Data Analytics Professional Certificate

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Module 1: Foundational Statistical Techniques

• Define population, sample, and sampling methods, identifying best practices and potential biases.
• Use GenAI to brainstorm sampling methods and debug spreadsheet errors.
• Analyze data using measures of central tendency, variability, and skewness, with histograms and box plots for summarization.
• Perform segmentation with group-by analysis, calculating measures like frequencies and percent changes, and visualizing results with bar charts.

Module 2: Probability and Simulation

• Explain the role of probability in quantifying uncertainty and apply probability rules (multiplication, addition, complement).
• Distinguish between independent and non-independent events and compare discrete vs. continuous probability distributions.
• Calculate z-scores using normal distribution and simulate random variates with uniform and normal distributions for business decisions.

Module 3: Confidence Intervals

• Define estimation and its link to sampling and descriptive statistics.
• Calculate and interpret confidence intervals to estimate population parameters, such as means and proportions.
• Use LLMs to assist in selecting, conducting, and analyzing inferential statistical techniques.

Module 4: Hypothesis Testing

• Explain the importance of hypothesis testing and conduct single-variable tests including t-tests and z-tests
• Control Type I and II error rates using statistical mechanisms.
• Describe the many types of hypothesis testing available to data analysts and their relevant applications.
• Use LLMs to assist in selecting, conducting, and analyzing inferential statistical techniques.

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Module 1: Getting started with Python

Learning Objectives:

• Explain what programming languages are used for
• Explain the analogues between programming languages and spreadsheets
• Setting up and navigating a Python notebook environment (create markdown and code cells, run blocks, restart kernel, use table of contents, upload files, etc.)
• Distinguish between code and comments in Python
• Print to the console
• Create variables of different data types
• Implement lists and tuples in Python
  Identify and use common pre-defined functions (e.g. str, len, sum, min, max) 
• Write code with proper Python indentation
• Describe and use conditional statements
• Write for and while loops
• Write custom functions using def
• Explain the concept of order of execution and apply it to code statements, loops, and conditionals
• Use print statements for debugging
• Identify the source of an error using an error stack trace
• Use generative AI to help identify the source of an error, e.g. by interpreting a stack trace

Module 2: Data structures and descriptive statistics

Learning Objectives:

• Import Python packages using aliases and use helper functions from imported modules
• Create and manipulate DataFrames and Series using pandas, explaining how they improve upon basic Python lists
• Explain the concept of vectorization and its importance for efficient data operations
• Differentiate between pandas attributes and methods when working with DataFrames
• Read CSV files into pandas DataFrames and perform initial data exploration
• Select specific columns and rows from DataFrames using proper syntax
• Filter DataFrame rows using boolean conditions
• Sort DataFrames by one or multiple columns using sort_values()
• Apply functions to DataFrame columns using the apply() method
• Calculate basic descriptive statistics using pandas methods including:
  • count() and sum() for numerical summaries
  • mean(), median(), std(), and other statistical measures
  • describe() for comprehensive statistical summaries
  • value_counts() for categorical data
• Create basic visualizations directly from pandas including:
  • Histograms for numerical data
  • Bar charts for categorical data
  • Scatter plots for relationships between variables
• Calculate correlations between numerical variables using pandas .corr()
• Segment data using: groupby() for single-feature segmentation and pivot_table() for multi-feature segmentation

Module 3: Visualization

Learning Objectives:

• Use Pandas to efficiently generate visuals directly from DataFrames and Series, including line graphs, bar charts, histograms, and scatter plots
• Use the most popular Python data visualization packages (matplotlib, pandas, Seaborn) to develop custom visualizations
• Use matplotlib to create a variety of common visuals, including line graphs, bar charts, histograms, scatter plots,to effectively communicate data insights.
• Use matplotlib’s styling and annotation options to enhance readability and highlight temporal patterns, trends, and anomalies in data
• Employ Seaborn to craft advanced visuals, including distribution plots (such as boxplots, histograms, stripplots, and kdeplots), categorical plots (like box plots, violin plots, and swarm plots), and heat maps

Module 4: Inferential statistics

Learning Objectives:

• Construct confidence intervals in Python using the SciPy module
• Conduct one and two sample t-tests in Python using the SciPy module
• Use NumPy’s random module to create simulated data using parameters for both the uniform and normal distribution
• Develop a simple linear regression model, from identifying the most appropriate predictive variable to training the model and interpreting its output
• Use key linear regression metrics to interpret the model’s fit, including r-squard, p-values, and coefficients
• Use a linear regression model to predict new data points
• Develop a multiple linear regression model to improve fit over a simple linear regression model
• Encode categorical variables using pd.get_dummies() to use them in a regression analysis
• Interpret multiple linear regression r-squared, p-values, and coefficients appropriately in the context of the model
• Identify model errors using mean absolute error, multiple r, and by plotting residuals
• Use an LLM to iterate on a linear regression model to improve its fit and provide an appropriate interpretation

Module 5: Time series

Learning Objectives:

• Use DateTime objects to model dates and times effectively in Python and create time series DataFrames
• Apply DateTimes as indices to a Series or DataFrame
• Develop single and multiple line charts with appropriately formatted date axes
• Apply descriptive statistics to time series, including moving averages and percent change
• Segment time series by date features like day, month, quarter, and year
• Apply resampling and reshaping techniques to time series data
• Use simple and multiple linear regression to develop time series forecasting models for both trend and seasonality in a dataset
• Apply appropriate error metrics to evaluate a time series forecasting linear regression model

Available in May, 2025

Available in June, 2025