Datascience with AI: 125-Hour Complete Data Science & Artificial Intelligence Training at ₹12995
Trained by working IT professionals from leading companies
125 HOUR Datascience with AI SYLLABUS
Launch a high-growth career in Data Science and Artificial Intelligence.
This complete program guides you from foundational Python and relational database SQL querying
to data analytics (Pandas, NumPy, Matplotlib, Tableau), Machine Learning algorithms (Supervised, Unsupervised, Reinforcement Learning),
and state-of-the-art Deep Learning, NLP, and Computer Vision AI applications.
Fee: ₹12995
Training Mode: Instructor-led live class
Duration: 125 hours
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What you will learn from this course?
- Module 1: Python Programming Foundations
- Python Setup, Environment & IDEs
- Installing Python, Anaconda, and VS Code / Jupyter Notebook
- Basic Python syntax and program structure
- Variables, Data Types & Type Conversion
- Integers, Floats, Strings, Booleans
- Dynamic typing and type casting (`int()`, `float()`, `str()`)
- Input/Output & String Formatting
- User input via `input()` and console output via `print()`
- Formatted string literals (f-strings)
- Operators & Expressions
- Arithmetic, Assignment, Comparison, and Logical operators
- Control Flow
- Conditional branching: `if`, `elif`, `else`
- Looping structures: `for` loops, `while` loops, `break`, `continue`
- Core Data Structures
- Lists: Creation, slicing, indexing, and list methods
- Tuples: Immutability and tuple packing/unpacking
- Sets: Mathematical set operations (union, intersection, difference)
- Dictionaries: Key-value mapping, nested structures, and dict methods
- User-Defined Functions
- Defining functions, parameters, return values, and variable scope
- Module 2: Database Management & SQL Querying
- Relational Database Management Systems (RDBMS)
- Database Setup & MySQL Workflows
- SQL Constraints: `PRIMARY KEY`, `FOREIGN KEY`, `UNIQUE`, `NOT NULL`, `CHECK`
- Data Definition Language (DDL): `CREATE`, `ALTER`, `DROP`, `TRUNCATE`
- Data Manipulation Language (DML): `INSERT`, `UPDATE`, `DELETE`, `SELECT`
- Filtering & Pattern Matching: `WHERE`, `LIKE`, `IN`, `BETWEEN AND`
- Sorting & Row Limiting: `ORDER BY`, `LIMIT`
- Aggregate Functions & Grouping: `COUNT`, `SUM`, `AVG`, `MIN`, `MAX`, `GROUP BY`, `HAVING`
- Joins in SQL: `INNER JOIN`, `LEFT JOIN`, `RIGHT JOIN`, `CROSS JOIN`, `UNION`
- Subqueries and nested analytical queries
- Module 3: Data Analytics & Visual Intelligence (Pandas, NumPy, Matplotlib, Tableau)
- NumPy for Numerical Computing
- Creating 1D, 2D, and multi-dimensional arrays
- Array indexing, slicing, and reshaping (`reshape`, `shape`)
- Copy vs. View semantics
- Joining, splitting, searching, sorting, and filtering arrays
- Statistical operations: Mean, Median, Mode, Standard Deviation, Variance, Percentiles
- Pandas for Data Manipulation & Wrangling
- Pandas Series and DataFrames
- Reading data from CSV, JSON, Excel, and SQL tables
- Data cleaning: Detecting and imputing missing data, fixing wrong formats, duplicate removal
- DataFrame transformations, filtering, sorting, and aggregation operations
- Matplotlib for Data Visualization
- Pyplot basics, plot styling, labels, titles, and legends
- Line plots, scatter plots, bar charts, histograms, and pie charts
- Creating multi-panel visual grids with subplots
- Tableau for Business Intelligence
- Connecting to external datasets and data preparation
- Creating interactive charts: Bar, Line, Scatter, Geographic Maps
- Filters, parameters, groups, calculated fields, and aggregations
- Building dynamic, interactive dashboards and sharing reports
- Module 4: Machine Learning Foundations & Algorithms
- Machine Learning Overview & Scikit-learn Pipeline
- What is Machine Learning? Supervised, Unsupervised, Reinforcement Learning
- Feature engineering, scaling (`StandardScaler`, `MinMaxScaler`), train-test split
- Model evaluation metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC, RMSE, MAE, R²
- Supervised Learning Algorithms
- Linear Regression & Multiple Linear Regression
- Logistic Regression for Binary and Multi-Class Classification
- Decision Trees and Random Forest Ensembles
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes Classifier
- Unsupervised Learning & Clustering
- K-Means & K-Means++ Clustering (Elbow Method for optimal K)
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Hierarchical Clustering (Agglomerative & Divisive) and Dendrograms
- Gaussian Mixture Models (GMM)
- Reinforcement Learning Fundamentals
- Key concepts: Agents, Environments, States, Actions, and Rewards
- Markov Decision Processes (MDP)
- SARSA (State-Action-Reward-State-Action) algorithm
- Q-Learning and Bellman Equation
- Deep Q-Networks (DQN) architecture overview
- Module 5: Deep Learning & Applied Artificial Intelligence
- Deep Learning Foundations
- Biological vs. Artificial Neurons
- Artificial Neural Networks (ANN) architecture: Input, Hidden, and Output layers
- Activation functions: Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax
- Forward propagation, Loss functions, Backpropagation, and Gradient Descent
- Advanced Neural Network Architectures
- Convolutional Neural Networks (CNN): Convolutional layers, pooling layers, feature maps
- Recurrent Neural Networks (RNN): Handling sequential and time-series data
- Long Short-Term Memory (LSTM) networks: Vanishing gradient resolution and cell gates
- Generative Adversarial Networks (GANs): Generator and Discriminator dynamics
- Natural Language Processing (NLP)
- Text preprocessing: Tokenization, stemming, lemmatization, stop-word removal
- Feature extraction: Bag of Words (BoW), TF-IDF, Word2Vec embeddings
- Sentiment analysis and text classification
- Computer Vision & OpenCV
- Image reading, writing, filtering, color conversions with OpenCV
- Edge detection, contour detection, and object tracking
- Image classification and object recognition with deep learning models
- Real-World AI & Data Science Capstone Projects
- Project 1: Exploratory Data Analysis & Tableau Business Dashboard
- Project 2: End-to-End Predictive Machine Learning Classification/Regression System
- Project 3: Deep Learning Image Recognition & NLP Sentiment Analysis Application
