Python for Data Science

Skill Goal:
Build a strong understanding of Data Science, its lifecycle, and its business relevance.

Skills Developed:

• Explain what Data Science is and why it matters

• Describe the complete data science lifecycle

• Differentiate statistical modeling and algorithmic modeling

• Explain the relationship between Data Science, Machine Learning, and AI

• Identify real-world business use cases of data science

Sprint Outcome:

Ability to clearly articulate how data science creates value and connect business problems to analytical workflows.

Skill Goal:
Establish and work confidently within a Python-based data science environment.

Skills Developed:

• Explain why Python is widely used in data science

• Install Python and required packages

• Work with Jupyter Notebook and cloud-based environments

• Execute Python code in local and cloud platforms

Sprint Outcome:

Ability to independently set up and operate Python environment for data analysis tasks.

Skill Goal:
Develop structured Python programs to solve data-related problems.

Skills Developed:

• Use Python data types, operators, and control flow

• Write reusable functions and modular programs

• Manipulate lists, tuples, dictionaries, and strings

• Read from and write to files

• Apply exception handling for robust code

• Understand foundational object-oriented programming concepts

Sprint Outcome:

Ability to write clean, efficient Python scripts for data processing and transformation.

Skill Goal:
Apply industry-standard Python libraries to perform data analysis.

Skills Developed:

• Perform numerical operations using NumPy

• Manipulate and analyze datasets using Pandas

• Work confidently with DataFrames and Series

• Create basic visualizations using Matplotlib

• Understand Scikit-learn workflows at a high level

Sprint Outcome:

Ability to analyze structured datasets using Python’s core data science libraries.

Skill Goal:
Apply foundational statistical concepts to interpret data effectively.

Skills Developed:

• Differentiate data types and summarize datasets

• Measure central tendency (mean, median, mode)

• Calculate variance and standard deviation

• Detect outliers using IQR and percentiles

• Interpret distribution patterns

Sprint Outcome:

Ability to apply statistical techniques to extract meaningful insights from data.

Skill Goal:
Understand probability concepts and their role in data analysis.

Skills Developed:

• Explain random variables and probability fundamentals

• Interpret normal, binomial, and Poisson distributions

• Apply z-scores in analysis

• Use the 68–95–99.7 rule

• Analyze skewness and symmetry in datasets

Sprint Outcome:

Ability to interpret uncertainty, variability, and distribution behavior in datasets.

Skill Goal:
Prepare raw datasets for accurate analysis and modeling.

Skills Developed:

• Clean and format raw datasets

• Handle missing values effectively

• Transform categorical variables

• Apply normalization and scaling

• Use binning and one-hot encoding techniques

Sprint Outcome:

Ability to prepare real-world datasets for analytical and modeling tasks.

Skill Goal:
Communicate analytical insights through effective data visualization.

Skills Developed:

• Apply visualization best practices

• Create line plots, bar charts, and scatter plots

• Build histograms and box plots

• Identify trends, patterns, and correlations visually

Sprint Outcome:

Ability to present clear, visually compelling insights using Python visualizations.

Skill Goal:
Discover patterns, relationships, and trends within datasets.

Skills Developed:

• Conduct structured exploratory data analysis

• Identify variance and patterns

• Compute and interpret correlation coefficients

• Understand introductory regression concepts

Sprint Outcome:

Ability to perform systemic EDA to generate actionable insights.

Skill Goal:
Develop and evaluate predictive models using Python.

Skills Developed:

• Understand model development workflows

• Build simple and multiple linear regression models

• Train models using Scikit-learn

• Interpret evaluation metrics

• Apply models for prediction and business decision-making

Sprint Outcome:

Ability to build and access predictive models for business applications.

Skill Goal:
Understand core machine learning principles and applications.

Skills Developed:

• Explain machine learning fundamentals

• Differentiate supervised and unsupervised learning

• Identify classification vs regression problems

• Understand recommender systems and deep learning

• Recognize real-world ML applications

Sprint Outcome:

Ability to interpret machine learning workflows and recommend appropriate approaches for business scenarios.

Project Goal:
Design, analyze, and present a complete data-driven solution to a real-world business problem using Python.

Skills Demonstrated:

• Analyze a real-world business scenario provided in class

• Understand the defined business objective and success criteria

• Collect, clean, and preprocess relevant datasets

• Perform Exploratory Data Analysis (EDA)

• Apply statistical techniques and probability concepts

• Build and evaluate a predictive model

• Visualize insights using Python

• Present actionable business recommendations

• Demonstrate responsible data usage