Python Data Science
Course Overview
Course Curriculum
Week 1: Python Basics
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Python introduction and installation
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Variables and data types
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Operators and expressions
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Control flow: if, elif, else
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Loops: for and while
Week 2: Data Types
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Numbers and arithmetic operations
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Booleans and logical operators
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Strings and string methods
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Lists and list comprehensions
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Dictionaries and their methods
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Sets and set operations
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Tuples and their properties
Week 3: Functions and Modules
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Defining and calling functions
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Variable scope and lifetime
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Using built-in modules and libraries
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Packages and pip installation
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File I/O: reading and writing files
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Error handling: try, except, finally
Week 4: OOP Concepts
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Object-oriented programming introduction
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Classes and objects
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Inheritance and polymorphism
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Method overriding
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Abstraction and encapsulation
Week 5: Basic Statistics
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Types of statistics
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Population vs sample
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Mean, median, mode
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Variance and standard deviation
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Random variables
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Percentiles & quartiles
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5-number summary and histograms
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Normal distribution and Z scores
Week 6: Numpy and Pandas
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Introduction to Numpy
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Creating and manipulating arrays
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Indexing, slicing and iterating arrays
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Reshaping and concatenating arrays
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Introduction to Pandas
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Series and DataFrame structures
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Data loading and storage formats
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Data cleaning and transformation
Week 7: Data Visualization
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Introduction to Seaborn
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Statistical plots with Seaborn
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Plotting categorical data
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Customizing Seaborn plots
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Introduction to Matplotlib
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Creating and customizing plots
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Advanced plotting techniques
Week 8: EDA and ML Basics
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Exploratory data analysis (EDA)
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Feature engineering and selection
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Introduction to machine learning
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Supervised, unsupervised, and semi-supervised learning
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Linear regression
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Logistics regression
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Overfitting and underfitting
Week 9: Advanced Regression and Metrics
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Ridge and lasso regression
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Linear vs logistic regression
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Performance metrics
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Confusion matrix
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Precision, recall and F-beta score
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ROC and AUC curves
Week 10: Classification Models
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Decision trees
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Random forest
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Support vector machines (SVM)
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K-nearest neighbors (KNN)
Week 11: Boosting Techniques
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Introduction to boosting
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AdaBoost
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Gradient boosting
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XGBoost
Week 12: Unsupervised Learning
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Introduction to unsupervised learning
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K-means clustering
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Principal component analysis (PCA)
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Dimensionality reduction with PCA
About This Course:
- Free IT Training
- Free Certification
- Free Interview Support
- Free Project Support
- Contact & Address
$1,000.00
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