Self Paced Career Path in Data Analytics
Data Analytics

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.


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

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