Self Paced Career Path in Data Analytics
Considering
a career path in data analytics can be very demanding due to the various things
one has to learn. The learning process can be time-consuming, usually ranging
from 6 months to even more reckoning on the hours per day invested in learning.
So, what is
data analytics?
This is a
process of gathering, interpreting and trying various analytical techniques to
visualize and extract data in order to create a structure order which communicates
the results to help make decisions.
Before
becoming an established data analyst, one should be proficient in Programming
languages such as Python which is
widely used in the field, R, Query
programming languages such as SQL,
Data visualization tools like Tableau,
Microsoft power BI and others tools like Microsoft Excel etc.
What you need to know before becoming a data analyst
Before
learning, it is advised to find a suitable site such as Edx, Coursera, khan academy, w3schools, Udemy or bootcamps such as FreeCodeCamp, DataCamp etc. where you will
gain extensive knowledge on the skills and tools and also have practical
experience.
Skills you need to acquire
in becoming a data analyst
1). A
Programming Language
·
PYTHON
(a general-purpose programming language): having an adept knowledge on the foundation
of python is very essential in becoming a data analyst. In data analytics, libraries
such Pandas which is used for data
manipulation, NumPy which is used for
math operation and matplotlib which
is used for data visualization are all indispensable tools used by the data
analyst in analyzing data while using python. In 2018, approximately 2/3 of
data scientists/analytics reportedly use python for analysis.
·
R
(a statistical programming language) -: This is an alternate programming
language widely used by data analytics to store & manipulate data, as well
as perform data analysis tasks and using varieties of data sets and making use
of charts and graphs to visualize the results. It is the number one choice by a
data analyst when considering using a statistical programming language.
2). Query programming Language
·
SQL
(Structured Query Language): - This is used to create and query databases, retrieve
information from relational databases, store, access and manipulate data stored
in a database. Example of SQL tools used by a data analyst: Microsoft SQL Server, MySQL, PostgreSQL
etc.
3). Data visualization tools: - this deal with the graphical representation
of information and data. It aids in viewing data in a more dynamic way, giving
it an improved perception. Examples of data visualization tools are:
·
TABLEAU
·
POWER
BI
·
Google
Data studio
4). Other Data Analysis Tools
·
Microsoft
Excel: - This is a spreadsheet application used for calculations, statistical
operations, reporting and analyzing data. It is a flexible tool which can also
be used to illustrate datasets.
Building a portfolio
Now after gaining knowledge on the listed above you can build a portfolio of your own which will consist of projects solving real life situations in tandem with skills you’ve acquired over the course of learning. This portfolio poses a decent reflection of your accomplishments and achievements. GITHUB is a popular tool used in storing software projects.
Internship
You can then
proceed on applying for Internships. This gives you a first-hand experience on
real life situations by working on real time project. It also facilitates in
exposing you to new tasks thereby aspiring you to complete goal specific tasks
hence giving you quality experience with projects relating with career goals.
Internships also aids in acquiring research experience, building a strong
resume, creating a robust professional network, providing unique opportunities,
and securing good reference transition into a permanent job. LinkedIn is a
great professional social networking tool where application of Internships is
ubiquitous. You can also opt for an online internship which can be done
remotely e.g. Zep Analytics, Indium Software, etc.
Job application and offer
In applying
for jobs, having a bachelor’s degree will aid you in potentially landing you employment.
Most times, having a degree in computer science, related engineering field, mathematics,
statistics, finance or economics can be of added advantage. Note that you simply
could encounter more job opportunities with a degree than without one. In
getting employment, you then begin your data analytics career as a Junior data analyst. You could also opt
for freelancing jobs using sites such as Upwork, Toptal, VanHack, Turing etc.
where you could be a freelancer within the data analytics field. Getting
certificates just like the Google data analytics certificates, IBM data analyst
professional certificate, etc. would be of great value too. Being a part of the
ISDSA (The international society for Data science and Analytics) which is a globally
recognized data science and analytics association is a big perk.
I hope this
article has inculcated the adequate knowledge you will need in commencing your
Data Analytic career.
I wish you
all the best.
Thank You.
- Mubaraq
- Mar, 28 2022