If you are interested in pursuing a career in data analysis and want to know how to get started and what steps to take. It doesn’t matter if you have a tech career or not, you can learn the necessary skills and tools to become a data analyst. In this article, you’ll be guided through six steps that will help you get a data analysis job from scratch.
1) Learn the Tech Skills Required
The first step is to learn the essential technical skills for data analysis. These skills include:
– Excel: Excel is a powerful spreadsheet software that allows you to perform calculations, manipulate data, and create charts and graphs. Excel is a great tool to start with, as it provides a strong foundation for data analysis and is widely used in many industries.
– Power BI or Tableau: Power BI and Tableau are data visualization tools that enable you to create interactive dashboards and reports from various data sources. Data visualization is a key skill for data analysts, as it helps you communicate your findings and insights to stakeholders and audiences.
– SQL: SQL is a programming language that is used to store, manage, and retrieve data from relational databases. SQL is a must-have skill for data analysts, as it allows you to query and manipulate large amounts of data efficiently and effectively.
– Python or R: Python and R are programming languages that are used for processing, cleaning, analyzing, and modeling data. Python and R offer a wide range of libraries and packages that can help you perform various data analysis tasks, such as data wrangling, statistical analysis, machine learning, natural language processing, and more. Python and R are optional skills for non-IT people, and can be learned later after mastering the other skills.
You can learn these skills through online courses, books, tutorials, blogs, podcasts, and other resources. Some of the popular platforms that offer data analysis courses are Coursera, Udemy, edX, DataCamp, and Codecademy.
2) Learn and Show Your Skills by Doing Practical Projects
The second step is to work on hands-on projects that will allow you to practice and apply your skills to real-world problems. Projects are very important, as they will help you build your portfolio, showcase your abilities, and demonstrate your value to potential employers.
You can find datasets and project ideas from various websites, such as Kaggle, data.gov, GitHub, and more. You can also join my Power BI course just for 8 dollars to do a hands-on project from scratch!
Another option is to do guided projects, where you follow along with an instructor or a mentor who will teach you how to complete a project step by step. A great YouTube channel for guided projects is Alex the Analyst, where you can learn how to use Power BI, SQL, and Excel to analyze different datasets.
You should aim to do diverse projects that cover different domains, such as sales, marketing, tech, HR, and more. This way, you will get an overall idea of how data analysis is applied in different contexts and scenarios.
3) Certifications
The third step is to obtain certifications that will validate your skills and knowledge in data analysis. Certifications are not necessary, but they can help enhance your resume and credibility, especially if you have no prior experience or formal education in data analysis.
There are many certifications that you can choose from, depending on your interests and goals. Some of the popular certifications are:
– Google Data Analytics Professional Certificate: This is a beginner-friendly certificate that covers the fundamentals of data analysis, such as data collection, data cleaning, data analysis, data visualization, and data presentation. The certificate consists of eight courses and a capstone project, and can be completed in about six months.
– IBM Data Analyst Professional Certificate: This is an intermediate-level certificate that covers the skills and tools required for data analysis, such as Excel, SQL, Python, and IBM Cognos Analytics. The certificate consists of nine courses and a capstone project, and can be completed in about 10 months.
– Microsoft Certified: Data Analyst Associate: This is an advanced-level certificate that focuses on the skills and tools required for data analysis using Power BI. The certificate requires passing one exam, which covers topics such as data preparation, data modeling, data visualization, and data analysis.
– Salesforce Certified Data Analyst: This is a specialized certificate that focuses on the skills and tools required for data analysis using Salesforce. The certificate requires passing one exam, which covers topics such as data management, data quality, data analysis, data visualization, and data storytelling.
4) Practice Soft Skills
The fourth step is to practice soft skills that are important for data analysts. Soft skills are the interpersonal and communication skills that enable you to work effectively with others, solve problems, and deliver results. Some of the soft skills that data analysts need are:
– Presentation: Presentation skills are the ability to present your data analysis results and insights in a clear, concise, and engaging manner. Presentation skills are essential for data analysts, as they help you convey your message and recommendations to your audience, whether it is your boss, your client, or your team.
– Communication: Communication skills are the ability to communicate your data analysis process, findings, and insights in a way that is understandable and relevant to your audience. Communication skills are crucial for data analysts, as they help you collaborate with others, ask questions, provide feedback, and explain your rationale and assumptions.
– Teamwork: Teamwork skills are the ability to work well with others, share ideas, support each other, and achieve common goals. Teamwork skills are important for data analysts, as they help you leverage the strengths and perspectives of your team members, and deliver better and faster results.
– Critical Thinking: Critical thinking skills are the ability to think logically, analytically, and creatively, and to evaluate information and arguments. Critical thinking skills are vital for data analysts, as they help you define the problem, collect and analyze data, identify patterns and trends, and draw conclusions and recommendations.
You can practice these skills by giving mock presentations, recording yourself, asking for feedback, joining online communities, participating in discussions, and taking online courses.
5) Refresh Your Resume and LinkedIn Profile
The fifth step is to update your LinkedIn profile and resume to reflect your skills and experiences in data analysis. Your LinkedIn profile and resume are the first impressions that you make on potential employers, so you want to make sure they are professional, relevant, and attractive.
Some tips to update your LinkedIn profile and resume are:
– Use keywords: Use keywords that are related to data analysis, such as Excel, Power BI, SQL, Python, data visualization, data wrangling, etc. Keywords will help you rank higher in search results and attract more attention from recruiters and hiring managers.
– Highlight your projects: Highlight your projects that you have done in data analysis, and include links to your portfolio, GitHub, Kaggle, or other platforms where you have showcased your work. Projects will help you demonstrate your skills and abilities, and show your passion and interest in data analysis.
– Quantify your achievements: Quantify your achievements and results that you have achieved in data analysis, such as how much data you have analyzed, how much time or money you have saved, how much revenue or profit you have generated, etc. Quantifying your achievements will help you show your impact and value, and make your profile and resume more impressive and convincing.
– Use a template: Use a template to format your resume and make it look neat and organized. A template will help you save time and avoid mistakes, and make your resume more readable and appealing. You can use resumeworded.com to get sample templates and tips for your resume.
6) Apply for Jobs and Prepare for Interviews
The final step is to apply for jobs and prepare for interviews in data analysis. You can find jobs through various channels, such as referrals, online portals, networking events, career fairs, etc. Some of the popular online portals that offer data analysis jobs are LinkedIn, Glassdoor, Indeed, and more.
You can also find jobs as a BI/data viz analyst (only requires Excel, Power BI/tableau and SQL) or jobs such as sales analyst/HR analyst in your domain as a non-tech person.
When you apply for jobs, make sure you tailor your resume and cover letter to the specific job description and requirements, and highlight your relevant skills and experiences. When you get invited for interviews, make sure you research the company and the role, and practice common interview questions and scenarios.
Some of the platforms that offer data analysis interview questions and solutions are Leetcode, Stratascratch, Datalemur, and Interview Query.
