Udemy – Statistics for Data Science: Solve Real World Data Problems. 2025-4

Statistics for Data Science: Solve Real World Data Problems. This course teaches statistical concepts for solving data science problems with a practical, case-based approach. Suitable for those interested in data science, data analysis, and machine learning, this course introduces statistics not through dry theory, but through case studies, data visualization, and step-by-step implementations in Python. Using libraries like Statsmodels, participants apply statistical techniques directly to real data and gain hands-on experience right from the start. Topics include analyzing data distributions, comparing groups, measuring relationships, and building regression models. This course makes learning effective and engaging with clear explanations, coding exercises, and helpful resources. Designed for all levels, from beginners to those looking to brush up on their statistical knowledge, the only prerequisites are a basic familiarity with Python and a curiosity about data. Combining theory and practice, this course provides the foundations necessary for real-world data analysis and evidence-based decision-making.

What you will learn

• Different types of data: Understand the types of data: categorical, numerical, and how they are measured.
• Data visualization techniques: Mastery of data visualization techniques (bar charts, line charts, pie charts, histograms, box plots, etc.).
• Key Statistical Measures: Understand key statistical measures such as mean, median, mode, variance, and standard deviation.
• Data analysis: Analyze central tendency, dispersion, and outliers to effectively summarize and interpret data.
• Measuring relationships: Measuring relationships using correlations, covariance, and heatmaps.
• Hypothesis Testing: An in-depth look at hypothesis testing with real-world applications of t-tests, chi-square tests, and more.
• Linear Regression Models: Learn to implement linear regression models and interpret p-values, coefficients, and R-squared values.
• Statsmodels: Use Statsmodels, a powerful Python library, to perform statistical analysis and modeling in your projects.

This course is suitable for people who:

• Aspiring data scientists: Those who want to build a strong statistical foundation.
• Business analysts and professionals: Those looking to improve their data interpretation skills.
• Students and graduates: from non-technical disciplines entering data-related roles.
• Programmers and developers: Those migrating into data science or analytics.
• Anyone preparing for data science interviews or technical case studies.

Course Description Statistics for Data Science: Solve Real World Data Problems.

• Publisher:  Udemy
• Instructor:  Temotec Learning Academy , Temotec Academy
• Training level: Beginner to advanced
• Training duration: 3 hours and 59 minutes
• Number of lessons: 21

Course syllabus in 2025/6

Prerequisites for the Statistics for Data Science: Solve Real World Data Problems course.

• No prior statistics knowledge required — everything is taught from the ground up.
• Basic understanding of Python (lists, functions, loops) is helpful.
• A willingness to learn through hands-on examples and real datasets.
• Access to a computer with internet connection (you’ll install Jupyter Notebook, use Google Colab) or VScode.

Course images

Sample course video

Download link

Download file – 651 MB

File(s) password: www.downloadly.ir

File size

651 MB