15
Modules
Verified
Hands-on Lab
Certificate
What You Will Master
In-Depth Foundation
Build strong core fundamentals matching industry level standard expectations.
Hands-on Projects
Work on mock design patterns and complex assignments for active practice.
Study Materials
No study materials available for this course at the moment.
Course Overview
Detailed description goes here.
Eligibility Criteria
Candidates with 10+2 qualification with minimum 50% marks from a recognized board.
Admission Process
Direct merit-based admission or common institutional admission entrance test.
Detailed Curriculum Roadmap
15 Modules- Introduction to Data Science
- Data Science Lifecycle
- Applications of Data Science
- Roles and Responsibilities of a Data Scientist
- Data Science Tools
- Python for Data Science
- Jupyter Notebook
- Anaconda Installation
- Data Types
- Data Sources
- Variables and Data Types
- Operators
- Conditional Statements
- Loops
- Functions
- Modules and Packages
- Lists
- Tuples
- Dictionaries
- Sets
- File Handling
- Exception Handling
- Introduction to NumPy
- NumPy Arrays
- Array Operations
- Array Indexing
- Array Slicing
- Broadcasting
- Mathematical Functions
- Linear Algebra
- Random Numbers
- Introduction to Pandas
- Series
- DataFrame
- Reading CSV & Excel Files
- Data Selection
- Filtering
- Sorting
- GroupBy
- Merge & Join
- Missing Data Handling
- Data Cleaning
- Data Transformation
- Introduction to Visualization
- Matplotlib
- Seaborn
- Line Chart
- Bar Chart
- Pie Chart
- Histogram
- Scatter Plot
- Box Plot
- Heatmap
- Pair Plot
- Descriptive Statistics
- Mean
- Median
- Mode
- Variance
- Standard Deviation
- Probability
- Normal Distribution
- Correlation
- Covariance
- Hypothesis Testing
- Data Collection
- Data Cleaning
- Handling Missing Values
- Outlier Detection
- Feature Engineering
- Feature Scaling
- Label Encoding
- One-Hot Encoding
- Data Splitting
- Train-Test Split
- Database Fundamentals
- MySQL Basics
- SELECT Queries
- WHERE Clause
- ORDER BY
- GROUP BY
- HAVING
- Joins
- Subqueries
- Aggregate Functions
- Views
- Introduction to Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Model Training
- Model Evaluation
- Bias & Variance
- Cross Validation
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Naive Bayes
- Model Performance Metrics
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Principal Component Analysis (PCA)
- Association Rule Mining
- Dimensionality Reduction
- Introduction to Deep Learning
- Artificial Neural Networks (ANN)
- TensorFlow
- Keras
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Natural Language Processing (NLP)
- Text Processing
- Sentiment Analysis
- Introduction to Big Data
- Hadoop
- Apache Spark
- Data Lakes
- AWS for Data Science
- Google Cloud Platform (GCP)
- Azure Machine Learning
- Cloud Storage
- Model Serialization
- Flask API
- FastAPI
- Streamlit
- Docker Basics
- Git & GitHub
- CI/CD Basics
- MLflow
- Model Monitoring
- Exploratory Data Analysis (EDA)
- Kaggle Projects
- Sales Prediction
- Customer Churn Prediction
- House Price Prediction
- Recommendation System
- Credit Risk Analysis
- Time Series Forecasting
- Dashboard Creation
- Resume Building
- Interview Preparation
- Portfolio Development
Syllabus Designers & Instructors
Our high-fidelity premium courses are curated and kept industry-updated by a globally accredited team of technical experts and corporate mentors.
Dr. Anjali Sharma
Ex-IIT Professor, Researcher
Arvind Krishnan
Principal Software ArchitectAdmission Window Open
50,000
Transparent Course Tuition Fee
Key Enrollment Features
- Shareable Certificate on success
- Interactive doubt session support
- Lifetime access to syllabus materials
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