Analyze and Visualize Data With Python

This Python For Data Analysis Course is a very comprehensive, practical, and hands-on course.

It will guide professionals in the preparation and analysis of research data and interpret the results generated

Key Knowledge Gained:

1. Methods for integrating and analysing different types of research data with Python
2. Techniques and algorithms available for data analytics by using Python language
3. Evaluate the Strengths and weaknesses of different types of analytical methods within Python.

With this knowledge, you’ll also gain the ability to perform several detailed analysis.

Some key abilities the learners will gain are:

1. Determine how data should be prepared in Python to facilitate intended analysis
2. Research data preparation with Python libraries like Pandas, and Numpy
3. Learn summarization and visualization of data with Python Libraries
4. Guide analyses of quantitative and qualitative data through the various Python libraries and modules
5. Guide interpretation of results generated through the analysis
6. Review the Python Analysis of the research data to ensure credibility of the analysis.

On this course, you’ll also look at:

• Conducting simple statistical analyses: The Python Numpy Module
• Combining For/While loops and Numpy for complex analysis
• Using Pandas to import and manipulate datasets.

For this course, you’ll need some basic experience with Python. You can do our Beginners in Python course first if you do not have the basics yet.

Why Use Pandas?

Pandas allows us to analyze big data and make conclusions based on statistical theories.

Pandas can clean messy data sets, and make them readable and relevant.

Relevant data is very important in data science.

What Can Pandas Do?

Pandas gives you answers about the data. Like:

• Is there a correlation between two or more columns?
• What is average value?
• What are the Min & Max values?

Pandas are also able to delete rows that are not relevant, or contains wrong values, like empty or NULL values. This is called cleaning the data.

Data Analysis With Pandas

• Creating, Reading & Writing Data & Files
• Indexing, Selecting & Assigning Data
• Using Summary Functions and Maps to Extract Insights From Data
• Grouping & Sorting Data to Scale up you level of insights. Valuable for larger, complex data sets.
• Solving Data Type & Missing Values Problems
• Renaming & Combining Data to begin making Sense
• Pandas Correlations
• Plotting Pandas Data
• Quiz & Practical Exercises for analyzing data

Data Analysis Project With Pandas & Python.

DETAILED COURSE OUTLINE

COURSE INTRODUCTION: 

Introduction to Python for Data Analysis

• Introduction to Python
• Introduction to Anaconda & JupyterLab
• The Python Standard Libraries

Installing Python

• Anaconda, JupyterLab
• Installing additional packages

Anaconda & JupyterLab

• Using Anaconda
• Working with environments
• Launching JupyterLab
• Working in JupyterLab
• Using Jupyter Notebooks
• Basics of running code
• Markdown
• Shutting down kernels and the Jupyter Server

MODULE 1: INTRODUCTION TO PANDAS

Learning Objective 1: Learners will be able to assess & plan data preparation based on the required analysis

• Data Types and Suitable Analysis
• Assess Data using Pandas library in Python using Dataframs as the primary method.

• Pandas Series and DataFrames
• Which data structure should I use?
• Pandas DataFrame

Using Pandas functions

• Creating DataFrames
• Importing data into a DataFrame
• Uploading data in JupyterLab

Accessing data within DataFrames

• Accessing specific rows
• Accessing specific columns
• Accessing data subsets by name or position

Manipulating DataFrames in Pandas with in Python

• Making changes in place
• Renaming columns and rows
• Replace a single value
• Replace multiple values
• Add data to a DataFrame
• Remove rows or columns
• Filter based on condition
• Sort data

Working with data in DataFrames

• Calculating summary statistics

MODULE 2: DATA PREPARATION FOR ANALYSIS

Learning Objective 2: Learners will be able to transform data for analysis

• Import & Export Data from files using Pandas Data frames
• Clean, Sort, Filter Data and Prepare for Analysis within Pandas library

MODULE 3: DATA SUMMARIZATION & VISUALIZATION

Learning Objective 3: Learners will be able to visualize data using charts

• Functions & Methods of Data Aggregation using Pandas library
• Group & Pivot Data using Pandas
• Data visualization with Matplotlib & Seaborn Libraries

MODULE 4: DATA ANALYTICS

Learning Objective 4: Learners will be able to analyze using statistical techniques

• Statistical analysis techniques with Pandas library in Python
• Time Series analysis with Python libraries
• Using Correlation and Linear Regression in Python
• Techniques for Categorical Data Analysis in Python