Data is the new oil -Igbo proverb

Are you fascinated by the power of data to drive decision-making and solve complex problems? You're not alone. In our increasingly data-driven world, the role of a data analyst has never been more crucial.

From healthcare and finance to marketing and sports, data analysts are the unsung heroes behind the scenes, turning raw data into actionable insights.

If you want to pursue a career as a data analyst , the following steps will guide on what you need to take.

1. Programming Languages
2. Data Visualization tools
3. Machine Learning basics.
4. Data analyst projects.
5. Conclusion

In this article, we will take a look at each points in details, giving you everything you need to get started on your journey to Data Analyst.

1. Programming Languages
Data analysts will usually work with several programming languages, i.e. there is no wrong or right choice. Essentially, you will need to master SQL for querying and manipulating databases, but you will then need to choose between R and Python for your next programming language.

Learning R or Python, you will able to discover some of the libraries such as Pandas, NumPy that can help you with various tasks and grow your programming skills. Using some of the libraries, you will be learning how to import, clean, manipulate and visualize data with your preferred programming language.

2. Data Visualization tools
Consuming large sets of data isn't always straightforward. Sometimes, data sets are so large that it's downright impossible to discern anything useful from them. That's where data visualization comes in.

Data visualization tools provide data visualization designers with an easier way to create visual representation of large data sets.

The data visualizations can be used for variety of purposes: dashboards, annual reports, sales and marketing materials, investor slide decks and anywhere else information need to be interpreted immediately.

The most common visualization tools used is Excel, Power BI, Tableau.

Other visualization tools include Infogram, chartblocks, data wrapper, D3.js, Google charts and fusion charts.

3. Machine Learning Basics
This involves mastering fundamentals of statistics which covers the topics such as probability distributions, hypothesis testing, measure of central tendency.
Key components include feature engineering, data visualization and encoding

4.Data Analyst Projects
Once you have mastered some of the basic essential skills, you will need to start developing them on your own by working on individual projects.

However, you will need to undertake individual projects where everything will be your responsibility: selecting the topic, fetching the necessary data, contemplating the direction of your research, designing the project structure, making and checking the hypothesis, effectively communicating your findings and laying out the way forward.

Practicing your skills and solving real-world problems will give you a solid basis for your future work experience. As a result, projects usually take much more time but they will help you stand out.

5. Conclusion
By now, you should know how to become a data analyst and what you need to do to make your career goal a reality.

However, to get in front of potential employers, you will need to have a portfolio of your work. Use your portfolio to make your passion and interests shine through.

Ideally, demonstrate both your technical and soft skills in your portfolio and design it in your resume or cv. The more you develop your portfolio or resume, the more you can remove the broad-scope, common projects.

Will all this information in hand, it's time for you to go ahead and start learning today.

Happy learning, happy coding!