About this Course
One of the keys to success in any organization is turning data into clear insights that support effective action, problem-solving and decision making. The process of developing well-informed business strategies that produce measurable results includes converting business questions into data analysis questions, using data to frame a problem, collecting and modeling the data, analyzing it and then accurately interpreting your findings.
In this course, we’ll cover the fundamental data modeling techniques and statistical knowledge at the heart of accurate and effective data analysis. You’ll learn to use the advanced features of industry-standard tools to work on real-world data sets and generate valuable insights. Gain skills that you can apply to your work today, and prepare for more advanced courses in data visualization, predictive analytics and data science.
What You’ll Learn
- Key processes for selecting, wrangling and exploring data
- Core statistical methods and measures for data analysis
- Programming syntax, packages and libraries for working with data
- How to test hypotheses and use inferential statistics
- The basics of accurately drawing conclusions, predicting outcomes and classifying data
Get Hands-On Experience
- Use advanced features of Microsoft Excel, Jupyter Notebook, SQL and the R programming language to import, wrangle, visualize and analyze data
- Apply descriptive statistics, data cleaning and treat outliers to prepare data for exploration and analysis
- Create basic machine learning regression and classification models using R programming