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Data Analyst Full Roadmap

"Companies are collecting large amounts of data and analyzing it to make strategic decisions in various processes within their businesses, which is why there is an increasing demand for data analysts. Additionally, data analysis technology and tools are constantly evolving, which makes data analysts' work more efficient."

Role of a Data Analyst

"A data analyst collects, cleans, and interprets data sets in order to answer a question or solve a problem."

Six steps of data analysis:

  • Business Question: Define what problem you want to solve.
  • Get Data: Collect the data required for analysis.
  • Explore Data: Explore data with visual exploration to understand what is in a dataset.
  • Prepare Data: Data cleaning, calculated fields and data validation.
  • Analyze Data: Use data analysis techniques to understand, interpret, and derive conclusions based on the requirements.
  • Present Findings: Share insights with stakeholders.

Roadmap

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1. Statistics

"Statistical foundations are crucial for a data analyst because they form the basis of the data analysis process. Through statistical methods, a data analyst can use a scientific approach to understand, interpret, and report on data results."

Youtube Channels & Videos

Cheat Sheets & Books

2. Microsoft Excel

"Excel is a commonly used tool for data analysis and is important for data analysts because it helps to organize, analyze, visualize, and manipulate data. Additionally, it is user-friendly and accessible to most data analysts."

Youtube Channels & Videos

Cheat Sheets & Books

3. SQL

"SQL is a programming language used by database management systems for storing, querying, organizing, and managing data. Knowing SQL is important for a data analyst because it is a tool used for querying, filtering, joining, and analyzing data. SQL is used by data analysts for managing and analyzing data, and it helps make analysis processes more efficient and effective."

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Books

Blogs

Youtube Channels & Videos

Courses

Tutorials

Practice & Online Databases

Cheat Sheets

4. BI (Business Intelligence) Tools - Power BI & Tableau

"BI (Business Intelligence) tools are important for data analysts because they are used to analyze, visualize and make sense of data. These tools help data analysts to speed up their workflow and better understand data. BI tools offer different visualization techniques and graphs to analyze data and visualize it for management decisions. This enables data analysts to better understand data, interpret results more effectively and create a better foundation for business decisions."

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Microsoft Power BI

Tableau

5. Programming - Python

"Python is important for data analysts because it is considered a programming language that can be used for many data analysis processes. Python can be used for many data analysis processes, such as analyzing large datasets, data manipulation, data visualization, machine learning modeling, data mining processes, and data cleaning processes. Additionally, Python's open-source nature, free availability, and ease of learning are advantages for a data analyst. Therefore, knowing Python as a data analyst creates a versatile tool for data analysis processes and can help make data analysis processes faster, more efficient, and more accurate."

Web Sites

Courses

Youtube Channels & Videos

6. Soft Skills for Data Analyst

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"What are the soft skills that a Data Analyst should have?"

  • Analytical Thinking: Analytical thinking skills are required to understand, manage and interpret data. A data analyst must use logical and critical thinking skills to analyze data and make decisions based on the results.

  • Communication Skills: Data analysts must have effective communication skills to explain complex data by translating technical terms into easily understandable language and to interact with other team members. Sharing the results of data analysis and discussing action plans with other team members is important for a successful data analyst.

  • Problem Solving: A data analyst must identify problems by analyzing data and develop effective strategies to solve them. Therefore, problem-solving skills require a flexible approach that can adapt to the complexity and variability of data.

  • Teamwork: Data analysts must be effective in teamwork and be able to collaborate with team members to exchange ideas during the data analysis process. A data analyst may also interact with other teams (such as marketing, engineering, etc.), so teamwork skills are of great importance.

  • Business Understanding: A data analyst must have an understanding of business. Understanding business needs and goals can help in asking the right questions and obtaining the right results in data analysis.

  • Storytelling: A data analyst must have effective storytelling skills to communicate data. By presenting data in an understandable way, a data analyst can help in the effective use of data.

7. Resume & Interview Preparation

Resume

  • There is nothing called PERFECT resume, so keep learning and updating!
  • Prepare one page resume and use professional template.
  • Based on above learnings and projects update your resume.
  • Also, if you have done any courses/certificates do add them as well.
  • Tailor your resume based on the role/company you’re applying.

Free resume template websites:

Interview Preparation:

  • Once you have completed all the above steps, just start applying for related jobs. Giving interview is also a part of your learning.
  • Be thorough with your resume, even with minute details.
  • Again, watch podcasts and interview experience shared on YouTube.
  • Read interview questions available on sites like: LinkedIn, Indeed, Glassdoor.

Congratulations!

Ta ta ta ta congratulations!, you are now a Data Analyst and you can apply for your dream job and company. 🥳🎉

If you've come this far and reviewed or completed all the steps, you can help me by giving this repository a star ⭐.

Contact Me

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Contribution Guideline

You can open an issue and give your suggestions as to how I can improve this roadmap, or what I can do to improve the learning experience.

You can also fork this repo and send a pull request to fix any mistakes that you have found.