Course Overview:
Python is one of the most popular programming languages in data science and analytics. Mastering it will enhance your employability and career prospects in a rapidly evolving job market. The course emphasizes practical skills, allowing participants to work on real-world projects and datasets, ensuring they can apply what they learn in professional environments. Covering key concepts from data manipulation to machine learning, participants will gain a well-rounded understanding of Python's role in data science.
Participants will explore Python's libraries and tools essential for data analysis, visualization, and machine learning. The course will provide a hands-on approach, enabling participants to work with real-world datasets and apply Python programming concepts effectively to derive insights and make data-driven decisions.
Duration
10 Days
Who Should Attend
- Aspiring data scientists and analysts
- Business analysts looking to leverage data for decision-making
- Professionals in fields such as finance, marketing, or healthcare who wish to analyze data more effectively
- Anyone interested in a career in data science or analytics with a basic understanding of programming concepts
Course Objectives
By the end of this course, participants will be able to:
- Understand the fundamentals of Python programming and its libraries for data science.
- Perform data manipulation and cleaning using Pandas.
- Visualize data effectively with Matplotlib and Seaborn.
- Apply statistical analysis techniques to draw insights from data.
- Implement machine learning algorithms using Scikit-learn.
- Work with real-world datasets to develop data-driven solutions.
Course Outline:
Module 1: Introduction to Python for Data Science
- Overview of Data Science and Python
- Setting up the Python environment (Anaconda, Jupyter Notebooks)
- Basic Python syntax and data types
Module 2: Data Structures and Control Flow
- Lists, tuples, dictionaries, and sets
- Control flow (if statements, loops)
Module 3: Data Manipulation with Pandas
- Introduction to pandas
- DataFrames and Series
- Data cleaning and preparation techniques
Module 4: Data Visualization with Matplotlib and Seaborn
- Introduction to data visualization
- Creating visualizations using Matplotlib
- Advanced visualizations with Seaborn
Module 5: Numpy for Numerical Computing
- Introduction to NumPy
- Array manipulation and operations
- Performance improvements with NumPy
Module 6: Exploratory Data Analysis (EDA)
- Techniques for EDA
- Descriptive statistics and data summarization
- Identifying trends and correlations
Module 7: Introduction to Machine Learning
- Overview of machine learning concepts
- Supervised vs. unsupervised learning
- Implementing simple models using scikit-learn
Module 8: Supervised Learning Algorithms
- Linear regression and logistic regression
- Decision trees and support vector machines
- Model evaluation techniques
Module 9: Unsupervised Learning Algorithms
- K-means clustering and hierarchical clustering
- Principal Component Analysis (PCA)
- Anomaly detection
Module 10: Capstone Project and Course Wrap-Up
- Real-world project where participants apply learned skills
- Presenting findings and insights
- Course review and next steps for further learning
