Learn Core Statistical Concepts and Apply Them to Real Data with Python
Statistics is at the heart of data science, and a solid understanding of it is essential for analyzing, interpreting, and drawing insights from data. This course, Essential Statistics for Data Science with Python, is designed to help you build that strong foundation by blending core statistical concepts with hands-on coding in Python.
What you’ll learn
- Explain the role of statistics in data science and why it is essential for data analysis..
- Analyze dataset distributions using skewness, kurtosis, and visualization techniques..
- Apply probability rules and concepts to model uncertainty and randomness in data..
- Use Python (with pandas) to perform descriptive statistical analysis and visualize key insights..
- Bridge theoretical concepts with practical coding to prepare for inferential statistics and machine learning..
Course Content
- Introduction –> 1 lecture • 8min.
- Statistics Terminology –> 4 lectures • 37min.
- Descriptive Statistics –> 6 lectures • 1hr 33min.
- Python –> 4 lectures • 42min.
- Descriptive Statistics with Python – basics –> 7 lectures • 1hr.
- Knowledge Check –> 0 lectures • 0min.
- Probability –> 18 lectures • 2hr 17min.
- Knowledge Check –> 0 lectures • 0min.
- Probability with Python –> 8 lectures • 57min.
Requirements
Statistics is at the heart of data science, and a solid understanding of it is essential for analyzing, interpreting, and drawing insights from data. This course, Essential Statistics for Data Science with Python, is designed to help you build that strong foundation by blending core statistical concepts with hands-on coding in Python.
We begin with descriptive statistics, where you will learn how to summarize and explore datasets using measures such as mean, median, and mode to identify central tendencies. We also cover how data spreads out using variance, standard deviation, range, and interquartile range, as well as advanced measures like skewness and kurtosis to understand distribution shapes.
The course then introduces probability, a key building block for data-driven decision making. You will understand basic probability rules, distributions, and how randomness impacts real-world data. These concepts will prepare you for more advanced topics like hypothesis testing and inferential statistics in future modules.
What makes this course practical is the integration of pandas, one of the most powerful Python libraries for data analysis. You will learn how to calculate summary statistics, manipulate datasets, and visualize results directly in pandas, making your statistical learning immediately applicable to real-world problems.
By the end of the course, you will not only understand essential statistical concepts but also be confident in applying them programmatically for data analysis. This combination of theory and practice ensures you are well-prepared for deeper explorations into inferential statistics, machine learning, and advanced data science techniques.