This article was originally posted here
I’ve never been good enough to make complex manipulations on data but to writing algorithms and build web applications a big Yes.
→ This is not a guide to be a full Data Science Engineer, I’m just sharing what I started with in this field.
→ This is not a unique path too.
Instead, you can consider it as one start study plan for those coming from Software Development and want to be Junior Data Science engineer.
The voice of a beginner for others beginners. :)
Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.
For me, it is all the things we are doing with data that can solve some problems and come out with business value or growth.
As we said up there it is about manipulating data the whole time, but what kind of data can we manipulate?
On the internet or large enterprise applications there are a lot of data coming from different sources as social media, call to actions, simple forms, log data, transactions, emails …
All things we are doing online or others place required the most times that we input data, these data can be in different types:
Also in different formats:
There are a lot of languages to Data Sciencing(hahaha), some of them are very popular and more used than others.
There are a few: R, Python, Java …
I have not just chosen to learn Data Science because it is also better paid in the tech industry. I’m firstly a passionate developer, Ok? Then love to implement and discover a lot and I will surely use it in a venture we launched with some partners.
Python because it is also used a lot in this field and is one of the first programming languages I started with and found easy.
I start Data Science with a free and very educative certification available on www.datacamp.com, it helps me to introduce myself to this field to be able to start in a new way in my career.
There are a lot of exercises and XP I earned there on this free course, I will show you some examples of things I learn there, but impossible to put all here, it is not the intent of this post and it is too much:
mySimpleList = [12, 43, 54, 34, 90] #Simple list with same type
myWeirdList = ['a', 43, 54, 'c', 90] #Different types of items
print(myList) # Knowing that the list is already created
countries = [["Cameroon", "CM"],
To print the type of variable, just hit this:
list = list[-2] = 5
A subsetting always return a list. Here the first index is included in the result and the last is not.
list[1:4] = [2, 3, 4] # From index 1 to index 3 included
list[:4] = [1, 2, 3, 4] # From the start to index 3 included
list[1:] = [2, 3, 4, 5, 6] # From index 1 to the end
There is some manner to import python package/function, let’s focus on these two for this post:
import numpy # Here we will address numpy array with numpy.array
import numpy as np # Address numpy array with np.array
# Using countries list declared up there
countries_np_array = np.array(countries)
countries_np_array[:, 1] = array([‘CM’, ‘NG’, ‘FR’, ‘GA’])
# Return all country code, all rows and the second column
age_array = np.array([2, 4, 6, 8])
age_selector = age_array >= 4
# result array([False, True, True, True], dtype=bool)
# Now use this selector to index the new array
# result array([4, 6, 8])
Note: Numpy does not allow multiple types on an array and will force all type to be same.
age_array = np.array([True, 4, False, 8])
#result array([1, 4, 0, 8])
age = [2, 4, 6, 8]
div = [2, 2, 2, 4]
age_array = np.array(age) # Numpy array of ages
div_array = np.array(div) # Numpy array of divs
print(age/div) # divide python list
# Traceback (most recent call last):
# File “<stdin>”, line 1, in <module>
#TypeError: unsupported operand type(s) for /: ‘list’ and ‘list’
print(age_array/div_array) # Will compute without issue on each item
array([ 1., 2., 3., 2.]) # result
Data Science deals with a lot of information to analyze, sort and do other things on, then it needs to do mathematical operations over collections quickly.
Supposed we have an array representing the grade of 3 students of a class in two courses(French and English):
import numpy as np
student_grades = np.array([[12, 16], [15.5, 9], [5, 16]])
# Average of student's grade in French
# Here we select all the rows and the french axis(the first column)
french_average = np.average(student_grades[:, 0])
# result: 10.833333333333334
# Standart deviation of student's grade in English
# We select all the rows and the english axis(the second column)
english_std = np.std(student_grades[:, 1])
# result: 3.2998316455372216
There are also a lot of interesting community courses available but non-certifying:
Ping me if you are looking or you would like to have a partner to study and master Data Science with, I’m available for peer learning.
I think that start by practice using a simple and detailed course with a good scope is worth it for learning new things.
Thrilled to have it and excited to learn more using online resources and other posts about Data Science. I would also like to know how you started or which advice can you give to a beginner like me.