Comprehensive Data Science With Python
This Python programming data science training course teaches engineers, data scientists, statisticians, and other quantitative professionals the Python skills they need to use the Python programming language to analyze and chart data.
What is Python?
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.
For more information, please check P2L’s website.
Skills You Will Gain
- Understand the difference between Python basic data types
- Know when to use different python collections
- Ability to implement python functions
- Understand control flow constructs in Python
- Handle errors via exception handling constructs
- Be able to quantitatively define an answerable, actionable question
- Import both structured and unstructured data into Python
- Parse unstructured data into structured formats
- Understand the differences between NumPy arrays and pandas dataframes
- Overview of where Python fits in the Python/Hadoop/Spark ecosystem
- Simulate data through random number generation
- Understand mechanisms for missing data and analytic implications
- Explore and Clean Data
- Create compelling graphics to reveal analytic results
- Reshape and merge data to prepare for advanced analytics
- Find test for group differences using inferential statistics
- Implement linear regression from a frequentist perspective
- Understand non-linear terms, confounding, and interaction in linear regression
- Extend to logistic regression to model binary outcomes
- Understand the difference between machine learning and frequentist approaches to statistics
- Implement classification and regression models using machine learning
- Score new datasets, evaluate model fit, and quantify variable importance
All attendees should have prior programming experience and an understanding of basic statistics.
Who Can Benefit From This Course?
- Data Scientists