Data Analysis Professional
Overview
In this course, you will learn fundamental concepts of data analysis, including statistical methods, probability, and data visualization. You will explore data cleaning, transformation, and feature engineering using Python and its key libraries, such as NumPy, Pandas, and Seaborn.
As you progress, you will gain hands-on experience with SQL and NoSQL databases, data warehouses like Redshift and BigQuery, and the role of data lakes in AI and data science. You will also be introduced to big data analytics, decision trees, and graph analytics.
Finally, you will apply advanced data science techniques, including regression analysis, and complete a real-world project to showcase your skills. This course is suitable for beginners.
Outcomes
By the end of this course, you will be proficient in:
- Analyzing and interpreting data using statistics and probability.
- Cleaning, transforming, and visualizing data with Python.
- Working with SQL and NoSQL databases for data storage and retrieval.
- Applying regression analysis and other data science techniques to real-world problems.
Please ensure to have a computer that meets the specified requirements:
- Supported operating systems: macOS, Linux, or Windows (Pro edition required).
- Latest OS version, fully up to date.
- All security updates installed.
- At least 100GB of free space on the hard drive.
- At least 16GB of RAM, 32GB RAM is strongly preferred.
- Support for video conferencing and screen-sharing, with a reliable webcam and microphone.
Statistical Foundations
- Descriptive and inferential statistics for analyzing data trends.
- Probability distributions, correlation, and regression analysis.
- Exploring time series analysis and pattern recognition in datasets.
Python for Data Analysis
- Data manipulation and visualization with NumPy, Pandas, and Seaborn.
- Exploratory data analysis (EDA) and feature engineering.
- Cleaning, preprocessing, and handling missing data for better insights.
Databases & Data Storage
- SQL and NoSQL databases for efficient data management.
- Data warehouses, data lakes, and their role in big data.
- Time-series and dimensional databases for advanced analytics.
Advanced Data Science Techniques
- Linear regression and key metrics like R-squared and p-values.
- Decision trees and graph analytics for complex data analysis.
- Real-world project to apply learned concepts and showcase skills.
To successfully pass the class, students should aim to reach a minimum of 90% of the available points. We’ve created a flexible environment which will enable you to have the best learning experience and elevate you on to greater heights.
Punctuality, participation in discussions, completion of assignments, and demonstration of professional courtesy to others are required, in accordance with our Code of Conduct. Attendance will be taken at the beginning of every class. Passing requires at least 90% attendance. Students should always contact the instructors ahead of time if they are unable to attend all or part of the published class/lab hours.
