Machine Learning with Python

Skill Goal:
Understand how Python supports data science and machine learning workflows.

Skills Developed:

• Explain how Python is used in data analysis and machine learning

• Write and execute Python code interactively

• Perform calculations and basic program execution

• Install and import essential Python libraries

• Work with Python environments such as Jupyter and Anaconda

Sprint Outcome:
Ability to confidently write and run Python code for data-related tasks.

Skill Goal:
Work efficiently with structured datasets using Python libraries.

Skills Developed:

• Create and manage Pandas Series and DataFrames

• Perform indexing, slicing, and filtering operations

• Import datasets from CSV, Excel, and JSON files

• Sort, rank, and analyze structured datasets

• Handle missing values and dataset inconsistencies

Sprint Outcome:
Ability to organize and manipulate structured datasets using Pandas and NumPy.

Skill Goal:
Prepare raw datasets for analysis and modeling.

Skills Developed:

• Clean and preprocess raw data

• Merge and combine datasets using joins and concatenation

• Remove duplicates and correct data inconsistencies

• Transform data formats and structures

• Perform group-based aggregations

Sprint Outcome:
Ability to transform messy datasets into analysis-ready structured data.

Skill Goal:
Create meaningful visualizations to interpret and communicate data insights.

Skills Developed:

• Understand visualization structures in Python

• Create line charts, scatter plots, and histograms

• Customize axes, legends, and chart formatting

• Highlight trends and patterns visually

• Export visualizations for reporting and presentation

Sprint Outcome:
Ability to convert data insights into clear and compelling visual representations.

Skill Goal:
Apply statistical methods to summarize and interpret datasets.

Skills Developed:

• Calculate mean, median, and mode

• Measure variance and standard deviation

• Analyze data distribution patterns

• Interpret spread and variability in datasets

• Use Python to summarize numerical data

Sprint Outcome:
Ability to extract meaningful statistical insights from datasets.

Skill Goal:
Understand and apply statistical hypothesis testing concepts.

Skills Developed:

• Explain null and alternative hypotheses

• Interpret Type I and Type II errors

• Perform z-tests and one-sample t-tests

• Visualize sampling distributions

• Apply chi-square and variance testing concepts

Sprint Outcome:
Ability to apply hypothesis testing to evaluate statistical assumptions in data.

Skill Goal:
Compare and evaluate differences between two groups or populations.

Skills Developed:

• Perform independent sample t-tests

• Conduct paired sample tests

• Evaluate equal and unequal variance assumptions

• Interpret statistical test results

• Apply hypothesis testing to real-world scenarios

Sprint Outcome:
Ability to statistically compare datasets to identify meaningful differences.

Skill Goal:
Analyze differences across multiple groups using statistical methods.

Skills Developed:

• Understand the purpose of ANOVA

• Perform one-way ANOVA analysis

• Interpret group-based statistical comparisons

• Identify statistical significance across groups

Sprint Outcome:
Ability to evaluate statistical differences among multiple datasets.

Skill Goal:
Analyze relationships involving multiple variables.

Skills Developed:

• Perform two-way ANOVA analysis

• Evaluate interaction effects between variables

• Interpret complex statistical outputs

• Analyze multi-variable comparisons

Sprint Outcome:
Ability to perform advanced statistical analysis involving multiple variables.

Skill Goal:
Understand the principles and workflow of machine learning systems.

Skills Developed:

• Explain what machine learning is and how it works

• Differentiate supervised and unsupervised learning

• Identify classification and regression problems

• Understand the machine learning pipeline

• Map business problems to machine learning solutions

Sprint Outcome:
Ability to identify machine learning opportunities and design ML workflows.

Skill Goal:
Build predictive models to forecast numeric outcomes.

Skills Developed:

• Build simple linear regression models

• Develop multiple linear regression models

• Evaluate model accuracy and performance

• Understand overfitting and model fit

• Interpret RMSE and goodness-of-fit metrics

Sprint Outcome:
Ability to create and evaluate regression models for forecasting problems.

Skill Goal:
Develop models to classify outcomes into categories.

Skills Developed:

• Understand binary classification problems

• Prepare datasets for classification models

• Build logistic regression models

• Evaluate models using confusion matrices

• Interpret classification accuracy and results

Sprint Outcome:
Ability to build and evaluate classification models for decision-making.

Skill Goal:
Analyze and forecast time-based datasets.

Skills Developed:

• Identify trends and seasonality in time series data

• Perform autocorrelation analysis using ACF and PACF

• Build AR, ARMA, and ARIMA models

• Apply automatic ARIMA forecasting

Sprint Outcome:
Ability to analyze historical data and forecast future trends.

Skill Goal:
Identify hidden patterns and groups within datasets.

Skills Developed:

• Understand unsupervised learning concepts

•  Apply K-Means clustering techniques

• Visualize clustering results

• Evaluate clustering effectiveness

• Compare clustering approaches

Sprint Outcome:
Ability to discover patterns and group similarities within complex datasets.

Skill Goal:
Build decision tree and ensemble-based machine learning models.

Skills Developed:

• Understand decision tree algorithms

• Build decision tree classification models

• Train random forest models

• Compare tree-based modeling approache

• Evaluate model accuracy and performance

Sprint Outcome:
Ability to apply tree-based machine learning models to solve classification problems.

Project Goal:

Apply Python-based data analysis, statistical methods, and machine learning techniques to solve a real-world business problem and generate actionable insights.

Skills Demonstrated:

• Analyze a real-world dataset to understand the business problem

• Define the project objective and success metrics

• Clean and preprocess raw data using Pandas and NumPy

• Perform Exploratory Data Analysis (EDA) to identify patterns and trends

• Apply statistical techniques to interpret dataset characteristics

• Build and evaluate machine learning models such as regression, classification, or clustering

• Apply model evaluation metrics to assess performance

• Visualize findings using Python charts and plots

• Generate meaningful insights and predictions from the data

• Present clear, data-driven business recommendations