Data Science and Analytics

About Course

This comprehensive course in Data Science and
Analytics equips students with the essential skills to
extract meaningful, actionable insights from complex
real-world datasets. Students will master the end-to-
end data lifecycle: collecting, meticulously cleaning,
transforming, analyzing, and visualizing diverse data
types. The curriculum fosters robust statistical
reasoning and programming proficiency in Python and
SQL, enabling data-driven decision-making to solve
critical business problems like predicting customer
churn, optimizing supply chains, and understanding
market trends.

Master the complete data science workflow, from precise problem definition and data acquisition to advanced modeling, interpretation, and compelling
results communication.
Apply rigorous statistical methods, including inferential statistics, regression analysis, and hypothesis testing, to effectively analyze and interpret complex data patterns.
Develop advanced proficiency in data manipulation, cleaning techniques for noisy data, and sophisticated feature engineering using libraries like Pandas and Scikit-learn.
Create compelling, interactive data visualizations and executive dashboards that clearly communicate complex insights to both technical and non-technical stakeholders
Implement and evaluate a range of predictive analytics models, including machine learning algorithms for accurate forecasting, classification, and clustering
Translate technical analytical findings into clear, actionable business recommendations that drive strategic decisions and measurable impact.

Course Content

Data Science Fundamentals
Explore the comprehensive data science process and workflow, covering problem formulation, various data types (structured, unstructured, semi- structured), and data collection methods. Understand the ethical considerations of data privacy, bias, and responsible AI in data science practice.

Data Wrangling and Preparation
Learn advanced techniques for data cleaning, handling missing values (imputation strategies) and outliers, and data transformation (scaling, normalization, one-hot encoding). Master feature engineering, selection, and data integration from disparate sources using Python with Pandas and SQL for relational databases.

Statistical Analysis
Cover descriptive statistics, probability distributions, statistical inference, confidence intervals, and hypothesis testing (t-tests, ANOVA, Chi-squared). Dive into correlation, simple and multiple linear regression, and explore non-parametric methods. Compare Bayesian versus frequentist approaches in statistical modeling.

Data Visualization
Understand principles of effective data visualization, including static and interactive chart types (bar charts, line plots, scatter plots, heatmaps, geospatial maps). Learn dashboard design and advanced storytelling with data using Python libraries like Matplotlib, Seaborn, Plotly, and concepts from Tableau or Power BI.

Predictive Analytics
Examine time series analysis and forecasting, along with various classification (Logistic Regression, Decision Trees, SVM, Random Forests) and regression models (Linear, Polynomial). Learn about ensemble methods, robust model evaluation techniques (cross-validation, confusion matrices, ROC curves), hyperparameter tuning, and operational deployment strategies for predictive models.

Business Applications and Communication
Discover how to translate complex analytical insights into clear, quantifiable business value. Focus on effective stakeholder management, crafting compelling narratives, and communicating technical results to diverse audiences through presentations, reports, and interactive tools, fostering a data-driven culture within organizations.

Capstone Project
Students will undertake a comprehensive, industry-relevant data analysis project using real-world datasets. This project encompasses defining a specific business problem, end-to-end data collection and preparation, conducting exploratory data analysis, developing and evaluating predictive models, and effectively presenting actionable insights. Students will create a portfolio-quality technical report and an interactive dashboard (e.g., using Dash or Streamlit) to communicate findings to both technical teams and business leadership. Examples include predicting customer churn for a telecom company, optimizing marketing campaign spend for an e-commerce platform, or analyzing public health trends from open data sources.

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Data Science and Analytics

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Data Science and Analytics

Data Science and Analytics

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