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The Full Stack Data Science course is designed to provide comprehensive knowledge in data analysis, data processing, and machine learning.
You’ll master key technologies such as Python, R, SQL, and TensorFlow while gaining experience with data visualization and cloud-based tools.
This course is ideal for those who want to pursue a career as a Data Scientist and work across the entire data pipeline, from data collection and cleaning to modeling and deployment.
What you will learn?
- Python & R Programming Data structures, libraries (NumPy, Pandas, Seaborn), data manipulation
- SQL for Data Handling: Queries, Joins, Subqueries, Data Extraction, Integration with Python
- Data Cleaning & Processing: Handling missing values, feature engineering, scaling
- Model Deployment: Flask, Streamlit apps, basics of AWS/GCP for hosting models
- Exploratory Data Analysis (EDA): Visualization, outlier detection, statistical summaries
- Machine Learning: Regression, Classification, Clustering using Scikit-learn
- Deep Learning (TensorFlow/Keras): Neural networks, CNN basics, model training
- Projects & Case Studies: Real-world data, industry-specific use cases, capstone projects
We Providing ISO Certification to all Students
Curriculum
- 6 Sections
- 46 Lessons
- 26 Weeks
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- Month 1: Python Programming & Data Handling9
- 1.1Week 1–2: Python Fundamentals
- 1.2Variables, Data Types, Operators, Control Structures
- 1.3Functions, Loops, Exception Handling
- 1.4File I/O, Modules, and Packages
- 1.5Week 3–4: Python for Data Science
- 1.6NumPy for numerical computing
- 1.7Pandas for data manipulation
- 1.8Working with CSV, Excel, and JSON files
- 1.9Mini Project: Data wrangling and preprocessing on real-world dataset
- Month 2: Statistics, SQL & Data Analysis10
- 2.1Week 5–6: Statistics for Data Science
- 2.2Descriptive & Inferential Statistics
- 2.3Probability, Normal Distribution
- 2.4Hypothesis Testing & p-values
- 2.5Week 7–8: SQL for Data Analysis
- 2.6Week 7–8: SQL for Data Analysis
- 2.7SELECT, JOINs, Subqueries, Aggregations
- 2.8Window functions, CTEs
- 2.9Creating analytical queries from real datasets
- 2.10Mini Project: Analyze a database (e.g., sales or customer data) using SQL
- Month 3: Machine Learning – Supervised & Unsupervised9
- 3.1Week 9-10: Supervised Learning
- 3.2Linear & Logistic Regression
- 3.3Decision Trees, Random Forests, KNN
- 3.4Model evaluation metrics (accuracy, precision, recall, AUC)
- 3.5Week 11-12: Unsupervised Learning
- 3.6Clustering: K-Means, Hierarchical
- 3.7Dimensionality reduction: PCA, t-SNE
- 3.8Anomaly detection
- 3.9Mini Project: Customer segmentation or sales prediction
- Month 4: Deep Learning & NLP9
- 4.1Week 13-14: Neural Networks & Deep Learning
- 4.2Introduction to Neural Nets using TensorFlow/Keras
- 4.3CNNs for image classification
- 4.4Hyperparameter tuning, dropout, batch normalization
- 4.5Week 15-16: NLP (Natural Language Processing)
- 4.6Text cleaning, tokenization, vectorization (TF-IDF, Word2Vec)
- 4.7Sentiment analysis, classification
- 4.8Intro to transformers (BERT, GPT)
- 4.9Mini Project: Sentiment analysis or image classification
- Month 5: Power BI, Tableau & Model Deployment9
- 5.1Week 17-18: Data Visualization Tools
- 5.2Power BI: DAX, dashboards, publishing
- 5.3Tableau: Storytelling, filters, actions, dashboard design
- 5.4Comparison and integration with ML outputs
- 5.5Week 19-20: Model Deployment
- 5.6Saving models (pickle, joblib)
- 5.7Deploying with Flask/FastAPI
- 5.8Hosting on Heroku, Streamlit or AWS
- 5.9Mini Project: ML-powered dashboard with deployment
- Month 6: Capstone Project, Resume & Interview Prep7
- 6.1Week 21-22: Capstone Project (End-to-End)10 Minutes0 Questions
- 6.2Real-world dataset10 Minutes0 Questions
- 6.3EDA → Modeling → Visualization → Deployment10 Minutes0 Questions
- 6.4Week 23-24: Portfolio & Career Preparation10 Minutes0 Questions
- 6.5Build portfolio on GitHub10 Minutes0 Questions
- 6.6Create resume & LinkedIn profile for data roles10 Minutes0 Questions
- 6.7Mock interviews & case studies10 Minutes0 Questions
This course is ideal for students, graduates, working professionals, or anyone looking to build a career in data science, data analytics, or machine learning—no prior programming experience required.
Basic computer skills and foundational knowledge of mathematics are recommended. No prior coding experience is necessary, as the course starts from the basics.
You will gain hands-on experience with Python, R, SQL, TensorFlow, Scikit-learn, Pandas, Matplotlib, Seaborn, Flask, Streamlit, and cloud platforms like AWS or GCP.
Yes, you will receive an industry-recognized certificate upon successful completion of the course, which you can showcase on your resume, LinkedIn, and professional portfolio.
We offer both formats. You can attend live sessions (online/offline) or follow a structured self-paced learning path with full access to recorded lectures and mentor support.
You will work on real-world projects involving domains like healthcare, finance, e-commerce, and more. The course also includes capstone projects to demonstrate end-to-end skills.
Yes, we offer resume building, mock interviews, portfolio reviews, and job placement assistance to help you land your first role in data science.
You’ll need a laptop or desktop with internet access. We recommend at least 8GB RAM for smooth performance with data science tools.
The course duration is typically 6 months, including live sessions, hands-on labs, assignments, and projects.
Our refund policy allows for a full refund within 3 days of enrollment. After the 3-day period, no refunds will be processed. Please ensure you review the course content and terms before enrolling.
Requirements
- Learners should be comfortable using a computer, navigating the internet, and working with basic software applications such as Microsoft Excel or Google Sheets.
- A basic grasp of high school-level mathematics, including algebra and introductory statistics, is recommended to understand data analysis and machine learning concepts.
- This course is beginner-friendly and includes foundational training in programming using Python and R. However, prior exposure to any programming language will be an added advantage.
- A personal computer with a stable internet connection is essential for attending online sessions, working on coding assignments, and accessing the LMS platform.
- Learners should have a strong desire to build a career in data science and be committed to engaging with course materials, completing projects, and participating in assessments.
Features
- Learn how to deploy data models on the cloud.
- Mini-projects and a final capstone project.
- Interactive quizzes and exercises to reinforce concepts.
- Hands-on projects using real-world datasets.
- Comprehensive curriculum covering the entire data science pipeline.
Target audiences
- Professionals seeking a transition into data science. .
- Business analysts looking to expand into data-driven roles .
- Aspiring Data Scientists looking for in-depth, full-stack knowledge.
- Students in computer science or data-related fields.
