introduction to Generative AI

- Difference between Predictive AI and Generative AI

➔ Foundation Models & LLM Basics

- Foundation Models overview

o GPT, Gemini, Claude, LLaMA

- How Large Language Models (LLMs) work

o Tokenization

o Embeddings

o Attention mechanism (conceptual overview)

➔ Transformers and Attention Mechanisms

- Introduction to transformers and self-attention

- BERT, GPT, and other transformer-based architectures.

- Hands-on exercise: Finetuning a transformer model for text generation

➔ Text, Embeddings & Retrieval

- Traditional text techniques (context-setting only)

o TF-IDF

o n-grams

- Embeddings and semantic similarity

- Vector databases and similarity search

- Retrieval-Augmented Generation (RAG)

o Why RAG is needed

o High-level RAG workflow

o Use cases in enterprise data

➔ Prompt Engineering & Productivity Tools

- Prompt engineering fundamentals

o Clear and contextual prompts

o Role-based prompting

o Prompt refinement techniques

- Text generation using ChatGPT / Gemini

- AI tools for productivity and creativity

o ChatGPT, Copilot, Gemini

o Writing, summarization, email replies

AGENTIC AI & APPLIED AI SYSTEMS (6 HOURS)

➔ Introduction to Agentic AI

- What is Agentic AI

- Difference between Chatbots and AI Agents

- Core concepts

o Goal-driven behaviour

o Memory

o Tool usage

o Feedback loops

- Real-world examples of agent-based systems

➔ AI Agents with APIs & Tools

- Integrating LLM APIs (OpenAI / Gemini – conceptual + demo level)

- API fundamentals

o Authentication

o Rate limits

o Error handling

o Data handling best practices

- Tools and function calling (conceptual)

- Multi-step reasoning workflows

➔ Applied AI Systems & AIOps Overview

- Introduction to AIOps

- How AIOps extends DevOps

- Core pillars

o Data ingestion

o Analytics

o Automation

- Use cases

o Anomaly detection

o Predictive alerting

o Automated incident resolution

- Tools overview (no deep dive)

o Splunk, Dynatrace, IBM Watson AIOps

➔ Responsible & Ethical AI

- Ethics and risks in AI

- Bias in AI models

- Privacy and data protection

- Deepfakes and misinformation

- Responsible usage in enterprise AI systems

➔ Mini Project

- Title: Conversational AI model

- Description: AI model that simulates a customer service assistant for a chosen domain (e.g., retail or tech support)

- Tools Covered: LLM, ChatGPT, GPT-3

Career Track

Once you have enrolled for the program then you will have access to a wide range of resources which will help you in becoming a Job-Ready Candidate. We have a dedicated placement team of professionals who excel in their individual domains committed to assist you in our journey.

1 Month

Sign ISA Agreement

The students need to pay after they get placed. They need to pay INR 20,000 to StarAgile as a career services fee which can be paid in easy EMIs.

Sign ISA

You are eligible
for placement program

Pay the career services
fee of INR (80,000) 20,000

Pay Career Services Fee

Pay ₹20,000/- (Career Services Fee) Only after Getting Placed

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What Our Data Science Certification Offers

Comprehensive Tool Mastery - Gain expertise in Python, SQL, Machine Learning, Power BI, Excel, and advanced analytics
8 Live Capstone Projects - Build industry-ready portfolio projects that demonstrate real-world capabilities
Dedicated Personal Mentor - Instant doubt clarification and career guidance throughout your journey
4 Month Internship Certificate - An Additional credential that strengthens your professional profile

Data Science prerequisites

Educational Background - UG/PG degree in Mathematics, Statistics, Computer Science, Economics, or related fields
Recommended Skills - Basic familiarity with Python, R, or MATLAB enhances your learning experience
Foundational Knowledge - Understanding of basic mathematical and statistical concepts

International Placement Assistance Details

StarAgile assists learners by sharing job opportunities that we receive through various sources, including company job portals, HR networks, and industry contacts. These opportunities are shared based on the requirements. Placement assistance support will be provided for a period of up to 6 months from the date of course completion.

We only focus on job opportunities within the US, Canada, Europe and Remote opportunities.
Remote opportunities shared may still require candidates to be legally authorized to work in specific regions, states, or provinces as defined by the employer.
StarAgile does not provide visa sponsorship, work permits, immigration support, or employer sponsorship. Learners are responsible for ensuring they have valid work authorization for the country and role they are applying to.
StarAgile does not offer or guarantee placements or interview opportunities.
We at StarAgile, including all employees, do not provide job referrals, job opportunities, or direct recommendations.
Compensation, salary ranges, and employment benefits are solely determined by the hiring employer and vary based on experience, skills, location, and market conditions. StarAgile does not influence or guarantee compensation outcomes.
Learners are responsible for actively applying to job opportunities shared and participating in the job search process.

Comprehensive FAQs on Data Science Certification

Distinctions and Achievements

Explore the milestones of our journey!

Best Skill Learning
Institute of the Year

The Education Excellence Award
by Brands Impact

Edutech Company
of the Year

The Education Awards by
The Corporate Titan

Institute with the
Best Placement

The Education Awards
by Mantra

About the Data Science Certification Course

What is Data Science?

Data science is a field that combines various disciplines, including mathematics, statistics, and computer programming, to extract valuable insights from large datasets. It combines ideas from mathematics, statistics, technology, and business to analyse information, identify trends, create predictive models, and assist companies in making informed decisions. Main tasks involve collecting and organizing data, using advanced analysis techniques, and working with tools like Python and R to tackle real business challenges for companies.

Key areas of study

Programming: Python is the most popular language for data science work. You'll start with Python basics and then learn important libraries like Pandas for handling data.
Databases: SQL skills are necessary for managing databases, and the course covers SQL for data science basics to help you work with data stored in databases.
Statistics: You'll learn fundamental statistics for data science and probability concepts that form the building blocks for analyzing data properly.
Machine Learning: The course takes you from basic machine learning ideas to more complex topics like deep learning and AI applications.
Data Visualization:Data Visualization Create clear charts and dashboards using tools like Power BI, Tableau, and Python libraries, including Matplotlib and Seaborn.

The Data Science Workflow

At its core, Data Science encompasses the entire analytical lifecycle: collecting data from diverse sources, cleaning and preparing information, exploring patterns through visualization, building predictive models using machine learning algorithms, and communicating findings to stakeholders. Students in a data science course learn to leverage tools like Python, SQL, Excel, Power BI, and advanced analytics platforms to process massive datasets and uncover hidden trends.

Three Foundational Pillars

The field rests on three foundational pillars. First, mathematics and statistics provide the theoretical framework for hypothesis testing, regression analysis, and probability theory. Second, computer science and programming enable automation, algorithm development, and scalable solutions. Third, domain expertise ensures insights are contextually relevant and actionable. Quality data science classes develop proficiency across all three areas through hands-on projects and real-world applications.

Modern Applications and Career Impact

Modern data science online course programs increasingly integrate Artificial Intelligence and Generative AI, reflecting how these technologies are reshaping the profession. From Netflix recommendations to fraud detection systems, Data Science applications in ecommerce impact daily life. The data scientist certification course pathway prepares professionals for this high-demand field, where entry-level positions in India average USD 14,988 annually.

Whether through structured data science online classes or comprehensive AI and data science course training, mastering this discipline opens doors to rewarding careers solving complex problems with data-driven solutions across virtually every sector.

Benefits of Data Science

Why Choose StarAgile for Data Science Certification?

StarAgile's comprehensive data science certification program is designed to transform aspiring professionals into industry-ready data scientists through expert-led training. This data scientist certification course combines rigorous academics with practical application, ensuring you gain the skills employers actively seek in today’s competitive job market while preparing you thoroughly for real-world roles outlined in a data scientist job description.

What's Included in Our Program:

148 hours of Live Training - Expert instructors with 15+ years of experience deliver comprehensive data science online classes covering Python, SQL, Machine Learning, Power BI, and Excel
Practical Assignments - Hands-on work that builds real-world competency in data analysis and modeling
8 Live Capstone Projects - Industry-relevant projects that create portfolio-worthy demonstrations of your capabilities
Dedicated Personal Mentor - One-on-one guidance for instant doubt clarification throughout your learning journey
4-Month Internship Certificate - Additional credential strengthening your professional profile

Career Benefits & Placement Support:

Complete Career Services - Resume building, mock interviews, and LinkedIn optimization included
Flexible Online Learning - The Data Science online course format allows learning at your own pace from anywhere

Whether you're enrolled in our data science course for beginners or advancing your existing career, StarAgile provides the complete pathway from data science online training through successful employment in this high-demand field.

Comprehensive Training Solutions for Enterprises

Comprehensive training solutions to empower enterprises with skills needed for growth and innovation. Tailored programs to boost productivity and improve workforce capabilities.