Emmanuel Udoh
Emmanuel Udoh

Emmanuel Udoh

How To Become A Data Analyst In 2022

Photo by Luke Chesser on Unsplash

How To Become A Data Analyst In 2022

Emmanuel Udoh's photo
Emmanuel Udoh
·Jan 28, 2022·

4 min read

Do you know that Data Analytics is earning more visibility? And what you should know is that if ever there was any time when companies need to make smart, business, and data-driven decisions, it is now.

Data analysts are taking that lead. So, if you’re an aspiring data analyst, you can expect to play a crucial role in helping companies adapt to the rapidly evolving world around them.

For any kind of project, first, there will be a requirement for a meeting.

The next question will be, what goes on at these gatherings?

To simply answer this, there will be a domain expert, product donor, and business analyst present.

Business analysts work to hold down all the required documents and they will interact with the product donors to form the product requirements.

After this, the requirement is actually sent to the data analyst or the data scientist along with the analytics manager.

These people will have discussions with domain experts as to where they will be collecting the data (Whether from a primary or secondary source) to solve a particular problem.

Upon getting the data, the data will be stored in the database (SQL, or NoSQL) and then we go into the life cycle of a data science project.

Going to the project proper, data analysts work with the data collected to Check which data are important.

Furthermore, the data analyst will do a lot of analysis, data visualization, and they with try to create lots of reports that will help the stakeholders to make the right decisions.

Differences between Data Analysts and Data Scientists.

Data analysis is concerned with the analytical path which is, creating analytical reports for stakeholders.

Note they are not involved in model creation, model hyper tuning. They are to analyze the data, visualize the data and create amazing reports about the data.

It is said that data scientists can do the work of data analysts. But data analysts cannot do the work of data scientists. The reason is that a data scientist is to do model creation, model deployment, model retraining of projects, and many more.

Roadmap To Becoming A Data Analyst

Things required to become great on your journey

  1. Programming Languages: Knowing your Programming Language like “Python” or R is very necessary to complete you as a data analyst, data engineer, or data scientist.

In the course of working with these programming languages, libraries, and tools one should focus on especially for python users are Numpy, Pandas, Seaborn, Scipy, Matplotlib, Plotly, and Dask. They are used for creating powerful visualizations.

Then that for R programmers, are dplyr for data manipulation in R, ggplot2 for data visualization in R, esquisse for exploring data quickly and extracting the information they hold. Lubridate is used for data wrangling, knitr for dynamic report generation in R, and shiny for formatting work.

  1. Statistics: is a must to know. So you must know statistics. Freecodecamp and several YouTube channels teach it. For this, Google is your friend.

  2. Basic tools: knowledge of PowerPoint, and excel is a plus because you really need to create lots of reports, presentations. At the end of the day, it is your presentation and graphical skills, the stakeholders want to see.

  3. Databases: You will need to know at least one of these; MYSQL, MongoDB, Microsoft SQL Server.

  4. Business Intelligence Tool: Examples of the ones used regularly are Tableau and Microsoft Power BI. Peradventure your company is working with Azure cloud, Microsoft Power BI is best to use. Otherwise, you can go with Tableau. They are both powerful and useful.

We also have other business intelligence tools like Qlikview, Dundas data visualization, Pentaho, Fusionchat, Google charts, Looker, Zoho analytics, and many more.

  1. Cloud: It is worthy of note to know that cloud services are rapidly growing in the market today. Cloud computing and cloud platforms offer enterprises an alternative to building structures required for record-keeping.

With cloud computing, anybody using the internet can enjoy scalable computing power on a plug-and-play basis.

These Cloud platforms include Microsoft Azure, Google cloud platform, Amazon web services, and many more.

 
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