 # Data Analysis with Python Pandas and NumPy

This is a 2 days course on Python Pandas and NumPy. The world generates data at an increasing pace. Consumers, sensors, or scientific experiments emit data points every day. In finance, business, administration and the natural or social sciences, working with data can make up a significant part of the job. Being able to efficiently work with small or large datasets has become a valuable skill.

NumPy is an open-source extension to Python that adds support for multidimensional arrays of large sizes. This support allows the desired acquisition, storage, and complex manipulation of data mentioned previously. NumPy alone is a great tool to solve many numerical computations. This library contains algorithms and mathematical tools to manipulate NumPy objects, with very definite scientific and engineering objectives. The combination of Python, NumPy has been the environment of choice of many applied mathematicians for years.

The Python data analysis course will teach you data manipulation and cleaning techniques using the popular Python Pandas data science library.

#### Learning Objectives

• Understand data import and export with Python Pandas.
• Learn how to use Series and DataFrame data types.
• Learn how to use functions such as groupby, merge and pivot tables for data aggregation.
• Understand the fundamental of NumPy and Matplotlib.
• Learn how to use Linear Algebra package and Optimization package.
• Understand the Statistics package and Signal Processing Package.

NA

#### Target Audience

Engineers or Programmers who want to learn basic Python

#### Training Outline

1.     Basics of NumPy

• Array Creation
• Array Operations
• Indexing & Slicing
• Shape Manipulation
• Polynomial
• Linear Algebra
• Statistics

2.     Numerical Analysis

• Curve Fitting
• Finding Roots
• Interpolation
• Integration

3.     Linear Algebra

• Matrix Operations
• Matrix Solve
• Eigenvalues
• Matrix Decomposition

4.     Statistics

• Basic Statistics
• Waveforms

5.     Data Preparation

• Data Analytics with Pandas
• Pandas DataFrame and Series
• Import and Export Data
• Filter and Slice Data
• Clean Data

6.     Data Transformation

• Join Data
• Transform Data
• Aggregate Data

7.     Data Visualization

• Data Visualization with Matplotlib and Seaborn.
• Visualize Statistical Relationships with Scatter Plot.
• Visualize Categorical Data with Bar Plot.
• Visualize Correlation with Pair Plot and Heatmap.
• Visualize Linear Relationships with Regression

8.     Data Analysis

• Statistical Data Analysis
• Time Series Analysis