Advanced Data Analytics Syllabus
In this 12-week course, you will gain a comprehensive understanding of how to use data to drive informed decisions. You will learn how to acquire data from various sources, visualize data effectively, and apply machine learning, regression, and statistics to your analysis. You will also learn how to use Python for data analytics, make predictions and forecasts for the future, optimize decision-making processes, and tell impactful stories with data. Through hands-on projects and real-world examples, you will develop the skills and knowledge needed to become a successful data scientist.
In the Data Analytics, Python, Machine Learning, Predictive Analytics, and Prescriptive Analytics course, you'll learn how to acquire and visualize data, use Python for data analytics, apply machine learning algorithms and regression for data analysis, and optimize decision-making processes. You'll also learn predictive analytics techniques and effective data visualization to tell impactful stories. By the end of the 12-week course, you'll have the skills to become a successful data scientist.
- Overview of data analytics
- Types of data analytics
- Importance of data analytics
- Key concepts and terminologies
- Tools and technologies used in data analytics
- Data sources
- Data collection techniques
- Data preparation techniques
- Data cleaning and pre-processing
- Univariate analysis
- Bivariate analysis
- Multivariate analysis
- Data visualization
- Overview of machine learning
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Overview of classification
- Decision trees
- Random forests
- Neural networks
- Simple linear regression
- Multiple linear regression
- Non-linear regression
- Logistic regression
- Probability theory
- Descriptive statistics
- Inferential statistics
- Introduction to Python
- Python libraries for data analytics
- Data manipulation and cleaning using Python
- Data visualization using Python
- Overview of predictive analytics
- Time series analysis
- ARIMA modeling
- Exponential smoothing
- Overview of forecasting
- Time series forecasting
- ARIMA modeling for forecasting
- Exponential smoothing for forecasting
- Overview of decision making
- Decision trees and random forests for decision making
- Neural networks for decision making
- Overview of data storytelling
- Data visualization for storytelling
- Communicating insights through storytelling
- Integrating all aspects of data analytics
- Hands-on project to apply all concepts learned in the course
- Presentation of the project to showcase skills and knowledge acquired in the course
