WA3364
Data Science Using Python Deep Dive Training
This Python for Data Science training course is ideal for engineers, data scientists, statisticians, and other quantitative professionals looking to hone their Python programming skills. Our experienced instructors will guide you through all the basics, helping you to become a proficient Python programmer.
Course Details
Duration
5 days
Prerequisites
Programming experience and an understanding of basic statistics.
Skills Gained
- Understand the basic Python types, collections, and control flow.
- Learn how to use NumPy for matrix computing and data analysis.
- Master the fundamentals of Pandas for data manipulation and exploration.
- Apply exploratory data analysis techniques to visualize and understand data.
- Implement inferential statistics in Python to test hypotheses and make predictions.
Course Outline
- An Accelerated Introduction and Overview to Python for Data Science Foundations
- Introduction to course and computing environment
- Up and running with Jupyter notebooks
- Fundamental Python types: String literals, numeric, Boolean, and dates
- Understanding Python ‘variables’ (reference assignment)
- Slicing syntax
- Fundamental collections: tuples, lists, dictionaries, and sets
- Control flow iteration in Python (if/then, for, while, list comprehension)
- Writing your own functions
- Handling exceptions
- Matrix Computing with NumPy
- Introduction to the ndarray
- Dtypes in NumPy
- NumPy operations, uFuncs
- Broadcasting
- Missing data in NumPy (masked array)
- Random number generation
- Managing, Exploring, and Cleaning Data with Pandas
- Fundamental Pandas: Series and DataFrames
- Exploring objects with attributes/methods
- Importing data from different structured sources
- Basic DataFrame summaries
- Creating new variables (columns)
- Scaling and standardizing data elements
- Discretizing continuous data
- Mapping categorical data to new values
- Establishing dummy codes (one hot encoding)
- Filtering rows and selecting columns
- Managing the indices
- Identifying duplicate rows
- Quantifying and managing missing data
- Combining datasets
- Merging datasets
- Transposing datasets
- Changing data from long to wide formats and back
- Exploratory Data Analysis with Pandas (including visualization with Seaborn)
- Univariate Statistical Summaries and Detecting Outliers, visually with graphical approaches and numerically.
- Multivariate Statistical Summaries and Outlier Detection, visually with graphical approaches and numerically.
- Groupwise calculations
- Pivot Table type operations to aggregate by group
- Pandas DataFrame plotting methods
- Data Pseudo-Coding Process, Extension to Data-Centric Problems
- Identifying data verbs
- Answering a question using a well-formatted analytic dataframe
- Understanding the unit of analysis
- Identifying the unit of analysis for a given question – is my dataframe organized this way?
- Leveraging normalized data to create the analytic dataframe through combinations of data verbs
- Identify the question and unit of analysis
- Define the desired analytic dataframe
- Examine the normalized source data
- Create data pseudo-code to map source data to the final analytic dataframe
- Implement with Python
- Focus on Graphics with Python: Seaborn, Matplotlib, and Plotly
- Using seaborn for 1 and 2 variable summaries
- Advanced statistical plots with Seaborn
- Controlling plot details through Seaborn
- Making graphs interactive with Plotly
- Introduction to Matplotlib for full control of parameters
- Overview of Descriptive versus Inferential Analytics
- Identifying the null hypothesis
- P-value interpretation
- The idea of statistical power and type 1/2 errors
- Implementing Inferential Statistics in Python
- Analyzing an A/B randomized test:
- T-tests/ANOVA
- Chi-square tests
- Correlation methods
- Analyzing an A/B randomized test:
- Multivariate Models: Linear Regression
- Estimating the mean
- Identifying p-values of interest
- Adding a categorical predictor and the link to t-tests
- Nonlinear trends: Polynomial regression and spline modeling
- Interaction terms
- Confounding
- Model building approaches (choosing the best model)
- Scoring new data from the model (making predictions)
- Multivariate Models: Logistic Regression
- GLMs and the link function
- Understanding the logit function
- The binomial distribution and
- Recovering the average event probability from the model
- Interpreting the coefficient – the odds ratio
- Categorical predictors and the connection to the chi-square test
- Expansion to more complex models (non-linear trends, multiple predictors)
- Confounding
- Interaction terms
- Making predictions
- Comparing models and picking the ‘best’ model
- Conclusion
- Optional modules depending on student interest and timing
- Analyzing unstructured data with Python
- Overview of structure versus unstructured data
- Implementing regular expressions in Python
- Converting unstructured data to structured data for analysis
- Missing Data
- Exploring and understanding patterns in missing data
- Missing at Random
- Missing Not at Random
- Missing Completely at Random
- Data imputation methods
- Analyzing unstructured data with Python
Upcoming Course Dates
