**Why using AngularJs’s global objects instead of native ?**

*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**.

**Notes**:

→ 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.*

**What is Data Science?**_"We have lots of data - now what?" (How can we unlock real value from our data?) Data science is a multidisciplinary…_datajobs.com

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:

- Text
- Photo
- Audio
- Video
- …

Also in different formats:

**Structured data**: those with a certain degree of organization for further querying and/or analysis. As the one stored in**Relational****Database Management System**or in**Json, Xml, Xls**files.**Semi-structured and Unstructured data**: easy to understand(not formatted), the opposite of the first.

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 …

**Percentage of search interest in R and Python for Data Science: R in Red and Python in sky Blue**

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.

**Learn Python for Data Science - Online Course**_DataCamp's Into to Python course teaches you how to use Python programming for data science with interactive video…_www.datacamp.com

- Manipulating python list in deep
- Manipulating Numpy array
- Subsetting Numpy array
- Subsetting 2D Numpy array
- Simple exploration of data
- Basic statistic

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:

**4700**XP Earned**1**Courses Completed**57**Exercises Aced

**Create a list:**

mySimpleList = [12, 43, 54, 34, 90] #Simple list with same typemyWeirdList = ['a', 43, 54, 'c', 90] #Different types of items

**Print a list**

print(myList) # Knowing that the list is already created

**List of list**

countries = [["Cameroon", "CM"],["Nigeria", "NG"],["France", "FR"],["Gabon", "GA"]]

**Type of a variable**

To print the type of variable, just hit this:

print(type(myVariable))

**Index**

list[4] = list[-2] = 5

**Subsetting list**

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

**Install Numpy**: pip3 install numpy**Import Numpy**

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

**From list to numpy Array**

# Using countries list declared up therecountries_np_array = np.array(countries)

**subsetting np array**

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**print(age_array[age_selector])**

# 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])**print(age_array)**

#result array([1, 4, 0, 8])

**Operation over collections**

age = [2, 4, 6, 8]div = [2, 2, 2, 4]

age_array = np.array(age) # Numpy array of agesdiv_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.

- Average: np.mean(your_numpy_array_or_axis)
- Median: np.median(your_numpy_array_or_axis)
- Standard Deviation: np.std(your_numpy_array_or_axis)

Supposed we have an array representing the grade of 3 students of a class in two courses(French and English):

import numpy as npstudent_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])**print(french_average)**# 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])**print(english_std)**# result: 3.2998316455372216

There are also a lot of interesting community courses available but **non-certifying**:

**Free Data Science and Analysis Training Courses | DataCamp**_Are you looking to build your data analysis skill set? Try one of our free open courses and see why over 460,000 data…_www.datacamp.com

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.**

Follow me on** **Facebook****,** **Twitter****,** **LinkedIn** **and visit my** **blog****.**

**Cheers!**

L O A D I N G

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