**Data Understanding and Preparation**: Statistics helps in understanding data through descriptive statistics such as mean, median, mode, variance, and standard deviation. These metrics provide insights into the data’s central tendency, dispersion, and overall distribution. Understanding these aspects is vital for data cleaning and preparation.**Modeling and Algorithm Selection**: Many machine learning algorithms are grounded in statistical theories. For instance, linear regression, logistic regression, and various types of clustering methods are directly based on statistical concepts. Selecting the right algorithm often requires understanding these statistical underpinnings.**Inference and Prediction**: Statistics is key to making inferences and predictions from data. It helps in estimating the relationships between variables and in making predictions about future observations. For example, statistical hypothesis testing is used to infer if the observed data can be explained by a model or is due to random chance.**Performance Evaluation**: After training a machine learning model, statistics is used to evaluate its performance. Metrics like confusion matrix, precision, recall, F1 score, and ROC curves are based on statistical concepts. These metrics help in understanding the strengths and weaknesses of a model.**Experimentation and Validation**: In machine learning, experimentation is essential. Statistical methods such as A/B testing and cross-validation are used to validate models and ensure their effectiveness and reliability before deploying them in real-world applications.**Dealing with Uncertainty**: Machine learning models often have to deal with uncertainty in data. Statistics provides tools to quantify, manage, and make decisions under uncertainty, for instance, through probabilistic models and Bayesian methods.**Feature Engineering and Selection**: Statistical methods help in identifying significant variables (features) that have more predictive power. Techniques like correlation analysis and principal component analysis (PCA) are used for feature selection and dimensionality reduction.**Ethical and Responsible AI**: Statistics plays a role in ensuring that machine learning models are fair, ethical, and unbiased. Statistical analysis can help identify and mitigate biases in data and models.

In essence, statistics forms the backbone of data science and machine learning, providing the necessary tools and methodologies for extracting insights and knowledge from data. Its importance cannot be overstated, as it enables practitioners to make data-driven decisions and build intelligent systems that are effective, reliable, and ethical.

Thanks worthy sir, JazakAllah o Khair!

this blog is very useful for those who is beginner in data scientists

اسلام علیکم! سر بہت عمدہ ہم کافی دنوں سے شماریات کو پڑھنے کے لیے انٹر نیٹ پر تلاش کر رہا تھا ، ماشاءاللہ آپ کے لیکچرز نےبہت متاثر کیا اور سیکھ رہاہوں جزاء ک اللہ ، اللہ رب العزت آپکو جزاء خیرعطاء فرمائے اور ہمیشہ خوش و خرم رکھےآمین۔

GREAT SIR G

done

Very easy way to teach. Understand very easily

This is very simple way to teach students, really sir I get much knowledge from this blog.

AOA, This blog post provides an introduction to statistics and covers topics such as the definition of statistics, types of data, and the importance of statistics in data science. You have done a good job of explaining the concepts in a simple and easy-to-understand manner. The use of examples and illustrations makes the blog post engaging and informative. Overall, I found the blog post to be a great resource for me.ALLAH PAK ap ko dono jahan ki bhalian aata kry AAMEEN.

very well explaination of Statistics in this blog , this blog is very helpful to understand statistics for Datascience and Machine Learning … Thank u Sir

Gr8 lecture

This is very simple way to teach students, really sir I get much knowledge from this blog, Jazak Allah

Well explained.

Asslam o allakam , I was a student of Physics ,these nots can help me to analyse data batter

This blog is superb and easily understandable for all those who have no any background to statics ……Its first time in my life i take interest in statistic and this because of yours easy way of explaining of Statistic

done

DOING GOOD WORK

This blog post contains the starter topics of statistics, if anyone follows these topics then they can easily improve their statistical expertise.

Very well explained