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Python for Data Analysis Online Course & Guides
 
 
This entry-level Python for Data Analysts training prepares learners to master what is easily the best programming language in the world for data analysis: Python and its data analysis libraries.
 
Good data analysis makes sense of the past, guides present action and can even predict the future – Python puts all that possibility within reach. And doing data analysis with Python almost always requires a good foundation in Python and comfort in its libraries like Pandas, NumPy and even IPython.
Python is the world's leading programming language for data analysis for a reason: it's versatile, powerful, intuitive, relatively easy, and the open source language has libraries and add-ons that are kept up-to-date by programmers and analysts all over the world. You can learn it and master it for data analysis with this course.
For anyone who manages their Python training, this Python training can be used to onboard new aspiring data analysts, curated into individual or team training plans, or as a Python reference resource.
 
Python for Data Analysts: What You Need to Know
This Python for Data Analysis training covers Python topics including:
 
Analyzing data with Python and Python libraries special made for data analysis
Identifying and acquiring the right data and types of data with Python
Performing simple and complex statistical analyses
Generating data visualizations and creating charts automatically
Using Python to predict future trends from data
Who Should Take Python for Data Analysts Training?
This Python for Data Analysis training is considered specialist-level Python training, which means it was designed for aspiring data analysts.
New or aspiring data analysts. This course is perfect for someone who just wants to make their everyday job a bit better with data analysis as well as people hoping to start a career as a full-fledged data analyst. Learning Python for data analysis is the key to taking huge sets of data, making sense of them, and making better decisions – and that's useful for anyone.
Experienced aspiring data analysts. If you've been working as a data analyst for a few years already, ask yourself how comfortable you are with Python, Pandas and NumPy. If you're even remotely uncertain about those libraries, why wouldn't you take a course that walks you through using the most powerful and popular data analysis languages and libraries and gives you plenty of chances to practice them?.
 
 
 
 
Python Development Environments
1. Introduction to Development Environments
2. Code Editors and IDEs vs. Interactive Notebooks
3. Installing Google Colab
4. Installing Jupyter notebook with Anaconda
5. Managing Projects Environments with Conda
6. Running Anaconda with Docker
 
Python for Data Analysis: Data Types
1. Introduction to Python Data Types
2. Data Types
3. Numbers and Bools
4. Strings and Keywords
5. Collections
6. Code Challenge
 
Python for Data Analysis: Numbers
1. Introduction to Python Numbers
2. Variables
3. Math Operators
4. Math with Python
5. Boolean Values
6. Code Challenge
 
Python for Data Analysis: Strings
1. Introduction to Strings
2. Working with Strings
3. Indexing and Slicing Strings
4. String Methods
5. Strings Code Challenge
 
Python for Data Analysis: Functions
1. Introduction to Python Functions
2. Python Operators
3. Functions and If Statements
4. Python Scope
5. Pseudocode
6. Functions Code Challenge
 
Python for Data Analysis: Loops
1. Introduction to Python Loops
2. If, elif, and else
3. For Loops
4. While Loops
5. Code Challenge
 
Python for Data Analysis: Collections
1. Introduction to Python Collections
2. Lists
3. List Methods
4. Dictionaries
5. Dictionary Methods
6. Code Challenge
7. Data Analysis Preview with pandas