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Applied Python for Data Science 
(Next Batch Starts: Monday, April 8th)

  

   Next Batch: Monday, April 8th       Duration: 3 Weeks       Number of Seats: 20 

   

   Highlight of the Course

  •    Hybrid  (Option for Classroom / Remote )*

            Project Work / Applied Learning (including  Prep & Practice / Quizzes )

  •    Internship (12 Weeks), following the Course.

          

    Course Fee :  $1295   (Sign up by March 22nd and receive 10% off)

 

        ** Include Microsoft Exam Fee   

 

Week 1: Introduction to Python for Data Science

Day 1: Introduction to Python Basics (4 hours)

  • Overview of Python and its importance in data science

  • Installing Python and Jupyter Notebook

  • Python syntax basics: variables, data types, basic operations

Day 2: Control Flow and Functions (4 hours)

  • Conditional statements (if, elif, else)

  • Loops (for, while)

  • Introduction to functions and their significance in data science

Day 3: Data Structures in Python (4 hours)

  • Lists, tuples, and dictionaries

  • List comprehensions

  • Understanding data structures and their application in data manipulation

Day 4: NumPy Basics (4 hours)

  • Introduction to NumPy arrays

  • Array manipulation and operations

  • NumPy functions for numerical computing

Week 2: Data Manipulation and Visualization

Day 5: Pandas Introduction (4 hours)

  • Introduction to the Pandas library

  • Series and DataFrame objects

  • Data manipulation with Pandas

Day 6: Data Cleaning and Preparation (4 hours)

  • Handling missing data

  • Data preprocessing techniques: cleaning, transforming, and merging data

  • Introduction to data visualization with Matplotlib and Seaborn

Day 7: Data Visualization with Matplotlib and Seaborn (4 hours)

  • Basic plotting with Matplotlib

  • Introduction to Seaborn for statistical data visualization

  • Customizing plots and enhancing visualization

Week 3: Advanced Topics and Project Work

Day 8: Introduction to Machine Learning (4 hours)

  • Overview of machine learning concepts

  • Introduction to scikit-learn library

  • Basics of supervised and unsupervised learning

Day 9: Supervised Learning: Regression (4 hours)

  • Introduction to regression analysis

  • Simple linear regression

  • Multiple linear regression and model evaluation

Day 10: Supervised Learning: Classification (4 hours)

  • Introduction to classification algorithms

  • Logistic regression

  • Decision trees and ensemble methods

Day 11: Unsupervised Learning: Clustering (4 hours)

  • Introduction to clustering algorithms

  • K-means clustering

  • Evaluation of clustering algorithms

Day 12: Capstone Project (4 hours)

  • Students work on a hands-on project applying Python for data science

  • Project guidance and assistance provided by instructors

  • Presentation of projects and peer feedback

Assessments and Review

Quizzes and Homework (Throughout the 3 weeks)

  • Weekly quizzes to assess understanding of concepts covered

  • Homework assignments to reinforce learning and practice programming skills

Final Assessment and Review (Last day)

  • Comprehensive final assessment covering topics from all weeks

  • Review of course material and discussion of key takeaways

This outline provides a structured approach to learning Python for data science, covering fundamental concepts, data manipulation, visualization, and machine learning techniques within a 3-week timeframe, with ample opportunities for hands-on practice and assessment.

Prerequisite 

  • Basic programming understanding in any language.

  • Familiarity with elementary mathematics concepts.

  • Understanding of fundamental statistics.

  • Proficiency in basic computer skills and file management.

  • Access to a computer with internet and commitment to self-guided learning.

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