Data AnalysisHow Do I Start A Career In

How to pursue a high paying career as a Data Analyst: complete guide

Data Analysis? What is it? What’s the scope in the field? And is it really worth dedicating a career to it?

In this article, we’ll be answering all these questions, and more!

A data analyst gathers, processes, and runs statistical analyses on large datasets. They learn how to use data to answer questions and solve problems. Data analysis has evolved as a result of the advancement of computers and the ever-increasing trend toward technological intertwinement. 

The development of the relational database breathed new life into data analysts, allowing them to retrieve data from databases using SQL. This is not enough to say about data analysts, so let us delve deeper to learn more about this in-demand profession in the twenty-first century.

Data Analysis is a very rapidly evolving and highly rewarding professional career. This can be exemplified by the fact that there is a very high demand for competent data analysts in the industries by corporates across the world.

Now, If you are the one who loves to play with numbers and find charts and tables fascinating, then data analysis can be a promising career for you too. 

Since you have read this article so far, it shows your interest and it seems that you might easily develop data analyst skills. 

Important things you need to know to be a data analyst. 

Data analytics jobs can be found in a variety of industries, and there is more than one way to get your first job in this in-demand field. 

Here are some steps to becoming a data analyst, whether you’re just starting out in the professional world, or changing careers.

Get Formal Education

Though it is a technical domain, you can still harness data analyst qualifications by gaining the required skills with formal education.

Earlier, most entry-level data analyst positions used to require a bachelor’s degree. While many positions still require a degree, this is changing now. 

While a degree in math, computer science, or another related field can help you develop foundational knowledge and boost your resume, you can also learn what you need through alternative programs such as professional certificate programs, boot camps, or self-study courses.

Get technical skills 

After studying the data analyst job descriptions for a variety of companies, we have concluded that there are a few skills and a technological knowledge base, that are universally acknowledged to be a data analyst. 

Here are some of the important ones:

R/SAS/python Programming Languages:

Data analysts should be fluent in at least one programming language and have a working knowledge of a few others. For data collection, data cleaning, statistical analysis, and data visualization, data analysts employ programming languages such as R and SAS. Python is also one of the most emerging and widely used programming languages in the industry.

Machine Learning (ML):

Data analysts with machine learning skills are extremely valuable, despite the fact that machine learning is not a required skill for most data analyst jobs. It provides businesses with insight into customer behavior and business operational patterns, as well as assists in the development of new products. 

Machine learning is at the heart of many of today’s most successful businesses, including Facebook, Google, and Uber.

With the help of machine learning algorithms, data analysts can build and process models to formulate data and extract valuable insights from it.

Analytical and creative thinking:

Curiosity and creativity are essential characteristics of a good data analyst. It’s critical to have a solid understanding of statistical methods, but it’s even more important to approach problems creatively and analytically. 

This will assist the analysts in developing intriguing research questions that will improve the company’s understanding of the subject at hand.

Microsoft Excel Advanced:

Excel is, after all, the heart of the corporate world. This is because it is the most commonly used software to represent and store tabular data.

Of course, there are many data types that can’t be stored in Excel format, but as a data analyst, you should be very well furnished in your MS Excel skills.

Data analysts should be comfortable with Excel and be familiar with advanced modeling and analytics techniques. 

Strong and effective Communication

Data analysts must clearly communicate their findings, whether to a readership or a small team of executives making business decisions. You will be expected to communicate your findings and recommendations to non-technical colleagues as a data analyst thus effective communication is the key to success. 

There are many professionals that are able to fine-tune their technical skills but are often met with criticism in regard to their soft skills.

Don’t be that kind of a professional.

Data Mining, Cleaning, and Munging:

When data isn’t neatly stored in a database, data analysts must rely on other tools to collect unstructured data. When they have a sufficient amount of data, they clean and process it using programming.  

Visualizing Data:

It takes trial and error to create an effective data visualization. A successful data analyst knows which graphs to use, how to scale visualizations, and which charts to use based on their audience. 

There are numerous data visualization tools available, but two of the most popular are Tableau and Power BI.

Querying Languages for Databases:

SQL is the most common querying language used by data analysts, and there are many variations of this language, including PostgreSQL, T-SQL, and PL/SQL (Procedural Language/SQL).

Python is currently the most popular data science programming language on the planet. It’s an open-source, user-friendly language that’s been around since 1991. This dynamic and general-purpose language is inherently object-oriented. 

When working with data science applications, Java is extremely useful because it provides a slew of other features. Many top companies, such as Spotify and Uber, continue to host business-critical data science applications in Java and Python.

Data Warehousing:

Some data analysts are employed on the back end. They create a data warehouse by connecting databases from various sources and using querying languages to find and manage data.

These were all the basic technical skills you’d need as a data analyst to excel at your job.

After gaining these skills, examine some job postings for roles you’d like to apply for, and focus your learning on the programming languages or visualization tools that are listed as requirements. Enrolling in some high-quality data analytical courses is a good idea. They might help you to brush up on your fundamentals, and give you a strong data-centric foundation.

In addition to these hard skills, hiring managers also look for workplace skills such as strong communication skills—you may be asked to present your findings to those with less technical knowledge—problem-solving ability, and domain knowledge in the industry in which you want to work.

Work on projects that use real-world data.

Working with data in a real-world setting is the best way to learn how to find value in it. Seek out degree programs or courses that include hands-on projects with real-world data sets. 

There are also a number of free public data sets available that you can use to create your own projects.

Of course, if you don’t already have a Kaggle account, make one first, and then start practicing your data analysis skills.

For example, if you are looking for a serious project to work with, here’s what you can do,

Investigate climate data from the National Centers for Environmental Information, delve deeper into the news, and try to use NASA’s open data to devise solutions to looming challenges on Earth and beyond. 

This is just one example of the incredible amount of data available on these platforms. Choose a topic that interests you and look for data to practice with.   

Experiment with presenting your findings.

It’s easy to get caught up in the technical aspects of data analysis, but don’t forget about your communication skills. 

Presenting your findings to decision-makers and other stakeholders in the company is an important aspect of working as a data analyst. When you can tell a story with data, you can assist your organization in making data-driven decisions. 

Practice presenting your findings as you complete projects for your portfolio. Consider the message you want to convey and the visuals you’ll use to support it.

Slow down your speech and make eye contact. Practice in front of a mirror or with a group of classmates. Try recording yourself as you present so you can go back and fix any flaws.

Important tools to learn for a Data Analyst

GA (Google Analytics):

GA assists analysts in gaining a better understanding of customer data, such as trends and areas of customer experience that need to be improved on landing pages or calls to action (CTAs)

Tableau: 

Data analysts use Tableau to aggregate and analyze data. They can design and share dashboards with other team members, as well as create visualizations. 

Jupyter Notebook software:

Data analysts can easily test code using Jupyter notebooks. Because of the markdown feature, non-technical people prefer the simple design of Jupyter notebooks.

Github: 

Github is a platform for sharing and developing technical projects. For data analysts who use object-oriented programming, this is a must-have tool. These are important skills to learn on the way to becoming a data analyst. 

There are many  Data analytics courses to learn all these tools and other relevant skills. 

Create an online portfolio of your work.

As you experiment with data sets on the internet or complete hands-on assignments in your classes, keep your best work for your portfolio in mind. Hiring managers can see your skills through a portfolio. A strong portfolio can go a long way toward helping you get the job. 

As you begin to curate work for your portfolio, choose projects that have the following aspects:

  • Data should be scraped from various sources.
  • Raw data should be cleaned and normalized.
  • Make graphs, charts, maps, and other visual representations of your findings.
  • Data can be used to generate actionable insights.

Consider including one of your group projects if you’ve worked on them as part of your learning. This demonstrates your ability to work as part of a team. If you’re not sure what to include in your portfolio (or need some project inspiration), look through other people’s portfolios to see what they’ve chosen to include.

Get a job as an entry-level data analyst

After you’ve gained some experience working with data and presenting your findings, it’s time to polish your resume and apply for an entry-level data analyst job

Don’t be afraid to apply for jobs for which you don’t feel completely qualified. Your skills, portfolio, and enthusiasm for a role may be more important than checking every bullet point on the qualifications list.

If you’re still in school, inquire about internship opportunities at your university’s career services office. With an internship, you can begin gaining real-world experience for your resume while also putting what you’re learning on the job to use. 

Consider obtaining a certification or an advanced degree.

Consider how you want to advance in your career as a data analyst and what other qualifications can help you get there? 

Certifications such as the Certified Analytics Professional or the Cloudera Certified Associate Data Analyst may help you qualify for more advanced positions with higher pay grades.

If you want to work as a data scientist, you may need to get a master’s degree in data science or a related field. Although advanced degrees are not always required, having one can lead to more opportunities. 

Check out this article on how to pursue a career as a Data Scientist!

Well, we hope that you find this information useful in your further career progression.

If you want to learn more about data analysis, then take a look at this.

Thanks for Reading!

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