Data Science with Python

Live Online (VILT) & Classroom Corporate Training Course

More and more businesses today are using Data Science to add value to every aspect of their operations. This course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.
Data Science with Python

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This Data Science with Python training course teaches engineers, data scientists, statisticians, and other quantitative professionals the Python programming skills they need to analyze and chart data.



At the end of Data Science with Python training course, participants will be able to

  • Understand the difference between Python basic data types
  • Know when to use different python collections
  • 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
  • Understand 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.


Course Outline

  • 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 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

  • Structured Data
    • Structured Text Files
    • Excel workbooks
    • SQL databases
  • Working with Unstructured Text Data
    • Reading Unstructured Text
    • Introduction to Natural Language Processing with Python

  • Introduction to the ndarray
  • NumPy operations
  • Broadcasting
  • Missing data in NumPy (masked array)
  • NumPy Structured arrays
  • Random number generation

  • Introduction to the ndarray
  • NumPy operations
  • Broadcasting
  • Missing data in NumPy (masked array)
  • NumPy Structured arrays
  • Random number generation

  • 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

  • Univariate Statistical Summaries and Detecting Outliers
  • Multivariate Statistical Summaries and Outlier Detection
  • Group-wise calculations using Pandas
  • Pivot Tables

  • Histogram
  • Box-and-whiskers plot
  • Scatter plots
  • Forest Plots
  • Group-by plotting

  • Introduction to the difference in Python, Hadoop, and Spark
  • Importing data from Spark and Hadoop to Python
  • Parallel execution leveraging Spark or Hadoop

  • Exploring and understanding patterns in missing data
  • Missing at Random
  • Missing Not at Random
  • Missing Completely at Random
  • Data imputation methods

  • Comparing Groups
    • P-Values, summary statistics, sufficient statistics, inferential targets
    • T-Tests (equal and unequal variances)
    • ANOVA
    • Chi-Square Tests
  • Correlation

  • 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 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

  • Linear Regression
  • Penalized Linear Regression
  • Stochastic Gradient Descent
  • Scoring New Data Sets
  • Cross Validation
  • Variance Bias-Tradeoff
  • Feature Importance

  • Logistic Regression
  • Random Forest
  • Ensemble Methods
  • Feature Importance
  • Scoring New Data Sets
  • Cross Validation