How to Become a Data Analyst in 3 Months

09 November5 min read
How to Become a Data Analyst in 3 Months

By the end of 2030, the market for data analytics is projected to be worth about $346.24 billion

Every day, 2.5 quintillion bytes of data are produced, which is only increasing. The proliferation of data is driving the industry that uses it.

Despite what you may fear, learning data analysis is easier than you think. This blog will examine a three-month journey you can take to start a career in this industry. To give you a rough concept of how to start the journey, we will break down the competencies you need each month.

The 3-Month Journey to Becoming a Data Analyst

It takes effort to improve as a data analyst in just three months. However, success in data analysis requires focus and challenging endeavors. Following our guide will give you sufficient expertise over the next three months to launch a successful data analysis profession. 

Image by storyset on Freepik

Image by storyset on Freepik

Learn the Fundamentals

You should be knowledgeable about the field you are working in. Therefore, you must have a solid understanding of the fundamentals before moving on to the technology stacks. You can develop a foundational skill set in this manner and then build on it. This exercise may take two to four weeks, based on how much work you have to put in.

Although most entry-level data analyst jobs require a bachelor's degree, this is starting to change. With a degree in math, computer science, or a related topic, you can gain the fundamental knowledge you need and boost your CV. Still, other ways exist to acquire the required skills, such as through professional certificate programs, boot camps, or self-study courses.

Polish and Acquire Technical Skills

After nearly 4 weeks of mastering the fundamentals, you understand the theoretical underpinnings of data analysis and statistical and quantitative components.

A particular set of technical skills is often necessary to land a job in data analysis. More specifically, business users will extract data and produce reports using BI and analytics tools without having to comprehend the underlying algorithms. Therefore, you must excel in the relevant tech skills. These skills will include: 

  • Data Cleansing
  • R or Python
  • SQL (Structured Query Language)
  • Data Visualization

Unsurprisingly, you’d need magic to learn R, Python, and SQL in under three months. But that's not what you're supposed to do. Nobody expects you to manage petabytes of data and draw insights that will change the world in three months. Your objective is to develop into a data analyst well-competent to handle the basics — in three months. 

SQL and Data Cleaning

Become hands-on with SQL. Although many individuals don't begin there, trust us when we emphasize that it is the foundation for all data applications and analyses performed globally.

To extract and transform the data you are studying, SQL is required, and it is also not that difficult. The first two weeks should be devoted to learning SQL. While the language's capabilities are limited, learning the fundamentals of SQL only takes around two days.

Sign up for one of the 4-6 week courses offered by boot camps on websites like Coursera and Udemy for SQL. You only need to complete the first two weeks of this journey. To advance to the next stage, build the fundamentals.

On top of that, you will require additional capabilities later in more formal settings. SQL will also assist you in learning a lot about data, allowing you to gain more knowledge about how to clean the data with SQL and other tools. 

Python or R Programming and Data Visualization:

Python and R are powerful programming languages with a wide range of capabilities. Learning Python would be wise because it can be helpful in many other contexts, such as web development, software development, the Internet of Things, etc. Even if you don't want to work as a data analyst, you can use your skills in other fields.

Python and R for Data Science courses are offered on Coursera and Udemy, just like SQL. If you sign up for those courses, they will assist you in building the foundational skills for Python or R in data analysis.

You could learn most of what you need to know to get started in data science in a six-week course for Python or R; the rest is practice. Visualization is also essential in this case. Just like R studio, Python allows you to visualize data using Jupyter Notebook, and you will also learn how to do it in the courses. 

Enhance Your Soft Skills

Although the soft skill requirements vary by industry, some are relevant to practically every function for a data analyst. 

The five C's of data analysis soft skills are:

  • Communication: Due to business and technical complexity, data, and insight, customers need to be more specific in communicating their needs. Clarifying and framing their precise requirements and demands is made more accessible by actively listening and asking insightful questions.
  • Collaboration: A data analyst can successfully collaborate with different teams to achieve a shared objective. It can be further developed by fostering open-mindedness, respecting other team members, appreciating their opinions, and soliciting their thoughts on various issues and difficulties.
  • Critical Thinking: Questioning the hypothesis, spotting biases, verifying assumptions, choosing suitable models, reviewing the correctness of the analysis, obtaining actionable insights, and evaluating the ethical implications for decision-making are all aspects of critical thinking abilities in data analysis.
  • Curiosity: Being curious is a crucial soft skill in data analysis since initiatives involving data analysis often face various difficulties, including unclear decision objectives, time and resource limitations, a lack of knowledge, poor data quality, and ethical and privacy concerns.
  • Creativity: A data analyst works tirelessly to produce fresh insights that are accurate, timely, and pertinent to the organization. Creativity enables analysts to investigate different options and viewpoints before settling on a particular analytics solution by considering numerous methods for addressing user requirements.

Get Down to Business

After completing the 12-week, three-step Bootcamp, you should be competent to perform simple data analysis tasks independently. Working with data in practical contexts is the best approach to discovering its worth. Either you can practice on your own or join an organization that will assist you in practicing and learning while you work. Your current skill set is sufficient for you to obtain an entry-level position.

At first, you practice with smaller data sets, and then you expand upon that. Save your best work for your portfolio as you experiment with online data sets or finish practical assignments as you learn. 

The Bottomline

We've outlined a few practices to help you master data analysis work in three months. You must practice a lot because analyzing data requires practice, like any other skill. It will get you going and prepare you well for a career in data analysis.