Module #1 Introduction to Data Science Overview of data science, its importance, and applications in business and research
Module #2 Data Science Tools and Technologies Overview of popular data science tools and technologies, including Python, R, Excel, and Tableau
Module #3 Data Types and Sources Understanding different types of data, including numerical, categorical, and text data, and sources of data, including surveys, sensors, and social media
Module #4 Data Preprocessing and Cleaning Techniques for preprocessing and cleaning data, including data wrangling, handling missing values, and data transformation
Module #5 Data Visualization Introduction to data visualization, including types of plots, charts, and dashboards, and tools such as Tableau and Power BI
Module #6 Descriptive Statistics Measures of central tendency, variability, and distribution, including mean, median, mode, range, and standard deviation
Module #7 Inferential Statistics Introduction to hypothesis testing, confidence intervals, and significance testing
Module #8 Regression Analysis Introduction to simple and multiple linear regression, including assumptions and interpretation of results
Module #9 Machine Learning Fundamentals Introduction to supervised and unsupervised learning, including types of algorithms and evaluation metrics
Module #10 Supervised Learning Techniques for building predictive models, including logistic regression, decision trees, and random forests
Module #11 Unsupervised Learning Techniques for clustering and dimensionality reduction, including k-means and principal component analysis
Module #12 Text Analytics Introduction to text mining, including text preprocessing, sentiment analysis, and topic modeling
Module #13 Time Series Analysis Introduction to time series data, including components, trends, and forecasting techniques
Module #14 Big Data and NoSQL Databases Introduction to big data, including Hadoop, Spark, and NoSQL databases such as MongoDB and Cassandra
Module #15 Data Mining Introduction to data mining, including pattern evaluation, association rule mining, and clustering
Module #16 Business Analytics Applying data science to business problems, including customer segmentation, market basket analysis, and revenue forecasting
Module #17 Research Methods Introduction to research design, including surveys, experiments, and quasi-experiments
Module #18 Academic Writing and Publishing Guidelines for writing and publishing research papers, including structure, tone, and style
Module #19 Data Science in Python Hands-on experience with Python libraries such as NumPy, Pandas, and Scikit-learn
Module #20 Data Science in R Hands-on experience with R libraries such as dplyr, tidyr, and caret
Module #21 Case Studies in Business Real-world examples of data science applications in business, including marketing, finance, and operations
Module #22 Case Studies in Research Real-world examples of data science applications in research, including social sciences, healthcare, and environmental studies
Module #23 Ethics and Governance Discussing ethical considerations and governance in data science, including privacy, bias, and transparency
Module #24 Course Wrap-Up & Conclusion Planning next steps in Data Science for Business and Research career
