Why you should learn data analysis. Data analysis is observing, cleaning, analyzing, and interpreting raw data for a productive decision making process in solving a problem. It basically understands patterns, trends, for a business improvement. Data analysis is used in different fields or industry including science, business, engineering, and art. Most people generalize data analysis only to survey, and census data, but scientific and numerical data is what we are discussing about here. To become a data analyst, you’ll need a combination of technical skills, analytical thinking, and domain knowledge. A data analyst role is quite broad but with the right tools, one can achieve success in the career path.
Let’s break it down a little.
Here are mistakes businesses make and how to improve on it.
First Example;
Whenever you walk into a supermarket to get a product and you noticed it’s always expired before it’s finished at its stand. That’s a huge loss to the brand. So, what should they do?
This brand will need data to help them have a sustainable business growth. Let’s take a food product as a sample,
The brand gets a list of the product sold each day in a year, number of customers buying it in a week at that supermarket, peak hours of the store, its purchase rates during certain season, if the product is in demand at that region, etc. Now with this data, this brand can figure out their marketing step for that supermarket they host their product. They know when to sell, what to sell, and how to sell.
This is “data analysis”. It is finding raw information for decision making to help solve a problem.
Types of Data Analysis Process
Table of Contents
Descriptive
Diagnostic
Predictive
Prescriptive
Skills Required for Data Analysis Career:
The number one skill you need in your data analysis career is critical thinking, and the ability to learn. You need to be able to approach a problem as a critic, as a skeptic, and be able to break it apart, find flaws in it, and how to solve it in a logical manner.
Now, you can focus on the software and hardware tools that can help with this analysis.
- Proficiency in programming languages like Python, R, SQL, or others used for data manipulation and analysis.
- Machine learning
- Knowledge of statistical methods and techniques for data analysis.
- Spreadsheets/Excel
- Familiarity with data visualization software tools like Tableau, Power BI, or Matplotlib.
- Understanding of databases and data querying.
- Effective communication skills to present findings and insights to colleague.
How to Become a Data Analyst:
Start by learning the basics of programming languages commonly used in data analysis, such as Python or R.
Take online courses or attend workshops on statistics, data analysis, and visualization from site like Coursera, Dataquest, etc.
Practice by working on real-world datasets and projects to build your portfolio.
Earn relevant certifications to validate your skills and knowledge.
Pursue internships or entry-level positions to gain practical experience.
Continuously learn and stay updated with the latest tools and techniques in the field.
Certifications for Data Analysis:
There are different certifications one can acquire, such as:
Microsoft Certified: Data Analyst Associate
IBM Data Science Professional Certificate
Google Data Analytics Professional Certificate
SAS Certified Data Scientist
How to Apply for Data Analysis Jobs
- Tailor your resume to highlight relevant skills, projects, and experiences.
- Prepare for technical interviews by practicing coding exercises and discussing data analysis methodologies.
- Network with professionals in the field through online platforms like LinkedIn or attending industry events.
- Apply for entry-level positions, internships, or freelance opportunities to gain experience.
Bonus
Tips for Productivity and Results in a Data Analyst Career:
Keep honing your technical skills and stay updated with industry trends.
Develop domain expertise in specific industries or areas of interest.
Build a strong professional network and seek mentorship from experienced data analysts.
Continuously improve your communication skills to effectively convey insights to non-technical stakeholders.
Be proactive and curious, always seeking to learn and explore new techniques and technologies in data analysis.
What niche is your business and let’s site an example on how data analysis works for it. Let me know in the comment section.
