Python for Data Science

• 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

• 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

• 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

• 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

• 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

• 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

• Clean and format raw datasets

• Handle missing values effectively

• Transform categorical variables

• Apply normalization and scaling

• Use binning and one-hot encoding techniques

• Apply visualization best practices

• Create line plots, bar charts, and scatter plots

• Build histograms and box plots

• Identify trends, patterns, and correlations visually

• Conduct structured exploratory data analysis

• Identify variance and patterns

• Compute and interpret correlation coefficients

• Understand introductory regression concepts

• 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

• 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

Design and implement a Python-based data analysis solution using real-world datasets, data transformation techniques, and visualization workflows.