Data Science

Data Science Course

Data Science Course Overview

Our Data Science course is designed to equip you with in-demand analytical skills and hands-on experience in tools like Python, SQL, Machine Learning, and more.

Whether you’re a beginner or want to enhance your data expertise, this course will transform your career path in today’s data-driven world.


Module 1:Introduction to Data Science

  • Description:
    Understand what Data Science is, its lifecycle, and how it solves real-world problems. Explore use cases across industries like finance, healthcare, marketing, and technology.

    Topics Covered:

    • What is Data Science?

    • Data Science vs Data Analytics vs Machine Learning

    • Applications of Data Science

    • Career Opportunities

2. Python for Data Science

Description:
Master Python from basics to advanced, tailored for data manipulation and analysis.

Topics Covered:

  • Python Basics (Variables, Data Types, Loops, Functions)

  • NumPy for Numerical Operations

  • Pandas for Data Manipulation

  • Matplotlib & Seaborn for Data Visualization

3. Statistics & Probability

Description:
Learn the statistical foundation essential for data modeling and interpretation.

Topics Covered:

  • Descriptive & Inferential Statistics

  • Probability Distributions

  • Hypothesis Testing

  • Statistical Measures (Mean, Median, Mode, Variance)

4. Data Wrangling & Preprocessing

Description:
Handle real-world datasets by cleaning, transforming, and preparing data for analysis.

Topics Covered:

  • Handling Missing Values

  • Data Transformation Techniques

  • Outlier Detection and Treatment

  • Encoding Categorical Data

  • Feature Scaling

5. Exploratory Data Analysis (EDA)

Description:
Gain insights from raw data using visualization and pattern recognition.

Topics Covered:

      • Univariate and Bivariate Analysis

      • Correlation Analysis

      • Interactive Charts

      • Data Distribution Analysis

6. SQL for Data Science

Description:
Learn to query, filter, join, and analyze structured data using SQL.

Topics Covered:

        • SQL Basics (SELECT, WHERE, GROUP BY, HAVING)

        • Joins and Subqueries

        • Window Functions

        • Data Aggregation

7. Machine Learning Fundamentals

Description:
Build predictive models and make data-driven decisions with supervised and unsupervised learning.

Topics Covered:

  • Supervised vs Unsupervised Learning

  • Regression Analysis

  • Classification Algorithms (KNN, SVM, Decision Trees)

  • Clustering (K-Means, Hierarchical)

8. Advanced Machine Learning & Model Tuning

Description:
Optimize models with cross-validation and hyperparameter tuning.

Topics Covered:

  • Ensemble Techniques (Random Forest, Gradient Boosting)

  • Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)

  • Overfitting vs Underfitting

  • Grid Search and Random Search

9. Deep Learning with TensorFlow & Keras (Optional Advanced Module)

Description:
Dive into neural networks and AI with hands-on deep learning techniques.

Topics Covered:

  • Neural Network Basics

  • CNNs for Image Processing

  • RNNs for Time Series & NLP

  • Using TensorFlow/Keras for Model Building

10. Capstone Project & Portfolio Building

Description:
Work on real-time projects to showcase your data science skills to employers.

Sample Projects:

  • Customer Churn Prediction

  • Sentiment Analysis

  • Sales Forecasting

  • E-commerce Recommendation System

Who Can Join This Course?

  • Students & Graduates

  • Working Professionals

  • Engineers & Analysts

  • Anyone with a curiosity for data

Tools & Technologies Covered

    • Python, NumPy, Pandas

    • SQL

    • Matplotlib, Seaborn

    • Scikit-Learn

    • TensorFlow & Keras (Advanced)

    • Jupyter Notebook

    • Tableau or Power BI (Optional)

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