COURSE DESCRIPTION:
The Data Science and Analytics course aims to equip students with fundamental concepts and methodologies in the field of data science and analytics. Emphasizing the development of skills for analyzing extensive datasets, the course enables students to derive insights and make informed decisions through statistical methods, machine learning, and visualization techniques. Participants will gain experience in handling both structured and unstructured data, employing data cleaning and preprocessing methods, conducting exploratory data analysis (EDA), and developing predictive models.
Additionally, the curriculum includes critical subjects such as data visualization, statistical analysis, and business intelligence, utilizing widely-used programming languages and tools like Python, R, SQL, and Excel. Upon completion, students will possess a robust understanding of data science, positioning them for roles as data analysts, data scientists, or business analysts.
CERTIFICATION
Upon finishing the Data Science and Analytics course, participants will be awarded a Certificate in Data Science and Analytics.
To qualify for certification, students must fulfill several requirements: complete all weekly assignments, quizzes, and practical labs; successfully pass both the midterm and final exams; and submit a capstone project that showcases their ability to analyze a real-world dataset and derive significant insights. The certificate is granted to individuals who exhibit proficiency in data science tools and methodologies, positioning them for careers as data scientists, data analysts, and business intelligence analysts.
LEARNING OUTCOME
By the conclusion of the Data Science and Analytics course, students will acquire the ability to effectively collect and clean data. They will learn various data collection methods, including sourcing from databases, APIs, spreadsheets, and web scraping, while also mastering techniques to ensure data reliability and cleanliness. Students will become proficient in handling missing values, outliers, and duplicates, utilizing data transformation methods such as normalization and encoding, with tools like Pandas in Python and dplyr in R.
Additionally, students will engage in exploratory data analysis (EDA) to uncover insights within datasets through visual and statistical methods. They will utilize summary statistics and visualizations, including histograms and scatter plots, while gaining expertise in data visualization tools such as Matplotlib, Seaborn, ggplot2, and Tableau to effectively communicate findings. The course will also cover essential statistical concepts and hypothesis testing, enabling students to conduct tests like t-tests and chi-squared tests, while understanding p-values and confidence intervals.
Finally, students will delve into the fundamentals of machine learning, focusing on both supervised and unsupervised learning techniques. They will learn to implement algorithms such as Linear Regression and Decision Trees, evaluate model performance using various metrics, and apply unsupervised methods like k-Means clustering and Principal Component Analysis (PCA) to analyze data patterns.
Course Features
- Lectures 35
- Quiz 0
- Duration 54 hours
- Skill level All levels
- Language English
- Students 28
- Assessments Yes
