A data analyst looking at a graph on a computer screen

What Is Data Analytics?

Emily Stevens

Data analytics: Everyone’s talking about it, but what exactly is it?

Perhaps you’ve heard that data analytics is the next big thing for business and want to finally understand what all the hype is about. Maybe you’re thinking about becoming a data analyst and want to know what a career in the field really involves. Either way, you’ve come to the right place!

In this guide, you’ll find a complete and comprehensive introduction to data analytics—starting with a simple, easy-to-understand definition and working up to some of the most important tools and techniques. We’ll also touch upon how you can start a career as a data analyst, and explore what the future holds in terms of market growth. By the end of this post, you’ll have a clear idea of what data analytics is, how it differs from data science, and what it takes to break into the field. Want to skip ahead to a specific section? Just use the clickable menu below.

  1. What is data analytics?
  2. What’s the difference between data analytics and data science?
  3. What are the different types of data analysis? 
  4. What is data analytics used for? 
  5. What does a data analyst do?
  6. What is the typical process that a data analyst will follow?
  7. What tools and techniques do data analysts use?
  8. What skills do you need to become a data analyst?
  9. What are some of the best data analytics certifications and courses?
  10. What does the future hold for data analytics?
  11. Key takeaways and further reading

So, what exactly is data analytics? Let’s find out.

1. What is data analytics?

Most companies are collecting loads of data all the time—but, in its raw form, this data doesn’t really mean anything. This is where data analytics comes in. Data analytics is the process of analyzing raw data in order to draw out meaningful, actionable insights. These insights are then used to inform and drive smart business decisions. So, a data analyst will extract raw data, organize it, and then analyze it, transforming it from incomprehensible numbers into coherent, intelligible information. Having interpreted the data, the data analyst will then pass on their findings in the form of suggestions or recommendations about what the company’s next steps should be.

You can think of data analytics as a form of business intelligence, used to solve specific problems and challenges within an organization. It’s all about finding patterns in a dataset which can tell you something useful and relevant about a particular area of the business—how certain customer groups behave, for example, or how employees engage with a particular tool. Data analytics helps you to make sense of the past and to predict future trends and behaviors; rather than basing your decisions and strategies on guesswork, you’re making informed choices based on what the data is telling you. Armed with the insights drawn from the data, businesses and organizations are able to develop a much deeper understanding of their audience, their industry, and their company as a whole—and, as a result, are much better equipped to make decisions and plan ahead.

Throughout this post, we’ll delve deeper into what data analytics is and what it looks like in action. First though, let’s clear up a common area of confusion: the difference between data analytics and data science.

2. What’s the difference between data analytics and data science?

You’ll find that the terms “data science” and “data analytics” tend to be used interchangeably. However, they are two different fields and denote two distinct career paths. What’s more, they each have a very different impact on the business or organization.

One key difference between data scientists and data analysts lies in what they do with the data and the outcomes they achieve. A data analyst will seek to answer specific questions or address particular challenges that have already been identified and are known to the business. To do this, they examine large datasets with the goal of identifying trends and patterns. They then “visualize” their findings in the form of charts, graphs, and dashboards. These visualizations are shared with key stakeholders and used to make informed, data-driven strategic decisions.

A data scientist, on the other hand, considers what questions the business should or could be asking. They design new processes for data modeling, write algorithms, devise predictive models, and run custom analyses. For example: They might build a machine to leverage a dataset and automate certain actions based on that data—and, with continuous monitoring and testing, and as new patterns and trends emerge, improve and optimize that machine wherever possible. In short: data analysts tackle and solve discrete questions about data, often on request, revealing insights that can be acted upon by other stakeholders, while data scientists build systems to automate and optimize the overall functioning of the business.

Another main difference lies in the tools and skills required for each role. Data analysts are typically expected to be proficient in software like Excel and, in some cases, querying and programming languages like SQL, R, SAS, and Python. Analysts need to be comfortable using such tools and languages to carry out data mining, statistical analysis, database management and reporting. Data scientists, on the other hand, might be expected to be proficient in Hadoop, Java, Python, machine learning, and object-oriented programming, together with software development, data mining, and data analysis.

Despite their differences, it’s important to recognize that data science and data analytics work together, and both make extremely valuable contributions to business. We’ll explore the role of the data analyst in more detail in section three, and you can learn more about the differences between a data scientist and a data analyst in this guide.

Another term you’ll often come across when researching the field of data is machine learning. We explain the differences between data science, data analytics, and machine learning in this guide.

3. What are the different types of data analysis?

Now we have a working definition of data analytics, let’s explore the four main types of data analysis: Descriptive, Diagnostic, Predictive, and Prescriptive.

Descriptive analytics

Descriptive analytics is a simple, surface-level type of analysis that looks at what has happened in the past. The two main techniques used in descriptive analytics are data aggregation and data mining—so, the data analyst first gathers the data and presents it in a summarized format (that’s the aggregation part) and then “mines” the data to discover patterns. The data is then presented in a way that can be easily understood by a wide audience (not just data experts). It’s important to note that descriptive analytics doesn’t try to explain the historical data or establish cause-and-effect relationships; at this stage, it’s simply a case of determining and describing the “what”.

Diagnostic analytics

While descriptive analytics looks at the “what”, diagnostic analytics explores the “why”. When running diagnostic analytics, data analysts will first seek to identify anomalies within the data—that is, anything that cannot be explained by the data in front of them. For example: If the data shows that there was a sudden drop in sales for the month of March, the data analyst will need to investigate the cause. To do this, they’ll embark on what’s known as the discovery phase, identifying any additional data sources that might tell them more about why such anomalies arose. Finally, the data analyst will try to uncover causal relationships—for example, looking at any events that may correlate or correspond with the decrease in sales. At this stage, data analysts may use probability theory, regression analysis, filtering, and time-series data analytics.

Predictive analytics

Just as the name suggests, predictive analytics tries to predict what is likely to happen in the future. This is where data analysts start to come up with actionable, data-driven insights that the company can use to inform their next steps. Predictive analytics estimates the likelihood of a future outcome based on historical data and probability theory, and while it can never be completely accurate, it does eliminate much of the guesswork from key business decisions. Predictive analytics can be used to forecast all sorts of outcomes—from what products will be most popular at a certain time, to how much the company revenue is likely to increase or decrease in a given period. Ultimately, predictive analytics is used to increase the business’s chances of “hitting the mark” and taking the most appropriate action.

Prescriptive analytics

Building on predictive analytics, prescriptive analytics advises on the actions and decisions that should be taken. In other words, prescriptive analytics shows you how you can take advantage of the outcomes that have been predicted. When conducting prescriptive analysis, data analysts will consider a range of possible scenarios and assess the different actions the company might take. Prescriptive analytics is one of the more complex types of analysis, and may involve working with algorithms, machine learning, and computational modeling procedures. However, the effective use of prescriptive analytics can have a huge impact on the company’s decision-making process and, ultimately, on the bottom line.

4. What is data analytics used for? 

By now, we’re pretty familiar with the theory behind data analytics, but it’s all still feeling rather abstract. What does data analytics actually look like in action? How is it used in the real world?

The truth is, data is absolutely everywhere—which means that data analytics has almost infinite uses across all kinds of businesses and organizations. Broadly speaking, data analytics is used to make faster and more informed decisions, to reduce overall business costs, to develop more effective products and services, and to optimize processes and operations. In more specific terms, data analytics might be used for the following:

  • To predict future sales and purchasing behaviors
  • For security purposes—for example, to detect, predict, and prevent fraud within the insurance and banking industries
  • To evaluate the effectiveness of marketing and advertising campaigns
  • To optimize marketing efforts through more accurate targeting and personalization
  • To identify and eliminate bottlenecks within a certain process
  • To boost customer acquisition and retention
  • To boost customer engagement on social media
  • To develop risk management solutions
  • To identify opportunities for innovation and create new revenue streams
  • To increase supply chain efficiency

The above list is by no means exhaustive; anywhere data is being collected, data analytics can be used to glean useful insights and inform future practices. Let’s now take a closer look at data analytics in action with some real-world case studies.

Data analytics case study: Healthcare

One area where data analytics is having a huge impact is the healthcare sector. Junbo Son, a researcher from the University of Delaware, has devised a system which helps asthma patients to better self-manage their condition using bluetooth-enabled inhalers and a special data analytics algorithm.

So how does it work? First, the data is collected through a Bluetooth sensor which the user attaches to their asthma inhaler. Every time the patient uses their inhaler, the sensor transmits this usage data to their smartphone. This data is then sent to a server via a secure wireless network, where it goes through the specially devised Smart Asthma Management (SAM) algorithm. Over time, this unique algorithm helps to paint a picture of each individual patient, giving valuable insight into patient demographics, unique patient behaviours—such as when they tend to exercise and how this impacts their inhaler usage—as well as each patient’s sensitivity to environmental asthma triggers. This is especially useful when it comes to detecting dangerous increases in inhaler usage; the data-driven SAM system can identify such increases much more quickly than the patient would be able to. What’s more, the SAM system has been found to outperform traditional models, with a false alarm rate that is 10-20% lower than that of current models, together with a 40-50% lower misdetection rate.

This case study highlights what a difference data analytics can make when it comes to providing effective, personalized healthcare. By collecting and analyzing the right data, healthcare professionals are able to offer support that is tailored to both the individual needs of each patient and the unique characteristics of different health conditions—an approach that could be life-changing and potentially life-saving. You can learn more about this case study in the following journal article: A Data Analytics Framework for Smart Asthma Management Based on Remote Health Information Systems with Bluetooth-Enabled Personal Inhalers.

Data analytics case study: Netflix

Another real-world example of data analytics in action is one you’re probably already familiar with: the personalized viewing recommendations provided by Netflix. So how does Netflix make these recommendations, and what impact does this feature have on the success of the business?

As you might have guessed, it all starts with data collection. Netflix collects all kinds of data from its 163 million global subscribers—including what users watch and when, what device they use, whether they pause a show and resume it, how they rate certain content, and exactly what they search for when looking for something new to watch. With the help of data analytics, Netflix are then able to connect all of these individual data points to create a detailed viewing profile for each user. Based on key trends and patterns within each user’s viewing behavior, the recommendation algorithm makes personalized (and pretty spot-on) suggestions as to what the user might like to watch next.

This kind of personalized service has a major impact on the user experience; according to Netflix, over 75% of viewer activity is based on personalized recommendations. This powerful use of data analytics also contributes significantly to the success of the business; if you look at their revenue and usage statistics, you’ll see that Netflix consistently dominates the global streaming market—and that they’re growing year upon year.

As you can see from these two case studies alone, data analytics can be extremely powerful. For more real-world case studies, check out these five examples of how brands are using data analytics—including how Coca Cola uses data analytics to drive customer retention, and how PepsiCo uses their huge volumes of data to ensure efficient supply chain management.

5. What does a data analyst do?

If you’re considering a career as a data analyst (or thinking about hiring one for your organization), you might be wondering what tasks and responsibilities fall under the data analyst job title.

In an interview discussing what it’s actually like to work as a data analyst, Radi, a data analyst at CENTOGENE, describes the role as follows:

“I like to think of a data analyst as a ‘translator’. It’s someone who is capable of translating numbers into plain English in order for a company to improve their business. Personally, my role as a data analyst involves collecting, processing, and performing statistical data analysis to help my company improve their product.” 

A job ad for a Graduate Data Analyst posted by Pareto Law describes the position as “a unique opportunity to work across all verticals as a knowledge broker, acting as an intermediary between clients and experts, connecting customers with the organization.”

In their ad for a Data Analyst, Shaw Media writes: “This role will primarily focus on turning datasets into an actionable direction for our newsrooms. You will be responsible for more than just monitoring our analytics—it’s communicating with the newsroom about what is working, what is not working, updating our dashboards, identifying trends and making sure we’re on top of data privacy.”

As you can see, the role of the data analyst means different things to different companies. However, there are some common threads that you’ll find among most data analyst job descriptions. Based on real job ads, here are some of the typical tasks and responsibilities of a data analyst:

  • Manage the delivery of user satisfaction surveys and report on results using data visualization software
  • Work with business line owners to develop requirements, define success metrics, manage and execute analytical projects, and evaluate results
  • Monitor practices, processes, and systems to identify opportunities for improvement
  • Proactively communicate and collaborate with stakeholders, business units, technical teams and support teams to define concepts and analyze needs and functional requirements
  • Translate important questions into concrete analytical tasks
  • Gather new data to answer client questions, collating and organizing data from multiple sources
  • Apply analytical techniques and tools to extract and present new insights to clients using reports and/or interactive dashboards
  • Relay complex concepts and data into visualizations
  • Collaborate with data scientists and other team members to find the best product solutions
  • Design, build, test and maintain backend code
  • Establish data processes, define data quality criteria, and implement data quality processes
  • Take ownership of the codebase, including suggestions for improvements and refactoring
  • Build data validation models and tools to ensure data being recorded is accurate
  • Work as part of a team to evaluate and analyze key data that will be used to shape future business strategies

To learn more about the kinds of tasks you can expect to take on as a data analyst, it’s worth browsing job ads across a range of different industries. Search for “data analyst” on sites like Indeed, LinkedIn, and icrunchdata.com and you’ll soon get a feel for what the role entails. We also recommend checking out this post: Am I a good fit for a career as a data analyst?

6. What is the typical process that a data analyst will follow?

Now we’ve set the scene in terms of the overall data analyst role, let’s drill down to the actual process of data analysis. Below, we’ve outlined the five main steps that a data analyst will follow when tackling a new project:

Step 1: Define the question(s) you want to answer

The first step is to identify why you are conducting analysis and what question or challenge you hope to solve. At this stage, you’ll take a clearly defined problem and come up with a relevant question or hypothesis you can test. You’ll then need to identify what kinds of data you’ll need and where it will come from. For example: A potential business problem might be that customers aren’t subscribing to a paid membership after their free trial ends. Your research question could then be “What strategies can we use to boost customer retention?”

Step 2: Collect the data

With a clear question in mind, you’re ready to start collecting your data. Data analysts will usually gather structured data from primary or internal sources, such as CRM software or email marketing tools. They may also turn to secondary or external sources, such as open data sources. These include government portals, tools like Google Trends, and data published by major organizations such as UNICEF and the World Health Organization.

Step 3: Clean the data

Once you’ve collected your data, you need to get it ready for analysis—and this means thoroughly cleaning your dataset. Your original dataset may contain duplicates, anomalies, or missing data which could distort how the data is interpreted, so these all need to be removed. Data cleaning can be a time-consuming task, but it’s crucial for obtaining accurate results.

Step 4: Analyze the data

Now for the actual analysis! How you analyze the data will depend on the question you’re asking and the kind of data you’re working with, but some common techniques include regression analysis, cluster analysis, and time-series analysis (to name just a few). We’ll go over some of these techniques in the next section. This step in the process also ties in with the four different types of analysis we looked at in section three (descriptive, diagnostic, predictive, and prescriptive).

Step 5: Interpret and share the results

This final step in the process is where data is transformed into valuable business insights. Depending on the type of analysis conducted, you’ll present your findings in a way that others can understand—in the form of a chart or graph, for example. At this stage, you’ll demonstrate what the data analysis tells you in regards to your initial question or business challenge, and collaborate with key stakeholders on how to move forwards. This is also a good time to highlight any limitations to your data analysis and to consider what further analysis might be conducted.

7. What tools and techniques do data analysts use?

Much like web developers, data analysts rely on a range of different tools and techniques. So what are they? Let’s take a look.

Data analytics techniques

Before we introduce some key data analytics techniques, let’s quickly distinguish between the two different types of data you might work with: quantitative and qualitative.Quantitative data is essentially anything measurable—for example, the number of people who answered “yes” to a particular question on a survey, or the number of sales made in a given year. Qualitative data, on the other hand, cannot be measured, and comprises things like what people say in an interview or the text written as part of an email. Data analysts will usually work with quantitative data; however, there are some roles out there that will also require you to collect and analyze qualitative data, so it’s good to have an understanding of both. With that in mind, here are some of the most common data analytics techniques:

  • Regression analysis: This method is used to estimate or “model” the relationship between a set of variables. You might use regression analysis to see if certain variables (such as a movie star’s number of Instagram followers and how much her last five films grossed on average) can be used to accurately predict another variable (e.g. whether or not her next film will be a big hit). Regression analysis is mainly used to make predictions; however, it’s important to note that, on their own, regressions can only be used to determine whether or not there is indeed a relationship, or correlation, between a set of variables—they can’t tell you anything about cause and effect.
     
  • Factor analysis: This technique helps data analysts to uncover the latent or underlying variables that drive people’s behavior and the choices they make. Ultimately, it condenses the data in many variables into a few “super-variables”, making the data easier to work with. For example: If you have three different variables which represent customer satisfaction, you might use factor analysis to condense these variables into just one all-encompassing customer satisfaction score. For this reason, factor analysis is sometimes known as dimension reduction.
     
  • Cohort analysis: A cohort is a group of users who have a certain characteristic in common within a specified time period—for example, all customers who purchased using a mobile device in March may be considered as one distinct group or cohort. In cohort analysis, customer data is broken up into smaller groups or cohorts; so, instead of treating all customer data the same, companies can see trends and patterns over time that relate to particular cohorts. In recognizing these patterns, companies are then able to offer a more targeted service.
     
  • Cluster analysis: This technique is all about identifying structures within a dataset. Cluster analysis essentially segments the data into groups that are internally homogenous and externally heterogeneous—in other words, the objects in one cluster must be more similar to each other than they are to the objects in other clusters. Cluster analysis enables you to see how data is distributed across a dataset where there are no existing predefined classes or groupings. In marketing, for example, cluster analysis may be used to identify distinct target groups within a larger customer base.
     
  • Time-series analysis: In simple terms, time-series data is a sequence of data points which measure the same variable at different points in time. Time-series analysis, then, is the collection of data at specific intervals over a period of time in order to identify trends and cycles. This is useful because it enables data analysts to make accurate forecasts for the future. For example, if you wanted to predict the future demand for a particular product, you might use time-series analysis to see how the demand for this product typically looks at certain points in time. 

These are just a few of the many techniques that data analysts will use, and we’ve only scratched the surface in terms of what each technique involves and how it’s used. Some other common techniques include Monte Carlo simulations, dispersion analysis, discriminant analysis, and text or content analysis (the latter being a technique for analyzing qualitative data). We’ve covered seven of the most useful data analysis techniques in this guide.

Data analytics tools

Now let’s take a look at some of the tools (and programming and querying languages) that a data analyst might work with. If you’re looking to become a data analyst, you’ll need to be proficient in at least some of the tools listed below—but, if you’ve never even heard of them, don’t let that deter you! Like most things, getting to grips with the tools of the trade is all part of the learning curve.

  • Microsoft Excel is a software program that enables you to organize, format, and calculate data using formulas within a spreadsheet system. Microsoft Excel may be used by data analysts to run basic queries and to create pivot tables, graphs, and charts. Excel also features a macro programming language called Visual Basic for Applications (VBA).
     
  • Tableau is a popular business intelligence and data analytics software which is primarily used for data visualization. Data analysts use Tableau to simplify raw data into visual dashboards, worksheets, maps, and charts. This helps to make the data accessible and easy to understand for everyone, allowing data analysts to effectively share their insights and recommendations.
     
  • Python is an open-source programming language which is used to organize and wrangle large sets of data. Data wrangling is a term used in the industry to describe the processing of data in various formats—merging and grouping data, for example, to get it ready for analysis. Python has many built-in features which help with data wrangling, making it a popular alternative to Microsoft Excel (especially when it comes to working with more complex datasets).
     
  • R is another open-source programming language used for statistical computing, often serving as a complementary tool to Python. R is especially popular among data analysts due to its output; it offers a great variety of tools for presenting and communicating the results of data analysis.
     
  • SAS is a command-driven software package used for carrying out advanced statistical analysis and data visualization. Offering a wide variety of statistical methods and algorithms, customizable options for analysis and output, and publication-quality graphics, SAS is one of the most widely used software packages in the industry.
     
  • SQL stands for “structured query language”, and it’s a language used to access and manipulate databases. You can think of SQL as a tool that allows you to communicate with the data stored in a database, which is necessary if you want to retrieve useful data and prepare it for analysis. Most large businesses use some variation of SQL to store their data, so learning SQL is essential if you want to work as a data analyst.
     
  • RapidMiner is a software package used for data mining (uncovering patterns), text mining, predictive analytics, and machine learning. Used by both data analysts and data scientists alike, RapidMiner comes with a wide range of features—including data modeling, validation, and automation.
     
  • Power BI is a business analytics solution that lets you visualize your data and share insights across your organization. Similar to Tableau, Power BI is primarily used for data visualization. While Tableau is built for data analysts, Power BI is a more general business intelligence tool.
     
  • FineReport is another business intelligence tool used to monitor performance, to identify trends within data, and to create reports and dashboards. This is an especially user-friendly tool which is popular with both data analysts and non-data experts. 

Again, this is just a brief introduction to some of the key tools and languages used in the field. You can get a good idea of what tools are currently most popular in the industry by browsing through data analyst job descriptions; employers will often list the tools and languages that they expect a data analyst to work with. We’ve also shared our top nine data analytics tools in this post.

8. What skills do you need to become a data analyst?

In addition to being well-versed in the tools and techniques we’ve explored so far, data analysts are also expected to demonstrate certain skills and abilities. Here are some of the most important hard and soft skills you’ll need to become a data analyst:

  • Mathematical and statistical ability: Data analysts spend a large portion of their time working with numbers, so it goes without saying that you’ll need a mathematical brain!
     
  • Knowledge of programming languages such as SQL, Oracle, and Python: As we’ve seen, data analysts rely on a number of programming languages to carry out their work. This may seem daunting at first, but it’s nothing that can’t be learned over time.
     
  • An analytical mindset: It’s not enough to just crunch the numbers and share your findings; data analysts need to be able to understand what’s going on and to dig deeper if necessary. It’s all in the name—an analytical mindset is a must!
     
  • Keen problem-solving skills: Data analysts have a wide variety of tools and techniques at their disposal, and a key part of the job is knowing what to use when. Remember: data analytics is all about answering questions and solving business challenges, and that requires some keen problem-solving skills.
     
  • Excellent communication skills: Once you’ve harvested your data for valuable insights, it’s important to share your findings in a way that benefits the business. Data analysts work in close collaboration with key business stakeholders, and may be responsible for sharing and presenting their insights to the entire company. So, if you’re thinking about becoming a data analyst, it’s important to make sure that you’re comfortable with this aspect of the job. 

Learn more in this guide: What are the key skills every data analyst needs?

9. What are some of the best data analytics certifications and courses?

Having read about what a career in data analytics entails and the skills you’ll need to master, you may now be wondering: How can I become a data analyst?

As more and more companies recognize the importance of data, data analytics has become something of a buzzword. With that, we’ve seen a whole host of courses and programs emerging which focus on teaching data analytics from scratch and, ultimately, facilitating a career-change into the field. It’s a great time to be an aspiring data analyst! So what courses are worth considering? We’ve outlined just three of the best data analytics courses below—for a more extensive comparison, check out this list of data analytics courses.

  • The CareerFoundry Data Analytics Program: CareerFoundry offers a flexibly-paced online program which comes complete with an expert one-to-one mentor, a personal tutor, career coaching, and a job guarantee. You don’t need any prior knowledge or experience, and you can try a free introductory short course here.
     
  • The Springboard Data Analytics Bootcamp: Another online option which also comes complete with a job guarantee. Unlike the CareerFoundry program, this bootcamp is designed for people who can demonstrate an aptitude for critical thinking and who have two years of work experience. 
     

  • The Certified Analytics Professional (CAP) Credential: This is a general certification offered by INFORMS, the leading international association for operations research and analytics professionals. If you’ve already got some experience in data analytics, a CAP credential can help to certify and formalize your skills. 

You can learn more about how to become a data analyst in this guide.

10. What does the future hold for data analytics?

Data has become one of the most abundant—and valuable—commodities in today’s market; you’ll often hear about big data and how important it is. However, while it’s often claimed that data is the new oil, it’s important to recognize that data is only valuable when it’s refined. The value of the data that a company has depends on what they do with it—and that’s why the role of the data analyst is becoming increasingly pivotal. Still, the sheer value of data (and data analytics) is reflected in the way the market has surged in recent years: in 2019, the global data analytics market was valued at $49 billion USD—that’s more than double what it was worth in 2015. And, from 2020 to 2023, the market is expected to grow at a rate of 30% per year, taking it up to $77.6 billion USD. 

So what does this mean in terms of career prospects? At the time of writing, a search for data analyst jobs on indeed.com turns up 20,000 vacancies in the United States alone. And we can expect this figure to rise: according to a report published by the World Economic Forum, data analysts will be one of the most in-demand professionals in 2020 and beyond.

If you’re considering a career in data analytics, there has never been a better time. As the market grows and businesses face a significant skills shortage, data analysts will increasingly benefit from high demand, a rich variety of opportunities, and competitive compensation.

11. Key takeaways and further reading

So there you have it: a complete introduction to the fascinating field of data analytics. We’ve covered a lot of information, from fundamental tools and techniques to some of the most important skills you’ll need to master if you want to become a data analyst. If you’re brand new to the field, all these skills and requirements (not to mention the technical terminology) can seem overwhelming—but it’s important not to let that put you off! Remember: Data analytics is a rapidly growing field, and skilled data analysts will continue to be in high demand. With the right training, anyone with the passion and determination can become a fully-fledged, job-ready data analyst. Keen to learn more about data analytics? Check out the following:

What You Should Do Now

  1. Get a hands-on introduction to data analytics with a free, 5-day data analytics short course.
  2. Take a deeper dive into the world of data analytics with our Intro to Data Analytics Course.
  3. Talk to a program advisor to discuss career change and find out if data analytics is right for you.
  4. Learn about our graduates, see their portfolio projects, and find out where they’re at now.

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Emily Stevens

Emily Stevens

Managing Editor at CareerFoundry

Originally from England, Emily moved to Berlin after studying French and German at university. She has spent the last five years working in tech startups, immersed in the world of UX and design thinking. In addition to writing for the CareerFoundry blog, Emily has been a regular contributor to several industry-leading design publications, including the InVision blog, UX Planet, and Adobe XD Ideas.