In an age where the collection and storage of data is more prevalent than ever, understanding how best to analyze and extract information from this data is the key to success for many businesses and organizations. Enter data analytics: a field that emerged way back in the 1960s. Since the advent of big data, cloud computing, machine learning and other various software and hardware, data analytics has evolved significantly, becoming an integral part of modern-day business decision-making.
As a result of this industry growth, data analytics has become a popular field for those seeking career change. But the uninitiated may have many questions about the field, such as: what exactly is data analytics, anyway? And: how do I become a data analyst? We’ll answer these questions and more in this data analytics for beginners guide.
In this guide, we’ll address the following topics and questions. If you’d like to skip ahead to a specific section, just use the clickable menu:
- What is data analytics?
- Types of data analytics
- The data analysis process
- What skills do I need to become a data analyst?
- How do I become a data analyst?
- Data analytics for beginners: Recommended bootcamps and courses
- Data analytics projects for beginners
- Best data analytics books for beginners
- Summary and further reading
Ready to get into data analytics for beginners? Let’s get started!
1. What is data analytics?
Every time we open an app, buy something at the supermarket, answer a survey, or fill out a CAPTCHA to log into our email—we’re creating data that is collected by businesses and organizations.
As you can imagine just from these activities alone, colossal volumes of data are being collected every day! So what happens with this data? Well, for a large proportion of it—up to 99.5%—the answer is: nothing. And the other 0.5%? That gets used for data analytics purposes.
In the field of data analytics, data analysts aim to extract meaningful insights from the swathes of raw data presented to them. By doing this, businesses and organizations are able to unleash their predictive power, giving them the ability to make informed business decisions. With data analytics, businesses are able to answer the following questions: what’s happened in the past? What’s happening now? What might happen in the future?
You can learn more about what data analytics is in the following video:
2. Types of data analytics
When analyzing data, there are different methods of extracting the information you need in order to draw out insights, patterns, and trends which guide business decisions. In data analytics and data science, we primarily focus on the following four methods:
Descriptive analytics: What happened?
As the name suggests, this type of analysis purely describes what has happened and presents it in a digestible snapshot. Descriptive data analysis makes use of data aggregation and data mining to provide an overview of past actions, which is often the starting point for more in-depth analysis.
Diagnostic analytics: Why did it happen?
The point of difference between descriptive and diagnostic analyses is that while descriptive analysis seeks to give an objective overview of what’s happened, diagnostic analysis seeks to establish why those things may have happened. This can be done by identifying and handling outliers or anomalies within your data.
Predictive analytics: What is likely to happen in the future?
Predictive analysis makes use of past patterns and trends in data in order to estimate the likelihood of a future outcome or event. In order to do this, a data analyst will devise predictive models that use the relationship between a set of variables.
predictive model may, for example, use the correlation between seasonality and sales figures to predict what points of the year are best for sales, and which are the worst. Based on this information, you may want to create marketing campaigns that will boost the quieter sales periods, and increase team power during intense sales periods.
Prescriptive: What is the best course of action?
Think of prescriptive analysis as the conclusion of the other forms of analysis: now that we’ve found out what happened, why it happened, and what may happen in the future, what should be done next?
How can you avoid a future problem, or capitalize on an emerging trend? An everyday use of prescriptive data analysis is in maps and traffic apps. Think about Google Maps, for example. You type in your start and end destinations, and the app will come up with the best way to get you there, whether it’s by foot, by public transport, bike, or by driving. It will also take into consideration the current traffic conditions, as well as reported quiet, flat, or scenic routes. Loaded with all of this information, you can make travel decisions that best suit your needs.
For a more in-depth look at each type, check out this guide: What Are the Different Types of Data Analysis?
3. The data analysis process
While different types of data analysis require differing methodologies, skills, and know-how in order to glean useful insights, the underlying process remains the same. Let’s take a look at the process a data analyst might follow:
Step 1: Define the question
In order to establish the foundations of your analysis, a data analyst will first need to define their objective, otherwise known as a ‘problem statement.’ To start, the data analyst may ask: what business problem am I trying to solve? By defining this, it can set the framework for the entire analysis.
Step 2: Collect the data
Once the analyst has established their objective for the analysis, they’ll need to design a strategy for collecting the appropriate data. Firstly, they’ll need to determine what kind of data they’ll need: quantitative (numeric) data such as sales figures, or qualitative (descriptive) data, which may include customer surveys. You can learn more about qualitative vs. quantitative data here.
Step 3: Clean the data
So, the data has been collected. Now what? It’s time to clean! In this step, a data analyst will need to clean the data to make sure it’s of high quality. This cleaning—or “scrubbing”—process involves:
- Removing unwanted data points
- Removing major errors, duplicates, and outliers
- Filling in any missing data
- Bringing structure to the data
As you can imagine, this is a crucial part of the process. It’s also the most time-consuming! To learn more about data cleaning, check out our in-depth guide.
You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.
Step 4: Analyze the data
Right! By this point, the data analyst has climbed the biggest mountain of the data-analyzing-journey—that being the data clean—and now they’re ready for the fun part: the analysis! We’ve already explained the basics of the four types of data analysis—descriptive, diagnostic, predictive, and prescriptive. This is the part where the data analyst will apply the methodologies associated with the analysis type that will best “solve” their problem statement.
Step 5: Visualize and share your findings
The data has been analyzed and insights have been gathered. However, this isn’t the end of the data analytics process: the data analyst must now present their findings in a way that’s clear and easily understood by key stakeholders. In order to do this, an analyst may use visualization software—such as Tableau or Microsoft Power BI—that will generate reports, dashboards, or interactive visualizations. At this stage of the process, it’s important that the data analyst is as clear and transparent in their findings as possible so that the relevant stakeholders can make informed decisions. You can learn more about data visualization here.
This is just a basic overview of the data analytics process. To learn more, read more in this article: A Step-by-Step Guide to the Data Analysis Process
3. What skills do I need to become a data analyst?
While there’s no clear-cut career path to become a data analyst, there are a few standard hard and soft skills that every data analyst entering the field will need. This list is by no means exhaustive, but see this as a starting point if you’re considering a career change.
Hard skills needed to become a data analyst
- Demonstrable knowledge in programming and querying languages, such as Python and SQL
- Demonstrable proficiency in business intelligence and data analytics software, which may include RapidMiner, Tableau, and SAS
- Solid understanding of each step of the data analysis process
- Solid numerical and statistical skills
Hard skills are, as you may have already figured out, the technical skills required to fulfill the requirements of a role. They are generally measurable in terms of proficiency—ranging from basic proficiency to advanced expertise.
Soft skills required to become a data analyst
- Great collaboration and communication skills
- An eye for detail
- A methodical and logical approach
- A problem-solving mindset
Soft skills, compared to hard skills, are not measurable. Think of soft skills as being more like characteristics that are a part of your existing personality, though you may have picked up or refined these skills through other roles or experiences you’ve had.
4. How do I become a data analyst?
Now that we’ve gone over the basics of data analytics for beginners: what data analysis is, the types of data analysis, the data analysis process, and the skills possessed by data analysts, you might be wondering, “Great! So, how exactly do I become a data analyst, then?”
It’s very possible to get hired as a data analyst without any formal training. For example, if you’re interested in becoming a healthcare analyst and you already work within the healthcare field and possess the soft skills required, your employer may be interested in providing a traineeship to skill you up on the hard skills required.. However, this would be considered a non-traditional route to entry.
For a more structured route into the field, here are some practical steps you can take:
Complete a data analytics bootcamp or program
Especially if you’re thinking about entering the field with little to no experience, taking a dedicated data analytics bootcamp is the best way to cover all of the basic skills and knowledge needed to become a data analyst.
It helps to look out for a course that has a project-based curriculum—as you can use these projects in your future portfolio—as well as one-on-one mentoring and a certificate of completion. Other nice-to-haves would include a focus on job preparation, networking opportunities, and a job guarantee.
We’ll talk a little more about data analytics bootcamps and courses a little later on in this article, so read on!
In this post, we review some of the top data analytics schools on the market.
Write a dedicated resumé
The job market can be tough—no matter the industry—so having a solid resumé is key to stand out to recruiters and potential future employers. If you’re looking to change careers into data analytics, you’ll need to re-write your resumé to highlight the new skills you’ve acquired during the course of your data analytics program—or any other skills from previous roles that may be relevant!
In this guide, we show you how to write a data analyst resume from start to finish.
Create a solid data analytics portfolio
You may think that once you’ve written a bulletproof resumé, you’re good to go, right? Wrong! Recruiters and employers want to see your skills and experience exemplified in previous projects, which is why most career-changers will have also built up a data analytics portfolio in addition to their resumé.
It’s a good idea to host this portfolio online, so that you can update it regularly. You should include a range of projects that highlight different aspects of your data analytics skillset. Consider including projects you completed on your own as well as projects you completed as part of a team; projects using different programming languages; projects run using different methods of analysis; projects using visualizations and clearly-written explanations of your findings.
Learn more about data analytics portfolios (with examples!) in this article.
Do your research, network, and apply for jobs
The field of data analytics is wide-ranging, and roles you’ll find online won’t all come under the same name. We outline many of the job titles you might find online, and what the job descriptions may entail in this guide. However, we recommend that you do your own research to discover which fields—and more specifically, which companies—suit your personal wants and needs best.
Once you’ve narrowed down the list of companies and organizations you may be interested in, networking is key. This can be done by attending career fairs, getting in contact with recruiters, or reaching out to people on LinkedIn. It’s a good way of getting information about upcoming roles that isn’t always listed on a careers page.
Finally, take the plunge and start applying for jobs! Make sure that you tailor your cover letter to each individual job posting you’re interested in. Yes, it’s some extra work, but it pays off—recruiters can spot a generic cover letter from a mile away. Putting in the extra effort shows that you have a genuine interest in the role. It may take a while for your efforts to pay off, but it’s worth it in the long run! Plus, every interview is good practice for the next one.
You might enjoy this recording of a webinar we hosted about becoming a data analyst. We often host live workshops and webinars related to data analytics—you can check out our upcoming events here.
5. Data analytics for beginners: Recommended bootcamps and courses
You’ve read this far into this article, and maybe you’re at a point where you’re considering data analytics as a career path. Great!
If you’re coming into the field from a related discipline that works with data or statistics, you may only need to upskill in a few areas. If you’re a relative beginner to data analytics, you may find a dedicated bootcamp or course useful to give you an overall understanding of the field.
There are many data analytics bootcamps and courses on the market, but here are a few of the best:
The CareerFoundry Data Analytics Program
- Mode of study: Online
- Duration: 8 months (15 hours per week, self-paced)
- Price: $6,900 USD
For beginners who want to fit their studies around their own schedule, the data analytics program offered by CareerFoundry may be a good fit.
This comprehensive, online, self-paced program will take you from a relative newbie to job-ready data analyst in anywhere from 5-8 months. Being a self-paced program, you can complete modules whenever it suits your schedule—as long as you hit certain milestones within the overall 8-month course duration.
You’ll be teamed up with a dedicated mentor and tutor, who’ll coach you through the modules and give you direct feedback on any projects you complete.
CareerFoundry’s offering comes in at $6,900 for the entire program, but the cost of the tuition is dependent on your location and is competitively priced.
A range of flexible payment options include paying upfront, or getting a small course discount. Contact one of their program advisors to find out your local pricing and if there are any partial scholarships available.
You can also try out the free introductory five-day short course before committing to the full course.
The General Assembly Data Analytics Course
- Mode of study: Online or on campus (USA, Europe, Asia, and Australia)
- Duration: 10 weeks part-time (4 hours per week) or 1 week intensive
- Price: $3,950 USD
This course is well-suited for those who are interested in learning the basics of data analytics, or employees working adjacent to data looking to upskill. Held either online or on-campus, you can study in the evenings over the space of ten weeks, or take a more intensive approach with the one-week accelerated course.
By the end of the course, you’ll have completed a capstone project that you can include in your portfolio, have access to the extensive General Assembly alumni network, and receive a certificate of completion.
The Harvard University Business Analytics Course
- Mode of study: Online
- Duration: 8 weeks (5-6 hours per week, self-paced)
- Price: $1,600 USD
This program is best suited for those looking for a university-endorsed course, without having to commit to a four-year degree. O
ffered through the Harvard Business School online platform, this online course gives a solid introduction to the key concepts of data analytics, including how to interpret data, how to develop and test hypotheses, and how to perform single and multiple variable regression analysis.
If you’re looking to learn how to develop a “data mindset” in order to make smarter business decisions through a flexible, reasonably-priced course, this is a great option.
The Springboard Data Analytics Bootcamp
- Mode of study: Online
- Duration: 6 months (15-20 hours per week)
- Price: $10,140 USD
Remember our list of hard and soft skills we mentioned earlier? If you feel like you possess some—but not all—of these skills, and want to complete the list in order to change careers to work in data analytics, the Springboard Data Analytics Bootcamp may be suitable for you.
Over the course of the 6-month bootcamp, you’ll learn the fundamentals of the data analytics process, including how to frame structured thinking, analyze business problems, connect data using SQL, visualize data using Python, and communicate your analysis. There are some prerequisites for enrollment, but if you don’t initially qualify, you can take their Intro to Business Analytics course instead.
For a closer look at courses and qualifications, check out this round-up of the best data analytics certification programs.
6. Data analytics projects for beginners
If you’re working towards changing careers to become a data analyst, you’ll need to create a data analytics portfolio. Portfolios are an easy way to show recruiters and potential employers—through projects—your understanding of the data analytics process, as well as your proficiency using industry-standard tools.
What kinds of processes should you highlight in your beginner data analytics projects? You could check out free datasets online—or build your own dataset!—and use them to show some of the following beginner processes as projects:
Scraping the web for data
Data scraping means pulling data from an existing website and compiling it into a usable format. You could scrape data from job websites, or even sports analytics! Just make sure you have the appropriate permissions before you start scraping.
Carrying out exploratory analyses
Exploratory data analysis is the process of identifying initial trends, patterns, and characteristics in a dataset using languages like R and Python, which have swathes of pre-existing algorithms that you can use to perform this analysis.
Cleaning untidy datasets
Data cleaning—otherwise known as data cleansing or data wrangling—is a lengthy part of the data analysis process. It’s also very important to clean data properly in order to achieve accurate results. Showing a simple dataset “before and after” will highlight your competency in this task.
Communicating your results using visualizations
For the stakeholders you’ll work with as a data analyst, visualizations are of utmost importance. Instead of being bogged down with numbers and algorithms, your stakeholders will see the meaningful information you’ve gleaned from your dataset in the form of a visualization, which may look like a chart, graph, or map. The type of visualization you land on will depend on the insights you’ve gleaned and how effectively you can present them.
Need some more inspiration to kickstart your own data analytics portfolio? Check out some of our favorite portfolios here.
7. Best data analytics books for beginners
For anyone looking to take a deeper dive into data analytics outside of the practical aspects of the field, there are a wealth of data analytics books available. Here are four of our top picks for data analytics beginners:
Data Analytics Made Accessible, by Anil Maheshwari
As the title states, this book is an overview of the field of data analytics, made accessible for those without any prior knowledge or experience of the field. At the beginning of each chapter (which span the fundamentals of data analytics, from data warehousing to decision trees) Maheshwari includes a ‘caselet’, to provide real-world context to the reader. It also includes beginner tutorials in the appendix, to get a taste for the data analytics process.
Hello World: Being Human in the Age of Algorithms, by Hannah Fry
British mathematician Hannah Fry takes a deep dive into the world of artificial intelligence, stripping it down to its simplest form—algorithms. In Hello World, Fry looks at how data and algorithms have the power to transform our world—seemingly either for better or for worse, and nowhere in-between. It’s an entertaining introduction to the world of AI, written in a way that can be understood by anyone interested in how AI functions, as well as its ethical dilemmas.
The Drunkard’s Walk: How Randomness Rules Our Lives, by Leonard Mlodinow
This book, by American theoretical physicist Leonard Mlodinow, explores the issues of randomness, chance, and probability in our daily lives. And how does this relate to data analytics, you may ask? While seemingly ‘random’, Mlodinow uses these themes to explore the opposite—human’s reliance on statistics and data to dictate future actions and decisions is not foolproof on its own, and actually requires strong critical thinking skills in order to make the ‘best’ decisions. This book is great for anyone interested in the more complex aspects of probability and statistics, while also reminding you of the human side of data-based decision making.
How Smart Machines Think, by Sean Gerrish
Have you ever wondered about how self-driving cars work, or how your streaming service manages to find exactly what you want to watch, without you having to search for it? Wonder no more! This book, written by an expert machine learning engineer, outlines some of the key ideas that enable some of our ‘smart’ machines to perceive and interact with the world, through the theory and practice of creating machine learning algorithms. For any data analyst looking to get into machine learning and artificial intelligence, this is a must-read.
If you’re looking to get offline and learn more about the world of data analytics, you can get a full data analytics reading list here.
Bonus reading: 12 Must-Read Data Analytics Blogs
8. Summary and further reading
So, there you have it! Our guide to data analytics for beginners. In this post, we’ve explored the fundamental basics of data analytics as a field, the main types of analysis, as well as an overview of the data analytics process. Then, we looked at the basics of entering the field—what skills do you need, and what process should you follow in order to become a data analyst? What are some of the best data analytics bootcamps on the market? What kind of beginner projects could you take on in order to show off your newly-learned data analytics skills? Finally, what are the best books on the topics geared towards beginners?
This guide is by no means exhaustive, but we’ve provided links to other guides that should round out the information we’ve covered here.
To learn more about the fundamentals of data analytics for beginners, sign up for this free, 5-day introductory data analytics short course. You’ll receive a short course on everything data analytics-related, delivered daily to your inbox. You may also be interested in these articles: