Full Stack Data Science & AI Bootcamp with Job Assurance [English]
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AI & Data Science Course Curriculum
💻 Python
💻 SQL
💻 Stats For Data Science
💻 Python For Data Science
🔵 Data Science and Machine Learning
💻 Power BI
💻 DSA Python
🔵 Projects
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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
- 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
15. 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
16. 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
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1. Introduction to SQL
- Why SQL?
- Importance of SQL
- Introduction to SQL
- What are the Purpose of SQL
- How it is Used
2. Know About DBMS and RDBMS
- Introduction to Database Management System (DBMS)
- Introduction to Relational Database Management System (RDBMS)
- DBMS vs RDBMS
3. Understanding Databases
- Difference between Database and Database Server
- Single Database
- Database Server?
- What is Client Server Architecture
- Installation of MySQL
4. Commands in SQL – I
- Introduction to Commands in SQL
- Types of Commands in SQL
- Data Types in MySQL
- String Data Type in MySQL
- Numeric Data Type in MySQL
- DDL Command in SQL
5. Commands in SQL – II
- DML Command Statements in SQL – INSERT, SELECT, DELETE, UPDATE Commands
- Install SQLyog
- Use MySQL Workbench
- SQL Relational Operators
- Between and In Command in SQL
- LIKE Operator in SQL
6. Working with SQL
- SQL Aggregate Functions
- Use of aggregate functions in SQL
- SQL aggregate function Advantages
- SQL Nested Queries
- Use of nested query
7. Clause and Aliases in SQL
- What is ORDER BY Clause in SQL
- Use of ORDER BY Clause
- What are the Advantages of ORDER BY Clause
- Use GROUP BY and HAVING Clause in SQL
- Aliases in SQL
- Use of Aliases in SQL
8. SQL Constraints – I
- Constraints in SQL
- Use of Constraints in MySQL
- SQL NULL Values
- IS NULL and IS NOT NULL Values in SQL
9. SQL Constraints – II
- What is Primary Key and Foreign Key Relationship in SQL
- SQL CHECK Constraint
- SQL DEFAULT Constraint
- SQL NULL Function
- SQL Auto Increment
10. SQL Joins
- Joins in SQL
- Different Types of SQL Joins
- inner join, left join, right join, outer join, full join in SQL
- Why we use Joins in SQL?
- How to join tables in SQL?
- How to Fetch records from multiple tables
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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
- Histograms, Box Plots, Bar Charts, and Pie Charts
- Scatter Plots and Correlation
4. Probability Theory
- Basics of Probability
- Axioms of Probability
- Conditional Probability
- Bayes’ Theorem
- Applications of Bayes’ Theorem
5. Probability Distribution
- Random Variables
- Discrete Variables
- Probability Distributions: PMF, PDF and CDF
- Binomial, Poisson, Exponential, Uniform, Log-Normal
6. 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
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1. Introduction to NumPy
- Overview of NumPy
- Steps to Install NumPy in Python
- Installing NumPy in PyCharm IDE
2. Creating and Understanding NumPy Arrays
- Different Ways to Create Arrays in NumPy
- Core Concepts of NumPy Arrays
- Creating Arrays in NumPy
- Understanding Attributes of NumPy Arrays
- Practical Exercise on Array Attributes
3. Operations and Functions in NumPy
- Using arange(), linspace(), and logspace() for Array Generation
- Array Initialization with zeros(), ones(), full(), and eye()
- Comparing Arrays in NumPy
- Understanding any(), all(), and where() Methods
- Performing Arithmetic Operations on Arrays
- Using Statistical Functions in NumPy
4. Merging and Splitting NumPy Arrays
- Reference vs View vs Copy in Arrays
- Combining Arrays with Concatenation
- Merging Two Arrays in NumPy
- Merging Arrays with concatenate(), stack(), vstack(), hstack(), and Depth Methods
- Techniques to Split Arrays
- Splitting Arrays with split(), array_split(), vsplit(), and hsplit()
5. Introduction to Data Analysis with Pandas
- Overview of Pandas for Data Analysis
- Installing Pandas on Windows
- Setting Up Pandas in PyCharm
- How to Download a Dataset for Analysis
6. Pandas Series
- Understanding Series in Pandas
- Key Properties of Pandas Series
- Performing Mathematical Operations on Series
7. Pandas DataFrames
- Introduction to Pandas DataFrames
- Creating DataFrames from Excel, CSV, and Clipboard
- Multiple Ways to Create a DataFrame in Pandas
- Exporting DataFrames to CSV and Excel
- Understanding DataFrame Attributes
- Slicing DataFrames for Analysis
- Sorting DataFrames in Pandas
- Removing Duplicate Entries from DataFrames
- Handling Missing Data with fillna() and dropna()
- Using loc() and iloc() for Data Selection
- Filtering Data in Pandas DataFrames
8. Advanced Data Analysis with Pandas
- Introduction to Advanced Data Analysis Techniques
- Merging DataFrames with Join
- Performing Joins Without a Common Column in Pandas
- Concatenating DataFrames for Complex Analysis
- Using the where() Function in Pandas
- Grouping Data with Pandas groupby()
- Performing Aggregations in Pandas
9. SQL in Pandas
- SQL-like Operations in Pandas
- Writing SQL-Equivalent Queries Using Pandas
- SQL Queries Translated for Pandas DataFrames
- Using isin() and not isin() for DataFrame Filtering
- Finding the Largest Values with nlargest()
- Inserting, Deleting, and Updating Data in Pandas DataFrames
10. Introduction to Data Visualization with Matplotlib
- Overview of Matplotlib for Data Visualization
- Installing Matplotlib in PyCharm
- Setting Up Matplotlib in Python
- Steps to Design a Basic Chart
11. Working with Matplotlib
- Using Markers in Matplotlib
- Different Types of Markers in Charts
- Drawing Lines in Matplotlib
- Customizing Line Properties (Style, Width, and Color)
- Modifying Lines in Charts
- Changing Title, x-axis, and y-axis Colors and Fonts
- Customizing Fonts for Titles and Axes in Charts
- Adding a Legend in Matplotlib
- Adding Grid Lines to Charts
- Applying Grids to Graphs in Matplotlib
12. Creating Beautiful Graphs with Matplotlib
- Using Subplots in Matplotlib
- How to Plot Subplots in Python
- Using xticks(), yticks(), xlabel(), ylabel(), xlim(), and ylim()
- Creating a Scatter Plot in Matplotlib
- Using Cmap and ColorBar in Scatter Plots
- Creating Vertical and Horizontal Bar Graphs
- Plotting Multiple Bars in a Single Graph
- Creating a Pie Chart in Matplotlib
- Creating Histogram Graphs
13. Introduction to Data Visualization
- The Role of Data Visualization in Data Science
- Why Use Seaborn for Visualization?
- Comparison of Seaborn and Matplotlib
- Installing Seaborn and Setting Up the Environment
- Seaborn’s Key Features and Benefits
14. Basic Plots in Seaborn
- Creating Line Plots and Bar Plots
- Scatter Plots for Bivariate Data
- Histograms and KDE Plots for Distribution Analysis
- Using distplot() and displot() for Data Distributions
- Creating Heatmaps for Correlation Analysis
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1. Data Science Essentials
- Introduction to Data Science
- The Evolution of Data Science
- Big Data vs. Data Science
- Key Terminologies in Data Science
- Data Science vs. AI and Machine Learning
- Data Science vs. Analytics
2. 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
3. Analytics Classification
- Types of Analytics
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
4. 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
5. Machine Learning Introduction
- What is Machine Learning? How is it Different from AI?
- The Machine Learning Workflow
- Popular Machine Learning Algorithms
- Reinforcement Learning
6. Supervised Learning – Classification
- What is Classification in Machine Learning?
- Logistic Regression
- k-Nearest Neighbors (k-NN)
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- Naive Bayes Classifier
7. Supervised Learning – Regression
- What is Regression in Machine Learning?
- Linear Regression
- Ridge and Lasso Regression
- Decision Trees and Random Forest Regression
- Support Vector Regression (SVR)
8. Unsupervised Learning – Clustering
- Introduction to Clustering Algorithms
- What is Clustering?
- Differences Between Supervised and Unsupervised Learning
- k-Means Clustering
- Hierarchical Clustering
9. Machine Learning – Deep Dive
- Feature Engineering
- Support Vector Machine (SVM)
- Introduction to Decision Trees
- How Decision Trees Work
10. Naive Bayes in Machine Learning
- Introduction to Naïve Bayes
- How Naïve Bayes Uses Probability
- Text Classification with Naïve Bayes
- Building Naïve Bayes Models in Python
- Introduction to Gradient Boosting and XGBoost
11. Introduction to 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
12. Natural Language Processing
- What is Natural Language Processing?
- Applications of NLP
- Text Preprocessing
- Text Classification
- Sentiment Analysis
13. Deep Dive in AI
- Introduction to Artificial Intelligence
- Introduction to Gen AI
- Introduction to Generative Adversarial Networks
14. 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
15. 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).
- Diffusion Models.
- Transformers (foundation of GPT, DALL·E, and other tools).
Module 3: Popular Tools and Frameworks
- Overview of deep learning frameworks:
- TensorFlow and PyTorch.
- APIs and tools:
- OpenAI for text and code generation.
- Hugging Face for pre-trained Gen AI models.
- Working with pre-trained models to simplify tasks.
Module 4: Applications of Generative AI
- Text generation:
- Chatbots and virtual assistants.
- Automated content creation.
- Image and video generation:
- Tools like DALL·E and Stable Diffusion.
- Creative design and video synthesis.
- Audio synthesis:
- Voice cloning and music generation.
- Code generation:
- Tools like Copilot and Codex.
16. Introduction to 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.
- Artificial Neural Networks (ANNs)
- Basics of neurons, layers, and their roles.
- Activation functions: Sigmoid, ReLU, Tanh.
- Forward propagation and backpropagation.
- Deep Learning Frameworks
- Introduction to TensorFlow, Keras, and PyTorch.
- Types of Neural Networks
- Fully connected networks.
- Convolutional Neural Networks (CNNs) for images.
- Recurrent Neural Networks (RNNs) for sequential data.
17. Deep Dive into Deep Learning
- Advanced Architectures
- Long Short-Term Memory (LSTM).
- Gated Recurrent Units (GRU).
- Transformer models (used in GPT, BERT).
- Training Deep Neural Networks
- Data Preparation
- Data preprocessing techniques.
- Importance of balanced datasets.
- Optimization Techniques
- Gradient descent and its variants.
- Learning rate tuning and scheduling.
- Loss Functions
- Mean Squared Error (MSE) for regression.
- Cross-entropy loss for classification.
- Practical Implementation
- Building Neural Networks – Creating and training a neural network using TensorFlow.
- Implementing CNNs – Image classification with CNNs.
- Using RNNs – Sequence data analysis: Text, time-series data.
- Challenges in Deep Learning
- Overfitting and Underfitting – Causes and solutions (regularization, dropout).
- Computational Costs
- Hardware requirements: GPUs, TPUs.
- Strategies for optimizing performance in large-scale models.
18. 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).
- Fine-tuning and pre-training.
- Applications of LLMs
- Natural Language Processing Tasks
- Text generation and summarization.
- Language translation.
- Sentiment analysis.
- Question answering.
- Advanced Applications
- Chatbots and conversational AI.
- Code generation and debugging.
- Content creation and editing tools.
- Emerging Use Cases
- Healthcare, education, and finance.
- Research and scientific discoveries.
19. Hands-on with LLMs
- Understanding the Challenges in LLMs
- Ethical Concerns
- Bias in LLM outputs.
- Privacy and data security.
- Technical Challenges
- High computational requirements.
- Handling hallucinations in responses.
- Hands-On with LLMs
- Exploring Pre-trained Models
- Open-source LLMs (GPT-3, GPT-4, BERT, LLaMA).
- Using APIs (OpenAI)
- Building a simple chatbot with an LLM.
- Fine-tuning an LLM for a custom dataset.
- Future of Large Language Models
- Trends in LLM Development
- Evolution from GPT to GPT-4 and beyond.
- Multimodal LLMs (text, image, audio).
- Career Opportunities
- Key skills for working with LLMs.
- Roles in AI driven by expertise in LLMs.
20. 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
21. 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
22. PySpark
- Introduction to Spark
- RDDs
- Data Processing with PySpark
- Spark SQL
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1. Introduction to Power BI
- Introduction to Business Intelligence
- Business Intelligence Stages
- BI Use Cases
- Overview of Power BI
- Power BI Benefits
- Power BI Real-world Applications
2. Setting Up Power BI
- How to Install and Set up Power BI
- Download Power BI Desktop
- Installation Guide Steps
- Initial Setup and Configuration of Power BI
- Architecture and Components of Power BI
3. Power BI Components
- Power BI Desktop, Service, Mobile
- How These Components Work Together
- Introduction to Power BI Desktop
- Power BI Desktop Installation
- Power BI Desktop Interface
- Key Features and Tools of Power BI
- Different Panes and Menus
4. Connecting to Data Sources
- Get Data from Different Data Sources
- How to Identify and Connect to a Data Source
- Types of Data Sources in Power BI
- How to Get Data from Flat Files and Relational Data Sources
- Importing Data from Excel, CSV, and Other Flat Files
- Connecting to Databases
5. Power Query – Clean, Transform, and Load Data
- Introduction to Power Query
- Power Query Editor Interface
- Features and Functionalities of Power Query
- Power Query Ribbon and Tabs Overview
- Organizing and Structuring Data Tables
- Creating Relationships Between Tables
- Analyzing Data Quality and Handling Data Issues
- Changing Column Data Types for Accuracy
- Managing Data Source Settings
6. Designing a Data Model
- Fact and Dimension Tables
- Relationships and Cardinality (One-to-Many, Many-to-Many)
- Star Schema in Data Modeling
- Benefits of Using a Star Schema
7. Data Modeling and DAX
- Introduction to Data Modeling and DAX
- Creating Relationships and Managing Cross-filter Directions
- DAX Syntax and Functions Overview
- Context in DAX
- Creating Calculated Tables and Columns
- Practical Examples of Calculated Tables
- Measures and Calculations Using DAX
- Date and Time Functions in DAX
- Best Practices for Using DAX in Your Data Model
8. Report Creation in Power BI
- Creating Reports from Raw Data
- Adding and Customizing Visualization Items
- Choosing Appropriate Visualization Types
- Formatting and Configuring Power BI Charts
- Using and Importing Custom Visuals
- Applying and Customizing Themes
- Configuring Conditional Formatting
- Applying Sorting, Slicing, and Filtering
- Creating Data Hierarchies for Navigation
- Drilldown/Drillup with Interactive Visuals
- Using AI Visuals (Q & A, Key Influencers, Decomposition Tree, Smart Narratives)
- Publishing Reports to Power BI Service
9. Managing Workspaces in Power BI
- Creating and Configuring Workspaces
- Assigning Workspace Roles and Permissions
- Configuring and Updating Workspace Apps
10. Dashboard Creation
- Building Dashboards in Power BI
- Managing and Organizing Dashboard Tiles
- Pinning Live Report Pages to Dashboards for Real-time Updates
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1. Introduction to DSA using Python
- What is DSA using Python?
- Why Python is a great choice for DSA
- Why DSA with Python
- Types of Data Structures
2. Stack in DSA Python
- Introduction to Stack in DSA Python
- Stack using List
- Stack using Collection Module in DSA Python
- Stack using Queue Module in DSA Python
- Convert Infix to Postfix Expression using Stack
- Convert Infix to Prefix in DSA
3. Queue in DSA Python
- What is a Queue in Data Structures?
- Queue using List in DSA Python
- Queue using Collection Module in DSA Python
- Linear Queue using Collection Module
- Circular Queue in DSA Python
- Insert, Display and Delete in Circular Queue
- D Queue in Data Structures
- Deque using Collection Module
- Priority Queue in DSA Python
- Priority Queue using List
- Priority Queue using Module
4. Linked List in DSA Python
- What is Linked List in DSA Python?
- Create, Display, Insert and Delete in Linked List in DSA Python
- Searching in Linked List
- Sorting in Linked List
- Stack Linked List in DSA Python
- Queue Linked List in DSA Python
5. Circular Linked List in DSA Python
- What is a Circular Linked List?
- Create, Display, Insert and Delete in DSA Python
6. Doubly Linked List in DSA Python
- What is Doubly Linked List in DSA Python?
- Create, Display and Reverse Display in Doubly Linked List in DSA Python
- Insertion and Display in Doubly Linked List in DSA Python
7. Circular Doubly Linked List in DSA Python
- What is Circular Doubly Linked List in DSA Python
- Creation and Display in Circular Doubly Linked List in DSA Python
- Insertion and Reverse Display in Circular Doubly Linked List in DSA Python
- Deletion in Circular Doubly Linked List in DSA Python
8. Recursion in DSA Python
- What is Recursion in DSA Python?
- Types of Recursion
- Tail Recursion in DSA Python
9. Searching in DSA Python
- Linear Search in DSA Python
- Binary Search in DSA Python
10. Sorting in DSA Python
- Linear Sort in DSA Python
- Bubble Sort in DSA Python
- Selection Sort
- Insertion Sort
- Radix Sort
- Shell Sort
- Quick Sort
- Two way Merge Sort
- Merge Sort
11. Tree in DSA Python Part – 1
- What is Tree in Data Structures using Python?
- Binary Tree and Binary Search Tree in DSA Python
- Tree Traversals in DSA Python
- Tree Traversal Methods – Inorder, Preorder, Postorder
- Creation of Tree from Inorder to Preorder in DSA Python
- Creation of Binary Search Tree
- Inorder, Preorder and Postorder in BST in DSA Python
- Searching and Sorting in BST in DSA Python
- Delete Node From Tree in DSA Python
12. Tree in DSA Python Part – 1
- Strictly Binary Tree
- AVL Tree
- Red Black Tree
- Threaded Binary Tree in DSA
- B Tree in DSA Python
- B+ Tree in DSA Python
- Heap Tree in DSA Python
13. Time Complexity in DSA using Python
- Address Calculation of Array in DSA Python
- Address Calculation of 3D Array in DSA Python
- Address Calculation of Lower & Upper Bound of Array in DSA
- Tower of Hanoi in DSA Python
14. Graph in DSA using Python
- What is a Graph in DSA?
- Graph Representation
- Graph Algorithms
- Kruskal’s Algorithm
- Prim’s Algorithm for Minimum Spanning Tree in DSA
- Dijkstra’s Algorithm
- Dijkstra Algorithm for Directed Graph
- Floyd Warshall Algorithm
- Breadth First Search Algorithm
- Depth First Search Algorithm
- What is Hashing
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1. Sentiment Analysis
Analyze and classify movie reviews as positive or negative using natural language processing techniques. This project explores text preprocessing and sentiment classification.
2. Image Classification
Build and train a convolutional neural network to classify images into 10 different categories. This computer vision project introduces deep learning frameworks and image preprocessing.
3. Credit Card Fraud Detection
Detect fraudulent credit card transactions using anomaly detection algorithms. This project focuses on handling imbalanced datasets and implementing effective classification models.
4. Movie Recommendation System
Develop a recommendation engine to suggest movies to users based on their viewing history. This collaborative filtering project delves into matrix factorization and similarity measures.
5. Natural Language Processing for News Categorization
Classify news articles into predefined categories using NLP techniques. This text classification project covers tokenization, vectorization, and model training for multi-class classification.
6. Sales Forecasting
Predict weekly sales for Walmart stores across different departments using historical sales data. This project involves time series forecasting and multivariate regression.
7. Customer Churn Analysis
Build a model to predict customer churn based on usage patterns and demographic information. This project focuses on handling imbalanced datasets and implementing classification algorithms.
8. Energy Consumption Forecasting
Predict future energy consumption levels using historical usage data and external factors. Focuses on time series forecasting and model deployment.
9. Facial Emotion Recognition
Develop a deep learning model to detect emotions from facial images. Focuses on convolutional neural networks (CNNs) and image preprocessing.
10. Real-Time Stock Price Prediction
Develop a system that predicts stock price movements using live market data and technical indicators. Implement streaming data processing to update predictions continuously.
11. Retail Store Sales Forecasting
Predict future sales for a retail store using historical sales data and promotional information. This project focuses on time series analysis and regression techniques to help optimize inventory and marketing strategies.
12. E-commerce Product Classification
Automatically categorize e-commerce products into predefined categories using product descriptions and metadata. This project leverages text classification and machine learning algorithms.
13. Traffic Sign Recognition
Build a computer vision model to recognize and classify traffic signs from images. This project involves deep learning with convolutional neural networks (CNNs) and image preprocessing techniques.
14. Employee Attrition Prediction
Predict which employees are likely to leave the company based on their demographic and job-related features. This classification project focuses on handling imbalanced datasets and feature selection.
15. Music Genre Classification
Build a model to classify songs into their respective genres using audio features. This project involves audio data processing, feature extraction using libraries like Librosa, and applying machine learning algorithms for multi-class classification.
16. Spam Detection in Emails
Develop a classification model to identify spam emails based on their content and metadata. This project emphasizes text preprocessing, feature extraction, and implementing machine learning algorithms for binary classification.
17. Blockchain Transaction Analysis
Analyze and classify blockchain transactions to identify patterns, anomalies, and potential fraudulent activities. This project delves into network analysis, feature engineering from transaction data, and applying machine learning algorithms for anomaly detection.
18. Healthcare Patient Diagnosis
Develop a classification model to diagnose diseases from patient medical records. This project emphasizes data preprocessing, handling missing values, and building accurate diagnostic models.
19. Real-Time Language Translation
Build a real-time language translation model using neural machine translation techniques. This natural language processing project involves sequence-to-sequence models and attention mechanisms to facilitate multilingual communication.
20. Predicting Loan Approval
Build a classification model to predict loan approval outcomes based on applicant demographics, financial history, and loan details. This project emphasizes handling imbalanced datasets and feature selection to improve prediction accuracy.
21. Speech Emotion Recognition
Create a model to recognize emotions from speech recordings using audio signal processing and machine learning algorithms. This project explores feature extraction from audio data and classification techniques to interpret human emotions.
22. Detecting Fake News
Develop a natural language processing (NLP) model to classify news articles as real or fake based on their content and metadata. This project emphasizes text preprocessing, feature extraction, and implementing classification algorithms to combat misinformation.
23. Emotion Detection from Text
Build a natural language processing (NLP) model to detect emotions in text data, such as tweets or customer reviews. This project involves text preprocessing, sentiment analysis, and classification algorithms to interpret human emotions accurately.
24. Handwritten Signature Verification
Create a machine learning model to verify the authenticity of handwritten signatures. This computer vision project involves image preprocessing, feature extraction, and implementing classification algorithms to detect forgeries.
💻 Self-paced 🔵 Instructor-led Live
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Data Science and AI Projects
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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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Tools and Technologies
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In our AI & Data Science Bootcamp, you will learn
This Bootcamp is designed to make you job-ready and confident in the exciting world of AI and Data Science!
Learn Python from Scratch
Master Data Visualization
Understand Data Handling
Solve Real-World Problems
Grasp Machine Learning Concepts
Build Strong Analytical Skills
Learn Statistics for Data Science
Get Job Ready
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Data Science and AI Case Studies
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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.
AI & Data Science Job Roles
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70000+
Career Carved
100+
Comprehensive Courses
4.8
Average Rating
19 LPA
Average Package
90+
Countries Served
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Top Reasons to Choose AI and Data Science as a Career
Learning Path of AI & Data Science
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Our learners are working in leading organizations
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Meet Our Instructors from Industry
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TechVidvan’s Career Services
Career Support
Complete support from our dedicated placement team until you get a job.
Dedicated Instructor
Expert data science career instructor to guide you throughout the program.
Resume Building
Create professional, ATS-friendly resumes to impress recruiters.
Lifetime Learning Access
Get lifetime access to all courses for continuous learning and growth.
Job Application
Streamline your job applications with expert guidance and tools.
Job Guarantee Programs
Secure your future with programs guaranteeing jobs or money back.
TechVidvan Internship
Gain real-world experience with career-boosting internships.
Job Portal Access
Offer exclusive access to job listings through a dedicated job portal.
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Why Join TechVidvan’s Bootcamp
AI & Data Science Course FAQs
Who should take this Data Science course?
This TechVidvan Data Science Bootcamp is ideal for engineering students, IT professionals, and anyone interested in pursuing a career in data science or analytics.
Do I need programming skills to start?
While some basic programming knowledge is helpful, it’s not necessary. The course starts with introductory lessons in Python to get you comfortable with coding.
Which programming languages are used in this course?
The course primarily focuses on Python, the most widely used language in data science, along with relevant libraries like Pandas and NumPy and many more.
What tools will I learn to use?
You will get hands-on experience with tools like Jupyter Notebook, SQL, and Power BI to analyze and visualize data.
Why are statistics important in Data Science?
Statistics form the backbone of data science. You’ll learn how to use statistical methods to analyze data and make data-driven decisions.
Will I receive a certificate after completing the course?
Yes, upon successfully finishing the course, you’ll receive a certificate that you can share with employers or on your LinkedIn profile.
What kind of career opportunities can I expect after completing this course?
This course prepares you for a variety of roles, including Data Analyst, Data Scientist, and Machine Learning Engineer, across multiple industries.
Are there any practical projects in the course?
Yes, there are multiple projects where you will apply your learning to real-world problems using actual datasets.
What industries hire Data Science professionals?
Industries like tech, healthcare, finance, retail, and government are actively hiring data scientists to solve complex problems.
What is the typical salary range for a data scientist?
Starting salaries for data scientists are usually between $70,000 and $100,000 per year, depending on location and experience level.
