Statistics for Data Science in Python (Day-16)

This video explains the details about statistical analysis in Data Science part-. Part one for statistics for Data Science here:

only if you are interested in this 40 days long course (python_ka_chilla with baba_aammar.
More about me: I am Dr Aammar Tufail, your instructor in Python_ka_chilla. My aim is to train people in Data Science, machine learning, artificial intelligence, and deep learning by the end of the year (2022).

If you are keen to learn from this complete course, then here is the playlist for the course:

If you want to learn Data Science with R here is the completed and uploaded course in urdu, link:

If you have any questions, you can always write in the comment section of the video, you have a question about.
#DataScience
#artificailIntelligence
#deeplearning
#machinelearning
#python
#python_ka_chilla
#baba_aammar
#statistics in urdu
#statistics in Data science
# how to choose a statistical mehtod

[Codanics]

Python k chilla main banay rehnay k liay shuru se end tak dekhen videoo ko and keywords jaisay or jahan per kahay gaye hyn reply karen un ka....

[syedriazali]

1:22 You have spoke statistics 4 times
10:32 Example of comparison : Batting average of two different batsman
10:43 My favorite color is green
11:52 You have paused for 4 sec
12:45 Kashti ke data me
Numerical variable = age, fare
Categorical variable = sex, class, embark-town
Ordinal variable = who
18:19 Ordinal variable can be used for chi-squared test
26:35 Converting data into normal distribution
a- Standardization
b- Min - Max scaling
c- Log transformation
28:54 Chi squared is a non parametric test

[AbdulHannan-dgdl]

1:28 times you said statistics

5:44 Levene test

10:56 Height comparison, Age comparison, etc.
favorite color: Yellow

12:17 Relationship between market close time and load-shedding (increase or decrease in load-hshedding)
12:47 Embarked and sex(nominal) are categorical and age and p class are numerical variables
passenger id is ordinal variable


18:22 we can use ordinal variables here, we can mention level of things. like someone is young, someone is old or someone is in mid-age.These are the levels of variables. age: 17-21, 22-25 etc.

26:40
1) Log Transformation

2) Square root Transformation

3) Reciprocal Transformation

4) Exponential Transformation

5) Box-Cox Transformation
6) Standardisation
7) Min-max Scaling'
8) Z-test
else use Non-parametric Test(Chi_square, t-test, anova test)
28:52 Non-parametric

31:45 One-way ANOVA test:
one way analysis of the variance test is used two differentiate between means of more than three groups.it consist of one independent variable and one dependent variable.
Your independent variable is social media use, and you assign groups to low, medium, and high levels of social media use to find out if there is a difference in hours of sleep per night.
With a Two-way ANOVA: There are two independents. For example, a two-way ANOVA allows a company to compare worker productivity based on two independent variables, such as department and gender. It is utilized to observe the interaction between the two factors. It tests the effect of two factors at the same time.

The repeated measures ANOVA: Compares means across one or more variables that are based on repeated observations. A repeated-measures ANOVA model can also include zero or more independent variables. Again, a repeated-measures ANOVA has at least 1 dependent variable that has more than one observation.
For example, you could use a repeated-measures ANOVA to understand whether there is a difference in cigarette consumption amongst heavy smokers after a hypnotherapy program (e.g., with three-time points: cigarette consumption immediately before, 1 month after, and 6 months after the hypnotherapy program).
In Mancova, there is two more dependent variables.

in MANCOVA, there are more than two dependent variables with co-variance.
covariance is relationship between two random variables.

[aliwaqas]

آپ ایک عظیم معلم ہے آپ کے سمجھانے کا طریقہ کار انتہائی موثر ہے

[muhammadawon]

A wise man once told me that rereading the old book is more beneficial than picking up the new book. I say that instead of going forward, I recommend you go through this video again (but not today).
It's the third time I've gone through this video, and I realize how wrong I wrote in the assignment that I submitted conceptual-wise. I'm glad I made that choice now. It is worth every minute. Finally, I can say that everything is clear to me.

[MujahidAkram]

12:45 Data Set: Kashti
Numeric variables: age, fare
categorical variable: Survived, sex, sibsp, embarked, who, adult_male, deck, alive, alone
Ordinal variable: pclass

[aminaabdulrehman]

26:30 Methods to normally distribute data
1- Data transformation: This method involves transforming the data set in a way that makes it more normally distributed. For example, you could take the logarithm of each data point, or you could square each data point.
2- Resampling: This method involves randomly sampling data points from the data set and then re-calculating the mean and standard deviation. This process is repeated multiple times, and the resulting data sets are more likely to be normally distributed than the original data set.
3- Data imputation: This method involves filling in missing data points with estimates. This can help to normalize the data set by making it more complete.
4- Data binning: This method involves grouping data points into bins. This can help to normalize the data set by reducing the number of outliers.

[muhammadawon]

@12:40 kashti walay dataset mein variables types:
survived -> binomial
pclass -> ordinal
sex -> binomial
age -> ordinal
sibsp -> ordinal
parch -> discrete
fare -> discrete
embarked -> ordinal
class -> ordinal
who -> ordinal
adult_male -> binomial
deck -> ordinal
embark_town -> ordinal
alive -> binomial
alone -> binomial

[ImranMeeran-xw]

wah wah baba g maza a jata hy apki misaalon ka ....sari smaj a jati hy

[shafiqahmed]

MASHALLAH SIR ALLAH AAPKO KAMYAB KARY AAP AIK BOHT ACHY TEACTHER HAIN ALLAH AAPKO KHOSH RKHY AAMIN

[hassanorakzai]

@12:45
Numerical Data in kashti dataset: Age, Fare
Categorical Data types in Kashti dataset: survived, passenger class, sex, alone, etc
ordinal data in kashti dataset: class

[daniyalahmad]

Thanku sir abi ap ke he play list open ker k betha hun laptop per rest lenay k lia mobile open kia to dekha apke video agaye ❤️❤️❤️❤️

[NasirJumani]

26:34 Ways to transform non normal data into normal distribution:
1- Increasing the sample size
2- Reducing the resolution fo data
3- Removal of extreme values
4- Removal of long tail values
5- Log-normal transformation
6- Box-Cox Transformation
7- Yeo Johnson transformation

Baba jee yeh naam to milgaye saaray ab baari baari samajhna rehta hai :P

[sanashah]

present
01:18 statistics statistics statistics
10:49 comparison ... age difference thats y not mature
light gree color is my fav
chi-squared nonparametric hy q k ye categorical hy srf

31:45
Types of Anova

Anova stands for Analysis of variance
One-way ANOVA: at least three different groups or categories to see if there is a significant difference between them.
Two-way ANOVA: the two-way analysis of variance is an extension of the one-way ANOVA that examines the influence of two different categorical
Repeated measure of ANOVA compares the means of three or more groups where the same subjects are measured more than once.

ANCOVA: ANALYSIS OF CO-VARIANCE (that analyzes the differences between three or more group means)
MANOVA(Multi-Variate analysis of variance)
MANCOVA: (Multi-variate analysis covariance

Bhut bhut bhut mushkil ta .. statistic mene kabi ni parhi..

[usmanayaz]

Best and Excellent Quality Content Ma Sha ALLAH.

[AbaseenPodcast]

You are doing brilliant work in Urdu, bringing this knowledge to people! Great job!

[zainabmerchant]

10: 40 pink


26: 32
Normalization techniques:
scaling to a range
clipping
log scaling
z-score

28:50 - The Chi-square test is, also called a distribution free test.

12:52 -
categorical= who, sex
ordinal= may be class
continuous= age, fare

[technoo]

26:37 data normalisation: (according to database)
1st normal: putting repeated data in different files and assign appropriate keys.

2nd normal: specified non-key elements by complete key are placed in other table. Normally these non-key elements are dependent on only a part of a compound key.

3rd normal: enables eliminating redundant data elements and tables that are subsets of other tables.

[sardarabdullahkhawar]

1:20 4 times
10:30 sales of 2 branches
10:46 Black
11:52 4 sec
12:50 Numerical Variable ['Age', 'Fare'], Categorical Variable ['sex', who', 'alive', 'embark town', 'deck', 'adult male', 'embarked', 'survived', 'sibsp', 'parch' ], Ordinal Variables['class', 'pclass']
18:15 Ordinal variables can be used.

26:27
1)Standardization
2) Min - Max scaling
3)Log transformation
28:53 Chi Squared is non para-metric.

[amnafarooq]

26:00
Methods to do Normalize the data:
1. Scaling: Scaling means converting floating-point feature values from their natural range (for example, 100 to 900) into a standard range—usually 0 and 1 (or sometimes -1 to +1). A good example is age. Most age values falls between 0 and 90, and every part of the range has a substantial number of people.

2. Feature Clipping: f your data set contains extreme outliers, you might try feature clipping, which caps all feature values above (or below) a certain value to fixed value. For example, you could clip all temperature values above 40 to be exactly 40.