Python for Data Science 12 hours compilation-practice with us #pythonkachilla

#python #dataScience #baba_aammar

[mohammadikramm]

3:50 Chapter 1 ( Installation of Python)
25:50 Chapter 2 (Operators)
36:05 Chapter 3 (Strings)
41:54 Chapter 4 (Comments)
46:45 Chapter 5 (Variables)
1:00:15 Chapter 6 (Input Variables)
1:10:38 Chapter 7 (Relational Operators)
1:22:20 Chapter 8 (Type Conversion)
1:32:41 Chapter 9 (if else & elif)
1:43:48 Chapter 10 (Functions)
2:00:22 Chapter 11 (Loops)
2:08:15 Chapter 12 (Import Libraries)
2:12:57 Chapter 13 (Trouble Shooting)
2:17:05 Chapter 14 (Installating Anaconda)

[saiqaahmad]

6:00:00 Data manipulation:
1.take data from data base
2.cleaning of data
3.filter data
4.ignore or delete data
5.arrange data
6.joining and splitting of two datasets
7.remove missing and duplicate values
8.data scraping
after all these set u get meaning ful data
then do DATA ANALYSIS BY GRAPHS
9.reporting on basis on data analysis
10.dashboards

[saiqaahmad]

5:59:00 example of descriptive analysis: kids mostly get sick on onset in november, december and march, Mostly have flu and cough.
Diagnostic analysis: this sickness may be related to onset of new season, or these month have favourable temperature for germs carriers like mosquitos.
predictive analysis: if sprays r not done on time, the ratio of sick kids can be reduced, or if diet is mostly on junk food it worsen viral attack.
prescriptive analysis: control mosquitos by spraying, add soup and protein rich food in diet, and try least exposure to outside.

[NoreenSaba]

9 months ago
3:50 Chapter 1 ( Installation of Python)
25:50 Chapter 2 (Operators)
36:05 Chapter 3 (Strings)
41:54 Chapter 4 (Comments)
46:45 Chapter 5 (Variables)
1:00:15 Chapter 6 (Input Variables)
1:10:38 Chapter 7 (Relational Operators)
1:22:20 Chapter 8 (Type Conversion)

[saiqaahmad]

3:26:00 for count function should give arguement. like
list1=[1, 2, 3, 4, 3, 2, 7, 3, 3]
list1.count(3)
4
in order to know total elements in list use len(list1)

[saiqaahmad]

7:16:15 max value(2, 3)
min value(4)
percentage(5)
prediction(6)
variation(7)
ratio(8)
ranking(9)

[saiqaahmad]

6:34:14, the row location can be given here, as we set date index label here, so by df.loc[date[2], ['A', 'B']] it works

[khandiscovers]

08:08:30 Basic of Statistics "Ham lahore gay thy"😃

[saiqaahmad]

4:06:53
a=np.array(['john', 1111, 'employed'])
array(['john', '1111', 'employed'], dtype='<U11')

b=np.array([['john', 111, 'employed'], ['sara', 222, 'employed'], ['tom', 333, 'unemployed']])
array([['john', '111', 'employed'],
['sara', '222', 'employed'],
['tom', '333', 'unemployed']], dtype='<U11')

[saiqaahmad]

3:56:00 codanics and python for data science.
list is actually like a row, thats why contain different datatype, while numpy has columns bcz of this same data type, this is what i have learnt from description

[faizanmohiuddin]

Much needed video as I am new to coding and have forgotten most of the coding stuff that we learned during the recent chilla. Thanks for your effort, working on it for the challenge. fingers crossed.

[saiqaahmad]

4:32:00 it come 1 or 0(for2 d), but not empty. bcz it set the values arbitrarily and allocat memory.

[muhammadjawad]

@2:00:00 I understand functions really good

[MuhammadAbbas-dduo]

import numpy as np
a=np.array([5, 5, 5])
a
time stamp 4:06:54

[ibrahimnaeem]

Capitalization
Generally lower case letters represent the sample attributes and capital case letters are used to represent population attributes.

P - population proportion.

p - sample proportion.

X - set of population elements.

x - set of sample elements.

N - set of population size.

N - set of sample size.

Greek Vs Roman letters
Roman letters represent the sample attributs and greek letters are used to represent Population attributes.

μ - population mean.

x¯ - sample mean.

δ - standard deviation of a population.

s - standard deviation of a sample.

Population specific Parameters
Following symbols represent population specific attributes.

μ - population mean.

δ - standard deviation of a population.

μ2 - variance of a population.

P - proportion of population elements having a particular attribute.

Q - proportion of population elements having no particular attribute.

ρ - population correlation coefficient based on all of the elements from a population.

N - number of elements in a population.

Sample specific Parameters
Following symbols represent population specific attributes.

x¯ - sample mean.

s - standard deviation of a sample.

s2 - variance of a sample.

p - proportion of sample elements having a particular attribute.

q - proportion of sample elements having no particular attribute.

r - population correlation coefficient based on all of the elements from a sample.

n - number of elements in a sample.

Linear Regression
B0 - intercept constant in a population regression line.

B1 - regression coefficient in a population regression line.

R2 - coefficient of determination.

b0 - intercept constant in a sample regression line.

b1 - regression coefficient in a sample regression line.

sb1 - standard error of the slope of a regression line.

Probability
P(A) - probability that event A will occur.

P(A|B) - conditional probability that event A occurs, given that event B has occurred.

P(A′) - probability of the complement of event A.

P(A∩B) - probability of the intersection of events A and B.

P(A∪B) - probability of the union of events A and B.

E(X) - expected value of random variable X.

b(x;n, P) - binomial probability.

b∗(x;n, P) - negative binomial probability.

g(x;P) - geometric probability.

h(x;N, n, k) - hypergeometric probability.

Permutation/Combination
n! - factorial value of n.

nPr - number of permutations of n things taken r at a time.

nCr - number of combinations of n things taken r at a time.

Set
A⋒B - intersection of set A and B.

A⋓B - union of set A and B.

{A, B, C} - set of elements consisting of A, B, and C.

∅ - null or empty set.

Hypothesis Testing
H0 - null hypothesis.

H1 - alternative hypothesis.

α - significance level.

β - probability of committing a Type II error.

Random Variables
Z or z - standardized score, also known as a z score.

zα - standardized score that has a cumulative probability equal to 1−α.

tα - t statistic that has a cumulative probability equal to 1−α.

fα - f statistic that has a cumulative probability equal to 1−α.

fα(v1, v2) - f statistic that has a cumulative probability equal to 1−α and v1 and v2 degrees of freedom.

X2 - chi-square statistic.

[saiqaahmad]

5:50:00 example of data analysis. comparison of graduates pass yearly and graduates acquire job

[muhammadjawad]

@1:44:00 if, elif, else statement clear

[saiqaahmad]

8:04:00 all clear and thnx codanics for teaching statistics

[saiqaahmad]

7:13:31 data analyst mostly work on cleaning, organizing data and getting insights from data.
data scientist is a broader term who along with doing data analyst job, predict further action to be done on data, as ML and DL.

[saiqaahmad]

4:03:29 the ranking of players according to scores is array