How to Become a Data Analyst

Before we talk about how to become a Data Analyst, Let’s firstly talk about what Data analytics means.

What is Data Analytics?

From a bird’s eye view, the term “analyze” can mean scrutinizing something to derive useful information from it. Hence, data analytics can be defined as the process in which meaningful insights are drawn from raw data by scrutinizing it. Such insights can be your business information, product innovations, market trends, etc.

Data analytics can be compared to how you make a jigsaw puzzle. Your first step is to gather all the puzzle pieces and then fit them correctly to bring out the final picture. Similarly, in data analytics, you have to analyze data collected from several sources, clean it, and then transform it into information that humans can interpret.

The collected data can be structured, semi-structured, or unstructured in nature. The final result can be visually represented as graphs and charts which provide precise results of the analysis. Several tools and frameworks are used in the analysis process.

There is a high requirement for professionals who can help organizations convert raw data into meaningful information, which in turn can help the growth of the business. There are several job roles in the field of data analytics, and among the job pool, becoming a data analyst offers the most amazing career opportunities. So what’s next: understanding how to become a data analyst. And here’s how!

Who is a Data Analyst?

A data analyst collects, processes, and performs analysis of large datasets. Every business, be it small or big, generates, and collects data. This data can be in the form of customer feedback, accounts, logistics, marketing research, and so on.

A data analyst is a professional who takes this data and figures out numerous measures, such as how to improve customer experience, price new materials, and how to reduce transportation costs, to name a few. Data Analysts deal with data handling, data modeling, and reporting.

And once you know who is a data analyst, it’s paramount to know the roles and responsibilities of a data analyst.

A data analyst collects, processes and performs statistical analyses on large dataset. They discover how data can be used to answer questions and solve problems. With the development of computers and an ever increasing move toward technological intertwinement, data analysis has evolved. The development of the relational database gave a new breath to data analysts, which allowed analysts to use SQL (pronounced “sequel” or “s-q-l”) to retrieve data from databases.

Skills Required to become a Data Analyst

• Programming Languages (R/SAS): data analysts should be proficient in one language and have working knowledge of a few more. Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.
• Creative and Analytical Thinking: Curiosity and creativity are key attributes of a good data analyst. It’s important to have a strong grounding in statistical methods, but even more critical to think through problems with a creative and analytical lens. This will help the analyst to generate interesting research questions that will enhance a company’s understanding of the matter at hand.
• Strong and Effective Communication: Data analysts must clearly convey their findings — whether it’s to an audience of readers or a small team of executives making business decisions. Strong communication is the key to success.
• Data Visualization: Effective data visualization takes trial and error. A successful data analyst understands what types of graphs to use, how to scale visualizations, and know which charts to use depending on their audience.
• Data Warehousing: Some data analysts work on the back-end. They connect databases from multiple sources to create a data warehouse and use querying languages to find and manage data.
• SQL Databases: SQL databases are relational databases with structured data. Data is stored in tables and a data analyst pulls information from different tables to perform analysis.
• Database Querying Languages: The most common querying language data analysts use is SQL and many variations of this language exist, including PostreSQL, T-SQL, PL/SQL (Procedural Language/SQL).
• Data Mining, Cleaning and Munging: When data isn’t neatly stored in a database, data analysts must use other tools to gather unstructured data. Once they have enough data, they clean and process through programming.
• Advanced Microsoft Excel: Data analysts should have a good handle on excel and understand advanced modeling and analytics techniques.
• Machine Learning: Data analysts with machine learning skills are incredibly valuable, although machine learning is not expected skill of typical data analyst jobs.

With the increase in data generation, the term “data analyst” has found its prominence today. In this article on how to become a data analyst, you will get an in-depth understanding of what you must do to become a data analyst, unearth the skills required to bag this position and you’ll get a step closer to your dream of becoming a data analyst.

Responsibilities of a Data Analyst

The data analyst designation comes with a bunch of roles and responsibilities. And the first step to becoming a data analyst in understanding the responsibilities of one! Some of the common and expected responsibilities of a data analyst are:

1. Understanding the Goal

First and foremost, a data analyst must identify the organization’s goal. They must assess the available resources, comprehend the business problem, and collect the right data.

2. Querying

Data analysts write complex SQL queries and scripts to gather, store, manipulate, and retrieve information from relational databases such as MS SQL Server, Oracle DB, and MySQL.

3. Data Mining

Data is mined from a plethora of sources and organized to obtain new details from it. By doing so, data models are built to increase the efficiency of the system.

4. Data Cleansing

Cleaning and data wrangling is the vital duties of a data analyst. The data gathered initially will often be messy and have missing values. Hence, it’s crucial to clean the collected data to make it ready for the analysis purpose.

5. Data Examining

Data analysts use analytical and statistical tools, including programming languages, for carrying out a logical examination of data.

6. Interpreting Data Trends

Data analysts use various packages and libraries to spot trends and patterns from complex datasets, thereby discovering unseen business insights.

7. Preparing Summary Reports

Data analysts prepare summary reports with the help of data visualization tools. These reports guide the leadership team to make timely decisions.

8. Collaborating with Other Teams

Data analysts interact with the management team, development team, and data scientists to ensure proper implementation of business requirements and figure out process improvement opportunities.

And now that you’re well aware of the roles and responsibilities of a data analyst, you are ready to unlock the secret – how to become a data analyst.

Industries with top job opportunities for Data Analysts:

Finance and investments: From entry-level to managerial positions, financial institutions have a varied and extensive need for Data Analysts. It also lends itself to the opportunity to focus on niche areas of analysis. Focusing attention on areas such as investment analysis opens up opportunities for expert positions in the financial sector.

Technology: Unsurprisingly, tech companies are big employers of Data Analysts. In large part, this is due to the changing nature of the industry – as the field evolves and develops there is pressure to predict new trends before competitors do. As a result, Data Analysts are relied on to establish new challenges and new market opportunities.

Marketing: Marketing relies heavily on the expertise of Data Analysts in both quantitative and qualitative market analysis with 72% of marketers considering data analysis skills vital to thriving in today’s data-centric marketing landscape.6 Marketing departments need to understand the success of campaign efforts and they do this by turning to Data Analysts. Data Analysts can also be called on to look into market research for the launch of new products and services.

What tools do data analysts use?

Here are some other important tools data analysts use on the job:

• Google Analytics (GA): GA helps analysts gain an understanding of customer data, including trends and areas of customer experience that need improvement on landing pages or calls to action (CTAs)
• Tableau: Analysts use Tableau to aggregate and analyze data. They can create and share dashboards with different team members and create visualizations
• Jupyter Notebook system: Jupyter notebooks make it simple for data analysts to test code. Non-technical folks prefer the simple design of jupyter notebooks because of its markdown feature
• Github: Github is a platform for sharing and building technical projects. A must for data analysts who use object-oriented programming
• AWS S3: AWS S3 is a cloud storage system. Data analysts can use it to store and retrieve large datasets

Data Analyst Salary

Salary numbers are dependent on job responsibilities. A senior data analyst with the skills of a data scientist can command a high price.

Salaries for Data Analysts:

• Average salary for entry-level data analysts: $83,750
• Average salary for data analysts: $100,250
• Average salary for senior data analysts: $118,750-$142,500
• According to Glassdoor, a data analyst in the United States can earn a minimum salary of $43K to a maximum of $95K per year.
• In India, a data analyst can earn an average salary of 500K per annum.
• The average salary of a data analyst in Australia is around A$83K per annum.
• In the United Kingdom, a data analyst earns an average salary of £30K per annum.

How to become a Data analyst

1. Learn Data Analytics Fundamentals

When thinking about how to become a Data Analyst, your first step should be to learn the data analysis fundamentals and data analysis tools like advanced Microsoft Excel; programming languages SQL, Python and R; Spark and Hadoop for number-crunching; and Tableau, Matplotlib, or ggplot2 for creating beautiful visualizations that bring data to life.

Data analysis courses can be a great way to learn these fundamental data skills, giving you a strong footing on all these platforms, and the ability to gain hands-on experience with:

• Data management
• Statistical programming
• Programming languages
• Data cleaning
• Data visualization

2. Work on Projects to Develop Your Data Analytics Skills

If you want to become a Data Analyst, you’ll need to get a firm grasp on a Data Analyst’s basic tools. Once you’ve done so, you can begin putting your knowledge to work. Successful Data Analysts will need to keep up-to-date with the latest and greatest tools associated with data analysis, including:

• Spreadsheets and querying languages depended on by Data Analysts, such as XML and SQL
• Programming languages and frameworks commonly used by Data Analysts like Python, R, and Hadoop
• Visualization tools popular with Data Analysts like Tableau, PowerBI; Plotly, Bokeh, and Matplotlib

They will also need to have experience with one or several leading data analysis platforms, including Google Analytics and Adobe Analytics.

Put together practice projects that touch on all the different stages of data analysis: researching companies and market opportunities, establishing the parameters of the data you need to collect, gathering and cleaning that data, then modeling and analyzing it using custom-built algorithms.

Finally, turn the insights you’ve drawn from your work into beautiful visualizations, or even try organizing them into a dashboard that allows others to query and interact with your dataset in a user-friendly way.

Data Analytics Tools List

• XML
• SQL
• Python
• R
• Hadoop
• Tableau
• PowerBI
• Plotly
• Bokeh
• Matplotlib
• Google Analytics
• Adobe Analytics

Try taking on a range of practice projects to grow – and demonstrate – your ability to work with different types of data, mining structured data, text and images, audio, or even video to perform statistical analysis, identify causality, and even make predictions.

As you go, you’ll also be practicing the soft skills that elevate the best Data Analysts above their peers:

• A good eye for detail
• An ability to think creatively and analytically
• Excellent communication skills
• A head for numbers

Steps to Become a Data Analyst

1. Research companies and market opportunities
2. Establish the parameters of data you need to collect
3. Gather and clean data
4. Model and analyze data using custom-built algorithms
5. Develop Visualizations and Practice Presenting Them

To become a Data Analyst, you’ll want to start using programs like Tableau, PowerBI, Bokeh, Plotly, or Infogram. Practice building your own visualizations from scratch, finding the best way to let the data speak for itself.

Microsoft Excel comes into play even during this step: although the basic premise behind spreadsheets is straightforward – making calculations or graphs by correlating the information in their cells – Excel remains incredibly useful after more than 30 years and is virtually unavoidable in the field of data science.

But creating visualizations is just the beginning. Data Analysts use these visualizations to present their findings, and that requires strong presentation skills.

Presenting can be improved with practice. Start small with friends first before moving onto colleagues.

These communication skills may come naturally to you, but if not, you can improve with practice. Start small, if necessary, delivering presentations to a single friend for example, before moving on to colleagues.

Developing a Data Analyst Portfolio to Showcase Your Work

One of the most important steps to take when planning how to become a Data Analyst is deciding how you will demonstrate your data skills and knowledge. A professional portfolio is a must, and to get started, you should put the code you’ve written (even as part of your coursework) up on GitHub to show what you can do and begin building your professional portfolio.

3 Tips for Building a Data Analyst Portfolio

• Display code you’ve written on Github or another similar portfolio platform
• Join Data Analyst communities like Kaggle to grow your expertise and find inspiration
• Research, develop and complete a personal project, making sure to carefully document and visualize its creation

When it comes to choosing your data projects, pick something that you’re really interested in, ask a question about it, and try to answer that question with data.

A well-executed data project that you pull off on your own can be a great way to demonstrate your data analysis abilities and impress potential hiring managers.

Apply to Relevant Data Analyst Jobs

There are a wide variety of Data Analyst jobs you can get with data skills. All of the following roles draw heavily on data analytics and can be entry-level or more senior data roles.

There are many other variations out there—new applications for data analytics are being developed all the time, and even the jobs listed here will continue to evolve as data analysis becomes more prevalent.

Such a highly dynamic field, according to consulting firm Mckinsey & Co., means demand may outpace the projected supply of data professionals by 50 or 60 percent, making Data Analyst jobs even harder to fill. All of which is to say that if you have Data Analyst skills, you’re already in a great position when it comes to following a Data Analyst career path.

Data Analyst Job Titles

• Data Analyst
• Database Administrator
• Data Scientist
• Business Analyst
• Business Intelligence Analyst
• Transportation Logistics
• Statistician
• Systems Analyst
• Healthcare Data Analyst
• Operations Analyst
• Data Engineer
• Quantitative Analyst
• Data Architect
• IT Business Analyst

How Do I Become a Data Analyst With No Experience?

If you’re wondering how to become a Data Analyst with no experience in the field, your first step is to acquire the relevant data skills. Some of these skills are relatively easy to acquire individually, others are more complex.

Even so, the data analytics field as a whole is large and diverse enough that it can be difficult to know where to even start when looking to learn data analysis. A structured learning environment that systematically covers all the basics is the best introduction to the field and will ensure that, from the outset, you’ll be clear on what you still need to learn.

Data Analytics courses and data science bootcamps, for example, are a popular option for aspiring Data Analysts looking for how to get started in data analytics. Here, you can learn key data skills and gain hands-on experience in an accelerated learning format with the confidence that the time you’re spending on learning is focused on the areas where it will benefit you most.

Once you’ve acquired key data skills, the next step to becoming a Data Analyst is to practice using these skills, ideally by building your own projects that you can share publicly.

An effective way to display your work – and your data skills – is by posting the code you’ve written, even as part of your coursework, on GitHub. This will show off what you can do, and form the beginning of your professional portfolio.

Don’t stop at the basics. An ambitious, well-executed data project that you pull off on your own is a great way to demonstrate your data abilities and impress potential hiring managers hiring when applying for a Data Analyst job.

Document your journey and present your findings – beautifully visualized – with a clear explanation of your process, highlighting your technical data skills and creativity.

Finally, joining an online network of Data Scientists like Kaggle can be a great way to show that you’re engaged with the community, show off your chops as an aspiring Data Analyst, and continue to grow both your expertise and your outreach.

How to Become a Data Analyst With No Experience

1. Acquire the relevant data skills by studying or enrolling in a data analytics course/bootcamp
2. Practice using those skills by building and developing data analytics projects
3. Gather projects into a portfolio and display it on Github
4. Practice visualizing and presenting your data analytics projects to an audience
5. Join online communities such as Kaggle to grow your expertise and network

Financial Analyst vs. Data Analyst: An Overview

If you are a student or young professional who is great with numbers, analytical, and an expert problem-solver, consider a career as either a financial analyst or data analyst. Financial analysts use financial data to spot trends and extrapolate into the future, helping their employers and clients make the best investing decisions.

Data analysts perform a similar role, the primary distinction being that these professionals analyze data that may or may not relate to investing decisions. For example, a data analyst might study figures related to sales numbers, advertising efficacy, transportation costs, or wages versus productivity.

Because the required education and skills, income potential, work/life balance, and competitiveness of the job market are similar between the two fields, subtle differences in personality type and skill set determine whether someone is better suited for a career as a financial analyst or data analyst.

• Both financial analysts and data analysts should expect to see strong growth and a respectable starting salary.
• Financial analysts are more focused on big-picture outcomes.
• Data analysts tend to possess a higher level of computer proficiency.
• Data analysts can work in data centers and big tech companies, and financial analysts can work on Wall Street and with investment banks.
• Although the job requirements are similar, financial analysts tend to come from economic backgrounds whereas data analysts come from computer science and statistics backgrounds.

Financial Analyst

Financial analysts tend to take a general perspective when undertaking their work. They review financial decisions based on current market trends, stated business objectives, and possible investment options of companies while also reviewing economic data and financial forecasts.1

A degree in finance is probably most beneficial for aspiring financial analysts, although mathematics or economics could also suffice. A Master of Business Administration (MBA) may help a financial analyst, but it is not always required. Financial analysts may also opt to pursue the highly selective Chartered Financial Analyst (CFA) title.2 Unlike the CPA, which is focused on a professional understanding of public accounting standards in the United States, the CFA is focused on those who actively make investment decisions on behalf of clients or an employer. This test is in three parts and administered and overseen by the CFA Institute.3

$131,322

The average take-home salary for a Google financial analyst.

Many financial analysts are also CPAs, and many accountants have a CFA designation. Having both titles is considered a major advantage for nearly any career in the business world and requires a significant mastery of business accounting and investment knowledge.

Financial analysts earned a median annual salary of $83,660 in 2020, the most recent figures as of April 2022. Top earners brought home more than $159,560 and the lower rung made approximately $48,760 according to the Bureau of Labor Statistics.5 Financial analysts tend to earn the most in large financial hubs, such as New York City or San Francisco. Bridgeport, Connecticut is also a lucrative destination for analysts.6 Increased regulations and market complexity are driving the growth for financial analysts, particularly among larger firms with a lot of assets to manage.

Data Analyst

Ultimately, any piece of numerical data that could be used to make a business decision is potentially within the purview of a data analyst’s job. While they are not as laser-focused on the financial markets as their counterparts in the financial analyst world, data analysts are still expected to maintain up-to-date knowledge on investing practices.

Often, accessing and organizing necessary data in this role requires high-level computer skills, making an information technology background, or at least a working knowledge of the field, a definite plus for an aspiring data analyst.

They collect data and examine it to spot trends and glean information that can be used to make business decisions. In the information age, companies rely on big data more than ever to make decisions such as which customers to target, which products and services to focus on, which advertising methods to use, how many people to hire and for which positions, and new markets for expansion.

For virtually any business decision, data is available to steer the company in the right direction. The role of the data analyst is to procure this data and draw conclusions the company can use to make decisions.

$101,308

The average take-home salary for a Google data analyst as of 2022.7

Data analysts are in demand everywhere. This is not an industry-specific role. Any company savvy enough to understand the importance of parsing data needs skilled data analysts. While data analysts command above-average salaries, the returns on investment (ROI) for companies that employ them are even more impressive. The trends spotted and information gleaned by data analysts often make their employers millions of dollars per year.

Students and young professionals who are quantitatively inclined, logic-driven, computer-savvy, good communicators, and who want to make an above-average income while working reasonable hours, should look into data analysis as a career choice. Industry analysts have named it one of the hottest career choices for the 2010s, with projections indicating the demand for data analysts should increase rapidly as more businesses get on board with the importance of harnessing big data.

The fact that such a variety of companies in a variety of industries employ data analysts contributes to the position’s wide salary range. The size of the company, the industry, the geographic location, the candidate’s education, experience, and other factors combine to determine a data analyst’s first-year salary.

Special Considerations

Neither career imposes across-the-board, hard-and-fast educational requirements. This means there is no exam you have to pass, such as the bar exam or medical boards before you can even legally practice the profession. Individual employers set their own requirements for new hires. Generally, the more competitive the job market for financial analysts and data analysts in your local area, the more rigid the standards.

In either profession, most new hires have obtained at least a bachelor’s degree, with a master’s degree becoming more standard with each passing year. The best college majors for a financial analyst are economics, finance, and statistics. Most large firms that hire financial analysts look for one of these three, and as an added bonus, these majors look great when applying for an MBA program, especially when combined with a competitive GPA and work experience.

For aspiring data analysts, a degree in statistics is a great place to start; even better, double up on your major and add information technology, computer information systems, or another up-to-date technology major offered by your school. When hiring a data analyst, employers want to see a healthy mix of quantitative acumen and computer literacy that goes beyond knowing how to input numbers into Excel. A bachelor’s degree is not an official, but a de facto requirement, for data analysts, and a master’s degree makes you much more competitive in the job market.

Choosing Between the Two

These are both fine careers: Income potential is strong, the work hours, at an average of 40 to 45 per week, are not oppressive, and the job market is primed for growth. Distinctions between the two jobs are mostly nebulous, but the biggest difference is a financial analyst’s daily duties are much more involved with the investment markets and employers often expect many hours “off the books.”

If you have a keen interest in investing and keeping up with Wall Street but want to steer clear of the powder keg environment of investment banking and trading, financial analysis is a career to consider. If, on the other hand, you like working with numbers but also enjoy computers and technology, you likely possess the skill set and interests needed to become a good data analyst.

What Skills Are Needed for Financial Analysts or Data Analysts?

Financial analysts and data analysts should be great problem-solvers, excel at the use of logic, and possess strong skills in quantitative analysis. In addition, successful financial analysts have an in-depth understanding of various financial markets and investment products. For data analysts, it is helpful to maintain up-to-date computer skills and have at least a cursory understanding of some of the more common programming languages.

Strong people skills, leadership ability, and teamwork are beneficial for either career. A lot of financial and data analysis is done in teams, and analysts are expected to report their findings to various departments within the company in a clear, concise, and persuasive manner.

What Is the Job Outlook for a Financial Analyst?

The U.S. Bureau of Labor Statistics (BLS) reports an optimistic outlook for financial analyst jobs between 2018 and 2028, with job growth projected at 6%. However, the BLS cautions: “Despite employment growth, competition is expected for financial analyst positions. Growth in financial services is projected to create new positions, but there are still far more people who would like to enter the occupation than there are jobs in the occupation. Having certifications and a graduate degree can significantly improve an applicant’s prospects.”8

What Is the Job Outlook for a Data Analyst?

The BLS did not break out the data analyst position in its latest forecasts, but the broader “financial specialist” job market is expected to experience a 6% growth between 2018 and 2028.9 In the near future at least, strong demand should exist for quantitatively inclined professionals who can cull pertinent information from large pools of data and use it to draw inferences and make forecasts.

The Bottom Line

Both financial analysts and data analysts should expect a solid career outlook with a salary to match. There are terrific exit opportunities with both careers and once you get your foot in the door and start building a resume, there are many positions with crossover responsibilities that become available. Deciding between the two will often come down to your temperament, career goals, and education.

5 Tips On How To Become a Better Data Analyst

Becoming a data analyst is one of the most common early career moves for people educated in STEM fields. Not to mention according to the Bureau of Labor Statistics the demand for data analysts will grow about 20% over the next few years.

Working as an analyst allows you to put skills learned in college to work while also giving you an opportunity to develop new skills on the job. Average salaries ranging from $60,000 to $80,000 also make data analyst positions lucrative starting points for younger professionals.

As companies have become more data-driven, the skills that go into working as an analyst have grown more technical. Many analysts today are skilled in programming languages like Python and R that are suitable for processing large data sets. Before developing these highly technical skills, however, there are some basic tips and tricks that all data analysts should learn. Here are some of the fundamentals you should focus on in order to become a better data analyst or data engineer.

1. Set Up a Clear Data Analytics Process

One of the fundamental parts of becoming a successful data analyst is to have a clear process set up for your projects. This will save you the time and trouble of approaching each project in an ad hoc manner. A simple data analytics process is outlined below:

• Define the Question: Fully define the question you’re trying to answer and the goals of your data analytics project.
• Collect Data: Work with data engineers or other data professionals to gather relevant data for your project.
• Clean the Data: Standardize the data you’ve collected and remove any incorrect or irrelevant entries.
• Analyze the Data: Employ data analysis techniques to understand the data and drive answers to your question. This step can take many different forms, depending on the question you’re trying to answer.
• Share Your Results: Create data visualizations and resources that will help others understand the insights you’ve produced.

With this simple framework, you’ll have a clear road map for outlining and completing data analytics projects. Following this basic process will also keep you from getting sidetracked as you conduct your analysis.

2. Don’t Bury the Lede

When your analysis is finished, it’s important that you’re able to communicate your findings to others in an effective way. A key part of this is to keep your reports simple and concise. While it may be tempting to show all of your findings, it’s better to condense your results down to a simple, understandable message.

For optimal communication, consider telling the story of your data with a few carefully selected charts. These should be relevant to the core question and easy for your audience to understand. Sum up your findings with a conclusion that answers the question and drives value for your audience. By doing this, you’ll avoid confusion and keep your messaging focused on what your analysis has produced.

3. Data Analytics Peer Review

Putting a peer review process in place for your analysis is one of the best ways to ensure your work is sound and accurate. Getting a second set of eyes on your analysis can help you find potential errors or room for improvement. If a fellow analyst confirms your analysis, you’ll know that your work is ready for presentation.

Peer review is especially important for less experienced data analysts. If you can get a more experienced analyst to review your work, you’ll be able to learn from their insights and comments. It’s also helpful for analysts with more technical roles who may be working on projects that were once primarily the domain of data engineers.

4. Triple-Check Your Data

Whenever you’re working with data, it’s good practice to assume that the datasets have at least some flaws. These flaws can range from simple organizational errors to completely erroneous pieces of data. For this reason, you should get into the habit of triple-checking your data as you conduct your analyses.

Finding flaws or inaccuracies in your datasets will help you provide a better analysis. In many cases, you’ll be able to simply fix the issues you find and then proceed with your work. In others, though, you may discover much larger problems that require substantially more work to resolve.

There are times when it’s even advisable to ignore flawed data altogether. While you might lose some information, you have to know when a problem is too labor-intensive to be worth fixing. This decision will depend on the project and the nature of the problems with your data.

While checking your data, it’s a good idea not to assume that anything is accurate. Even something that seems foolproof may have errors that could throw your analysis off. Columns in which data are entered from a dropdown menu, for example, seem like they should be free from errors. If there are invalid options in the menu, though, you could end up with flawed data as a result. Assume that there are errors in any dataset and conduct a thorough search to find them. By doing this during the data cleaning phase of your project, you can save yourself from having to backtrack and fix mistakes later on.

5. Know When To Stop Your Analysis

A final critical skill that is frequently overlooked is knowing when to stop your analysis. Having a set endpoint is a key part of the data analytics process. When you reach that endpoint, you need to be able to stop and finalize your analysis.

Without a clear endpoint, you can easily think of new questions to ask and find yourself going down rabbit holes that aren’t relevant to your project. While there are times when further exploration delivers useful insights, endless data analysis frequently fails to produce valuable results.

Knowing when to stop relates directly to the first step of the data analytics process outlined above. If you don’t know exactly what question you’re trying to answer, it’s very difficult to know where to stop. With a clearly defined question, you should have a natural endpoint beyond which there’s no need for further analysis.

Conclusion

The tools and resources available to data analysts and engineers are constantly changing as technology evolves. The fundamental role of an analyst, however, remains the same. As a data analyst, your primary task will always be to provide valuable, data-driven insights that help your business or organization achieve its goals.

By focusing on these fundamental skills, you can give yourself an extremely strong foundation as an analyst. From there, you can build your technical skills to expand your capabilities. Whatever tools you’re using, though, keeping these basic principles in mind will help you improve as an analyst and create more value for your employer.