How to Become a Data Analyst from Scratch?

How to Become a Data Analyst from Scratch?

Data analysts are in high demand today. They collect, clean, and analyse data to help businesses make better decisions and achieve their goals. If you want to start or shape your career in data analysis, here is a step-by-step guide on how to become a data analyst.

What does a data analyst do?

Data analysts collect and review large amounts of data from a variety of sources for their employers. They translate this data into information that companies can use to guide and motivate business decisions. Some data analysts specialize in operations, marketing, financial or other types of data, depending on the industry they're in and their employers' requests. Some common responsibilities of data analysts include:

*Assessing questions their client's or employer's questions and determining which types of data can answer them

*Using data analysis and statistical modeling software applications to identify trends and patterns

*Converting numbers, figures and other types of data into narratives that answer questions

*Preparing graphs, diagrams and reports to share findings with stakeholders

What is the average salary for a data analyst?

The national average salary for a data analyst is $65,820 per year. The salary for a data analyst can depend on several factors, including level of experience and geographical location. To increase their earnings, professionals may pursue a graduate degree in a relevant field, or earn professional certifications.

How to become a data analyst

1. Get an Education

Bachelor's Degree: Start with a bachelor's degree in a relevant field such as Computer Science, Mathematics, Statistics, Economics, or a related field.

Alternative Education Paths: Online courses, bootcamps, and community college programs can also provide the necessary education if a traditional degree is not feasible.

Relevant Courses: Focus on courses that cover statistics, mathematics, computer science, and business.

2. Learn Key Computer Languages

  • SQL (Structured Query Language): Essential for querying databases.

  • Python: Widely used for data analysis and machine learning.

  • R: Another popular language for statistical analysis and visualization.

  • Excel: Basic tool for data manipulation and analysis.

  • SAS: Used for advanced analytics, business intelligence, and data management.

  • Additional Tools: Familiarity with tools like Tableau, Power BI, and Hadoop can be advantageous.

3. Get Relevant Work Experience

Internships: Look for internships or part-time positions during your studies to gain practical experience.

Entry-Level Positions: Start with roles such as junior data analyst, data entry clerk, or business analyst.

Projects: Participate in projects or competitions (e.g., Kaggle) to build a portfolio of real-world experience.

4. Get On-the-Job Training

Mentorship: Seek guidance from experienced data analysts or mentors within your organization.

Continuous Learning: Stay updated with the latest trends and technologies in data analysis by attending workshops, webinars, and conferences.

Internal Training Programs: Many companies offer internal training programs for their employees to enhance their data analysis skills.

5. Get Certified

Professional Certifications: Consider obtaining certifications to validate your skills and knowledge. Some well-regarded certifications include:

Certified Analytics Professional(CAP): Offered by INFORMS.

Microsoft Certified: Data Analyst Associate: Focuses on using Microsoft Power BI.

Google Data Analytics Professional Certificate: Comprehensive program offered through Coursera.

SAS Certified Data Scientist: For those proficient with SAS software.

IBM Data Analyst Professional Certificate: Available on Coursera and covers various data analysis tools and techniques.

Additional Tips

Build a Portfolio: Create a portfolio showcasing your projects, analyses, and visualizations to demonstrate your skills to potential employers.

Network: Connect with professionals in the field through networking events, LinkedIn, and professional organizations.

Soft Skills: Develop strong communication skills to effectively present your findings and insights to non-technical stakeholders.

Stay Curious: Cultivate a mindset of continuous learning and curiosity to keep up with evolving data analysis techniques and technologies.

By following these steps and dedicating time to develop your skills and experience, you can successfully become a data analyst and advance in this dynamic and growing field.