A Realistic Data Analyst Roadmap that I followed!

Introduction

Ever felt overwhelmed by complicated or unrealistic roadmaps for data analytics?

Look no further! I'm here to share a realistic roadmap that I followed, aiming to help you avoid the pitfalls I encountered and guide you toward success.

But first, let's establish a crucial mindset:

The journey to becoming a data analyst isn't a sprint; it's a marathon. If you lack patience, this field may not be for you.

However, it is not a 3 or 6-month journey, it's a never-ending story. But, with dedication, in around a year or a year and a half, you can become self-sufficient in making your projects and ready to land a job in data analytics.

But first, let's break this down. Imagine we're sitting down for a chat over coffee. You have questions, and I have answers. Let's dive in together.

Note: Need personalized guidance on your data analytics journey? Reach out to me directly! I'm here to help you navigate the complications of data analytics and designate you to achieve your goals. I have a personal course on the entire data analytics mentioned in the steps which I can share with you through Zoom or Google Meetings. Let's chat and learn together!

1. Understanding Your Field:

Explanation: Before diving into the technical aspects, let's understand the lay of the land. What is data science, and what are the different roles within the field?

How to Achieve: Start by researching and understanding the various domains within data science. Understand the overlapping figure of data science which includes statistics, Coding, and Domain knowledge. Search for different roles like Data Scientists, Data Engineers, Machine Learning Engineers, and especially Data Analysts. Explore job descriptions to identify the skills and knowledge required for each role.

Sources:

What Is Data Science 6 hours introductions by FreeCodeCamp

What is Data Science by IBM (Part of Data Science Specialization)

2. Embracing Data Analytics:

Explanation: Data analytics forms the bedrock of your journey. It involves acquiring, cleaning, analyzing, and interpreting data to extract actionable insights.

How to Achieve: Begin by familiarizing yourself with basic data analysis techniques, such as descriptive statistics, data cleaning, and visualization. Practice analyzing sample datasets and interpreting your findings. In short, know about data analytics.

Sources: Data, Data, Everywhere by Google (Part of one of the best data analytics Certificate)

3. Mastering Excel:

Explanation: Excel is a versatile tool widely used for data manipulation, analysis, and visualization. Mastering Excel is essential for effective data handling and reporting.

How to Achieve: Start by learning essential Excel functions and formulas for data manipulation, such as VLOOKUP, SUMIFS, and PivotTables. Practice creating charts and graphs to visualize data trends.

Sources: Excel Basic for data analysis by IBM

Excel Playlist by Alex the Analyst (One of my favorite Youtubers Alex Freberg ):

4. Learning Python (or R):

Explanation: Python is a powerful programming language commonly used in data analysis for its extensive libraries and versatility. Mastering Python allows you to perform complex data manipulation, analysis, and visualization tasks. Whereas, R is more easy to read and learn, and better for Data Analysis language (Recommended by Google). R has some limitations if we look outside this specific field, that's why I personally recommend Python which is more powerful than R and has better Library support.

How to Achieve: Start by learning the basics of Python programming, including syntax, data structures, and control flow. Then, dive into data analysis libraries like Pandas, NumPy, Matplotlib, and Seaborn for data manipulation and visualization.

Sources: Python for Data Science, AI, development by IBM

Learn Python - Full Course for Beginners by FreeCodeCamp

Data Analysis with R

5. Mastering SQL and Databases:

Explanation: SQL (Structured Query Language) is essential for querying and managing data stored in relational databases. Understanding SQL enables you to extract, manipulate, and analyze data efficiently.

How to Achieve: Start by learning About Databases, and relational databases, SQL fundamentals, including querying databases, filtering data, and performing joins. Practice writing SQL queries using sample databases to gain hands-on experience.

Sources: SQL Tutorial on FreeCodeCamp

Basic, Intermediate, and Advanced SQL Tutorials by Alex the Analyst

Databases and SQL for Data Analysis by IBM

6. Data Visualization with Power BI (or Tableau or Python):

Explanation: Data visualization tools like Power BI and Tableau allow you to create interactive dashboards and reports to communicate insights effectively. Mastering data visualization enhances your ability to convey complex information visually.

How to Achieve: Start by learning the basics of data visualization principles and best practices. Then, explore the features and functionalities of Power BI or Tableau through tutorials and hands-on projects. Choosing between Power BI and Tableau, I would recommend Power BI because of its user-friendly environment and better features to customize your dashboards. However, Tableau has a better community than Power BI. You should also use Python for basic visualization while exploring your data which makes Python my personal favorite tool for entire Data Analytics.

Sources: Power BI tutorial by Alex the Analyst

Data Visualization with Python by IBM

7. Real-world Projects:

Practice makes men perfect

Explanation: Applying your skills to real-world projects is essential for honing your expertise and building a portfolio. Undertaking personal projects allows you to demonstrate your abilities and problem-solving skills to potential employers.

How to Achieve: Start by identifying areas of interest or industry domains where you'd like to apply your skills. Then, brainstorm project ideas that align with your interests and showcase your strengths. Finally, execute your projects, documenting your process and outcomes along the way.

Note: You can always have a coffee chat with me if you ever feel difficulties on your projects.

Sources: Data Analytics Capstone by Google

Except for that source, I'm leaving the findings of your projects to you.

Again, if you wanna learn everything in a single place from scratch. Let's have a chat. I am always at your send click!

Thank you!

Email: [email protected]

Linkedin: https://www.dhirubhai.net/in/syed-izhan-ali-5b1257286/

phone: +923241839800