Course Description
Get ready to learn the fundamentals of using data analytics to drive business decisions and strategies.
This course aims to equip you with the skills to collect, analyze, interpret, and present data in ways that support strategic business decisions. It combines technical training with practical business applications, preparing you to leverage data analytics to drive organizational success.
What You’ll Learn?
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- Introduction to Business Analytics:
- Understanding the role of data analytics in business decision-making.
- Overview of different types of analytics: descriptive, diagnostic, predictive, and prescriptive.
- Data Collection and Management:
- Methods of data collection and data sources (e.g., transactional data, customer data, social media data).
- Basics of data management and data governance, including data quality and integrity.
- Data Preparation and Cleaning:
- Techniques for data cleaning, transformation, and preprocessing.
- Handling missing data and outliers.
- Statistical Analysis and Data Exploration:
- Basic statistical concepts and methods used in data analysis.
- Exploratory data analysis (EDA) techniques to uncover patterns, trends, and insights.
- Data Visualization:
- Principles and best practices for effective data visualization.
- Tools and software for creating visual representations of data (e.g., Tableau, Power BI, Python libraries like Matplotlib and Seaborn).
- Predictive Analytics and Machine Learning:
- Introduction to machine learning algorithms and their applications in business.
- Building and evaluating predictive models (e.g., regression, classification, clustering).
- Business Applications of Data Analytics:
- Use cases of data analytics in various business functions (e.g., marketing, finance, operations, HR).
- Case studies demonstrating successful implementation of data analytics in business contexts.
- Data-Driven Decision Making:
- Frameworks for integrating data analytics into business strategy and operations.
- Techniques for making data-driven decisions and measuring their impact.
- Tools and Technologies for Data Analytics:
- Overview of popular data analytics tools and software (e.g., Excel, SQL, R, Python).
- Introduction to big data technologies and platforms (e.g., Hadoop, Spark).
- Ethical and Legal Considerations in Data Analytics:
- Ethical issues related to data privacy and security.
- Legal frameworks governing data usage and compliance requirements.
- Capstone Project or Practical Application:
- Hands-on project or case study to apply learned concepts to a real-world business problem.
- Presenting data-driven recommendations and insights.
Overall, this course aims to equip you with the skills to collect, analyze, interpret, and present data in ways that support strategic business decisions. It combines technical training with practical business applications, preparing you to leverage data analytics to drive organizational success.
- Introduction to Business Analytics:
Lessons:
