What Does a Junior Data Analyst Do?<\/h2>\n\n\n\n
Junior data analysts have the following responsibilities:<\/p>\n\n\n\n
- \n
- Collecting data. <\/strong>The data analysis process requires finding and identifying authoritative sources of data.<\/li>\n\n\n\n
- Cleaning data<\/strong><\/a>. <\/strong>These datasets will have some inconsistencies and gaps. The data needs to be combed for these anomalies so that the data is standardized and complete.<\/li>\n\n\n\n
- Writing reports. <\/strong>After the data analysis is complete, junior data analysts help collate the findings and draft recommendations.<\/li>\n<\/ul>\n\n\n\n
Related Read: What Does a Data Analyst Do?<\/a><\/em><\/p>\n\n\n\n
Becoming a Junior Data Analyst: A Quick Guide<\/h2>\n\n\n\n
Let\u2019s look at the six steps to becoming a junior data analyst:<\/p>\n\n\n\n
Invest in Learning and Education<\/h3>\n\n\n\n
Here are the three routes you can take to get the required education needed to become a data analyst. <\/p>\n\n\n\n
University Degree<\/h4>\n\n\n\n
Though a college degree is unnecessary, it can help with the job search process. The obvious degree choices are data science<\/a>, statistics, mathematics, and computer science. Other options like psychology and economics can also help get your foot in the door.<\/p>\n\n\n\n
Bootcamp<\/h4>\n\n\n\n
A data analytics bootcamp<\/a> takes much less time than a college degree and offers hands-on experience. With a bootcamp, you\u2019ll be job-ready in under a year.<\/p>\n\n\n\n
Self-Taught Route<\/h4>\n\n\n\n
It takes discipline, but you can also teach yourself data analytics. These resources can help get you started:<\/p>\n\n\n\n
- \n
- Website\/Blogs: Analytics Vidhya<\/a><\/li>\n\n\n\n
- Newsletter: Data Science Weekly<\/a><\/li>\n\n\n\n
- Podcasts: Ken\u2019s Nearest Neighbors<\/a>, Analytics Power Hour<\/a>, The Sequel Show<\/a><\/li>\n\n\n\n
- Books: An Introduction to Statistical Learning<\/a>, Python Data Science Handbook<\/a>, R for Data Science<\/a><\/li>\n\n\n\n
- YouTube Channels: Keith Galli<\/a> for project tutorials and David Robinson\u2019s TidyTuesday<\/a> for live analysis of datasets<\/li>\n<\/ul>\n\n\n\n
Hone Your Skillset<\/h3>\n\n\n\n
To enrich your learning journey, sharpen these data analysis skills<\/a>:<\/p>\n\n\n\n
Technical Skills<\/h4>\n\n\n\n
Knowing your way around key data analytics tools<\/a> and processes will make data analysis much more manageable. Your technical skills should include:<\/p>\n\n\n\n
Microsoft Excel<\/h5>\n\n\n\n
Most data analytics companies<\/a> you work for will have Excel in their tech stack. Learn how to enable data models and relations and power query, create pivot tables, and nest conditions and formulas.<\/p>\n\n\n\n
Programming Languages<\/h5>\n\n\n\n
Structured Query Language<\/a> is the main programming language you\u2019ll use. You can polish your SQL skills with some tutorials<\/a>. Taking Python courses<\/a> will also help you learn how to analyze datasets and transition to complex data tasks.<\/p>\n\n\n\n
Data Visualization<\/h5>\n\n\n\n
Data visualizations<\/a>, usually with tools like Tableau and Power BI, are handy for presenting your findings to non-technical stakeholders.<\/p>\n\n\n\n
Statistical Modeling<\/h5>\n\n\n\n
Statistical modeling isn\u2019t just a matter of knowing how to do it. It\u2019s also a matter of picking the right tool for the data you\u2019re analyzing.<\/p>\n\n\n\n
Soft Skills<\/h4>\n\n\n\n
You\u2019ll need these soft skills to land a junior data analyst position.<\/p>\n\n\n\n
Research<\/h5>\n\n\n\n
You will be digging for hours to find the right source and will need enough material to frame questions that contextualize the data. Experiment with different research methods to leverage search engines, subject matter experts, and scholarly sources.<\/p>\n\n\n\n
Attention to Detail<\/h5>\n\n\n\n
When data analysts detect patterns, it\u2019s often based on small clues coming together across large datasets. This requires keen attention to detail as you sort through the data.<\/p>\n\n\n\n
Problem-Solving<\/h5>\n\n\n\n
Some snags you need problem-solving skills for include identifying anomalies across different datasets, facing accessibility issues, and encountering bugs during coding<\/a>.<\/p>\n\n\n\n
Collaboration<\/h5>\n\n\n\n
You will work with other data analysts, data scientists, and sometimes data engineers. Understanding how to work with people who have skillsets that are different than your own is key to succeeding.<\/p>\n\n\n\n
Gain Practical Experience and Build a Strong Portfolio<\/h3>\n\n\n\n
Here are some ways to gain practical experience that you can show off in a portfolio:<\/p>\n\n\n\n
Work on Open-Source Projects<\/h4>\n\n\n\n
Depending on the industry you want to work in, you will find open-source projects to contribute to on company websites. If not, you can try websites like Kaggle<\/a>, which has data sets you can use to create your own projects.<\/p>\n\n\n\n
Consider Volunteer or Freelance Work<\/h4>\n\n\n\n
Assist a small business to test your skills. Every business requires data analysis in some form, so why not help while gaining practical experience? You can also leverage freelancing platforms for small jobs. With both options, you get projects you can add to your analyst portfolio<\/a>.<\/p>\n\n\n\n
Create a GitHub Profile<\/h4>\n\n\n\n
When you have little to no previous experience, your portfolio will be exponentially more important than your resume. Create a GitHub profile<\/a> and add 2-3 projects demonstrating your skills. Look up project best practices such as scope, coding style, documentation, and version control.<\/p>\n\n\n\n
Find a Mentor<\/h4>\n\n\n\n
Mentors can help in many ways during the early stages of your career. It doesn\u2019t have to be a formal mentorship. Just ask them out for coffee!<\/p>\n\n\n\n
\n![\"YouTube](\"https:\/\/img.youtube.com\/vi\/0Xp3bnMt-TQ\/sddefault.jpg\")
- Newsletter: Data Science Weekly<\/a><\/li>\n\n\n\n
- Cleaning data<\/strong><\/a>. <\/strong>These datasets will have some inconsistencies and gaps. The data needs to be combed for these anomalies so that the data is standardized and complete.<\/li>\n\n\n\n