About This Course
Turning raw data into actionable insights is a cornerstone of success in any organization. Data-driven insights enable organizations to make smarter decisions, solve problems more effectively, and build stronger business strategies. The process begins with framing business questions as data analysis problems, collecting and modeling data, analyzing results, and interpreting findings accurately.
In this course, you’ll explore key data modeling techniques and the statistical knowledge at the core of effective analysis. You’ll use advanced features of industry-standard tools to analyze real-world datasets and generate insights that drive value. Build practical skills you can use today and prepare for 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
