How to Become a Data Analyst With No Experience (2023 Guide)

Are you an aspiring data analyst? Here’s a complete guide on what data analytics entails and five skills you need to become a data analyst without experience.

Introduction to Data Analytics

Data analysts are in high demand. And there are many reasons why. Today, virtually every sector uses data analytics to improve performance. It’s no coincidence that the industries that rely on data the most to make informed decisions are among the fastest-growing.

As businesses continue to use data to scale, becoming a data analyst is a smart career choice. But how do you become a data analyst? 

While there are many content sources, you can Get Started on Rolai with expert guidance, build a professional portfolio, and earn industry-recognized certificates.

This guide will show you five skills you need to become a data analyst. But your journey begins with understanding what the industry is about.

The Purpose of Data Analytics

Data analytics involves analyzing raw data to extract valuable and actionable insights. As a data analyst, your job is to help your company lead a competitive market by analyzing the vast amount of raw data available. 

To do this, you’ll need analytics tools like Power BI, Tableau, R & Python, SAS, MatLab, etc.

If you’re an aspiring data analyst, Rolai’s experimental learning offers a faster route to a data analyst job through our unique learning structure: 

• Learn
• Apply
• Solve

For example, you can learn Python, R, no-code functions, Live contests, hackathons, case studies, and live projects from a single platform – Rolai. 

Before your data analytics learning roadmap ends, you’ll already have a solid professional portfolio to get a data analyst job.

As a business, transform your people and data into a competitive advantage, increase ROI on data initiatives, and champion a data-driven culture through upskilling.

Generally, there are four types of data analytics that are vital to data analysts;

• Descriptive Analytics: “what happened?”
• Diagnostic Analytics: “Why did this happen?”
• Predictive Analytics: “What might happen in the future?”
• Prescriptive Analytics: “What should we do next”

Why Data Analytics Is Necessary in the Real World

We live in a data-driven economy. Only those that analyze raw data can effectively navigate the market, forecast correctly, and adapt their business operations to emerging customer trends and satisfaction. But this remains a problem. Forbes reports 95% of businesses struggle with managing unstructured data.

Here are some reasons why data analytics is necessary:

Reduce Overall Business Costs:

Data analytics can reveal areas of your business that use unnecessary finances and where you should invest more money into. 

Develop New and Innovative Products and Services:

Data analytics can help improve the current products by identifying the features that don’t work or are least used. As customer demands evolve, businesses will find ways to provide innovative products that meet customer expectations.

To Forecast Futures Sales or Purchasing Habits:

Businesses can analyze customer data or previous shipments to predict future demand. This helps them determine what products need to be reduced or to predict what a customer may need to complement their initial order.

Fraud Detection:

Data analytics allows companies to identify suspicious activity patterns and quickly prevent criminal attempts. The Association of Certified Fraud Examiners Report to the Nations shows that data monitoring/analysis is the most effective anti-fraud control strategy, reducing fraud by 56%.

To Analyze the Effectiveness of Marketing Campaigns:

Data analytics helps companies understand which marketing campaigns are most effective. By applying marketing analytics, companies will better understand what motivates consumer behavioral patterns and enhance their marketing strategies.

Enhance Customer Acquisition and Retention:

Customer data analytics gives marketers a detailed overview of their audience’s preferences and needs. This helps them create cross-channel marketing campaigns that convert highly qualified leads. 

Elevate Supply Chain Performance:

Using data analytics will help businesses identify risks, avoid supply chain disruptions, find opportunities to improve working capital, and increase planning accuracy. 

In What Capacity Do Data Analysts Work? 

The capacity of a data analyst depends on the company they work for and the tools they use. However, here are the common data analyst responsibilities you should expect to perform:

• Manage the delivery of user satisfaction surveys and report on results using data visualization software.
• Work with business owners to develop project requirements, define success metrics, manage and execute the analytical project, and evaluate the results.
• Monitor practices and systems to find opportunities for overall business growth and improvement.
• Translate important questions into concrete analytical tasks.
• Collect, clean, and sort data from multiple sources
• While basic coding skills are required for entry-level data analyst jobs, Power BI and Tableau knowledge can improve your career prospects. 
• Designing, building, testing, and maintaining backend code (SQL) can be an added advantage, along with R and Python, which are the most used tools. Since they’re open source, you can download them for free on sites like GitHub.
• Establish data processes, define data quality criteria, and implement data quality processes.
• Evaluate and analyze critical data that will use to shape future business strategies.

An Analyst’s Approach to a New Project: Five Steps

When starting a project as a data analyst, there are five key ways to analyze the data to find meaningful results.

1. Define the Question You Want to Answer

The first step to data analysis is defining your goal. What business problem do you want to solve? For example, why are we losing customers?

2. Collect Data From Different Sources

The next stage is to collect data from the sources of data that concern this problem. Data analysts can collect data from software, web analytics, social media activity, customer satisfaction surveys, or buying datasets from third parties. 

3. Data Cleaning

The collected data are usually fragmented, have errors, and contain missing values. So, cleaning and data wrangling prepares the data for analysis. The core tasks in data cleaning include:

• Removing errors, duplicates, and outliers
• Removing unwanted data points
• Making the data structured
• Filling missing data

4. Data Analyzing 

Your data analysis strategy depends on the goal you highlighted in the first step. Let’s look at some standard data analysis methods:

Cluster Analysis

Cluster analysis helps you identify homogenous groups of cases. It involves grouping objects in similar clusters. 

For example, customers don’t renew their subscription after the first time to get value for their money. You can group customers based on how many times they subscribed before leaving. This analysis will be helpful for them to analyze the customer data based on clustering.

Regression Analysis

Data analysts use regression analysis to identify the variables that impact the problem. This helps you establish which factors are most important, which ones to ignore, and how they influence each other. 

Time Series Analysis

Time series analysis involves analyzing a sequence of data points collected over time. Rather than capturing the data points intermittently or arbitrarily, a data analyst can use this method to record the data points at regular intervals over a predetermined length of time.

5. Data Interpretation and Result 

This is where you make meaning of the data. Most business decision-makers want straightforward answers, so your interpretation using graphs, charts, and other data visualization tools must communicate to them why there is a problem and the suggested solution.

How to Become A Data Analyst With These Skills 

To become a data analyst, you need specific skills that employers want to see. Here are a few in-demand data analyst skills that will land you a job in 2023:

Mathematical and statistical ability to solve problems:

A data analyst’s job involves both categorical (Qualitative) and numerical (Quantitative) data. So, a profound understanding of statistics and mathematics to solve problems is non-negotiable. 

Programming Languages:

Python and R are statistical programming languages that help you perform advanced analysis that Excel cannot. As an aspiring data analyst, you don’t have to learn both. You can choose one while you grow and learn the other later. However, R is used mainly for data analysis and statistics, while Python is mostly used for deployment and production.

On the other hand, Structured Query Language (SQL) is the industry-standard language for database management systems. It helps data analysts access large amounts of data sets directly where it’s stored. And by knowing SQL, data analysts can communicate with databases to retrieve and query data. Most data analysts are able to leverage their technical skills to learn several data analysis and visualization tools.

Analytical skills like Data Analysis, Research, and Critical thinking:

When using data to find answers, you need to know what to ask in the first place.  Being analytical involves uncovering details with research and aggregating connections that aren’t always obvious. 

Problem-Solving skills:

Data analysts work with real-life business problems and use facts and figures to solve them. To become a data analyst, you must understand complex data sets and extract meaning from them.

Excellent Communication Skills:

Data analysts must possess excellent communication skills to show the business leaders, managers, and coworkers the solution your analysis has found. Data visualization software, like Tableau, can also help you communicate findings effectively. 

In Conclusion

Data analytics involves telling stories through analytics. More companies now understand the importance of data analytics in every aspect of their business. The BLS forecasts data analyst jobs to grow by 25% in the next decade. The entry-level data analyst role’s compensation is around $70,000, significantly higher than most entry-level positions (See Forbes Entry Level Positions). When starting as an aspiring data analyst, take advantage of free resources. Rolai provides a complete content library as well as industry use cases and projects directly related to becoming a data analyst. Start your professional journey with the best resources today.