Data Cruise

By now, you might have heard a lot about Data Analysis and you may wonder what it entails, who a Data Analyst is, and how to become one. Well, these are legitimate questions that need to be answered.

I discussed the first two questions in my previous post “who is a Data Analyst” and I will be answering the third one in this post under 3 sub-topics.

1. Acquire the skills and qualifications
2. Prepare to get hired
3. Work as a Data Analyst

Becoming a Data Analyst is not unattainable. However, there are some things that must be in place before this can be achieved.

I have listed below, the general steps you need to follow to make this a possibility.

1. ACQUIRE THE SKILLS AND QUALIFICATIONS

SKILLS

You will need to be trained and be proficient in the following skills:

QUALIFICATIONS:

Although the skills are more important, having the right qualification will definitely help you on your journey to becoming a Data Analyst. This also comes in handy when it is part of your employer’s requirement but this varies from country to country. For example, countries like the US attach more importance to degrees when compared to Europe. The qualifications you will need are:

• Bachelors’s Degree in Computer Science, Statistics, Mathematics, Economics,  Finance or higher is preferred i.e. a Master’s Degree is required for some positions.
• Industry certification and cloud computing framework (Microsoft Azure and AWS)

However, you need a lot of experience and loads of projects than a degree to stand out from the crowd. In the long run, you may not need all these qualifications before getting a Data Analyst role but you will definitely need the skills and experience. This is why it is possible to easily maneuver from a different background once you have an open mind to learn the skills listed above and have logical thinking to work on requirements. Qualification is an added advantage. They give you a good platform to build on and help you have an in-depth understanding of the topic.

2. PREPARE TO GET HIRED

BUILD A PORTFOLIO

This is a form of personal marketing and branding which is centered on letting people know who you are and what you can do. It can be achieved by creating a public profile on Linkedin, Github, or a personal website and showcasing the hands-on Data Analysis projects you have done. You will have to show an end-to-end composition of the code you used in building the projects. But a question comes to mind: What projects should you display when you haven’t got a data analyst role? There are several ways you can gain practical experience before getting a role. You can get it through volunteering, freelancing, working on personal projects (See my post on “website to get free data for project work“), and practicing coding challenges. (See the top websites you can practice Data analysis coding challenges).

BUILD A RESUME

Your resume should show your qualities, skills, and experience. It is an important job search tool because it goes a long way in determining if the employer would decide to meet you or not. The structure and the information to include in your resume play a vital role in its preparation; you wouldn’t want to include irrelevant details. This information should convince your employer why you are a great match for that position and invaluable addition. Learn more on how to make your resume.

JOIN A LEARNING COMMUNITY AND read BLOGS

Reading blogs and joining a learning community keeps you “alive” and up-to-date. You will get to learn new things through participation, asking questions, and from the experiences of other data analysts. Furthermore, you will become aware of several job opportunities and possibly get a job reference. Examples of data analytics communities are Analytics vidya and SAS community.

PREPARE FOR INTERVIEWS

Before you go for an interview, you will need to do thorough research about the company and the position. This will let you properly align your skills and objective with the company’s requirements. You can do this by doing a google search, reading their review comments on Glassdoor, Trust pilot, or LinkedIn in order to understand their culture, vision, products, and clients. Also, you must continually practice several interview questions both technical and behavioral to keep you abreast of the industry knowledge, develop your self-confidence, know the right responses to give during an interview, and improve your communication skills.

Check this link for interview questions.

3. WORK AS A DATA ANALYST

JOB DESCRIPTION

This is a typical data analyst job description:

• Analyzing data using Statistical techniques and providing reports.
• Acquiring data from primary and secondary sources and meeting data systems.
• Identifying, analyzing and interpreting trends or patterns in complex data sets Filtering and cleaning data.
• Working with management to prioritize business and information needs.
• Locating and defining new process improvement opportunities

However, you must understand that the above job description is derived from the entire Data Analysis process. The entire job description must cover what you do at each stage of the data analysis process. The stages are:

• Problem definition: This is when you determine the client’s needs e.g. dashboard, reports,product analysis etc. , prepare questions based on these needs and create an action plan which you will communicate to the entire team.
• Data Collections: Data comes from multiple sources e.g database,backups, flat files, APIs. At these stage, you may need to work with programmers to create the Extract Transform Load (ETL) Process for data aggregation.
• Data Cleaning: Data is always messy and dirty. Cleaning data makes it more usable, normalised and standardised. Some of the areas you may want to look at during cleaning include the date fields, gender, measurements, data validation etc.
• Data Analysis: This is when you perform the data exploratory analysis to answer those questions you outlined in the problem definition stage. You will use statististical methods and graphical visualizations to help you solve these problems. This is usually done with SQL but preferrably Python when it involves statistics.
• Set data up for report/visualization: Create views, format data for its specific purpose and filter data to the subset you need.
• Create reports and visualizations: Connect the data to data visualization tools e.g. Power BI, Tableau(or python, R). NB: You can automate reports in SQL using stored procedures. Make sure your reports/visualization solve the defined problem and properly present your data.

salary expectation

This varies across different countries, sectors, and companies. Although Data Analysts are considered highly skilled, some companies pay lesser than others. Also, countries with emerging market awareness for Data Analysis pay lesser than those with a matured market and high demand for data analysts e.g US and Europe. The average salary expectation for a data analyst in the UK is £35000 per annum and US$69,517 per annum in the US.