Comprehensive Data Science With Python
Duration: 5 days
Industry: Information Technology
About this course
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.
Who can benefit?
- Engineers
- Data Scientists
- Statisticians
This is what you'll learn
- 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
Course Outline
Software Requirements
- Anaconda Python 3.5 or later
- Spyder IDE (Comes with Anaconda)
Data Science with Python Programming Training Outline
Base Python Introduction
- History and current use
- Installing the Software
- Python Distributions
- String Literals and numeric objects
- Collections (lists, tuples, dicts)
- Datetime classes in Python
- Memory Management in Python
- Control Flow
- Functions
- Exception Handling
Defining Actionable, Analytic Questions
- Defining the quantitative construct to make inference on the question
- Identifying the data needed to support the constructs
- Identifying limitations to the data and analytic approach
- Constructing Sensitivity analyses
Bringing Data In
- Structured Data
- Structured Text Files
- Excel workbooks
- SQL databases
- Working with Unstructured Text Data
- Reading Unstructured Text
- Introduction to Natural Language Processing with Python
NumPy: Matrix Language
- Introduction to the ndarray
- NumPy operations
- Broadcasting
- Missing data in NumPy (masked array)
- NumPy Structured arrays
- Random number generation
Data Preparation with Pandas
- Filtering
- Creating and deleting variables
- Discretization of Continuous Data
- Scaling and standardizing data
- Identifying Duplicates
- Dummy Coding
- Combining Datasets
- Transposing Data
- Long to wide and back
Exploratory Data Analysis with Pandas
- Univariate Statistical Summaries and Detecting Outliers
- Multivariate Statistical Summaries and Outlier Detection
- Group-wise calculations using Pandas
- Pivot Tables
Exploring Data Graphically
- Histogram
- Box-and-whiskers plot
- Scatter plots
- Forest Plots
- Group-by plotting
Advanced Graphing with Matplotlib, Pandas, and Seaborn
Python, Hadoop and Spark
- Introduction to the difference in Python, Hadoop, and Spark
- Importing data from Spark and Hadoop to Python
- Parallel execution leveraging Spark or Hadoop
Missing Data
- Exploring and understanding patterns in missing data
- Missing at Random
- Missing Not at Random
- Missing Completely at Random
- Data imputation methods
Traditional Inferential Statistics
- Comparing Groups
- P-Values, summary statistics, sufficient statistics, inferential targets
- T-Tests (equal and unequal variances)
- ANOVA
- Chi-Square Tests
- Correlation
Frequentist Approaches to Multivariate Statistics
- Linear Regression
- Multivariate linear regression
- Capturing Non-linear Relationships
- Comparing Model Fits
- Scoring new data
- Poisson Regression Extension
- Logistic regression
- Logistic Regression Example
- Classification Metrics
Machine Learning Approaches to Multivariate Statistics
- Machine Learning Theory
- Data pre-processing
- Missing Data
- Dummy Coding
- Standardization
- Training/Test data
- Supervised Versus Unsupervised Learning
- Unsupervised Learning: Clustering
- Clustering Algorithms
- Evaluating Cluster Performance
- Dimensionality Reduction
- A-priori
- Principal Components Analysis
- Penalized Regression
Supervised Learning: Regression
- Linear Regression
- Penalized Linear Regression
- Stochastic Gradient Descent
- Scoring New Data Sets
- Cross Validation
- Variance Bias-Tradeoff
- Feature Importance
Supervised Learning: Classification
- Logistic Regression
- LASSO
- Random Forest
- Ensemble Methods
- Feature Importance
- Scoring New Data Sets
- Cross Validation
Conclusion
Prerequisite Skills
All attendees should have prior programming experience and an understanding of basic statistics.
Schedule (iMVP)
Please contact P2L to schedule the dates for this course.