Data Science for Business and Research
( 24 Modules )

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

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Data Science for Business and Research ( 24 Modules )

Data Science for Business and Research
( 24 Modules )