Python for Data Science Online Course
About the Course
Python is an open-source community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python. This course will help you learn the tools necessary to perform data science.
In this course you will learn all the necessary libraries that make data analytics with Python a joy. You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the Numpy library used for numerical and scientific computation. You will also employ useful libraries for visualization, Matplotlib and Seaborn, to provide insights into data. Further you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, and implement your knowledge on projects.
By the end of this course, you'll have embarked on a journey from data cleaning and preparation to creating summary tables, from visualization to machine learning and prediction. This video course will prepare you to the world of data science. Welcome to our journey!
Course Curriculum
Beginning the Data Science Journey
- The Course Overview
- What Is Data Science?
- Python Data Science Ecosystem
Introducing Jupyter
- Installing Anaconda
- Starting Jupyter
- Basics of Jupyter
- Markdown Syntax
Understanding Numerical Operations with NumPy
- 1D Arrays with NumPy
- 2D Arrays with NumPy
- Functions in NumPy
- Random Numbers and Distributions in NumPy
Data Preparation and Manipulation with Pandas
- Create DataFrames
- Read in Data Files
- Subsetting DataFrames
- Boolean Indexing in DataFrames
- Summarizing and Grouping Data
Visualizing Data with Matplotlib and Seaborn
- Matplotlib Introduction
- Graphs with Matplotlib
- Graphs with Seaborn
- Graphs with Pandas
Introduction to Machine Learning and Scikit-learn
- Machine Learning
- Types of Machine Learning
- Introduction to Scikit-learn
Building Machine Learning Models with Scikit-learn
- Linear Regression
- Logistic Regression
- K-Nearest Neighbors
- Decision Trees
- Random Forest
- K-Means Clustering
Model Evaluation and Selection
- Preparing Data for Machine Learning
- Performance Metrics
- Bias-Variance Tradeoff
- Cross-Validation
- Grid Search
- Wrap Up
