Programming

    Data Science Course

    Learn the foundations of modern data science through Python, applied statistics, machine learning concepts, and hands-on work with real datasets. This course is designed to build practical analytical and predictive thinking.

    8 Months
    45,000

    One-Time Fee

    45,000

    Installment Option

    5 x ₹10,000

    Total ₹50,000

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    Who Should Join

    • Learners interested in data science careers
    • Python learners moving toward ML and analytics
    • Students exploring future-ready tech fields
    • Anyone who wants deeper data and prediction skills

    Career Outcomes

    • Understand how data science projects are structured
    • Use Python and common libraries for analysis
    • Learn the basics of machine learning and model evaluation
    • Interpret data using practical statistics concepts
    • Build stronger foundations for future AI and ML work

    Tools and Software Covered

    • Python (Advanced)
    • Prof. Advanced Excel
    • Machine Learning
    • Power BI
    • AI Tools

    What You'll Learn

    Python (Advanced)
    Prof. Advanced Excel
    Machine Learning
    Power BI
    AI Tools
    Python (Advanced for Data Science) - Same as Python course with focus on data analysis, automation, and real datasets
    Statistics & Probability (Applied)
    Basics of Statistics (Mean, Median, Mode)
    Data Distribution & Variance
    Probability Concepts (Events, Conditional Probability)
    Correlation & Regression Basics
    Hypothesis Testing (Real-world use)
    Understanding Data Patterns
    Practical Case Studies (Business Data)
    Machine Learning (Core Concepts)
    Introduction to Machine Learning
    Supervised vs Unsupervised Learning
    Regression Models (Linear, Logistic)
    Classification Algorithms
    Clustering Techniques
    Model Training & Testing
    Overfitting & Model Evaluation
    Real-world ML Applications
    Libraries (NumPy, Pandas, Matplotlib, Scikit-learn)
    NumPy (Arrays & Numerical Operations)
    Pandas (Data Cleaning & Manipulation)
    Matplotlib & Seaborn (Data Visualization)
    Scikit-learn (ML Model Implementation)
    Data Preprocessing Techniques
    Working with Real Datasets
    End-to-End Mini Projects

    Frequently Asked Questions

    I am weak in maths, can I still learn data science?
    Basic maths is enough to start. Advanced concepts are taught in a simple, practical way.
    Is this course very difficult compared to others?
    It is more advanced than basic courses, but structured learning makes it manageable.
    What is the difference between Data Analytics and Data Science?
    Data Analytics focuses on reports and insights, while Data Science includes prediction, machine learning, and advanced analysis.
    Will I build real machine learning projects?
    Yes, you will work on real datasets and create prediction models.
    Is Python enough for data science?
    Python is the main tool, along with libraries like Pandas and Scikit-learn.
    Can I get a job directly after this course?
    You can apply for roles like Junior Data Scientist, Data Analyst, and ML Intern.
    Is AI replacing data scientists in 2026?
    No, AI tools assist data scientists. Skilled professionals are still in high demand.
    What is the biggest benefit of this course?
    You learn future-ready skills like machine learning and data-driven decision making.