Python for Data Science and Machine Learning Course with Projects

This course provides a comprehensive introduction to Python programming for data science and machine learning. Students will learn Python basics, data manipulation, visualization, and machine learning fundamentals. By the end of the course, participants will develop a portfolio of projects, showcasing their new skills in data science and machine learning.

Key Learnings:

Master Python programming fundamentals and libraries for data science.
Perform data cleaning, manipulation, and analysis using Pandas.
Visualize data insights using Matplotlib and Seaborn.
Build and evaluate machine learning models using scikit-learn.
Develop practical, real-world projects to showcase data science and machine learning skills.

Course Modules:

Module 1: Introduction to Python for Data Science

Overview of Python for Data Science
Python basics: data types, variables, loops, and conditionals
Functions, modules, and packages
Introduction to Jupyter Notebooks

Module 2: Data Analysis and Manipulation with Pandas

Introduction to data analysis and Pandas
Data frames, series, and basic operations
Data cleaning and preprocessing
Handling missing data and data manipulation

Module 3: Data Visualization with Matplotlib and SeabornIntroduction to data visualization
Basic plots with Matplotlib (line, bar, scatter)
Advanced visualizations with Seaborn (heatmaps, pair plots)
Customizing and exporting visualizations

Module 4: Statistical Analysis and Hypothesis Testing Basics of descriptive statistics
Probability distributions
Inferential statistics and hypothesis testing
T-tests, chi-square tests, and ANOVA

Module 5: Introduction to Machine LearningOverview of machine learning and its applications
Types of machine learning: supervised, unsupervised, and reinforcement
Introduction to scikit-learn library
Building a simple machine learning model

Module 6: Supervised Learning Algorithms Linear regression and logistic regression
Decision trees and random forests
Model evaluation metrics: accuracy, precision, recall
Hyperparameter tuning and model selection

Module 7: Unsupervised Learning and Clustering Introduction to unsupervised learning
K-means clustering and hierarchical clustering
Dimensionality reduction techniques (PCA)
Practical applications of clustering

Module 8: Model Evaluation and Optimization

Cross-validation and model evaluation techniques
Grid search and randomized search
Ensemble learning: bagging, boosting, and stacking
Final project guidance and best practices

Target Audience:

Beginners to Python and data science.
Aspiring data scientists and machine learning engineers.
Professionals looking to integrate data science skills into their careers.

Career Pathways:

Data Analyst
Junior Data Scientist
Machine Learning Engineer (entry-level)
Python Developer for Data Science Applications

International Student Fees: USD295

Job Interview Preparation (Soft Skills Questions & Answers)

Tough Open-Ended Job Interview Questions
What to Wear for Best Job Interview Attire
Job Interview Question- What are You Passionate About?
How to Prepare for a Job Promotion Interview

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