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Statistics for Data Science & Machine Learning
English
Starting at ₹14 Per Day
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Course Highlights
Course Highlights
- Master All the Concepts & Practical Skills in Statistics for Data Science & Artificial Intelligence
- Practical Statistics Hands on Tutorials in Ms Excel
- Complete Guide of Statistics & Data Analysis Concepts Used in Education, Data Science & Corporations
- Easily Implement Regression, Classification, & Numerous Other Algorithms
About the Course
03 hrs 20 mins
13 Lessons
15 Study Materials
Q&A
12 Month Access
347 Enrolments
02 Hands-on-Tutorials
Assured Certification
Course Structure
- 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
Your Instructor
Sachchidanand Kumar
Data Science & AI ExpertCourse FAQs
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.
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