How To Become a Data Analyst in 2021?
To become a good data analyst, you must have vital data analysis skills and the capability to assemble comprehensions from large data sets. Data analytics is a field that is growing with great opportunities because companies across all industries have made huge commitments towards big data and building their analytics sectors, especially in insurance, financial services, telecommunications, advertising, technology, and healthcare. In addition, the data analysis department is expected to grow in the future in many industries like government, education, manufacturing, etc.
What does a data analyst do?
A data analyst performs their duties in three different areas.
- Data Mining: The analyst drags the information from any primary and secondary data resources
- Data Cleaning: They have to clean data from any errors or inconsistencies and proceeds further to elaborate any patterns, trends, and relationship among the gathered data.
- Data Visualization: The data analyst organizes the data into one easily understandable format to achieve business goals and strategies.
So, you can become a great data analyst in five steps
- Learn data analytics fundamentals
- Work on related projects to develop your skills
- Develop visualizations and practice of presenting them
- Develop a data analyst portfolio to showcase your work
- Apply for related data analyst jobs
Learn data analytics fundamentals
The first step while becoming a data analyst is to learn data analysis fundamentals and tools required for them, such as advanced Microsoft Excel, Programming languages such as Python, R, and SQL. Power BI or Tableau for creating unique visualizations that bring data to the data analyst’s life. Data analysis courses will provide a great way to learn all fundamental skills that will give you a firm grip on all desired platforms and the capability to get hands-on experience with statistical programming, data management, programming languages, data cleaning, data visualization, and many more. So, the following are concise explanations of the skills required to become a data analyst.
R and Python are popular and on-demand programming languages to become a data analyst. So, you must have proficiency in one language and have working experience of some others. These programming languages are used for data cleaning, data gathering, data visualization, and statistical analysis.
Analytical and Creative Thinking:
Creativity and curiosity are two significant attributes of an exemplary data analyst. It will prove very substantial if you have skills in statistical methods but even more critical if you have the ability to think through an analytical and creative lens. This approach helps data analysts to raise interesting questions that can enhance the company’s understanding of the matter at hand.
Practical and Strong Communication Skills:
Data analysts must have the capability to convey their findings whether he has to explain to an audience of readers or to a small team of executives to make business decisions. Thus, vital communication skill is key to success.
Valuable data visualization required trials and errors. So, a successful data analyst must understand what types of graphs are used for particular Visualization and how to measure Visualization.
Some data analysts have experience of backend working. For example, they connect databases from various sources to establish a data warehouse and then use query languages to discover and administer data.
These are relational databases with structured data. In this approach, data is stored in tables, and data analysts fetch data from different tables to perform analysis.
The most common querying languages used by data analysts are SQL, PostgreSQL, T-SQL, PL/SQL.
Data Mining, Cleaning, and Munging:
When the data is not stored in a clean and easily readable format, data analysts use other tools to gather unstructured data. When they collect required data, they clean, and process gathered data through various programming languages.
Advance Microsoft Excel:
Data analysts must have good hands-on experience in excel, like understanding advanced models and analytics techniques.
Data analysts with proficient machine learning skills are highly valuable, although this is not an expected skill for typical data analyst jobs.
Work on relevant projects to develop data analytics skills
If you desire to become a great data analyst, get strong commands on essential data analysis tools. When this has been done, then you can start to put your knowledge into work. Successful data analyst keeps their skills up to date by learning the most incredible and latest tools correlated with data analysis that includes,
- Spreadsheets and query languages that depend on data analysts like SQL and XML.
- Programming languages and frameworks most commonly used by data analysts such as Hadoop, R, and Python.
- Visualization tools are famous for data analysts like Tableau, Plotly, Matplotlib, PowerBI, and Boken.
It is also suggested to gain hands-on experience on leading data analysis platforms such as Google Analytics and Adobe Analytics. Put all practice projects you have done at different stages of data analysis, such as market opportunities, researching companies, establishing, and maintaining parameters of the data required for collecting, gathering, and cleaning data. After that, models and analyze them using custom or building algorithms. Finally, turn your insights into beautiful Visualization. Moreover, you can also try to organize them into a dashboard that permits others to inquire and interrelate with your data sets in a user-friendly manner (Lester et al., 2020).
Try to take practice and experience on these projects and showcase your ability to work on different data types such as structured data, text, images, audio, and videos to perform statistical analysis, causalities identification, and prediction making.
Develop Visualization and practice to present them
To make data, analysts take start from programs such as PowerBI, Boken, Tableau, Infrograms, or Plotly. Practice building your visualizations from the start; find an appropriate way to turn in a format that can speak for itself. During this step, Excel comes into the ground for making calculations and graphs by relating information in their cells. Visualization creation is just the beginning of the journey. Data analysts must have the skills to use these visualizations to present their results and findings. These skills can come naturally but can be improved by having practice. So, start from minor such as the present to the friend before moving towards colleagues.
Develop a data analyst portfolio to showcase your skills
One of the most exciting and essential steps while planning to become a data analyst is to decide how you will demonstrate your knowledge and skills. A professional portfolio must start by putting code you have written on GitHub to show that it fulfills all requirements of an experienced data analyst. Becoming a member of online data science community such as Kaggle is another effective approach to show that you are engaged with the data analyst community and willing to continue to grow your expertise and outreach.
Moreover, a well-executed project that has been done on your own can be proved a great way to elaborate your data analysis skills and to impress a hiring manager. So, for this pick, something that fascinates you, ask questions about that and try to answer all questions with data. Then, document your whole journey and elaborate your findings with beautiful Visualization and explanation of your process, technical skills, and creativity (Lester et al., 2020).
Apply on related data analyst jobs
There are a vast variety of data analysts’ jobs that you can get through data analysis skills. All of the following jobs rely heavily on data analytics skills and have entry level and senior level roles.
- Data and Analytics Manager
- Database Administrator
- Digital Marketing Manager
- Business Analyst
- System Analyst
- Operations Analyst
- Quantitative Analyst
- Data Scientist
There are many other variations available such as new applications for data analytics are developed all the time, and even the above listed jobs are continuously evolving as data analysis becomes more frequent. Such highly dynamic fields demand 50% to 60% jobs for data analysts. Finally, if you have data analysis skills, you are in a great position in the Data Analyst career path (IOVAN et al., 2015).
Data Analyst Job Description
A data analyst description requires the following attributes, experience, and education.
- Leadership skills and cooperative nature: To become successful in the data analysis domain, the focus is crucial. Each organization must understand what they desired to accomplish and how data analysts can fulfill those demands. So, the data analyst has to play the role of IT, data scientist, and leadership to achieve the targeted results they are desired for data.
- Programming language expertise: Every job description requires proficiency in basic programming languages, database languages, analytics tools such as Python, R, SQL, and Microsoft Excel.
- Analytics experience: A data analyst interact with statistical tools for data interpretation they have gathered to analyze. They worked on that data to extract relevant patterns or trends to deliver any predictive analysis. They must have the ability to recognize the relationship between any dataset and what each relationship wanted to explain.
- Data Maintenance: A data analytics also fixes any coding errors, tracks trends or patterns, deletes inappropriate data to free up space to create any required reports to update the team (Kozyrkoy et al., 2019).
FAQs related to Data Analyst Career
Below are some common and important questions that come into every individual who wanted to pursue his career as a data analyst.
How much time is required to become a data analyst?
After acquiring the proper education, anyone can become a data analyst after graduation. Suppose you don’t have a major in the related field, then no need to worry. Just go through online courses to develop an excellent grip on SQL, programming language, and Microsoft Excel. This will take less than one month with complete dedication to learn various lessons and just explore the interfaces. If you have a committed personality, then your career as a data analyst will be right around the corner.
What subjects are demanded to become a data analyst?
You have to receive a bachelor’s degree in related subjects such as computer science, statistical analysis, or mathematics. The subjects required to include are statistics, linear algebra, probability, business information system, information management, economics, or finance.
Does a data analyst required to be good in mathematics?
The answer is Yes. Understanding and thinking mathematics are important to effectively manipulate data and communicate different trends, relationships, and patterns. If you are not good at math, then keep in mind math is a learned skill. Start taking different courses to develop your mathematical thinking.
Does the data analyst code?
Coding is not a required skill for data analysts. However, surface level understanding of programming is helpful in processing. Therefore, some organizations include coding in data analyst responsibilities.
Is data scientist is growing field?
According to a recent report, United States faces a shortage of Data Analyst skills and managers and analysts who can understand how to use data analysis in decision making. However, in a report, IBM predicted that the number of jobs for US professionals would grow. And IBM has added analytically savvy workers who will fill the gap who wanted to change careers to become data analysts.
Here we elaborate a roadmap to introduce those individuals who are new in the data analysis field. We have discussed how they can start their career as a data analyst. It is suggested never to stop at basics. An impressive and well-executed data project will be proved a great way to demonstrate your data analysis abilities. It can impress the hiring manager while applying for any data analyst job. So, select a topic that fascinates you, find questions about that topic, and try to find those answers to those questions with data. Document your journey and present your find in beautiful Visualization with a clear explanation of your adopted procedure and highlight your technical data skills and creativity.
Moreover, join an online data scientist community such as Kaggle. This will prove a great way to show that you are engaged with such a fantastic community to show off you are an aspiring data analyst and wanted to grow your expertise and outreach.
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Stoudt, S., Vásquez, V. N., & Martinez, C. C. (2021). Principles for data analysis workflows. PLOS Computational Biology, 17(3), e1008770.
IOVAN, S., & IOVAN, A. A. (2015). THE ROLE AND IMPORTANCE OF DATA ANALYST IN USING LARGE VOLUMES OF DATA. Annals of’Constantin Brancusi’University of Targu-Jiu. Engineering Series, (3).
Maguire, M., & Delahunt, B. (2017). Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars. All Ireland Journal of Higher Education, 9(3).