Here is one of my skills, I'm currently working on to excel the skill of Statistics under my Professor Usha Mohan. 

Below is the complete description of the course:-

Statistics for Data Science I: Course Summary

This foundational course, Statistics for Data Science I (BSMA1002), delivered by Professor Usha Mohan of IIT Madras, provides a robust introduction to the essential statistical concepts crucial for data science. As a first-year student pursuing the IIT Madras BS Degree in Data Science and Applications, this 4-credit course has equipped me with fundamental skills in data handling and interpretation.

The curriculum is structured to build a strong understanding from the ground up, starting with types of data, descriptive and inferential statistics, and scales of measurement. I delved into various methods for describing both categorical and numerical data, mastering concepts like frequency distributions, measures of central tendency (mean, median, mode), and measures of dispersion (variance, standard deviation, Inter Quartile Range (IQR)). A significant portion of the course focused on understanding associations between variables, including the use of contingency tables, scatterplots, covariance, and correlation coefficients.

A core component of this course was the comprehensive introduction to probability. I explored basic definitions, properties of probability, and advanced concepts such as conditional probability, independence, the law of total probability, and Bayes’ theorem. This led naturally into the study of random variables, covering discrete and continuous types, probability mass functions, and cumulative density functions. The course culminated in understanding expectation and variance of random variables and applying the properties of key distributions like the Binomial and Normal distributions.

Through weekly online assignments, quizzes, and an end-term exam, I've gained practical experience in:

• Manipulating and analyzing datasets.

• Framing data-driven questions.

• Describing data using numerical summaries and visual representations.

• Applying probability laws to estimate chance and translate real-world problems into probabilistic models.

• Calculating expectation and variance of random variables.

This course has been instrumental in laying the groundwork for more advanced data science topics, solidifying my understanding of how data can be effectively analyzed and interpreted to derive meaningful insights.