This course aims to help professionals develop and enhance their data analysis and visualization skills, which are essential for working with various types of data and creating insightful reports and dashboards. The course will cover topics such as:
- What are the types and characteristics of data, and how can they be evaluated for quality and representativeness?
- How can you use descriptive statistics to summarize data, and profile two or more groups with statistical tests?
- How can you visualize multiple analytics with powerful smart charts, such as simple linear regression, simple logistic regression, and outlier detection?
- What is machine learning, and how can you use supervised learning algorithms to perform classification and regression tasks, such as multiple linear regression, multiple logistic regression, discriminant analysis, decision trees, support vector machines, k-nearest neighbors, naive Bayes, and neural networks?
- What is business intelligence, and how can you use databases, ETL, storage, analytics, and BI tools to create data warehouses, data marts, data lakes, OLAP, dashboards, etc.?
- How can you use forecasting techniques to predict future trends and outcomes, such as exponential smoothing, time series, ARIMA models, etc.?
- How can you compare R vs. Python for performing statistical tests and machine learning algorithms?
- What is unsupervised learning, and how can you use dimensionality reduction and clustering algorithms to discover patterns and groups in data, such as principal component analysis, hierarchical clustering, k-means clustering, correspondence analysis, multi-dimensional scaling, quadrant analysis, etc.?
- What is PMP (Project Management Professional), and how can you use it to plan and execute data science projects effectively?
- What are IoT (Internet of Things) and Big Data Ecosystems, and how can you use various protocols and technologies to collect, store, process, and analyze large-scale data from various sources?
