How to Become a Data Analyst with No Experience: Step-By-Step Guide

Becoming a data analyst without prior experience can be challenging, but it’s definitely achievable with the right approach. This article will guide you through the essential steps to embark on a successful data analyst career, even if you’re starting from scratch. We’ll cover the key skills to acquire, strategies for building a strong portfolio, and effective job search techniques.

Ready to launch your career in data analysis, even without prior experience? Enroll in Scaler’s Data Science Course today and gain the comprehensive training, mentorship, and hands-on experience you need to succeed.

Is It Possible to Become a Data Analyst with No Experience?

Yes. The industry’s diversity and growing demand for data analysts present opportunities for newcomers. If people put in the work, are willing to learn, and have access to the right training materials, they can acquire the abilities and information required to succeed in this field.

How to Become a Data Analyst with No Experience?

Not everyone can afford to obtain a professional certificate or a degree. If you don’t have the skills and expertise needed to become a data analyst but can’t afford to spend the time or money on education, here’s how to do it. Follow these steps to get the knowledge you need for a career as a data analyst.

1. Understand the Data Analyst Role

Start by learning everything there is to know about the role of a data analyst. Become familiar with the roles, duties, and effects of data analysis across a range of sectors. Having this core knowledge will enable you to adjust your learning plan to the needs of the industry. Some of the responsibilities include data collection and preparation, report generation and visualization, data Interpretation, and much more.

2. Develop Key Data Analytics Skills

Becoming a data analyst does not usually require a degree in a related discipline, such as computer science, statistics, or mathematics, however, it can be helpful. Even without any prior experience, you can succeed in this industry if you have the necessary skills and a strong work ethic. The following are some critical skills that you should prioritize honing:

2.1 Creating a Robust Statistics Foundation

The core of data analysis is statistics. It is essential to comprehend fundamental statistical ideas such as probability, regression analysis, hypothesis testing, and sampling strategies. To increase your understanding of statistics, you can sign up for local workshops, view video tutorials, or take online courses.

2.2 Understanding Techniques for Data Visualization

When presenting their conclusions, data analysts frequently need to use graphics to effectively convey complex information. Learn how to use Python libraries like Matplotlib and Seaborn or data visualization tools like Tableau and Power BI. Discover how to produce eye-catching visual data visualizations.

2.3 Gaining Expertise in Programming Languages

For data analysts, learning how to program in a language like Python, SQL, or R is quite helpful. These languages are frequently used for jobs involving automation, analysis, and data processing. Take the time to become familiar with these languages’ unique syntax, data structures, and libraries.

3. Master In-Demand Data Analytical Tools

Data analytics tools include SAS, Excel, Splunk, Python, Tableau, PowerBI, etc. Learn how to use the frequently used tools in the data analytics sector. Learn how to handle data using Excel, do statistical analysis using SAS or SPSS, and run SQL database queries. To help you get practical expertise with these tools, a multitude of resources are available on websites and in tutorials.

4. Complete Industry Ready Data Analytics Certification Courses

Enroll in industry-recognized data analytics certification courses as an investment in your education. Courses on a variety of subjects are available on platforms such as Scaler, edX, and Udacity. These include sophisticated machine-learning techniques and basic data analysis. A certification can help you become more marketable and legitimize your talents.

Here are some professional data analytics-related certifications:

• Google Data Analytics Professional Certificate
• IBM Data Analyst Professional Certificate
• Microsoft Certified: Power BI Data Analyst Associate

5. Hands-On Practice Through Case Studies and Projects

Utilize your theoretical understanding by working on projects and case studies from the actual world. You can practice with datasets from platforms like Kaggle, which enables you to obtain real-world experience and lay a solid foundation. Present your finished work in your portfolio to prospective employers as proof of your expertise.

One of the best ways to put your abilities to use and obtain real-world experience is through personal projects. Determine which practical issues can be resolved with data analysis methods. Gather pertinent information, evaluate it, and draw insightful conclusions. Keep track of your procedures, and communicate your results well.

Here’s a collection of case studies by Infosys you could refer to understand better how to build your projects.

If you’re looking for guided project experience, mentorship, and a structured approach to learning data science through real-world case studies, Scaler’s Data Science Course offers hands-on project-based learning that can help you develop a strong portfolio and accelerate your career.

6. Build a Solid Data Analytics Portfolio

Creating a portfolio that speaks for you professionally is one method to show off your data analytic abilities. Work on personal projects first so you can demonstrate your skills. Examine datasets that are accessible to the general public or offer your assistance to nonprofit groups in need of data analysis. Keep track of your assignments and their results to build a portfolio that demonstrates your abilities. You can upload all your projects to Github to maintain a portfolio there which could be easily shared with recruiters and peers.

Here are some portfolios you could refer to:

• Naledi Hollbruegge
• Tim Hopper
• Ger Inberg

7. Join Analytics Communities and Work on Networking

Joining a group of professionals who share your values can be incredibly helpful and enlightening. Participate in social media groups, internet forums, and communities devoted to data analysis. Participate in conversations, ask knowledgeable people in the industry for advice, and gain knowledge from them.

In any field of employment, networking is essential, and data analysis is no different. Participate in data analysis meetups, attend industry conferences, and establish connections with industry experts. Through networking, one can find insightful people, mentorship programs, and even practical internships.

These are a few of the top data analytics communities you could join and make the most benefit from networking:

• ​​Locally Optimistic 
• Data Talks Club
• Data Science Salon 
• Data Science Network

8. Showcase Transferable Skills

Emphasize any transferable skills, such as problem-solving, critical thinking, attention to detail, and teamwork, from prior experiences that are pertinent to data analysis. Your talents can make you stand out from the competition in the field of data analysis, even if they weren’t directly relevant to your past employment.

Transferable skills such as problem-solving, critical thinking, attention to detail, and teamwork are invaluable assets for transitioning into data analysis, even for individuals without a technical background. For example, a marketer’s ability to analyze market trends, evaluate campaign performance, and collaborate with teams demonstrates proficiency in data-driven decision-making and communication, essential qualities for success in data analysis. By showcasing these transferable skills, candidates can effectively position themselves for opportunities in the field of data analysis, regardless of their previous experience.

9. Tailoring Your Resume and Cover Letter

It’s important to update your cover letter and resume to emphasize your relevant experiences and talents when looking for jobs as a data analyst. When possible, quantify your accomplishments, highlight your projects, and emphasize your technical talents. To improve your chances of getting an interview, personalize your application materials for each employment opportunity.

When creating a CV and cover letter specifically for data analyst positions, you should emphasize your technical talents, highlight relevant experiences and measurable accomplishments, and customize your application for each position. Include sections for skills (such as programming languages, data visualization tools), projects (with descriptions and outcomes), work experience (highlighting data-related responsibilities), education, certifications, and keywords specific to job descriptions in your data analytics resume to make it ATS-friendly. Make use of professionally created resume templates that have been enhanced for readability and eye appeal.

10. Preparing for Entry-Level Positions

Applying for internships, apprenticeships, or entry-level jobs might help you get exposure to the industry and obtain real-world experience. These changes may eventually lead to full-time work prospects and can offer beneficial on-the-job training.

In order to land a career as a data analyst as a recent graduate, concentrate on obtaining real-world experience through internships or entry-level jobs. Practice common data analytics interview questions and become familiar with pertinent tools and approaches to help you prepare for interviews. By completing projects, attending workshops, and taking online courses, you can improve your abilities. Make connections with industry experts and demonstrate your excitement and openness to learning. Update your résumé with pertinent projects, credentials, and courses completed. Furthermore, maintain up-to-date knowledge of industry developments and trends to exhibit your enthusiasm and dedication to the field.

11. Stay Current with Tech Trends

The data analytics area is always changing, with new technology, methods, and tools appearing regularly. Participate in online classes, webinars, and blog reading to stay up to date on industry developments. Maintaining your competitiveness in the employment market is ensured by updating your abilities consistently.

Take into consideration the following sites to stay up to date on data analytics technology trends:

• Online courses and platforms: A variety of data analytics courses taught by professionals in the field are available on websites such as Scaler. Seek out classes covering subjects like big data analytics, data visualization, and machine learning.
• Blogs and Publications: To stay up to date on the newest trends and advancements, subscribe to data analytics blogs and publications. Towards Data Science, KDnuggets, and Data Science Central are a few well-known blogs.
• Podcasts: Take in industry experts and thought leaders’ ideas by listening to data analytics podcasts. A few are The Curiosity of the Analyst with Dr. Debbie Berebichez, Data Science Hiring Processes, and The Past and Present of Data Science (with Sergey Fogelson).

Ready to stay ahead in the ever-evolving world of data analytics? Enroll in Scaler’s Data Science Course and gain access to the latest industry insights, expert mentorship, and hands-on projects to keep your skills sharp and your career on track.

12. Final Tip – Stay Persistent and Positive

Any new field can be difficult to break into, but you can overcome challenges and accomplish your goals if you have perseverance, determination, and a good outlook. Remain committed to your goals, be willing to learn from mistakes, and acknowledge and appreciate your accomplishments along the route.

Although beginning a career in data analysis without any prior expertise can be difficult, success can be achieved with a determined and well-thought-out strategy. You can position yourself as a competitive candidate in the data analytics field by comprehending the role, honing essential skills, learning tools, earning certifications, practicing in person, developing a strong portfolio, networking within the analytics community, showcasing transferable skills, customizing your application materials, looking for entry-level opportunities, keeping up with tech trends, and keeping a positive outlook.

What is Data Analytics?

Data analytics is the process of examining large datasets to uncover patterns, trends, and insights that can be used to make informed decisions. It involves collecting, cleaning, analyzing, and interpreting data to extract meaningful information. Data analysts use various tools and techniques to analyze data, including statistical analysis, data mining, and machine learning.

Roles and Responsibilities of Data Analyst

Data analysts play a crucial role in extracting valuable insights from data to drive informed decision-making. Their responsibilities encompass various aspects of data analysis and reporting.

Key responsibilities of a data analyst include:

• Gathering data from diverse sources and ensuring its accuracy, completeness, and consistency.
• Utilizing statistical techniques and data mining tools to identify patterns, trends, and correlations within data.
• Creating clear and informative visualizations (charts, graphs, dashboards) to communicate data insights effectively.
• Developing comprehensive reports and presentations to summarize findings and provide actionable recommendations.
• Working closely with business teams to understand their needs, gather requirements, and deliver relevant data insights.
• Applying analytical and critical thinking skills to solve complex data-related problems.
• Keeping abreast of the latest data analysis techniques, tools, and industry trends.

Entry Level Data Analyst Salary

Entry-level data analysts in India typically earn between ₹3 lakhs to ₹6 lakhs per annum. However, salaries can vary depending on several factors. It’s important to research specific job postings and salary data in your region to get a more accurate estimation.

5 Entry Level Data Analyst Jobs

Here are 5 popular entry-level data analyst jobs along with their average salaries in India:

1. Data Entry Clerk: Responsible for accurately entering data into computer systems. This role often involves typing, scanning, and verifying data to ensure its accuracy and completeness.
   Average Salary: ₹2.5 LPA – ₹4 LPA
2. Quality Assurance Analyst: Ensures the quality of data by identifying and correcting errors. This role involves reviewing data for accuracy, consistency, and completeness, and implementing measures to improve data quality.
   Average Salary: ₹3 LPA – ₹5 LPA
3. Data Quality Analyst: Analyzes data quality and implements measures to improve accuracy and consistency. This role requires a deeper understanding of data quality concepts and tools to identify and address data quality issues.
   Average Salary: ₹4 LPA – ₹6 LPA
4. Business Analyst Consultant: Provides data-driven insights and recommendations to businesses. This role involves working closely with business stakeholders to understand their needs, analyze data, and develop solutions to improve business performance.
   Average Salary: ₹5 LPA – ₹8 LPA
5. Data Analyst: Performs various data analysis tasks, including data cleaning, exploration, and visualization. This role requires a strong understanding of data analysis techniques and tools to extract meaningful insights from data.
   Average Salary: ₹4 LPA – ₹7 LPA

Leading Companies and Industries Actively Seeking Entry-Level Data Analysts

Many sectors are actively looking for entry-level data analysts to help them leverage data for strategic planning and well-informed decision-making in today’s data-driven economy. Leading businesses in various industries, from software behemoths to healthcare providers, are growing their data analytics teams. Let’s investigate the sectors and particular businesses leading this desire.

Technology Sector:

1. Google: With its vast data environment, Google provides prospective data analysts with entry-level possibilities to engage in creative projects and initiatives.
2. Amazon: One of the biggest internet firms in the world, Amazon is always looking to improve customer experiences and streamline operations by hiring data analysts.

Banking and Finance:

1. JPMorgan Chase: Known for its strong data analytics section in the finance industry, JPMorgan Chase offers entry-level analysts chances to contribute to risk management and financial insights.
2. Goldman Sachs: Another significant player in the financial industry, Goldman Sachs provides entry-level positions for data analysts to assist with financial decision-making and investment strategies.

Pharmaceuticals and Healthcare:

1. Johnson & Johnson: This healthcare innovation-focused company aggressively seeks out entry-level data analysts to use data to optimize healthcare services and improve patient outcomes.
2. Pfizer: A well-known pharmaceutical corporation, Pfizer uses data analytics to spearhead research and development initiatives. It provides analysts with entry-level roles to help with drug discovery and development procedures.

E-commerce and retail:

1. Walmart: The retail giant uses data analytics to refine marketing tactics, assess consumer behavior, and optimize supply chain efficiency.
2. eBay: Entry-level analysts can support a variety of business tasks on eBay, an e-commerce platform that depends on data analysis to optimize its marketplace.

Professional Services and Consulting:

1. Deloitte: Consulting organizations such as Deloitte provide entry-level jobs for data analysts to help clients in many industries use data-driven insights for innovation and commercial expansion.
2. Accenture: In a similar vein, Accenture gives entry-level data analysts the chance to work on a variety of projects while assisting clients in utilizing data for operational and strategic decision-making.

Communications:

1. Verizon: To inform business choices and improve service offerings, Verizon, a telecommunications company, hires entry-level data analysts to examine network performance, consumer usage trends, and market conditions.
2. AT&T: Another significant participant in the telecom space, AT&T uses data analytics to enhance customer experiences, manage network operations, and create new services. Aspiring analysts can apply for entry-level jobs at this company.

These sectors and businesses just serve as a sample of the wide range of options accessible to beginning data analysts who want to make a splash in the fast-paced field of data analytics.

Additional Resources for Aspiring Data Analysts

Recommended Books and Blogs:

1. “Data Science for Business” by Foster Provost and Tom Fawcett: Intended for aspiring data analysts who want to comprehend the real-world applications of their work, this book offers an extensive introduction to data science ideas and their use in business situations.
2. “Storytelling with Data” by Cole Nussbaumer Knaflic: For data analysts, effective communication is crucial, and this book provides helpful advice on how to visualize and present data engagingly and insightfully.
3. DataCamp Blog: A plethora of tutorials, case studies, and articles covering a range of subjects related to data science, data analysis, and programming languages like Python and R can be found on DataCamp’s blog. It’s a great tool for learning about current business trends and recommended procedures.
4. Among the best platforms for data scientists and analysts is Kaggle, which provides a wealth of tools for continuing education and industry knowledge. The distinctive features and community-driven philosophy of Kaggle are advantageous to both seasoned professionals and aspirants.
5. In addition to Kaggle, related websites like GitHub, and DataCamp provide opportunities for continuing education and knowledge retention in the data analysis industry. GitHub provides open-source project and library repositories where analysts can examine code and participate in group projects. Interactive lessons and courses covering a range of data analysis tools and approaches are provided by DataCamp and Dataquest.

Online Courses and Webinars:

1. Scaler: Scaler provides a thorough program in Data Science and Machine Learning that is intended to give prospective data analysts abilities that apply to the industry. This course offers a systematic approach to ongoing skill development, emphasizing practical learning through real-world projects and industry expert mentoring.
2. Webinars by Data Science Communities: Aspiring data analysts can gain important insights and networking opportunities from the educational webinars that platforms like Data Science Central and Towards Data Science frequently provide.

Conclusion

Becoming a data analyst without prior experience is achievable with dedication and the right approach. By acquiring essential skills, building a strong portfolio, and effectively networking, you can successfully transition into a data analyst career. Start by gaining foundational knowledge, exploring data analysis tools, and practicing data analysis techniques. Remember, continuous learning and a passion for data are key to succeeding in this rewarding field.

FAQs