– Introduction to Data Cleaning
– Handling Missing Data
– Data Transformation
– Outliers Detection
– Normalization and Scaling Techniques
– Data analysts and scientists
– Business analysts
– Anyone interested in preparing data for analysis
– Hands-on data cleaning tasks
– Python and R-based exercises
– Techniques for handling real-world messy datasets
– Introduction to Data Science
– Overview of Business Analytics
– Understanding Data-Driven Decision Making
– Tools and Techniques in Data Science
– Beginners in data science and business analytics
– Professionals looking to transition into data-related roles
– Video tutorials
– Interactive lessons
– Case studies and examples from the business world
– Introduction to Python and R for Analytics
– Data Manipulation with Pandas and dplyr
– Analyzing Business Data with Statistical and Predictive Models
– Visualizations in Python & R
– Business analysts
– Data scientists
– Learners looking to use Python and R in business analytics
– Python and R coding exercises
– Hands-on business projects
– Data visualization techniques
– Case studies
– Introduction to Predictive Analytics
– Building Predictive Models (Regression, Classification)
– Time Series Forecasting
– Model Evaluation and Optimization
– Business decision-makers
– Data scientists and analysts
– Professionals interested in predictive modeling
– Predictive model development
– Practical business case studies
– Model evaluation techniques
– Python and R examples
– Introduction to EDA
– Data Visualization and Summarization
– Identifying Patterns, Trends, and Outliers
– Correlation and Causation Analysis
– Data scientists
– Business analysts
– Beginners to intermediate learners in data exploration
– EDA projects using real datasets
– Visualization techniques (histograms, scatter plots, etc.)
– Hands-on coding exercises
– Understanding Statistical Concepts
– Types of Statistical Tests (t-tests, chi-squared, ANOVA)
– Formulating and Testing Hypotheses
– Confidence Intervals and p-Values
– Business analysts
– Data scientists and statisticians
– Professionals using statistical methods in analysis
– Statistical test examples
– Hypothesis testing exercises
– Real-world problem solving
– Interactive coding assignments
– Overview of Machine Learning Algorithms
– Supervised and Unsupervised Learning in Business Analytics
– Building Predictive Models
– Model Deployment in Business
– Business analysts
– Data scientists
– Managers and decision-makers working with ML-driven business solutions
– Machine learning algorithm implementations
– Case studies for business applications
– Predictive analytics projects
– Principles of Data Visualization
– Tools for Data Visualization (Tableau, Matplotlib, ggplot)
– Visualizing Business Data
– Effective Storytelling with Data
– Data analysts and scientists
– Business professionals working with data
– Anyone interested in data storytelling
– Data visualization projects
– Hands-on with Tableau, matplotlib, and ggplot
– Real-world business data visualization examples
