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Mindset is everything because our mindset will be the guiding force behind our decisions and the actions we have to take.
You must believe that you can do it, you can learn data science as well!
Before we dive deeper, let's watch this video!
Programming means writing instructions for a computer to perform desired actions or tasks.
For writing instruction, we need a language, as humans talk to each other. Because of the computer can't understand our natural language---such as English, Bahasa Indonesia---we need to communicate with the computer using a language that the computer understands, programming language.
A programming language is a set of commands, instructions, and other syntax use to create a software program. The problem that programming languages solve is computers only understand 0s and 1s, but humans do not understand 0s and 1s. So, a programming language is an intermediary between a computer and a programmer.
There are hundreds of developing programming languages with various uses. The majority of popular programming languages are high-level languages (which are easy for humans to understand). Some of them, namely Python and R. We will learn more in Algoritma Data Science School course.
R is the name of a programming language as well as software for data processing and graphics. R is very popular today for three reasons:
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Lots of data processing options with a very complete number of features from graphics to machine learning.
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It is faster to learn and run to process data compared to other languages.
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R is free and open source, which means there is no need for licensing fees which are usually very expensive for data processing software.
📌 They have many Cheatsheets for you
Python is widely used in data science and has a robust suite of powerful tools to communicate with data. Python is also popular today for three reasons:
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Python is easy to learn.
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The syntax is easy to read and understand.
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There are many useful libraries to perform computations and other operations.
📌 Generally, Python code is also much shorter compared to other programming languages.
However, here we will provide a little starter for those of you who want to start learning both languages for free and can be accessed life-time. Please enjoy while studying!
A variable is a place to store data, while a data type is a type of data stored in a variable.
So if we say, data is food, then variable is where we store the food.
R
Use the assignment operator <- to create new variables.
x <- 5
x## [1] 5
Python
Use the assignment operator = to create new variables.
print("Hello Python!")## Hello Python!
x = 5
x## 5
This is one of the Decision-making statements in the programming language. It is one of the easiest decision-making statements.
R
x <- 5
if (x > 0) {
print('x is positive')
} else {
print('x is negative')
}## [1] "x is positive"
print('This cell execute after if-else statement')## [1] "This cell execute after if-else statement"
Python
x = -5
if (x > 0):
print('x is positive')
else:
print('x is negative')## x is negative
print('This cell execute after if-else statement')## This cell execute after if-else statement
This vector in R is a place to store values for elements that have the same class. But, if we want to store elements or components that have different classes and lengths, we can use list both in R or Python.
R
In R, if we want to store elements or components that have different classes and lengths, we can use list, but if the classes of the element that we want to store is same, instead of list() we can use concate function or c() or we can call it as vector.
Vector
my_vector <- c("a", "b", "c")
print(my_vector)## [1] "a" "b" "c"
List
my_list <- list("apple", 1, "cherry", my_vector)
my_list[1]## [[1]]
## [1] "apple"
my_list[length(my_list)] # That means printing the last element## [[1]]
## [1] "a" "b" "c"
my_list[-1] # That means excluding the first element to print## [[1]]
## [1] 1
##
## [[2]]
## [1] "cherry"
##
## [[3]]
## [1] "a" "b" "c"
Python
List in Python can be used for storing elements or components that have different classes and lengths. Python uses zero-based indexing. That means, the first element(value 'red') has an index 0, the second(value 'green') has index 1, and so on. Negative indexing in Python means the indexing starts from the end of the iterable.
my_list = ["apple", 1, "cherry", ["a", "b", "c"]]
my_list[0]## 'apple'
my_list[-1] # That means printing the last element## ['a', 'b', 'c']
my_list[1:] # That means excluding the first element to print## [1, 'cherry', ['a', 'b', 'c']]
The for loop is used to iterate over a sequence (list) or other iterable objects.
R
for (i in c("apple", "banana", "cherry"))
{
print(i)
}## [1] "apple"
## [1] "banana"
## [1] "cherry"
Using list()
for (i in list("apple", 1, "cherry"))
{
print(i)
}## [1] "apple"
## [1] 1
## [1] "cherry"
Python
my_list = ["apple", 1, "cherry"]
for i in my_list:
print(i)## apple
## 1
## cherry
DataFrame is a data structure that organizes data into a 2-dimensional table of rows and columns, much like a spreadsheet.
R
For creating a DataFrame in R, we can use data.frame()
iklan <- data.frame(Channel=c("Youtube", "Instagram", "Facebook", "Twitter"), budget=c(8.0, 4.5, 4.3, 2.5))
iklan## Channel budget
## 1 Youtube 8.0
## 2 Instagram 4.5
## 3 Facebook 4.3
## 4 Twitter 2.5
Python
For creating a DataFrame in Python, we need pandas library. So first of all we need import that library like this one.
import pandas as pdiklan = pd.DataFrame({
"Channel":["Youtube", "Instagram", "Facebook", "Twitter"],
"budget":[8.0, 4.5, 4.3, 2.5],})
print(iklan)## Channel budget
## 0 Youtube 8.0
## 1 Instagram 4.5
## 2 Facebook 4.3
## 3 Twitter 2.5
R
One can get the structure of the data frame using str() function in R.
str(iklan)## 'data.frame': 4 obs. of 2 variables:
## $ Channel: chr "Youtube" "Instagram" "Facebook" "Twitter"
## $ budget : num 8 4.5 4.3 2.5
Python
One can get the structure of the data frame using .info() function in Python pandas.DataFrame.
iklan.info()## <class 'pandas.core.frame.DataFrame'>
## RangeIndex: 4 entries, 0 to 3
## Data columns (total 2 columns):
## # Column Non-Null Count Dtype
## --- ------ -------------- -----
## 0 Channel 4 non-null object
## 1 budget 4 non-null float64
## dtypes: float64(1), object(1)
## memory usage: 192.0+ bytes
R
In R data frame, the statistical summary and nature of the data can be obtained by applying summary() function.
summary(iklan)## Channel budget
## Length:4 Min. :2.500
## Class :character 1st Qu.:3.850
## Mode :character Median :4.400
## Mean :4.825
## 3rd Qu.:5.375
## Max. :8.000
Python
In Python pandas.DataFrame, the statistical summary and nature of the data can be obtained by applying .describe() function.
iklan.describe()## budget
## count 4.000000
## mean 4.825000
## std 2.299819
## min 2.500000
## 25% 3.850000
## 50% 4.400000
## 75% 5.375000
## max 8.000000
Extract data from a data frame means that to access its rows or columns. One can extract a specific column from a data frame using its column name.
R
iklan$Channel## [1] "Youtube" "Instagram" "Facebook" "Twitter"
Python
iklan["Channel"]## 0 Youtube
## 1 Instagram
## 2 Facebook
## 3 Twitter
## Name: Channel, dtype: object
iklan.Channel## 0 Youtube
## 1 Instagram
## 2 Facebook
## 3 Twitter
## Name: Channel, dtype: object
R
A data frame in R can be expanded by adding new columns and rows to the already existing data frame.
iklan$Color <- c("Red", "Purple", "Blue", "Soft Blue")
iklan## Channel budget Color
## 1 Youtube 8.0 Red
## 2 Instagram 4.5 Purple
## 3 Facebook 4.3 Blue
## 4 Twitter 2.5 Soft Blue
Python
A Python pandas.DataFrame can be expanded by adding new columns and rows to the already existing data frame.
iklan["Color"] = ["Red", "Purple", "Blue", "Soft Blue"]
iklan## Channel budget Color
## 0 Youtube 8.0 Red
## 1 Instagram 4.5 Purple
## 2 Facebook 4.3 Blue
## 3 Twitter 2.5 Soft Blue
That's all basic programming that you must know before continuing the next level of data science learning.For the next level, let's learn in Algoritma Data Science School