Starting with Data Analysis: Essential Skills and Tools for Newbies

A Beginner's Guide to Data Analytics

Data is the currency of the modern world, so it's no surprise that analysts are in such high demand. Demand is so high that the US Bureau of Labor Statistics projects that the number of analyst job openings will increase by 23% by 2032, well above the national average. It's a direct result of digital technology proliferation across industries, which has led to an unprecedented explosion of data generation. Every interaction, transaction, and engagement in the digital realm generates vast amounts of data, and savvy business leaders are eager to capture and use it.

If you're a job seeker considering moving into data analysis, you're in luck! Organizations want to harness the abundance of data they have to gain insights, make informed decisions, and drive innovation, but they can only do it with skilled analysts! They need talented individuals ready to navigate complex datasets and extract meaningful conclusions—and business leaders know it. Whether identifying market trends, optimizing operational efficiency, or enhancing customer experiences, the insights data analysis provides serve as a guiding light for strategic decision-making.

As the volume and complexity of data continue to escalate, so does the need for proficient data analysts who possess the requisite skills and expertise to make sense of it all. Organizations need analysts with a deep understanding of statistical methods, data visualization techniques, and programming languages like Python and R to help them confidently navigate this data-driven landscape.

Read on to learn about the growing demand for skilled data analysts and the opportunities it presents for those looking to embark on a career in this dynamic field.

On this page:

• Understanding Data Analysis
• 4 Types of Data Analysis
• How to Perform Data Analysis: Examples for Each Type
• Skills Needed for Data Analysis
• How to Acquire Data Analysis Skills
• Getting Started with Practical Experience
• Is It Hard to Learn Data Analysis?
• Do You Need a Degree?
• Networking and Continuous Learning
• Conclusion

Understanding Data Analysis

Let's start with the basics: What is data analysis? In the simplest terms, it's the process of examining raw data to find patterns, draw conclusions, and make informed decisions. It involves organizing, cleaning, and interpreting data to uncover insights to help solve problems, identify opportunities, or understand trends.

There's data everywhere you look, providing organizations with the opportunity to understand past performance, decipher current trends, and make predictions. Analysts extract meaningful insights from data and use them to recommend how their organization should proceed. Data analytics empowers companies to optimize operations, enhance customer experiences, and identify growth opportunities.

It doesn't matter what industry you're in, data has a role to play. For example, in healthcare, data aids in disease prevention, treatment optimization, and public health management. In finance, it drives investment decisions, risk management strategies, and fraud detection efforts. In technology, it fuels innovation, product development, and user experience enhancements.

4 Types of Data Analysis

Data analytics has four main types of data analysis: descriptive, diagnostic, predictive, and prescriptive. Each type of data analysis provides serves a different purpose, depending on the answers you are looking for. The are four types of data analysis are:

• Descriptive Analysis

• Diagnostic Analysis

• Predictive Analysis

• Prescriptive Analysis

#1: Descriptive Analysis

Descriptive Analysis focuses on summarizing past data to understand what has happened. It involves several techniques, such as:

• Data aggregation: gathering raw data from a source such as a data lake or pivot table to organize it.

• Summarization: analysts calculate summary statistics such as mean, median, mode, standard deviation, range, and percentiles to convey the necessary information more effectively and efficiently.

• Visualization: organizing data into charts, box plots, and graphs to illustrate the distribution and patterns within the data. Visualization is essential for conveying vast and complex information clearly and intuitively so analysts can better identify trends, outliers, and relationships within the dataset.

#2: Diagnostic Analysis

Diagnostic Analysis is applied when you want to know why something has happened. This technique aims to identify patterns, correlations, and causal relationships within the data to explain why specific outcomes occurred.

• Hypothesis Testing: looking at past data to prove or disprove an hypothesis

• Root Cause Analysis: analysis to identify underlying factors to identify appropriate solutions

• Comparative Analysis: systematically comparting two or more data sets to pinpoint similarities and differences

#3: Predictive Analysis

Predictive Analysis uses historical data to answer the question, “What might happen next time?" It leverages historical data to forecast future trends, behaviors, or events. Analysts can anticipate potential outcomes and make proactive decisions by applying statistical models, machine learning algorithms, and business intelligence tools. It is never 100% accurate, but it provides valuable insight into critical business decisions.

For example, imagine a telecom company wants to reduce the number of times customers cancel their subscriptions. Based on existing customer data, including demographics, usage patterns, and service interactions, they can identify at-risk customers and confidently say, "This customer is at risk of canceling because of X, Y, and Z."

#4: Prescriptive Analysis

Prescriptive Analysis builds upon predictive analysis to determine what to do next. With the ability to predict what might happen, data can point to actions or interventions to optimize those potential future outcomes. Borrowing from the previous example, if the telecom company can identify customers at risk of canceling their service, it can prepare intervention strategies, such as personalized offers, service upgrades, and loyalty rewards to try and retain them.

How to Perform Data Analysis: Examples for Each Type

To understand how the different types of data analysis are done, we’ll look at a case study to illustrate each type of analysis used.

You work for a major telecommunications organization that had an increase in low survey scores for customer service, as well as an increase in the number of customers cancelling their services.

Perform Descriptive Analysis:

Data aggregation	Collect survey score and cancellation data
Summarization	Summarize the data to provide average metrics
Visualization	Using data visualization tools, the analysis results were shown in various charts for easy understanding

 

Your organization’s initial response was to create a training program for customer service to stem the tide of customer cancellations. However, though training was completed, it seemed as if there was no decrease in customer complaints and cancellations.

Perform Diagnostic Analysis:

Hypothesis Testing	Hypothesized that training didn’t solve the issues and was affirmed by comparing survey and cancellation data before and after training
Root Cause Analysis	Determined that new skills weren’t fully implemented and that product offerings were an issue
Comparative Analysis	Compared data among all demographics and determined certain demographics were more likely than others to have customer service and product issues

 

Once the diagnostic analysis was done, your organization needed to predict what could happen in the future.

Perform Predictive Analysis:

Prediction based on data

Using demographic, usage patterns, and service interactions data, customers most likely to cancel service due to X, Y, or Z were identified

 

Once customer behavior predictions were made, your organization needed to determine what actions it could take to prevent the further loss of customers.

Perform Prescriptive Analysis:

 

Prescription determined

Intervention strategies such as skills reinforcement, personalized offers, service upgrades, and loyalty awards programs created to retain customers most likely to cancel

Skills Needed for Data Analysis

Data analysts need a balance of both hard and soft skills to best perform in their position. The skills data analysts need are in three broad categories:

Technical	Ability to use Excel, programming languages such as Python and SQL for data aggregation, sorting and filtering, and calculations, and data visualization tools such as Tableau or Power BI.
Analytical	Strong math and statistical knowledge and critical thinking/problem solving skills.
Soft	Strong communication and collaboration skills to convey findings to the organization.

How to Acquire Data Analysis Skills

To start a data-analysis career, you must first master the foundational principles of data analysis. There are many ways to gain the necessary knowledge and skills, allowing you to use the option best suited to you.

Formal education	Degrees in computer science, statistics, or related fields
Bootcamps/Training	Immersive practical learning programs in Data and Analytics covering:   No-Code: data analysis that doesn’t require programming knowledge   Low-Code: data analysis using tools such as Tableau or Power BI that requires some programming knowledge   Traditional Data Analytics: courses intended for developers, analysts, data scientists, database administrators
Real-World Practice	Working on real-world projects or Kaggle competitions to apply data analysis skills in a simulated environment

Is It Hard to Learn Data Analysis?

Like any discipline, it depends on your existing skills and knowledge level. If you're not very analytical, it can be especially tough to change how you think. Likewise, you might have difficulty entering the field if you don't have the technical skills to learn programming languages or how to use new software.

While learning data analysis can be challenging, especially for beginners, numerous resources and learning paths are available to support skill development. With dedication, perseverance, and consistent practice, individuals can overcome initial hurdles and make significant progress in their learning journey. The key is to start with the basics, build a strong foundation, and gradually advance to more complex concepts and techniques.

If you've managed to land a data analysis role and are struggling out of the gate, our experts recommend that you take the time to learn the business and its current strategy. Be patient, and remember that it takes time to learn and understand the data you're working with, especially because it may come from many different sources and be different file types.

Do You Need a Degree?

While beneficial, our training experts agree that a degree in relevant fields is not necessary. While they can provide a structured learning environment and formal credentials, it is not a strict requirement for pursuing a career in data analytics. Knowledge of the business you're working in is more important than schooling.

Practical skills, experience, and a strong portfolio showcasing projects and achievements can often outweigh academic qualifications in the eyes of employers. What matters most is demonstrating proficiency in data analysis techniques and a passion for leveraging data to drive business value.

Getting Started with Practical Experience

For beginners looking to gain practical experience in data analysis, starting with small projects using public datasets is a great way to apply theoretical knowledge and develop technical skills. Platforms like Kaggle, GitHub, and Data.gov offer a wealth of publicly available datasets across various domains, allowing aspiring data analysts to explore different data analytics techniques. A portfolio showcasing projects, analyses, and insights is crucial to attracting potential employers or clients.

Networking and Continuous Learning

If you ask our data analytics experts, they will tell you that networking is essential for your professional growth. Join online communities, attend industry events, and participate in forums and discussion groups to connect with fellow data professionals. People are usually eager to share their knowledge, and these resources can help you stay updated with industry trends and best practices.

Continuous learning is essential in the fast-paced field of data analytics, where new tools, technologies, and methodologies emerge regularly. By staying curious, proactive, and open to learning, data analysts can adapt to evolving demands and maintain their competitive edge in the job market.

Conclusion

Data analytics is a rewarding and dynamic career path that applies to any industry. It's a good option if you want to make a tangible impact on your organization by leveraging data to drive insights and innovation. There are plenty of avenues you can take to acquire the necessary skills and expertise to position yourself for success in a rapidly growing, in-demand field.

Becoming a data analyst starts by assessing your options and making an informed decision. You can become a data analyst whether your first step is through online platforms, formal education, boot camps, or hands-on practice. Embrace the challenges, stay committed to continuous learning, and unlock the fantastic opportunities data analytics offers.