To ensure you are well-prepared and can get the most out of the Data Science and Machine Learning: Mathematical and Statistical Methods course, the following prerequisites are recommended:
- Basic understanding of mathematics, including familiarity with algebra and elementary statistics.
- Some knowledge of probability concepts, as you will be dealing with statistical methods.
- Ability to work with data in spreadsheets or similar tools, as data manipulation is a key part of data science.
- Basic computer literacy, as the course will likely involve the use of data science software or programming languages.
- A willingness to learn and engage with complex concepts, as data science and machine learning can be challenging but rewarding fields of study.
Please note that a deep expertise in mathematics or programming is not required to begin this course; however, a foundation in the above areas will enable you to grasp the concepts more quickly and fully.
Target Audience for Data Science and Machine Learning: Mathematical and Statistical Methods
This course offers an in-depth exploration of data science and machine learning through mathematical and statistical methods. Ideal for analytics-focused professionals.
Target Audience and Job Roles:
- Data Scientists
- Machine Learning Engineers
- Data Analysts
- Statisticians
- Business Analysts
- Research Scientists
- AI Engineers
- Software Developers interested in data science
- Quantitative Analysts
- PhD and Masters students specializing in data science or related fields
- Data Science Instructors/Educators
- Technical Project Managers overseeing data-driven projects
- Product Managers in tech companies focusing on analytics-driven features
- Data Engineers looking to enhance analytical skills
- Marketing Analysts interested in consumer data analysis
- Finance Professionals leveraging predictive analytics
Learning Objectives - What you will Learn in this Data Science and Machine Learning: Mathematical and Statistical Methods?
Introduction to Course Learning Outcomes
This course aims to equip students with a thorough understanding of statistical methods and machine learning techniques essential for data analysis and predictive modeling.
Learning Objectives and Outcomes
- Understand the foundational concepts of descriptive statistics, including measures of central tendency (mean, median, mode) and measures of variability (variance, standard deviation).
- Gain proficiency in identifying and handling outliers and anomalies within data sets to ensure the accuracy of statistical analysis.
- Learn the principles of probability distributions, particularly the normal distribution, and how it applies to data science methodologies.
- Develop skills in creating and interpreting visual data representations such as histograms, box plots, and scatterplots to extract insights from data.
- Acquire knowledge of correlation and regression analysis for determining relationships between variables and making predictions.
- Master the concepts of hypothesis testing, p-values, and A/B testing to validate data-driven decisions and scientific conclusions.
- Explore various machine learning algorithms, including Naive Bayes, K-Nearest Neighbors, and K-means clustering, for classification and pattern discovery.
- Understand how to construct and analyze confusion matrices, ROC curves, and other performance metrics to evaluate the effectiveness of machine learning models.
- Learn the importance of hyperparameter tuning to optimize the performance of machine learning algorithms.
- Develop the ability to implement sampling techniques, understand biases, and apply resampling methods to ensure representative data analysis.
Intermediate
