AI & Data Science Course with Certification [English]
The Data Science course is an enlightening learning experience that will provide students with the competence and understanding to traverse the increasingly intricate land of data successfully. This program breaks down the complex data collection, analysis, and interpretation processes into easily understandable components for a broad audience, from new learners to experienced professionals.
What will you take home from this AI & Data Science Course?
- 60+ hrs self-paced expert-led course
- 310+ hrs of comprehensive study material
- 290+ hrs of real-world practicals
- 45+ Interactive quizzes & assessments
- 670+ Interview questions for top MNCs
- 50+ Real-time projects with implementation
- 130+ Practical Code Examples
- 98% Positive reviews from learners
- 65+ Comprehensive assignments
- 30+ Real-time industry case-studies
- 245+ Data Science tutorials
- 1:1 Career counselling with expert
- Practical knowledge which industry needs
- Industry-renowned certification
Your AI & Data Science Journey Starts Here — Enroll Now
Master AI & Data Science from Scratch
Join our hands-on AI & Data Science course crafted by industry veterans and build real-world skills. It’s not just a course, it’s a job-ready bootcamp.|
Start 📅 12-Apr-2025 |
Schedule 🕗 8.00 PM IST | 09.30 AM EST (Sat-Sun) |
Duration 60+ Hrs |
Access Duration 🕗 Lifetime Access |
Price |
Enroll Now |
Why should you enroll in this AI & Data Science Course?
- Perform statistical analysis and apply machine learning techniques effectively
- Extract, clean, and visualize data to uncover insights
- Gain hands-on experience with industry-standard tools like Python and R
- Build predictive models and offer data-based recommendations
- Understand best practices in data handling and address ethical considerations
- Develop a portfolio of real-world projects to demonstrate your skills
AI & Data Science Course Objectives
- Data Collection, Analysis, and Interpretation: Students can gather, process, and analyze large, complex datasets to extract meaningful insights.
- Statistical Methods and Data Visualization: Teach statistical techniques and visualization tools essential for interpreting data and effectively communicating findings.
- Programming Languages: Develop proficiency in programming languages such as Python and R, which are critical for data manipulation and analysis.
- Machine Learning and Predictive Modeling: Integrate knowledge of machine learning algorithms and predictive modelling to create models for classification, regression, clustering, and recommendation systems.
- Model Development and Evaluation: Students should be equipped to develop, tune, and validate machine learning models to ensure optimal performance.
- Ethical Data: Practices Instill an understanding of the importance of ethical data management, including data privacy, security, and fairness in data science practices.
- Career readiness: Prepares students for meaningful employment by providing them with the skills and knowledge to drive innovation and solve real-world problems using data.
The overarching goal of the course is to empower students to use data effectively to drive innovation, gain competitive advantages, and address complex challenges across various industries through informed data-driven strategies.
Why should you learn AI & Data Science?
Working with data and making data-driven decisions is invaluable in today’s world. Here are a few compelling reasons why learning Data Science can be a transformative skill:
- Data is the New Currency: As famously stated, data is the “new oil” — a critical resource driving the modern economy.
- Growing Demand: Data Science roles consistently rank among the top-paying jobs worldwide – Glassdoor
- Market Growth: The global Data Science market, valued at over $64 billion in 2022, is overgrowing – Statista
- Business Impact: Data-driven companies see a sixfold increase in customer retention and a significant boost in client acquisition rates – Forrester Research
What is Data Science?
Data Science means extracting information, patterns, and knowledge from raw data. Together, Natural Sciences is an interdisciplinary subject that deals with the data of multiple sciences. Data Science is a multidisciplinary field that applies domain knowledge with the help of statistics and scientific methods to convert raw data into valuable insights that lead to strategic decision-making.
Data Science encompasses various activities, from data collection and preprocessing to advanced machine learning and predictive modeling. Today, we live in a data-rich environment where businesses, governments, and organisations capture lots of data from various sources, such as sensors, websites, and social media. Enter Data Scientists, whose professional job is to sift through the data using statistical methods and machine learning algorithms to determine hidden patterns or trends.
Such insights are massively relevant, helping businesses make critical decisions, streamline operations, and solve challenging problems in industries such as health care and finance.
Data Science emphasizes data as a strategic resource, supporting the widely held notion that “Data is the new oil.” As Big Data technologies and data-gathering tools evolve, the importance of data-driven decision-making continues to grow. Data scientists are in high demand for their analytical skills and ability to communicate findings effectively with both technical and non-technical audiences. Data Science drives innovation and provides a competitive edge across industries.
What to do before you begin?
You just need to learn:
- Strengthen Mathematical Foundations
- Familiarize Yourself with Basic Programming
- Cultivate Curiosity and Analytical Thinking
Who should go for this AI & Data Science course?
Data science training benefits diverse individuals who want to work with data and derive meaningful insights.
- IT Graduates
- Aspiring Analysts
- Statistical Research Enthusiasts
- Data Engineers
- Aspiring Economists
- Business Owners and Entrepreneurs
- Marketers
Whether at the beginning of your career or looking to expand your skill set, Data Science training can open up new and impactful career paths.
By enrolling in our AI & Data Science course, you can expect the following benefits:
- Expand Career Opportunities: Opens doors to diverse job prospects in industries with high demand for data expertise.
- Offers Practical Experience: Engage in real-world data projects that build hands-on experience and a professional portfolio.
- Improves Data Manipulation and Analysis: Strengthen your ability to handle and interpret data efficiently.
- Introduces Industry Standard Tools and Languages: Gain proficiency in widely used software and programming languages like Python and R.
- Builds a Professional Network: Connect with peers and industry professionals to foster valuable relationships.
- Addresses Complex Business Challenges: Learn to apply data solutions to significant business problems.
- Boosts Earning Potential and Marketability: Increase your value in the job market with in-demand data science skills.
Jobs after Learning this AI & Data Science Course
The AI & Data Science field offers a variety of career opportunities for those with the right skills.
Here are some roles:
- Data Consultant
- AI/ML Researcher
- Data Product Manager
- Healthcare Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- Data Engineer
- Quantitative Analyst (Quant)
Our students are working in leading organizations
Online AI & Data Science Training Course Curriculum
- The Data Science Process
- Role of a Data Scientist
- Data Science Tools and Environment
- Data Types and Sources
- Data Collection Methods
- Data Cleaning and Wrangling
- Exploratory Data Analysis (EDA)
- Data Visualization Techniques
- Descriptive and Inferential Statistics
- Probability Distributions
- Hypothesis Testing
- Confidence Intervals
- Regression Analysis
- Introduction to Python for Data Science
- Data Structures in Python
- Pandas for Data Manipulation
- NumPy for Numerical Operations
- Data Visualization with Matplotlib and Seaborn
- Supervised vs. Unsupervised Learning
- Model Selection and Evaluation
- Feature Engineering
- Model Training and Validation
- Overfitting and Regularization
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines
- k-Nearest Neighbors
- Clustering Techniques (K-Means, Hierarchical, DBSCAN)
- Principal Component Analysis (PCA)
- Association Rules and Apriori Algorithm
- Introduction to NLP
- NLP Applications
- Text Preprocessing
- Tokenization and Stemming
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Classification
- What is Artificial Intelligence?
- Applications of Artificial Intelligence
- What is Generative AI?
- What is Generative Adversarial Networks?
- Introduction to Prompt Engineering
- Working with AI models
- Real-world applications across industries
- Major fundamentals of prompt design
- Different types of prompt
- What are the challenges in prompt engineering?
- Limitations of AI models
- Ethical considerations
- Introduction to Generative AI
- Traditional AI vs Generative AI
- Real-world applications of Generative AI
- Fundamentals of Generative AI models
- Different types of Generative AI: GANs, VAEs, Diffusion models, Transforms
- Tools and Frameworks of Generative AI
- Real-world applications of Generative AI
- Introduction to Deep Learning
- Real-world applications of deep learning
- Introduction to Artificial Neural Networks
- Introduction to Neural Networks
- Different types of Neural Networks
- Feedforward Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Transfer Learning
- Deep Learning Frameworks
- Characteristics of Time Series Data
- ARIMA Models
- Exponential Smoothing
- Time Series Forecasting
- Anomaly Detection in Time Series Data
- Introduction to Large Language Models
- Key Features of LLMs
- Importance of LLMs
- What are the fundamentals of LLMs?
- Tokenization process of LLMs
- Real-world applications of LLMs
- Use cases of LLMs
- Challenges in LLMs
- Ethical Concerns and Technical Challenges
- Future and Trends of LLMs
- Career Opportunities of LLMs
- What is Data Engineering?
- Introduction to Big Data Concepts
- Big Data vs Data Science
- Evolution of Big Data
- Use cases of Big Data
- Introduction to Hadoop
- Hadoop Installation
- Hadoop Architecture
- Hadoop Ecosystem
- HDFS
- MapReduce
- PySpark for Data Processing
- Distributed Machine Learning
- RDDs
- Introduction to Spark SQL
Features of AI & Data Science Course
AI & Data Science Online Training FAQs
As the name suggests, data science is a field that combines science-based methods, principles, and knowledge from various disciplines to extract meaningful information from (structured and unstructured) data. It is essential because it allows businesses to make informed decisions based on data, solve complex problems, and gain a competitive advantage.
This AI & Data Science course can benefit anyone interested in working with data, including students, professionals from various industries like IT, finance, and healthcare, and those looking to change careers.
The AI & Data Science course covers data analysis, visualization, statistical analysis, machine learning, programming (primarily in Python), and domain knowledge. It also emphasizes critical thinking and problem-solving skills.
The duration is flexible and depends on your pace. Our AI & Data Science course is designed to accommodate your schedule, allowing you to learn at a comfortable speed.
While a background in programming and mathematics, especially statistics, is helpful, our course includes introductory modules to help beginners build these essential skills.
Graduates can pursue roles such as data scientist, machine learning engineer, business intelligence analyst, and more. There’s a high demand for data professionals across many industries.
Yes, the course features practical assignments that require you to work with accurate data to solve real-world problems. These projects are valuable for building a portfolio that showcases your skills to employers.
While not mandatory, having basic knowledge of programming and mathematics (algebra and statistics) is beneficial. The AI & Data Science course is beginner-friendly and designed to build foundational skills.
