Data Science With Python

S No. Course Name Modules
1 Python For Beginners Introduction to Python
    Installing Python and setting up the environment
    Importing/getting data set
    Variables and Data Types
    Reusable Functions
    Working with Text
    Object-Oriented Programming (Classes)
    Reusing Code with Modules and Packages/Libraries
    Executable Files
2 Advance Python Regular Expressions
    Iterator Objects (Functions and classes)
    Unit tests and test classes
    Extensions in Python
    Parsing in Python
    Web Scraping in Python
    Graphic User Interfaces in Python
    Libraries, Packages, and Modules
    Application Programming Interface (APIs)
    Working with files
    Advance Python Project to parse phone numbers and other information from a data file
3 Exploration And Visualization With Python Numpy basics
    Pandas basics
    Data importing, format, and storage
    Data cleaning and preparation
    Data Wrangling: Splitting, merging, and reshaping the dataset
    Data Visualization: Creating Charts and tables
    Data Aggregation: Grouping and pivoting
    Time series data
    Advanced Pandas
    Advanced Numpy
4 Machine Learning With Python Introduction and fundamentals
    Introduction to Statistics, Data Mining, Data Analytics, and Data Science
    Introduction to Machine Learning Python Packages and Core Libraries
    Introduction to Data Analysis Packages: NumPy, Pandas, and Matplotlib
    Exploratory Data Analysis (EDA)
    Supervised Learning
    Unsupervised Learning
    Model Building
    Model Evaluation and Improvement
    ML Project on Movie reviews
5 AI With Python Introduction to Prediction Models
    Classification and evaluation
    Decision trees
    Random Forests
    Use of confusion matrix
    Deep learning methods
    AI Project on positive or negative sentiments in user reviews

Practical Learning

Practice Sheets



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