Explanation
Python Data Science Expert, Master the skills you need to transition into a Professional Data Scientist with a Python degree and set yourself up for success in the field of data science. During this track, you’ll focus on using Python for data science, starting with the basics and moving on to more advanced topics like machine learning. You will cover many different areas, including data manipulation, visualization, and analysis, using popular Python libraries such as pandas, Seaborn, Matplotlib, and scikit-learn.
What will you learn?
- Introduction in Python
- data Activities with pandas
- Introduction to Statistics in Python
- Introduction To plot Matplotlib Data
- Introduction to draw Seaborn Data
- Python Data Science Tools
- Medium Image courtesy of Seaborn
- exploration Data Analysis in Python
- working with Python Component Data
- data Concepts of Communication
- Introduction Importing data in Python
- cleaning Data in Python
- working with Dates and Times in Python
- Text Python functions
- Introduction reversion to stasmodels in Python
- Sample in Python
- imagination Testing with Python
- Without protection Learning Python
- the machine Learning Tree-Based Patterns in Python
- Making an introduction Machine Learning in Python
- development Python packages
- the machine Business Studies
- Introduction in SQL
- joining Data in SQL
- Introduction in Git
A Data Science Professional’s Guide to Python
- Publisher: data camp
- Teacher: Hugo Bowne-Anderson, Richie Cotton
- Language : English
- Level : All Levels
- Number of courses: 31
- Duration : 116 hours and 0 minutes
Python Data Science Professional Content
Pictures
Sample Clip
Installation Guide
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Subtitle : English
Quality: 720p
Download Links
Introduction in Python
Python neutral
Joining Pandas Data
Introduction to Python Statistics
Python Data Science Toolbox (Part 1)
Seaborn Intermediate Survey
Working with Component Databases in Python
Data Communication Concepts
An introduction to importing data from Python
Data Cleaning in Python
Working with Dates and Times in Python
Writing Functions in Python
Introduction to Regression with statsmodels in Python
Python sampling
Hypothesis testing in Python
Supervised learning with scikit-learn
Unsupervised learning in Python
Machine Learning for Tree-Based Models in Python
Interpreting import data in Python
Preparing for Machine Learning in Python
Developing Python Packages
Business Machine Learning
Introduction to SQL
Intermediate SQL
Joining Data in SQL
Introduction to Git
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file size
2.7 GB