Course description
Highlights:
- Introduction to Business Analytics: Learn the fundamentals of business analytics, including
its importance and applications.
- Data Collection & Analysis: Understand how to collect, clean, and analyze data to derive
valuable insights.
- Statistical Techniques: Master statistical methods like regression, hypothesis testing, and
probability in business contexts.
- Data Visualization: Learn how to create visualizations using tools like Power BI, Tableau,
and Excel to present insights clearly.
- Predictive Analytics: Explore forecasting models, machine learning, and data-driven
decision-making.
Course Objective:
By the end of this course, you will be able to:
- Understand the importance of business analytics in improving decision-making.
- Collect, clean, and prepare data for analysis.
- Apply statistical techniques to analyze business data and identify trends.
- Visualize data and communicate insights effectively using analytical tools.
- Use predictive models and machine learning techniques to forecast future trends and
outcomes.
- Apply business analytics concepts to solve real-world business challenges
Course Structure:
1. Introduction to Business Analytics
- Overview of business analytics and its role in decision-making
- Key components of business analytics: Descriptive, diagnostic, predictive, and
prescriptive analytics
- The role of data in modern business strategy and performance
- The analytics process: From data collection to insight generation
2. Data Collection and Preparation
- Types of data: Structured, unstructured, and semi-structured data
- Data collection methods: Surveys, transactional data, web scraping, and APIs
- Data cleaning techniques: Removing duplicates, handling missing values, and
standardizing data
- Data transformation and preparation for analysis: Normalization, categorization, and
aggregation
3. Statistical Techniques in Business Analytics
- Descriptive statistics: Mean, median, mode, standard deviation, and variance
- Inferential statistics: Hypothesis testing, confidence intervals, and p-values
- Regression analysis: Linear regression, multiple regression, and logistic regression
- Correlation and causality: Identifying relationships between variables
- Probability theory and its applications i
4. Data Visualization and Reporting
- Importance of data visualization in communicating insights
- Tools for data visualization: Excel, Power BI, Tableau, and Google Data Studio
- Creating dashboards and reports for decision-makers
- Best practices for data visualization: Chart selection, design principles, and interactivity
- Presenting data-driven insights to non-technical audie
5. Predictive Analytics and Forecasting
- Introduction to predictive analytics and its business applications
- Time series analysis and forecasting techniques: Moving averages, exponential smoothing
- Machine learning models: Decision trees, random forests, and k-nearest neighbors (KNN)
- Evaluating predictive models: Accuracy, precision, recall, and F1 score
- Use cases of predictive analytics: Customer churn prediction, demand forecasting, and
financial modeling
6. Big Data Analytics
- Overview of Big Data and its importance in business
- The 5 V's of Big Data: Volume, Variety, Velocity, Veracity, and Value
- Tools for Big Data analytics: Hadoop, Spark, and NoSQL databases
- Working with large datasets and distributed computing environments
- Real-time analytics: Streaming data and
7. Advanced Business Analytics Techniques
- Advanced machine learning models: Neural networks, deep learning, and support vector
machines (SVM)
- Natural Language Processing (NLP) for text analytics
- Optimization and prescriptive analytics: Linear programming, simulation, and decision
models
- Business applications of advanced techniques: Supply chain optimization, pricing
strategies, and fraud detection
- Ethics and challenges in business analytics: Data privacy, fairness, and bias
8. Business Analytics in Practice
- Case studies of successful business analytics implementation across industries
- Applying business analytics to solve real-world business problems
- The role of business analysts in organizations: Stakeholder management and
communication
- Measuring the impact of business analytics on organizational performance
- Continuous learning in business analytics: Keeping up with emerging trends and
technologies
Learning Methodology:
- Interactive Lectures: Gain insights from experts and instructors on fundamental and
advanced business analytics concepts.
- Hands-on Practice: Work on real-world business problems using case studies, data sets, and
analytical tools.
- Assessments and Quizzes: Regular quizzes and assessments to reinforce learning and ensure
comprehension.
- Live Sessions: Participate in live Q&A sessions and discussions with instructors and peers
for clarification and deeper understanding.
- Project Work: Apply your learning to solve actual business challenges using data analysis
and reporting tools
Who Should Enroll:
- Business analysts and data analysts looking to enhance their analytical skills
- Professionals working in finance, marketing, sales, or operations who want to leverage data
for better decision-making
- Aspiring data scientists and machine learning enthusiasts
- Managers and decision-makers looking to use business analytics for strategic planning
- Students or professionals interested in a career in data analysis or business analytics
- Anyone interested in understanding how data can drive business decisions and improve
performance
This Business Analytics course is ideal for professionals who want to leverage data to
enhance business performance and make informed decisions. Whether you are looking to
improve your data analysis skills, learn advanced techniques, or get a comprehensive
overview of business analytics, this course provides the knowledge and tools needed to drive
success in today’s data-driven world.
