Becoming a data analyst: All you need to know to become a data analyst
All you need to know to become a data analyst in 2022.

All you need to know to become a data analyst.

Becoming a nerd in the data field is not as easy as talked about in our everyday conversations. Data is everywhere and it’s been produced each and every second of everyday, this has caused a dramatic increase in data jobs worldwide and if you’re thinking of getting started in the data field or transitioning into the data field then this article is definitely for you.
There exists numerous number of job roles in the data field ranging from database developer, data curator, data manager, data analyst, data scientists, data engineers, machine learning engineer /AI engineer etc. whiles most people target other job roles in the data field, most people use the data analyst role as an entry point to get into a much higher role.
This is not to say that data analysts are at a lower level in the data field. Becoming a senior data nerd depends on the type of roles you execute in your organization. Some of the data analyst job roles include data cleansing and wrangling, making reports and dashboards and also building predictive AI models.
Read through this article to the end as we unravel all the secrets related to becoming a data nerd specifically a data analyst. Without much ado let’s get straight into the technical and non technical stuffs you need to know in order to become a successful data analyst.
Excel : excel is by far the most underrated yet pertinent tool used in modern data analyst job roles. Although Mastering excel wouldn’t be much of a needful since that’s not the only tool to be used in your data analyst role, there are very helpful and a bit technical topics in excel that you must know, these are power query, pivot tables, formulas and functions such as countif/countifs, xlookup/vlookup, sumif/sumifs, index ,match, charts/graphs and macros. As a starter, getting to know the aforementioned areas of excel is needed to excel in your junior data analyst role.

SQL : SQL stands for Structured Query Language and it is used to communicate with databases. As a data analyst, you would need SQL to pull data from numerous sources so as to be able to perform your analysis. Functions such as max, min, count, average , group by, where clauses, in, not, if, loops, user defined functions, window functions, views and stored procedures are necessary for a data analyst to know. You can practice SQL here; hackerank, datalemur,w3shool

Data visualization: the act of building screens to accurately convey analysis and findings is very key in a data analyst role and knowledge of one or two data visualization tool(s) will be vey helpful in your data journey as a data analyst, business analyst or data scientist. In the space of data visualization today, the following are some if the top visualization tools you can choose from depending on personal preference, power bi, tableau, QlikView, Alteryx etc. All these are industry standard applications that can be used/are built to generate visualization. Practice visualizations here.

Python/R programming: In the world of data today, knowing one/two programming languages is a plus. Programming languages such as python and R are the leading programming languages that you can pick from and both are great, although python is more beginner friendly. One intriguing thing about programming languages is that, they can generate powerful visualization on their own just as you could do with the use a visualization tool. So in learning python or R you’ll also get exposed to the world of visualization without having to learn a specific industry standard visualization tool. Both languages have also gotten the luxury of being able to perform advanced data cleansing and wrangling which is a very crucial part of a data analyst’s job. Here are resources to help you learn/practice python or R. Learn python for free here.

Statistics and probability: starting your data analyst journey by learning statistics and probability could feel like a daunting task especially for people coming from a non STEM background. But there is no doubt that learning statistics and probability should be the first steps in transitioning into data analytics since these two areas of study will enable you to properly access, wrangle and clean datasets.  In studying statistics and probability, there are certain areas that you need to grasp as a data professional and they are descriptive statistics, inferential statistics and prescriptive statistics. Statistics and probability will go a long way to helping you understand the various data types, outliers, central tendency, distributions and so on and this will help  you  move from one data role to another as majority of data jobs require you to know a bit of statistics and probability. You should click here to learn and apply statistics and probability here.

It is also important to note that knowing the aforementioned technical stuffs can be of great help to you when starting out as a junior in the data industry but you can not downplay the impact of the following soft skillsets for a successful data analyst career, good communication skills, interpersonal relationship and being a good team player will do you a lot of good in the data industry.