Statistics for Data Science 1
5.0
(6)
50 learners
What you'll learn
This course includes
- 51.3 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 140 lessons • 51.3 hours of video
Statistics for Data Science 1
140 lessons
• 44.3 hours
Statistics for Data Science 1
140 lessons
• 44.3 hours
- W0: Statistics for data science 1 - introduction 05:32
- W0: Introduction - week wise of the course 28:54
- W1_L1: Introduction & types of data - basic definitions 14:37
- W1_L2: Introduction & types of data - understanding data 32:40
- W1_L3: Introduction & types of data - classification of data 14:21
- W1_L4: Introduction & types of data - scales of measurement 20:34
- W1_T1: Statistics for data science 1 07:41
- W1_T2: Statistics for data science 1 10:52
- W1_T3: Spreadsheet formulae 15:13
- W1_T4: Downloading & uploading spreadsheets 08:10
- W2_L1: Describing categorical data - frequency distributions 21:55
- W2_L2: Describing categorical data - charts of categorical data 23:33
- W2_L3: Describing categorical data - best practices while graphing data - 1 17:50
- W2_L4: Describing categorical data - best practices while graphing data - 2 08:05
- W2_L5: Describing categorical data - mode & median 32:32
- W2_T1: Problems charts & tables 11:05
- W2_T2: Problems misleading graphs 04:11
- W2_T3: SUMIF in google sheets 05:13
- W2_T4: VLOOKUP in google sheets 14:04
- Statistics for data science I 42:48
- W3_L1: Describing numerical data - frequency tables for numerical data 33:45
- W3_L2: Describing numerical data - mean 23:57
- W3_L3: Describing numerical data - median and mode 21:47
- W3_L4: Describing numerical data - measures of dispersion- range 32:19
- W3_L5 - Describing numerical data - percentiles, quartiles, and interquartile range 26:45
- W3_Tutorial 1 06:16
- W3_Tutorial 2 03:27
- W3_Tutorial 3 04:16
- W3_ Tutorial 4 05:51
- W3_Tutorial 5 08:22
- W3_Tutorial 6 02:31
- W4_Tutorial 7 04:39
- Week 3 - Box plot Tutorial 07:36
- Statistics for data science I 50:42
- Statistics for data science I 29:55
- W4_L1: Association between two variables - review of course 05:18
- W4_L2: Association between two categorical variables - introduction 26:04
- W4_L3: Association between two categorical variables - relative frequencies 35:08
- W4_L4: Association between two numerical variables - scatterplot 16:11
- W4_L5: Association between two numerical variables - describing association 10:42
- W4_L6: Association between two numerical variables - covariance 36:26
- W4_L7: Association between two numerical variables - correlation 28:06
- W4_L8: Association between two numerical variables - fitting a line 14:57
- W4_L9: Association between categorical & numerical variables 23:02
- W4_Tutorial 1 07:27
- W4_Tutorial 2 13:35
- W4_Tutorial 3 03:19
- W5_Tutorial 4 13:34
- Statistics for data science I 01:38:41
- W5_L1: Permutations & combinations - basic principles of counting 22:47
- W5_L2: Permutations & combinations - factorials 15:35
- W5_L3: Permutations & combinations - permutations: distinct objects 38:50
- W5_L4: Permutations & combinations - permutations : objects not distinct 34:13
- W5_L5: Permutations & combinations - combinations 38:04
- W5_L6: Permutations & combinations - applications 20:19
- W5_Tutorial 1 04:10
- W5_Tutorial 2 08:49
- W5_Tutorial 3 08:44
- W5_Tutorial 4 04:25
- W5_Tutorial 5 02:33
- W5_Tutorial 6 03:26
- Statistics for data science I 47:43
- Statistics for data science I 25:53
- W6_L1: Probability - basic definitions 28:24
- W6_L2: Probability - events & basic operations on events 23:43
- W6_L3: Probability - random experiment, same space events 21:25
- W6_L4: Probability - properties of probability 53:19
- Statistics for data science - I 21:59
- Statistics for data science - I 20:48
- W6_Tutorial 1 02:55
- W6_Tutorial 2 02:11
- W6_Tutorial 3 01:46
- W6_Tutorial 4 01:29
- W6_Tutorial 5 05:01
- W6_Tutorial 6 01:39
- W6_Tutorial 7 03:59
- Statistics for data science I 47:38
- Statistics for data science I 44:04
- W7_L1: Conditional probability - contingency tables 20:58
- W7_L2: Conditional probability - conditional probability formula 15:05
- W7_L3: Conditional probability - multiplication rule 24:57
- W7_L4: Conditional probability - independent events 12:51
- W7_L5: Conditional Probability - independent events: examples 16:39
- W7_L6: Conditional probability - independent events: properties 17:00
- W7_L7: Conditional probability - bayes' rule 29:31
- W7_Tutorial 1 01:53
- W7_Tutorial 2 02:27
- W7_Tutorial 3 05:16
- W7_Tutorial 4 08:45
- W7_Tutorial 5 06:05
- W7_Tutorial 6 03:34
- Statistics for data science I 41:59
- Statistics for data science I 45:02
- W8_L1: Random variables - introduction 45:38
- W8_L2: Random variables - application 17:17
- W8_L3: Random variables - discrete & continuous random variable 16:59
- W8_L4: Discrete random variables - probability mass function properties 29:39
- W8_L5: Discrete random variables - graph of probability mass function 23:06
- W8_L6: Discrete random variables - cumulative distribution function 19:22
- Statistics for data science I 07:11
- Week 8 - Tutorial 2 05:55
- Week 8 - Tutorial 3 03:48
- Week 8 - Tutorial 4 05:20
- Week 8 - Tutorial 5 08:53
- Week 8 - Tutorial 6 06:49
- Infinite Series Tutorial 07:11
- Lecture 9.1 - Discrete random variable - Application 15:02
- Lecture 9.2 - Expectation of a random variable 22:54
- Lecture 9.3 - Expectation of a random variable - Properties of expectation 26:28
- Lecture 9.4 - Variance of a random variable - Properties of variance 21:48
- Lecture 9.5 - Variance of a random variable - Properties of variance 17:41
- Lecture 9.6 - Standard deviation of a random variable 15:35
- week 9 - Practice Assignment Solutions 38:19
- Lecture 10.1 - Binomial distribution - Bernoulli distribution 13:40
- Lecture 10.2 - Binomial distribution - IID Bernoulli trials 08:05
- Lecture 10.3 - Binomial distribution - Distribution of Binomial random variable 15:26
- Lecture 10.4 - Binomial distribution - Modeling situations as Binomial distribution 37:54
- Lecture 10.5 - Binomial distribution - Expectation and variance of Binomial random variable 17:51
- week 10 - Tutorial 1 02:29
- week 10 - Tutorial 2 03:48
- week 10 - Tutorial 3 01:59
- week 10 - Tutorial 4 02:09
- week 10 - Tutorial 5 02:39
- Week 2 Practice Assignment Solutions 46:13
- Week 8 - Practice Assignment Solution 45:07
- week 9 - Expectation of Hypergeometric Random Variable 10:49
- Outliers tutorial 09:09
- Continuous random variable - Uniform distribution 20:29
- Continuous random variable - Uniform distribution: applications 18:12
- Continuous random variable - Non uniform and triangular distribution 16:30
- Continuous random variable - Exponential distribution 14:51
- Continuous random variable - Introduction 27:27
- Normal Distribution 39:58
- Standard Normal Distribution 29:20
- Standardizing a normal random variable 42:12
- Additive Property and percentiles 34:06
- Hypergeometric Distribution 31:03
- Expectation and variance of Hypergeometric Distribution 33:24
- Poisson Distribution 24:47
- Expectation and variance of Poisson Distribution 16:33
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