Enroll Course: https://www.udemy.com/course/istatistik-python-adan-zye-temel-istatistik-bilimi-6/
In the ever-expanding universe of data science and artificial intelligence, a strong foundation in statistics is not just beneficial, it’s essential. The Udemy course, “İstatistik & Python: A’dan Z’ye Temel İstatistik Bilimi (6)” by an unnamed instructor, promises to deliver exactly that. As the sixth installment in a comprehensive AI journey, this course bridges the gap between fundamental statistical concepts and their practical application using Python.
The course emphasizes a hands-on approach, starting each lesson with a blank page to build Python code from scratch. This method ensures learners understand the ‘why’ behind each line of code, fostering a deeper comprehension of programming and statistical logic. The instructor provides downloadable code templates and patterns, which are invaluable for practicing and kickstarting personal projects.
What truly sets this course apart is its commitment to explaining the theory and logic behind the code. It’s not just about syntax; it’s about understanding the underlying principles. Furthermore, the support system, featuring a team of professional data scientists, guarantees that students receive prompt responses to their queries, typically within 72 hours.
The curriculum is extensive, covering everything from the basics of data, measurement levels, and populations versus samples, to central tendency and dispersion. It delves into bivariate data, correlation coefficients (Pearson and Spearman), and effect sizes. The probability section is particularly robust, exploring its definition, permutations, combinations, conditional probability, Bayes’ Theorem, and various probability distributions (discrete and continuous, including the crucial Normal distribution and Z-scores).
The statistics module builds upon this foundation with topics like sampling, the Central Limit Theorem, standard error, and hypothesis testing. Real-world examples and explanations of Type 1 and Type 2 errors are included, along with discussions on T-distributions, A/B testing, ANOVA, and Chi-Square analysis.
For those looking to apply these statistical concepts, the course includes regression analysis, covering linear, multiple linear, and polynomial regression. While the course title is in Turkish, the instructor explicitly states that the lessons are conducted entirely in Turkish, making it accessible to a wider audience.
Overall, “İstatistik & Python: A’dan Z’ye Temel İstatistik Bilimi (6)” appears to be a thoroughly designed course for anyone serious about building a solid statistical understanding for data science, machine learning, or AI. The blend of theoretical depth, practical coding, and dedicated support makes it a highly recommended resource.
Enroll Course: https://www.udemy.com/course/istatistik-python-adan-zye-temel-istatistik-bilimi-6/