AI & Data Science Certification Course with AI & ChatGPT
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AI & Data Science Course Curriculum
💻 Python
💻 Stats
🔵 Data Science and ML with AI
1. Introduction to Python
- Overview of Python
- Why Choose Python for Programming?
- What is Python?
- Real-World Applications of Python
- Platform Dependent vs Independent Languages
- Key Features of Python
- Limitations of Python
- The Evolution and History of Python
2. Writing Your First Python Program
- Installing Python
- Installing and Setting Up PyCharm
- Introduction to IDLE
- How Python Code is Executed
- Writing and Running the “Hello World” Program
3. Data Types in Python
- Python Statements, Indentation, and Comments
- How to Print in Python
- Getting User Input in Python
- Understanding Methods, Identifiers, and Variables
- Python Data Types and Variables
- Rules for Declaring Identifiers
4. Input and Output in Python
- Input and Output Functions in Python
- Reading Data in a Single Line
- Using the print() Function
- Formatted Print Statements
- The Replacement Operator
- Printing with the format() Method
5. Operators in Python
- Different Types of Operators in Python
- Bitwise Operators in Python
- Arithmetic and Assignment Operators
- Number System Conversion in Python
- Identity Operators
6. Control Statements and Loops in Python
- Writing if-else Statements in Python
- Using if-elif Conditions
- Creating Pyramid Patterns with Control Statements
- Transfer Statements: Break and Continue
- Using the Pass Statement
- Introduction to Loops
- Types of Loops: while and for
- Using Nested Loops
7. Strings in Python
- What Are Strings?
- Built-in String Functions
- String Class and Methods
- Common String Operations
- Type Casting in Python
8. Collections in Python
- Understanding Collections in Python
- Working with Lists, Tuples, Sets, and Frozen Sets
- Using Dictionaries in Python
- Bytes and Bytearray Collections
9. Functions in Python
- Understanding Functions in Python
- Types of Functions and Methods
- Creating Functions in Python
- Parameters and Arguments
- Function Arguments and Call by Value
- Types of Function Arguments
- Returning Values from Functions
- Passing Functions as Arguments
- Global Variables and the global Keyword
- Using the eval() Function
10. Recursion in Python
- Writing Recursive Functions
- Implementing Factorial with Recursion
- Reversing Numbers with Recursion
- Fibonacci Series with Recursion
11. Lambda Functions in Python
- Anonymous Functions with lambda
- Using the filter() Function with lambda
- lambda with the map() Function
12. Arrays in Python
- Introduction to Arrays
- Arrays Operations
- Types and Concepts of Arrays
- Using Arrays in Python
- Array Methods in Python
- Creating Arrays with NumPy
- Comparing Arrays in Python
- Understanding Object References
- Difference Between View and Copy
- Exploring NumPy Array Dimensions and Attributes
- Working with Multidimensional Arrays
- Using Matrices in Python with NumPy
13. Searching and Sorting in Python
- Implementing Binary Search
- Writing a Bubble Sort Algorithm
14. Object-Oriented Programming in Python
- Procedural vs Object-Oriented Programming
- Key Concepts of OOP in Python
- Creating Classes and Objects
- Understanding the self Variable
- Using the __init__() Method
- Constructor Overloading in Python
- Exploring Polymorphism and Operator Overloading
- Relational Operator Overloading
- Overloading vs Overriding in Python
- Using Getters and Setters
15. Object-Oriented Programming part-2
- Static Variables and Methods
- Working with Inner Classes
- Understanding Is-A vs Has-A Relationships
- Implementing Inheritance and Using the super() Method
- Types of Inheritance in Python
- Role of Constructors in Multiple Inheritance
- Abstract Classes and Methods
- Creating Interfaces in Python
16. Exception Handling in Python
- Basics of Exception Handling
- Common Exception Types
- Using try, except, and finally Blocks
- Exception Handling with Practical Programs
- Using the finally Statement
- Working with Assertions
- Raising Exceptions in Python
- Writing Custom Exceptions
17. File Handling in Python
- Introduction to Files in Python
- File Modes (r+, w+, a+)
- Reading, Writing, and Appending Files
- Using with for File Handling
- Handling File Exceptions
- Writing Data to a File with Practical Programs
- Working with readlines() and writelines() Methods
- Counting Lines, Words, and Characters in a File
- Reading Files Character by Character
- Binary File Operations in Python
- Working with the Pickle Module
- Reading and Writing CSV Files
- Using tell() and seek() Methods
18. Python Libraries
- Core Concepts of NumPy Arrays
- Creating Arrays in NumPy
- Comparing Arrays in NumPy
- Performing Arithmetic Operations on Arrays
- Data Analysis with Pandas
- Working with DataFrames in Pandas
- Inserting, Deleting, and Updating Data in Pandas DataFrames
- Creating Beautiful Graphs with Matplotlib
- Creating Pie Chart, Bar Graphs, Scatter Plots, Histogram, etc.
- Draw Plot using Seaborn
- Work on Bar, Histogram, Scatter and Heatmap Plots.
1. Introduction to Statistics
- What is Statistics
- Importance in Data Science
- Understanding Data
- Types of Statistics: Descriptive vs. Inferential
- Types of Data: Qualitative vs. Quantitative
- Populations vs. Samples
2. Descriptive Statistics
- Central Tendency: Mean, Median, Mode
- Dispersion
- Range, Variance, Standard Deviation
- Coefficient of Variation
3. Shape of Distribution
- Skewness and Kurtosis
- Understanding Normal Distribution
- Visualization Techniques
4. Graphs and Plots in Statistics
- Histograms
- Box Plots
- Bar Charts
- Pie Charts
- Scatter Plots
- Correlation
5. Probability Theory
- Basics of Probability
- Axioms of Probability
- Conditional Probability
- Bayes’ Theorem
- Applications of Bayes’ Theorem
6. Probability Distribution
- Random Variables
- Discrete Variables
- Probability Distributions: PMF, PDF and CDF
- Binomial, Poisson, Exponential, Uniform, Log-Normal
7. Statistical Inference
- Sampling and Sampling Distributions
- Hypothesis Testing
- Defining Null and Alternative Hypotheses
- Type I and Type II Errors
- Using p-Value and Significance Levels
- Applying Z-test, T-test, Chi-Square Test, and ANOVA
- Confidence Intervals
- Margin of Error
8. Regression
- What is Regression and Its Types
- Root Mean Square Error
- K Nearest Neighbor Algorithm
- Support Vector Machine
- Random Forest Algorithm
- Clustering
1. Data Science Essentials
- Introduction to Data Science
- The Evolution of Data Science
- Big Data vs. Data Science
- Key Terminologies in Data Science
- Working with NumPy, Pandas & Matplotlib
- Data Science vs. AI and Machine Learning
- Data Science vs. Analytics
- Data Science Applications in the Real World
2. Career in Data Science
- Why Data Science is in Demand
- Career Opportunities in Data Science
- How to build a career in Data Science
- Required Skill Set to become a Data Scientist
- AI & Data Science Roadmap
- Salary Trends & Career Growth
- Future of Data Science
3. Deep Dive into Data Science
- A Real-World Use Case
- Preparing Data for Analysis
- Building a Machine Learning Model
- Making Predictions with the Model
- Delivering Business Value with Data Science
- The Data Science Project Workflow
- Stages of a Data Science Project
- Roles: Data Engineer, Data Scientist, ML Engineer
4. Analytics Classification
- Types of Analytics
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
5. Data Science Industry Applications
- Data Science in Finance and Banking
- Data Science in Retail
- Data Science in Healthcare
- Data Science in Logistics and Supply Chain
- Data Science in the Technology Industry
- Data Science in Manufacturing
- Data Science in Agriculture
6. Exploratory Data Analysis Unlocking Insights
- What is Exploratory Data Analysis
- Numerical vs Categorical Data
- Continuous vs. Discrete Data
- Feature Engineering
- Handling Missing Values
- Handling Outliers
- Univariate, Bivariate, Multivariate Analysis
- Correlation
- Data Cleaning Essentials
- Business Insights from EDA
7. Machine Learning Introduction
- What is Machine Learning?
- How is it Different from AI?
- Why Machine Learning?
- Types of Machine Learning
- Key ML Terminologies
- Features and Labels in ML
- Training and Testing in ML
- Overfitting vs. Underfitting
- The Machine Learning Workflow
- Popular Machine Learning Algorithms
- AI vs ML vs Data Science
- AI & ML Case Studies in Industry
- AI & ML Applications in Real World
- Reinforcement Learning
- Data Science Project Architecture
- Data Science & Engineering Data Flow
- Data Platform Strategy
- Data Ecosystem Project Architecture
8. Supervised Learning – Regression
- What is Regression in Machine Learning?
- Types of Regression Problems
- Linear Regression
- Evaluation Metrics
- Common Challenges with Regression
- Linear vs Polynomial vs Ridge vs Lasso Regression
- Regression Industry Applications
- Regression Real-World Use Cases
9. Classification – Logistic Regression
- What is Classification?
- Regression vs. Classification
- Classification Real-World Applications
- Types of Classification Problems
- Data Preparation for Classification
- Label Encoding vs One-Hot Encoding
- Feature Scaling in ML
- Train-Test Split in ML
- Handling Imbalanced Data in Classification
- Model Evaluation Metrics in Classification
- Accuracy in Classification
- Precision, Recall, F1-Score in Classification
- Confusion Matrix in Classification
- ROC Curve and AUC Score in Classification
- Overfitting & Underfitting in ML
- Regularization Techniques in Classification
10. Logistic Regression – KNN
- What is k-NN?
- How k-NN Works
- KNN Distance Metrics
- Choosing the Right ‘k’ in KNN
- Data Preparation for KNN
- Categorical Features Handling in KNN
- Strengths and Limitations of KNN
- Evaluation Metrics in KNN
- KNN Model Tuning
- Cross-Validation in KNN for Optimal ‘k’
11. Decision Tree
- Introduction to Decision Tree
- Types of Decision Trees
- Components of a Decision Tree
- Decision Tree Splitting Criteria
- Gini Impurity in Decision Tree
- Entropy & Information Gain in Decision Tree N
- Advantages and Limitations of Decision Tree
- Pre-Pruning and Post-Pruning in Decision Tree
- Decision Tree Evaluation Metrics
- Decision Tree Hyperparameter Tuning
- Decision Tree Cross-Validation
12. Random Forest Algorithm
- Introduction to Random Forest
- Why use Random Forest over a Single Tree?
- Concepts Behind Random Forest
- How Random Forest Works
- Random Forest Hyperparameters
- Random Forest Advantages and Limitations
- Random Forest Use Cases
13. Unsupervised Learning
- What is unsupervised learning?
- Supervised vs unsupervised learning
- Where to use Unsupervised learning
- Application of Unsupervised learning
- Popular Algorithms in Unsupervised Learning
- Real world use cases of Unsupervised learning
- Evaluation Metrics
14. Unsupervised Learning – Clustering
- Introduction to Clustering Algorithms
- What is Clustering?
- Differences Between Supervised and Unsupervised Learning
- Classification vs Clustering
- K-means Clustering
- K-means Clustering Real-world industry use cases
- Elbow method in K-means Clustering
- Hierarchical Clustering
- Agglomerative vs Divisive Approach in Hierarchical Clustering
- Hierarchical Clustering Real-world industry use cases
- Dimensionality Reduction
15. Machine Learning in Business
- Enterprise Applications Overview
- Predictive Modeling & Segmentation
- Personalization Engines
- Time Series & Anomaly Detection
16. Scikit-Learn
- What is Scikit-Learn and Why is it Important?
- Features of Scikit-Learn for Machine Learning
- Installing Scikit-Learn and Setting Up the Environment
- Understanding the Machine Learning Workflow with Scikit-Learn
17. Natural Language Processing
- What is Natural Language Processing?
- Applications of NLP
- Text Preprocessing
- Text Classification
- Sentiment Analysis
18. Deep Dive in AI
- Introduction to Artificial Intelligence
- Introduction to Gen AI
19. Deep Learning
- What is Deep Learning?
- Definition and its importance in AI.
- Comparison with machine learning.
- Applications of Deep Learning
- Computer vision: Image recognition, object detection.
- Natural language processing: Speech recognition, chatbots.
- Applications in healthcare, finance, and autonomous vehicles.
- Deep Learning vs Machine Learning
- Deep Learning case studies in industry
- Why Deep Learning?
- Need for Deep Learning in Industry
- Why Deep Learning is in Demand
- Key Deep Learning Terminologies
- Basics of neurons, layers, and their roles.
- Activation functions: Sigmoid, ReLU, Tanh.
- Forward propagation and backpropagation.
- CNN
- Activation Functions in Deep Learning
- Optimizers in Deep Learning
- ResNet50
- Vanishing gradients
- Transfer Learning
- DenseNet121
- RNN
- ANN vs CNN vs RNN
- LSTM
- RNN vs LSTM
- LSTM in deep learning
20. Deep Learning Frameworks
- Real-time implementation with TensorFlow
- Real-time implementation with Keras
- Real-time implementation with PyTorch
- PyTorch vs TensorFlow
- PyTorch Features
- PyTorch Use Cases
21. Types of Neural Networks
- Fully connected networks.
- Convolutional Neural Networks (CNNs) for images.
- Recurrent Neural Networks (RNNs) for sequential data.
22. Prompt Engineering
Module 1: Introduction to Prompt Engineering
- What is Prompt Engineering?
- Importance in working with AI models like GPT and DALL·E.
- Applications across industries (e.g., content creation, customer support, design).
Module 2: Fundamentals of Prompt Design
- Types of prompts:
- Instruction-based.
- Conversational.
- Contextual.
- Components of effective prompts:
- Clarity, specificity, and relevance.
Module 3: Challenges in Prompt Engineering
- Understanding limitations of AI models.
- Ethical considerations in designing and using pro
23. Generative AI
Module 1: Introduction to Generative AI
- What is Generative AI?
- Differences between traditional AI and Generative AI.
- Real-world applications:
- Chatbots and conversational AI.
- Image generation (e.g., DALL·E).
- Video synthesis and editing.
Module 2: Fundamentals of Generative AI
- Understanding Generative Models:
- How they create new data.
- Types of Generative AI:
- GANs (Generative Adversarial Networks).
- VAEs (Variational Autoencoders).
- Transformers (foundation of GPT, DALL·E, and other tools).
Module 3: Popular Tools and Frameworks
- Overview of deep learning frameworks
- APIs and tools
- Working with pre-trained models to simplify tasks
24. Large Language Models (LLMs)
- What are Large Language Models?
- Definition and key features.
- Examples: ChatGPT, Bard, Claude, GPT-4.
- Importance of LLMs in AI and Data Science
- Role in modern AI applications.
- Real-world use cases in various industries.
- Fundamentals of LLMs
- How Do LLMs Work?
- Overview of transformer architecture.
- Basics of the attention mechanism.
- Tokenization process.
- Training Large Language Models
- Data preprocessing for LLMs.
- Overview of training datasets (Common Crawl, Wikipedia).
- Applications of LLMs
- Natural Language Processing Tasks
- Emerging Use Cases
- Healthcare, education, and finance.
- Research and scientific discoveries.
25. Agentic AI & AI Agents
- Agentic AI Essentials
- Historical evolution: From reactive systems to proactive agents
- Working with Agentic AI
- Applications of Agentic AI
- Agent vs. Model vs. Tool: Understanding the distinction
- Agentic AI vs AI Agents
- Multi Agent Systems
- Core characteristics of AI agents: autonomy, proactivity, adaptability
- Real-world use cases of AI agents
- Agent architectures
- Open-source agent framework
26. Data Engineering
- Introduction to Data Engineering
- What is Big Data?
- Difference between Big Data and Data Science
- Characteristics of Big Data
- Five Vs
- Importance of Big Data in Today’s World
- Evolution of Big Data and Its Role in Business
- Use Cases of Big Data Across Industries
27. Working on Hadoop
- What is Hadoop?
- Installation of Hadoop
- Architecture of Hadoop
- Hadoop Ecosystem: Hive, Pig, HBase, and Sqoop
- Working with HDFS and MapReduce
- Setting Up and Navigating HDFS
- Basic HDFS Commands for File Management
- How MapReduce Works
28. PySpark
- Introduction to Spark
- RDDs
- Data Processing with PySpark
- Spark SQL
💻 Self-paced 🔵 Instructor-led Live
Data Science & AI Projects
Build Real-time Projects & Get Job-Ready!
-
Detecting Fake News
You’ll create a program that analyzes news articles and determines if they are true or fake by studying patterns in text data.Credit Card Fraud Detection
Build a system that examines transaction data and identifies unusual activities to detect fraud in credit card usage.Sentiment Analysis
Analyze customer reviews or social media comments to identify whether the emotions expressed are positive, negative, or neutral. -
Brain Tumor Detection
Learn to use data science techniques to analyze medical images and identify brain tumors.Breast Cancer Classification
Develop a program that classifies breast cancer as malignant or benign by studying patient data and history.Language Translation
Design a model that translates text from one language to another, making communication across languages seamless. -
Movie Recommendation System
Create a program that suggests movies to people based on their previous choices and preferences, just like Netflix or Amazon Prime.Generate Human Faces
Build a model that can create realistic human faces from scratch using data, similar to how gaming or animation industries work.Customer Segmentation
Group customers into categories based on their shopping habits, helping businesses understand and target them better. -
Speech Emotion Recognition
Create a system that listens to spoken words and identifies emotions like happiness, sadness, or anger from the tone of voice.Sales Forecasting
Predict future sales for a store using past data, helping businesses plan their inventory and strategies.Retail Market Basket Analysis
Discover which products are frequently bought together to help stores design better offers and improve sales.
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Course Objectives
Why
What
Prerequisites
Who
Course Benefits
Jobs
Companies
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
AI & Data Science Case Studies
Transforming Business Operations in Top Companies
-
Data Science at Netflix
Netflix uses data science to understand what people watch. It suggests shows and movies that match their interests, making it easier to find something they’ll enjoy.Data Science at Facebook
Facebook uses data science to learn how people connect and share. It helps show posts and ads that are most useful or interesting to each person.Data Science at Flipkart
Flipkart uses data science to predict what customers might want to buy. It helps suggest products and keeps stock ready for a smooth shopping experience. -
Data Science in Retail
Retail stores use data science to see what customers like to buy and when. It helps plan product placement and create better shopping deals for customers.Data Science in Digital Marketing
Digital marketers use data science to track customer behavior and improve ads. It ensures people see the right ads and companies get better results.Data Science in Healthcare
Data science helps doctors understand patient data, predict illnesses, and improve treatment. It makes healthcare more accurate and effective. -
Data Science in Education
Schools use data science to track how students are doing. It helps find learning gaps and improve teaching methods and students learning.Data Science in Movies & Entertainment
The movie industry uses data science to understand what audiences like. It helps make better movies and predict which ones will do well.
Features of AI & Data Science Course
Looking for Industry Expert Counselling?
We are a team of trainers who have 20+ years of Industry Experience. They provide project based training and cover real time scenarios during the interactive training sessions.
Contact our Industry Experts & get answers to all your queries.
AI & Data Science Online Training FAQs
Why is data science important, and what does it entail?
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.
Who should consider enrolling in a AI & Data Science course?
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.
What fundamental skills are covered in this Artificial Intelligence & Data Science course?
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.
How long does the average AI & Data Science course last?
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.
Do I need programming or math experience to enroll?
While a background in programming and mathematics, especially statistics, is helpful, our course includes introductory modules to help beginners build these essential skills.
What types of employment opportunities can I expect after completing this AI & Data Science course?
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.
Does the course include real-world projects?
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.
What prerequisites are needed before enrolling?
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.
