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Data Analytics for Beginners: What Is It and How to Learn It

The world is full of data-centric businesses these days. Those businesses rely on the expertise of data analysts to keep track of large volumes of data. These in-demand professionals help companies make data-driven decisions.

In this article, you will learn about data analytics for beginners and the skills you’ll need to get started in the field. We also explore education paths for data analytics and give you a few ideas for projects you can add to your portfolio. 

What Is Data Analytics?

Data analytics is a set of processes and techniques used to draw actionable insights from raw and undefined data. Simply put, as a data analyst you should be able to make sense of data that an untrained eye can’t interpret. You should also be able to model this data and make it easier for others to understand it. 

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What Are the Different Types of Data Analysis?

  1. Descriptive
  2. Prescriptive
  3. Diagnostic
  4. Predictive

Descriptive Data Analytics 

This method of data analysis involves making a description of a dataset over some time. For example, a data analyst working on a digital marketing campaign may make a descriptive report of how the campaign has performed over time. Did certain channels bring conversion? Did sales improve? They will use analytical skills to decide the next step. 

Prescriptive Data Analytics

You’ll make suggestions on the best course of action based on data collected and analyzed. If the information from a digital marketing campaign shows that Instagram posts are responsible for 50 percent of the total conversions, the data analyst would suggest that more effort is channeled toward Instagram posts. 

Diagnostic Data Analytics 

With these analysis skills, the data analyst provides reasons for why certain things happen. If Facebook posts don’t lead to conversions during a campaign, the data analyst would go through the data and figure out why this happened. It could be that the posts didn’t bring in enough sales because they were mainly text and people respond more to visual ads. 

Predictive Data Analytics 

This involves predicting what will happen in the future based on data that already exists. The data analyst studies the data, finds a pattern, and makes a prediction. For example, a data analyst can look at data and predict that Instagram posts with videos and pictures will lead to more conversations in a digital marketing campaign than those with plain text.

What Are the Stages of the Data Analysis Process?

  1. Evaluate 
  2. Clean 
  3. Summarize 

Evaluate 

This is the first stage of data analytics and involves data investigation. As a data analyst, you will be expected to go through the data thoroughly, looking for irregularities and confirming that the data is complete. Data evaluation simply means ensuring that the data you’ll be working with is adequate. 

Clean 

In this step, you’ll be making sense of raw and undefined data using a combination of analytical skills. Data cleansing also involves getting rid of corrupted, inaccurate, and duplicate datasets. Without clean data, database management systems and algorithms can’t be relied upon.

Summarize 

After evaluating and cleaning data, the next stage before the presentation is a summary. You need to summarize large datasets in simple language. No one wants to read long and boring reports with too many complicated words.  

When you’re summarizing, make sure that you differentiate facts from opinions and main points from details. Ensure also that there is a logical flow and that the data is easy to read and understand. 

What Data Analyst Skills Do I Need in 2021?

  • Machine learning. Machine learning is a branch of artificial intelligence that plays a key role in big data analysis. A basic understanding of artificial intelligence and machine learning is a great asset for any data analyst.
  • SQL. This is the most common programming language used by data analysts to communicate with databases. 
  • Data visualization. This is all about representing data in ways that are easy for your non-technical colleagues to understand. 
  • Statistical programming. Data analysts should know how to use programming languages like R and Python to analyze data. 
  • Probability and statistics. Mastering these technical skills will help you avoid biases, identify trends, and provide accurate results. 

How to Learn Data Analytics

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You can learn data analytics through online courses or bootcamps.

Your data analytics journey can begin in a number of ways. Two of the fastest ways to become a data analyst are enrolling in an online certificate program and attending a coding bootcamp. Below we take a close look at the best data analysis courses and coding bootcamps.

5 Best Online Data Analytics Courses for Beginners in 2021

  • The Data Science Course 2021: Complete Data Science Bootcamp by Udemy
  • Excel to MySQL: Analytic Techniques for Business Specialization by Coursera 
  • Data Analysis Nanodegree by Udacity
  • Become a Data Analyst by LinkedIn Learning 
  • Learning Python for Data Analysis and Visualization by Udemy 

The Data Science Course 2021: Complete Data Science Bootcamp by Udemy

  • Cost: $94.99 
  • Duration: 29 hours (self-paced) 

This certificate program covers the principles of data science and data analytics, Python, statistics, deep learning, deep neural networks, and machine learning. You’ll learn all the skills and tools you need to become a data scientist.

Excel to MySQL: Analytic Techniques for Business Specialization by Coursera 

  • Cost: Free enrollment, then $49 per month until you complete the program
  • Duration: 7 months (self-paced)

Coursera offers this certificate in collaboration with Duke University. You can expect to learn key skills in data analytics to improve a company’s profits. It covers binary classification, business analysis, linear regression, and data visualization as well as the technologies Tableau, SQL, Excel, and MySQL. 

Data Analysis Nanodegree by Udacity

  • Cost: $399 per month  
  • Duration: 4 months (self-paced) 

For this certificate program, you need basic knowledge of SQL and Python. You’ll build a solid foundation in data wrangling, data visualization, linear algebra, and practical statistics, among other topics. You’ll get to work on real-life projects and benefit from mentorship and career services.

Become a Data Analyst by LinkedIn Learning  

  • Cost: 1-month free trial, then $29.99 per month
  • Duration: 24 hours (self-paced)

This certificate program is divided into seven sections of bite-sized videos that even a complete beginner can follow. Each on-demand video covers one technical aspect of data analytics. You will also build your problem-solving, collaboration, and communication skills. 

Learning Python for Data Analysis and Visualization by Udemy 

  • Cost: $124.99
  • Duration: 21h 5m (self-paced) 

This certificate program requires a basic familiarity with math and Python. The course covers advanced Python and machine learning, and students get to work on projects for hands-on practice. You will also learn key aspects of NumPy and Pandas. 

5 Best Data Analytics Bootcamps in 2021

  • Thinkful 
  • BrainStation 
  • General Assembly 
  • Springboard 
  • Ironhack 

1. Thinkful 

  • Cost: $12,250 (full-time) or $8,000 (part-time)
  • Duration: 4 months (full-time) or 6 months (part-time) 
  • Location: Online 

Thinkful is one of the best online bootcamps for data analytics and data science because of its convenient online format. This program is ideal for those looking for a deep dive into SQL, Tableau, and statistical analysis. The bootcamp has a job placement rate of 85 percent and comes with a money-back guarantee: if you don’t get an entry-level data analyst job six months after graduation, your tuition will be refunded.

2. The Data Incubator 

  • Cost: $10,000
  • Duration: 8 weeks (full-time) or 20 weeks (part-time) 
  • Location: Online

The Data Incubator has an advanced data analytics program that covers Python, SQL, data communication, and machine learning. The curriculum is primarily designed for people who are already on a data analytics career path but need more expertise in the field to advance their careers. 

3. General Assembly 

  • Cost: $3,950 
  • Duration: 10 weeks (part-time) or 1 week (accelerated)  
  • Location: Online

General Assembly offers this data analytics program on a part-time or accelerated schedule. It covers SQL, Excel, Tableau, and a host of other technologies. By the end of the program, graduates would have built a professional portfolio that they can present to prospective employers. 

4. Springboard 

  • Cost: $8,500 
  • Duration: 6 months 
  • Location: Online 

Springboard has courses on data science, data analytics, data engineering, and machine learning. The data analytics bootcamp program comes with one-on-one mentorship, career services, hands-on experience, and a job guarantee. The projects you will work on at Springboard are based on real-world datasets from Khan Academy and Harvard Business School. 

5. Ironhack 

  • Cost: Contact the school for details
  • Duration: 9 weeks (full-time) or 24 weeks (part-time) 
  • Location: Online, Miami

Data analytics is just one of the many courses offered at Ironhack. The program is divided into four modules. The first module consists of 60 hours of prework and the other three modules are classes. It covers everything from statistical analysis to machine learning. 

5 Best Data Analytics Project Ideas for Beginners

  1. Credit card fraud detection. You can use programming languages like Python or R to investigate and detect credit card fraud. You’ll do this by analyzing data from real-world credit card transactions. 
  2. Movie recommendations. Using programming language R and tech tools like reshape2 or recommenderlab you can create your own recommendations system for shows and movies.
  3. Sentiment analysis. You can launch your sentiment analysis project using R as your go-to language. This project will use artificial intelligence to gather people’s opinions about certain issues.
  4. Exploratory data analysis. The best language for these data analytics projects for beginners is Python. Coupled with tools like NumPy, Pandas, and Matplotlib, you can explore real-world datasets to recognize patterns and give suggestions to help companies make smart business decisions.
  5. Chatbot. Using Python, you can build a machine learning chatbot. These chatbots serve as the first line of communication between customers and businesses. 

Why Learn Data Analytics?

You should learn data analytics because it is a profitable career path that is currently in high demand. According to the Bureau of Labor Statistics, the demand for these experts is expected to rise by 25 percent between 2019 and 2029. In addition, data analysts enjoy high job satisfaction as they provide valuable insights that are central to businesses’ success.

Data Analytics for Beginners FAQ


What is data analytics for beginners?

It’s a branch of data science that deals with the in-depth analysis of raw, undefined data and seeks to make sense of it. It involves collecting, analyzing, and giving suggestions for smarter decision-making. People from all sorts of backgrounds have found success in this field.


Can I teach myself data analytics?

Yes, you can teach yourself data analytics in three to nine months if you are a dedicated person who knows how to learn in an unstructured environment. Some things you’ll have to learn are Python, machine learning, SQL, and MySQL. There are many certificate programs to help you in your bid to learn a new skill.


What are the top three skills for a data analyst?

The three most important skills for data analytics are data visualization, statistical programming, and machine learning. Data wrangling, presentation, and critical thinking skills are also important. You should also know how to use at least one data analytics tool.


How do you start learning data analytics?

Start your learning journey by attending a university or bootcamp or by enrolling in a certificate program. When you have mastered the skills for data analytics, focus on the practical application of what you’ve learned by building your portfolio. To become more comfortable with programming, it’s a good idea to join additional certification courses.

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