Python has emerged as a popular and effective language in the world of data science. The dynamic nature of the language, the relative simplicity of the syntax, and the abundance of fast and powerful libraries have all been important contributory factors in this growth.
This course takes a detailed look at the most popular Python libraries for numeric processing, statistical analysis, machine learning, and visualization. We also show how to make use of common Python data types and algorithms to achieve real-world tasks.
What you will learn:• Using NumPy and Pandas for efficient data manipulation
• Using Matplotlib and Seaborn for visualization
• Working with time series data
• Machine learning concepts
• Using Scikit-Learn for machine learning
Prerequisites:• Some familiarity with Python or another contemporary language would be beneficial
Course outline:• Python Quick Start: Python Essentials; Language Fundamentals; Functions; Data Structures
• Getting Started with NumPy: Setting the Scene; NumPy Arrays; Manipulating Array Elements; Manipulating Array Shape
• NumPy Techniques: NumPy Universal Functions; Aggregations; Broadcasting; Manipulating Arrays using Boolean Logic; Additional Techniques
• Getting Started with Pandas: Introduction to Pandas; Creating a Series; Using a Series; Creating a DataFrame; Using a DataFrame
• Pandas Techniques: Universal Functions; Merging and Joining Datasets; A Closer Look at Joins
• Working with Time Series Data: Introduction to Time Series Data; Indexing and Plotting Time Series Data; Testing Data for Stationarity; Making Data Stationary; Forecasting Time Series Data; Scaling Back the ARIMA Results
• Introduction to Machine Learning: Machine Learning Concepts; Classification; Clustering
• Getting Started with Scikit-Learn: Scikit-Learn Essentials; A Closer Look at Datasets
• Understanding the Scikit-Learn API: Introduction; Scikit-Learn API Essentials; Performing Linear Regression
• Going Further with Scikit-Learn: Introduction; Understanding Naïve Bayes Classification; Naïve Bayes Example using Scikit-Learn
• Case Study: Worked example of a real-world data science problem