Data Science & Analytics Course
Master Python, ML, AI & Launch Your Analytics Career
Salaries vary by company, location & experience.
Course Overview
Become a Data Scientist and unlock one of the highest-paying tech careers. This comprehensive course covers Python programming, statistics, machine learning, AI, and real-world data analytics projects. Perfect for graduates from any background who want to enter the booming field of data science.
What You'll Get
Course Curriculum
Module 1: Python Programming Fundamentals2 weeks
- Python basics and syntax
- Data structures (lists, tuples, dictionaries)
- Functions and modules
- File handling and exceptions
- Object-oriented programming in Python
Module 2: Statistics & Mathematics2 weeks
- Descriptive statistics and probability
- Distributions and hypothesis testing
- Linear algebra for ML
- Calculus basics for optimization
- Statistical inference
Module 3: Data Analysis with Python2 weeks
- NumPy for numerical computing
- Pandas for data manipulation
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA)
- Data visualization (Matplotlib, Seaborn, Plotly)
Module 4: SQL & Databases1.5 weeks
- SQL basics and advanced queries
- Joins, subqueries, and aggregations
- Database design and normalization
- Working with PostgreSQL/MySQL
- NoSQL basics (MongoDB)
Module 5: Machine Learning3 weeks
- Introduction to ML and scikit-learn
- Supervised learning (regression, classification)
- Unsupervised learning (clustering, dimensionality reduction)
- Model evaluation and hyperparameter tuning
- Ensemble methods (Random Forest, XGBoost)
Module 6: Deep Learning & AI3 weeks
- Neural networks fundamentals
- TensorFlow and Keras
- Computer vision with CNNs
- Natural Language Processing (NLP)
- Transformers and pre-trained models
Module 7: Big Data & Cloud2 weeks
- Introduction to big data ecosystems
- PySpark basics
- AWS for data science (S3, EC2, SageMaker)
- Google Cloud Platform (BigQuery, Vertex AI)
- MLOps fundamentals
Module 8: Capstone Projects2 weeks
- Customer churn prediction
- Sales forecasting model
- Sentiment analysis of reviews
- Image classification system
- Recommendation engine
Module 9: Deployment & Interview Prep1.5 weeks
- Model deployment with Flask/FastAPI
- Building ML pipelines
- Version control with Git
- Portfolio building on GitHub
- Data science interview preparation
Course Features
Python Mastery
Become proficient in Python - the #1 language for data science and AI.
Hands-On ML Projects
Build 5+ real machine learning projects for your portfolio.
Real Business Problems
Work on actual business cases: churn prediction, forecasting, recommendations.
Cloud Platforms
Learn AWS and GCP for deploying ML models at scale.
System Access & Course Completion Certificate
System access and course completion certificate.
High Paying Field
Data scientists are among the highest-paid tech professionals.
Who Should Learn This Course?
Career Outcomes
Job Roles You Can Apply For:
Companies Hiring:
Frequently Asked Questions
Do I need coding knowledge to learn data science?
No prior coding knowledge needed! We start with Python basics. If you have logical thinking and problem-solving skills, you can learn data science. Most of our successful students had no coding background.
Is mathematics required for data science?
Basic mathematics (10th standard level) is enough to start. We teach necessary statistics, probability, and linear algebra as part of the course. Focus is on practical application, not heavy theory.
Can non-engineers learn data science?
Absolutely! We have successfully trained commerce, science, and MBA graduates. Data science is about analyzing data and solving business problems - not rocket science. Any graduate can learn it.
What is the difference between Data Science and Data Analytics?
Data Analytics focuses on analyzing past data for insights. Data Science includes predictive modeling and machine learning to forecast future trends. Data scientists earn more and have broader opportunities.
Which companies hire data scientists?
Product companies (Amazon, Flipkart, Swiggy), analytics firms (Fractal, Mu Sigma), banks, e-commerce, healthcare, and almost every industry now needs data scientists.
Is data science better than software development?
Both are great! Data science has higher starting salaries and is less crowded. Software development has more job openings. If you like math and analytics, choose data science. If you like coding and building products, choose development.
What is the career growth in data science?
Salary depends on experience, module, and company.
Will I get a job after completing this course?
Yes! We have 90% placement rate in data science. You'll build strong portfolio with projects, get system access and course completion certificate, and our placement team works with 30+ companies hiring data scientists.
Enroll in Data Science & Analytics
Start your journey — competitive fresher salary in your chosen domain.
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