Data Analyst Beginner Guide for 2023
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The document outlines the path to becoming a data analyst, highlighting the evolution of data analysis and its significance in various industries. It describes the essential skills required, including both soft skills like communication and curiosity, and technical skills such as SQL and proficiency in data visualization tools. Additionally, it contrasts data analytics with data science and suggests learning paths available for aspiring data analysts.
Data Analyst Beginner Guide for 2023
- 1. Data Analyst Learn Guide Data analysis has evolved with the advancement of computers and an ever-snowballing move toward a technological revolution. Want to learn more about this high-demand career and find out what all skills you must have to become a data analyst, then you have landed on the right page. We have developed a synopsis of a more detail-oriented learn path in this domain. You can find the link to it towards the end of this article. What is Data Analysis? Data Analysis is a robust process of identifying and collecting data that you want to analyze, cleaning and sorting the data in groundwork for analysis, analyzing and interpreting the results, and presenting it in a meaningful visual format with the help of charts and graphs. A Data Analyst is an individual who has the expertise and skills to process raw data extracted from large datasets into meaningful information and insight to make more informed business decisions. Data is Everywhere Businesses in all industries are more and more dependent on data to make crucial business decisions like what products to produce, which markets to enter, and which customer segment to target. Data is also being used to diagnose weak areas in a business that need to be addressed. They can work in several industries, including agriculture, banking, entertainment, education, and so on. Data Science Versus Data Analytics Data Science is an umbrella domain that encircles Data Analytics. Data Science is an amalgamation of multiple skill sets like Machine Learning, Mathematics, Artificial Intelligence, and Statistics. It encompasses concepts like data mining, predictive modeling, and ML algorithms to analyze multiform datasets and transform them into scientific business strategies. Whereas Data Analytics deals with Mathematical and Statistical Analysis. In simple words, Data Analytics is a subdivision of Data Science that is mainly concerned with deriving answers to specific questions that Data Science brings forth. Both the roles entail multiple responsibilities of collecting, cleaning, and analyzing data. Hence, their job roles often overlap.
- 2. Skills Required • Soft Skills A strong hold of the following soft skills for data analysts can help you all through your career: o Communication: A data analyst’s job requires you to have strong written and verbal communication skills to get your ideas across to your colleagues without leaving room for misunderstanding. o Curiosity and Exploratory Nature: Knowing all about the data you are working on beforehand will give you an upper hand in your job. Take some time and study all about the data you will be working on to get familiar with it and be updated on the latest trends. o Critical Thinking: It is essential to have a strong foundation in statistical methods and view problems with a critical and creative lens. It will also help in generating interesting research questions which will further enlighten the understanding of the data at hand. o Presentation: Presenting your conclusions in an easy-to-understand and compelling way will help a non-technical team to form meaningful insights and aid in well-rounded decision making. o Math and Statistics Understanding: Having a basic grasp over statistics and math will be advantageous for you to help you regulate which tools are best suitable to solve a particular problem and have a better judgment of the results.
- 3. • Technical Skills Also called hard skills that include explicit technical knowledge. The following are some hard skills that a company looks for in a data analyst to possess: • Structured Query Language (SQL): It is known for handling larger data sets and encompasses extracting, transforming, and loading the desired data. • Advanced MS Excel: Proficiency in MS Excel is a must. A data analyst should have a strong hand in Excel and understand and work on advanced techniques and analytics modeling. • Power BI or Tableau: It is used to cumulate and analyze data and present it in a more clear and concise way. It also helps in having a bird’s eye view of what's going on in the business. • R-Statistical Programming: It is used while performing predictive analytics on large and complex datasets.
- 4. Learning Paths If you are pondering how to become a Data Analyst, there are various online and offline options being offered. It is up to your requirements, preferences, and feasibility to sort down the right one like an on- campus learning option will be more suited for an integrated learning experience. However, in terms of flexibility, online learning is favorable. Conclusion A data analyst’s job is a gratifying passage to walk on, regardless of the industry you work in. If you want to know more about how to build a successful career in this field and explore various courses available to acquire desired skills and knowledge, then explore our comprehensive learn guide on ‘How to Become a Data Analyst’ and get started with your career.