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Statistics for Data Science & Machine Learning
About the Course
For every Artificial Intelligence, Machine Learning, and Data Science enthusiast, Statistics is fundamental to learning to dive deeper into these fields.
Correctly understanding Statistics will make it easy to implement regression, classification, and numerous other algorithms. This course is the combination of 4-5 available courses in the market, which will help you give you deeper insights and introduce you to various facets of statistics and concepts required to solve multiple data science problems.
With simplified and complete statistics training, quizzes, and practical steps – this is one of the most comprehensive Statistics courses available.
We’ll cover the fundamentals of statistics, Descriptive statistics, Inferential statistics, Hypothesis Testing, Correlation Analysis, Regression Analysis, Modelling, Ch- Squared tests, ANOVA, T-Test, Z Test, Index Number, Correlation, Regression and many more. By the end of this course, you will confidently implement techniques across the significant situations in Statistics, Business, and Data Analysis for research projects.
03 hrs 20 mins
15 Study Materials
12 Month Access
Statistics is one of the critical FIVE pillars of Data Science & AI. Knowledge of statistics not only helps in model (Algorithms) understanding but also lets you improve the performance of the models through parameters fine-tuning.
Statistics is a vast subject, and knowing everything about it demands massive effort and dedication. Luckily all the concepts of statistics are not required when it comes to Data Science & AI. However, you need to know basic statistical concepts like hypothesis testing and some crucial tests along with the concepts of central tendencies and probability density functions.
Some of the essential terms (but not limited to) include mean, median, mode, standard deviation, variance, skewness, kurtosis, central limit theorem, various statistical tests, descriptive statistics analysis, etc.
Hypothesis testing is a form of statistical inference that uses data from a sample to conclude a population parameter or a population probability distribution. First, a tentative assumption is made about the parameter or distribution. This assumption is called the null hypothesis and is denoted by H0.
Practising the relevant questions and implementing the concepts taught in the course in real case studies and real datasets will help you improve your overall confidence in this field.
- Concept 05:25 min
- Mindset 05:58 min
- Action 12:06 min
- Basics Of Statistics For Data Science & AI-Part 1 18:40 min
- Basics Of Statistics For Data Science & AI-Part 2 21:19 min
- Basics Of Statistics For Data Science & AI-Part 3 19:03 min
- Beyond The Basics Of Statistics For Data Science & AI-Part 1 14:15 min
- Beyond The Basics Of Statistics For Data Science & AI-Part 2 17:52 min
- Beyond The Basics Of Statistics For Data Science & AI-Part 3 23:39 min
- Beyond The Basics Of Statistics For Data Science & AI-Part 4 13:27 min
- Beyond The Basics Of Statistics For Data Science & AI-Part 5 16:40 min
- Beyond The Basics Of Statistics For Data Science & AI-Part 6 18:30 min
- Important Evaluation Metrics and Summary 13:28 min
Sachichidanand Kumar is a data scientist and AI practitioner. He is also a career coach with over 10 years of industrial experience in top MNCs and a PhD in computer science. Alumni of NIT and XLRI, he is also the co-founder of Fetchr-School Of Excellence Academy.
Data Science & AI Expert