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Startup Development Team

Applied Python for Data Science 

     Next Batch: Monday, April 8th   

     Duration: 3 Weeks   


     Course Highlights

     Applied Learning in Microsoft Cloud Environment

     Option to join Internship program

     Job Readiness, Placement Assistance 


    Course Fee :  $1295   

    (Sign up by March 22nd and receive 10% off)   


Python is a critical skill for job seekers in the field of data science due to its prevalence in the industry. Mastering Python equips job seekers with the ability to efficiently manipulate, analyze, and visualize data, essential for roles in data science and analytics. Many companies specifically seek candidates proficient in Python for their data-driven decision-making processes.  Ultimately, proficiency in Python enhances job readiness by providing job seekers with a competitive edge and increasing their employability in the rapidly evolving field of data science. 


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

Course 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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