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Ways to fix your code when you don’t know why it doesn’t work.
June 7, 2024
What’s the Difference Between Data Analyst vs. Data Scientist?
September 9, 2024

How to break into Data Analysis and get a job

If you’re passionate about data and enjoy problem-solving, a data analyst role might be the perfect fit. 

As a data analyst, you’ll be the driving force behind informed decision-making. By gathering, cleaning, and analyzing data, you’ll help businesses uncover trends, identify opportunities, and optimize their strategies. Let’s explore one potential path to becoming a data analyst.

  1. Foundation – Gain a solid understanding of data analysis fundamentals.
  2. Skills – Develop your technical proficiency in tools like SQL, Python, and data visualization.
  3. Projects – Apply your skills to real-world data projects to gain practical experience.
  4. Portfolio – Showcase your work through a portfolio that highlights your abilities.
  5. Presentation – Practice effectively communicating your findings to a variety of audiences.
  6. Job – Secure an entry-level data analyst position to kickstart your career.
  7. Advancement – Consider earning certifications or pursuing an advanced degree to specialize and advance your career.

You can build a strong portfolio, master key data analysis techniques, and launch your career in data with Torilo Academy’s  beginner-friendly Data Analyst Professional Certificate Training. It is designed to equip you with the essential skills for an entry-level role. Under the guidance of experienced tutors. And the best part? You can complete this comprehensive program in just 8 weeks! 

Get certified as a Data Analyst

Accelerate your career in data, and earn a data analysis certification teaching high-demand skills used by data analysts.

Learn More

Your Path to Becoming a Data Analyst: A Step-by-Step Guide

Data analysts are in high demand across industries, and there are multiple paths to breaking into this exciting field. Whether you’re a recent graduate or looking to switch careers, here’s how to get started.

1. Start with a Foundation.

New to the world of data analysis? The first step is to build a strong foundation. A comprehensive overview can help you determine if this career path is right for you and equip you with practical skills.

Traditionally, a bachelor’s degree was a prerequisite for most entry-level data analyst roles. This is now less common place, as the landscape is evolving. Alternative pathways are becoming increasingly the route to a Data Analytics career. Explore professional certificate programs, bootcamps, or self-study courses to acquire the necessary skills to help you start.

2. Master the Technical Skills for Data Analysis.

Landing a data analyst job often hinges on possessing specific technical skills. Whether you’re pursuing a bootcamp, training centre, or self-study, here are the essentials:

  • Statistics: A strong foundation in statistics is crucial for understanding and interpreting data.
  • SQL: SQL is indispensable for querying and manipulating databases.
  • Data Visualization: The ability to create clear and informative visualizations is a valuable asset.
  • Data Cleaning and Preparation: Mastering data cleaning techniques is essential for ensuring data accuracy and reliability.
  • Programming: Proficiency in R or Python, popular languages for data analysis, is a plus.

To tailor your learning, review job listings in your desired industry and focus on the specific extraction, manipulation or visualization tools listed as requirements.

Beyond technical skills, hiring managers also value workplace competencies. Strong communication skills, especially the ability to explain complex findings to non-technical audiences, are essential. Problem-solving skills and domain knowledge in your target industry can also set you apart.

Get certified as a Data Analyst

Accelerate your career in data, and earn a data analysis certification teaching high-demand skills used by data analysts.

Learn More

3. Gain Practical Experience with Real-World Data.

The best way to master data analysis is to apply your skills to real-world data. Seek out training programs or courses that offer hands-on projects using authentic datasets.

To jumpstart your learning, explore these valuable resources:

  • National Centers for Environmental Information: Dive into climate data to analyze environmental trends. National Centers for Environmental Information, 
  • BuzzFeed: Use news-related datasets to uncover interesting insights. BuzzFeed,
  • NASA Open Data: Tackle global challenges by working with NASA’s vast collection of data. NASA
  • Lagos Bureau of Statistics 
  • Nigerian Open Data for Africa
  • Nigeria Open Data for Africa (Banking)

These are just a few examples. Choose a topic that interests you and start exploring!

4. Build a Strong Portfolio to Showcase Your Skills.

As you delve into data projects, save your best work for your portfolio. This collection will demonstrate your skills to potential employers and can significantly boost your job prospects.

When curating your portfolio, focus on projects that showcase your ability to:

  • Scrape data: Extract data from various sources.
  • Clean and normalize data: Prepare raw data for analysis.
  • Visualize data: Create informative graphs, charts, maps, and other visualizations.
  • Draw insights: Extract meaningful conclusions from data.

If you’ve collaborated on group projects, include one to highlight your teamwork skills.

Need inspiration? Browse other data analyst portfolios to see what types of projects are valued by employers.

5. Master the Art of of Presenting Your Findings.

While technical skills are essential, effective communication is equally crucial for data analysts. Your ability to present findings to decision-makers and stakeholders will significantly impact your success. By telling a compelling story with data, you can guide your organization toward data-driven decisions.

As you work on your portfolio projects, practice presenting your findings. Consider the key message you want to convey and the visuals that will best support your narrative. Remember to speak clearly, maintain eye contact, and use gestures to emphasize your points. Rehearse in front of a mirror, classmates, or even record yourself to identify areas for improvement.

6. Launch Your Career: Securing an Entry-Level Data Analyst Job.

With experience in data analysis and strong communication skills, you’re ready to take the next step: securing an entry-level data analyst job.

Don’t be afraid to apply for positions that might seem slightly beyond your current skill set. Your enthusiasm, portfolio, and overall fit can often outweigh a few missing qualifications.

If you’re still in school, explore internship opportunities through your university’s career services office. Internships offer invaluable hands-on experience and can help you apply your classroom knowledge in a real-world setting.

7. Advance Your Career: Certifications and Advanced Degrees.

As you progress in your data analyst career, consider your goals and explore opportunities for advancement. Certifications and advanced degrees can enhance your qualifications and open doors to higher-paying roles.

Certifications like the Certified Analytics Professional or Cloudera Certified Associate Data Analyst can validate your skills and make you a more attractive candidate for advanced positions.

If you aspire to become a data scientist, a master’s degree in data science or a related field can be beneficial. While not always mandatory, an advanced degree can expand your career horizons and provide a competitive edge.

Learn more: Data Analyst vs. Data Scientist: What’s the Difference?

How to become a data analyst without experience

Many employers seek data analyst candidates with experience. However, you don’t need to wait for a formal job to start building your skills. Data is everywhere!

If you’re transitioning from another field, leverage opportunities to work with data. Many degree programs, certificates, and online courses include hands-on projects with real datasets. Additionally, you can find free datasets online or even collect your own to practice data collection, cleaning, analysis, and visualization.

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