How To Become a Data Analyst

Top Online Learning Platforms for Data Analyst Courses

DataCamp

For Professional Career Paths

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4.5

Pros & Cons

• Interactive & Engaging
• Experienced Experts
• Clear Pricing Plans
• Wide Range
• Community
• Rewarding Learning

• Not a Verified Certificate
• Video Content Lacking Detail
• Limited Interaction
• Course Topics

Coursera

Accredited Certificates from Top Unis

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4.8

Pros & Cons

• Partners
• Affordable
• Full Degree Programs
• Free Courses
• Learning Options
• Quality Of Courses
• Job-Relevant Skills
• App & Offline Access

• Confusing Pricing
• Prior Knowledge
• Limited Feedback
• Certificates Aren't Always Free
• Course Completion Pressure

What Does a Data Analyst Do?

We live in a time when companies and organizations receive an enormous amount of information and data. This data is gathered and analyzed by a Data Analyst, who will then provide the organization with the results that can help improve decision-making and predictions for their future business.

All types of organizations benefit from data analysts from the health care industry to retail, food sector, marketing and more. The data gathered could be based on a variety of subjects like; How to improve customer experience, How to reduce material costs, Which new products to develop and much more. All this collected data is to improve procedures and boost the profits of the company and organization.

The Role of a Data Analyst

Whether you are working for a client or organization in the food sector or marketing your day-to-day role as a Data Analyst will have some similarities. It will start with defining the problem, then collecting and cleaning data, then on to spotting patterns and finally creating reports and visualizations.

Define the Problem

The first and most important part is determining what the client or organization actually needs. So, that means defining the problem whether its analysis on a product, a dashboard or reports. Once you have that defined you can create a plan of action along with time frames which you may need to communicate to the rest of the team.

Collect & Clean the Data

A key part is collecting your data which may come from multiple sources (databases, backups, flat files or APIs) which you then need to put together into one place. You will be working alongside the programmers to extract this data. Once you have this data it will need to be cleaned up as inevitably it will be messy. It’s important the data collection is well organized, and the Data Analyst will work to develop a methodical technique which can be easily modified. Specialized tools and software are used to create this infrastructure. This is a critical part of the process as it means later on the data is easier to manage and use again.

Spotting patterns

A successful Data Analyst can spot patterns in the data, which could form its weekly, monthly or quarterly reports depending on how often the client wants the reports. These significant patterns help the Data Analyst find trends which they can use to make recommendations to the client or organization.

Collaborating with others

You may be led to believe that a Data Analyst works pretty solitary, but actually, it’s quite the opposite of the truth. The majority of Data Analysts responsibilities mean that they collaborate not only with their immediate data science team like database developers and data architects, but they also work closely with other employees in the organization like executives and marketers. Another key ingredient a Data Analyst needs is the ability to communicate well with his/her peers when delivering findings and the final presentation.

Create Reports & Visualizations

This is the final part of the process when the Data Analyst produces the reports or visualizations, which can be created automatically, and provided to the client or organization with the latest data as regularly as they would like it. These reports will give management an insight into new trends and areas that the company may need to improve on.

What You Need to Know to Become a Data Analyst?

There are some key subject areas that you need as a building block to become a Data Analyst. By putting together these key skills, you will have the ultimate package in becoming a Data Analyst. Or maybe you already have knowledge in some key subject areas, and you are lacking in others. Below I have highlighted these specific subject areas and recommended courses to help you on your Data Analyst path.

Maths

Being comfortable and familiar with numbers is another key component in becoming a Data Analyst. A knowledge of algebra is necessary to manipulate algebraic expressions and to solve equations.

Programming

It is the core skill that any Data Analyst needs, and you need to be proficient in at least one programming language. If you are familiar with more than one programming language then even better. Starting with either Python or R programming languages and then branching out to Java, MATLAB and PHP will give you a great foundation.

Statistics

A strong foundation in statistics is an important part of becoming a Data Analyst, as this will help you to analyze and explore the data you are working with.

Machine Learning

Having an understanding of machine learning algorithms enables data professionals to make predictions or calculated suggestions based on a huge amount of data. The three main types of machine learning that data analysts should know are supervised learning, unsupervised learning and reinforcement learning.

What Skills Does a Data Analyst Need?

To be a successful Data Analyst you need a variety of Technical, Practical and Soft skills. These must-have skills will help you to stay competitive and industry ready in your role. Below the table I have highlighted and described in detail what I consider the most important skills from the above list.

SQL

SQL or (Structured Query Language) is the industry-standard database language. It is often thought of as the graduated version of Excel and is for handling larger datasets that Excel simply can’t.

Python and R

Like SQL, R and Python can handle what Excel can’t. They are powerful statistical programming languages used to perform advanced analyses and predictive analytics.

Presentation Skills

Being comfortable presenting to a room of people isn’t something we should take for granted, actually most of us get very nervous and feel a sense of dread about standing in front of people. Like most things in life, with practice you will begin to feel more comfortable.

Creative and Analytical Thinking

Another important skill as a Data Analyst is to be able to look at problems with a creative and analytical eye. This way, the Analyst is able to ask interesting and thought-provoking questions which will make the client or organization think outside the box.

Effective Communication Skills

Feeling comfortable conveying your findings in a strong and effective way to your audience (the client or organization) means they will trust what you are saying and be on the same page.

Team Player

The role of a Data Analyst means being an active team player who can collaborate and work with programmers, IT specialists and executives. To be a Data Analyst, you should feel comfortable joining forces with others.

Data Analyst Salary Expectations

The field of Data Analysis is still fairly new, so the salary results are based on job search sites like Glassdoor, Monster and Indeed. The average Data Analyst can expect to have an annual salary of $70,000.

• Data Analyst (Entry level): $45,000 – $60,000
• Data Analyst (Mid-level): $65,000 – $85,000
• Data Analyst (Senior level): $85,000 – $110,000

It’s important to remember that careers in the Data Analytics field are highly employable, and it’s an industry that is ever-growing and most definitely is a job for the future.

Last Thoughts: How to Become a Data Analyst

I hope this detailed review has given you an insight into what it takes to become a Data Analyst. It’s important before taking any course or qualification to consider the following strengths and weaknesses of a career as a data analyst.

Data Analyst Pros

Data Analyst Cons

FAQs