How Hard Is Data Analytics to Learn and Do?

Data analytics, with its blend of statistics, programming, and business strategy, might seem intimidating. Many people question the difficulty of diving into this field. Is data analytics hard or is it one of the easier STEM disciplines to learn?

To put it simply, learning and doing data analytics is quite straightforward, provided you have an interest and a willingness to tackle technical tasks. The proliferation of user-friendly software has made the field more accessible to beginners. However, standing out as a data professional requires dedication, continuous learning, and a strong mathematical aptitude.

It’s important to remember that thriving as a data analyst isn’t just about technical prowess. As your career progresses, you’ll find that effective communication skills and the ability to drive impactful business results become increasingly crucial.

Let’s delve into what’s required to learn data analytics and successfully perform data analysis in a professional capacity.

Definition

Data analytics is the process of examining, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making.

Data professionals use a variety of techniques and approaches to manipulate and interpret data. This data, originating from diverse sources, comes in many sizes and formats. It can be as straightforward as numbers or text, or more intricate like images or voice recordings.

The process often needs to be transparent and understandable to others, including decision-makers who may not be technical experts. The insights derived from the data need to be easily interpretable, to guide business decisions effectively. As such, even though the field can get quite complex with advanced statistical models and machine learning algorithms, often the most impactful insights come from relatively simple transformations and analyses.

This simplicity promotes transparency and comprehensibility in the analysis, ensuring effective communication of results to all stakeholders. It fosters a shared understanding of the data and its derived insights, which is crucial for informed decisions. Thus, good data analysts strive to keep the process uncomplicated.

Hardest Parts of Learning Data Analytics

Learning data analytics can be a challenge. It requires time, effort, and some understanding of technical areas like coding and data visualization tools. Let’s look at some of the main hurdles you could encounter and discuss ways to simplify this 4-step process.

1. Finding the right data

The first step in data analytics is identifying relevant data. The data you choose should serve a purpose, particularly in aiding your company’s strategising. With the vast amount of data available, determining the most useful pieces can be challenging. This is where a data warehouse comes in handy. It consolidates data from various sources into one central location, simplifying access and analysis.

Keeping your data well-organized is also paramount. An orderly arrangement not only aids in quickly pinpointing the required data, but it also minimizes errors. You can maintain a clear inventory of your data assets, tagging and classifying them appropriately. This discipline ensures your data is ready for efficient, accurate use whenever needed.

2. Understanding your data

Understanding your data is key after collecting it. This involves interpreting its various aspects, such as categories or columns in a data warehouse. Simple techniques include descriptive statistics to summarize data and exploratory data analysis for visual insights.

Advanced methods involve correlation and regression analyses to understand variable relationships. Machine learning algorithms can reveal hidden patterns or predict trends in complex datasets.

Modern tools like Tableau for visualization, Python’s Pandas for data processing, and scikit-learn for machine learning speed up and automate these tasks, making data comprehension quicker and more precise.

3. Cleaning up

Raw data is often cluttered and messy. Cleaning it, a process that involves correcting or removing inaccuracies, can be a long and tedious task. This might require actions like adjusting variables, handling missing values, or discarding outliers—data points that significantly differ from others.

However, clean data is crucial for building reliable data models. It ensures that your analytics are based on high-quality, accurate data, which leads to more trustworthy results. This process, though mundane, forms the backbone of your analysis.

To ease this task, artificial intelligence and machine learning can be used. Tools equipped with these technologies can automate much of the cleaning process, detecting and fixing errors more quickly than manual methods. This significantly speeds up the task, allowing you to focus more on data interpretation and decision making.

4. Analyzing and interpreting

After your data is clean, it’s time for the actual analysis. This involves applying statistical techniques or machine learning models to your data to extract insights. You might look for trends, relationships, or patterns that can inform business decisions.

Analysis can range from simple descriptive statistics to complex machine learning models. The choice of analysis often depends on your data and the specific questions you’re trying to answer. With tools like Python’s scikit-learn for machine learning and Tableau for visualization, you can carry out comprehensive analysis and present your findings effectively.

Remember, the goal of data analysis is to draw meaningful conclusions that aid in decision making. Therefore, interpreting your results accurately and presenting them in a clear, understandable manner is as important as the analysis itself. This ability to derive and communicate insights is what ultimately drives value in the field.

Yes, Significant Math Is Involved

There is mathematics involved in learning and doing data analytics. You should be mathematically capable. But math isn’t normally the core part of an analyst’s daily activities.

You’ll need to grasp basic statistics to summarize and understand data. For more advanced work, you’ll use inferential statistics, which helps make predictions from your data. Probability is also a big player in data analytics, as it underpins statistical testing and a lot of machine learning methods.

While data analysts need to be good with numbers, and a foundational knowledge of Math and Statistics can be helpful, much of data analysis is just following a set of logical steps. As such, people can succeed in this domain without much mathematical knowledge.
CodeClan

If you dive deep into machine learning, you may come across linear algebra and calculus. But don’t worry, even though these topics sound scary, much of the heavy lifting is done by the software tools and libraries that data analysts use every day. It’s more about understanding and using mathematical concepts rather than crunching numbers and solving equations.

While the math is significant, it’s not the whole story. Having problem-solving skills, the know-how to clean and wrangle data, coding abilities, and most importantly, the capacity to clearly communicate results are all as vital in data analytics as mathematical proficiency.

Why Data Analysis Is Harder than Coding

With the rise of AI automating some aspects of coding, it’s fair to suggest that data analysis could be seen as relatively harder. AI is making coding more accessible and efficient, taking over some of the more repetitive tasks and reducing the need to learn many programming languages.

However, the core tasks of data analysis, such as interpreting data and making informed decisions, are less automated. These tasks often require a substantial level of human insight, domain knowledge, and statistical understanding.

Nonetheless, it’s important to note that both data analysis and coding still require human involvement for optimal outcomes. The perceived difficulty of each can depend on individual skills and interests. In many scenarios, these two skill sets can complement each other in problem-solving and decision-making processes.

Why Data Analytics Should Be Considered Low Stress

In general, data analytics should be considered a relatively low-stress job. The nature of the work often involves independently exploring data and drawing conclusions, which means daily activities may not be closely supervised.

Additionally, while there can be deadlines, many analytical projects are not typically time-critical. Timelines are typically measured in weeks and may include a fudge factor, allowing for a more measured and thoughtful approach to problem-solving.

Another aspect that reduces stress is the flexibility in the depth of analysis. If a deadline is looming, an analyst can adjust the scope of their work, focusing on the most significant insights first. This ability to control the depth and direction of analysis can provide a better balance between job demands and job control, often resulting in lower stress levels.

However, experiences can obviously vary depending on the specific role, company culture, and individual personalities. Analysts with low emotional intelligence, for example, may find themselves getting stressed over different aspects of the job or having their work scrutinized. Everyone has different stress triggers. But overall, many find a career as a data analyst to offer a balanced and manageable workload.

How Long Does It Take to Learn Data Analysis?

Becoming professionally competent in data analysis typically requires around 12-18 months, assuming you already possess some background knowledge in statistics and have been exposed to computer programming.

This estimated timeframe of 12-18 months corresponds to the time it generally takes to complete a Master of Analytics or a similar postgraduate program. Such degrees are recognized for providing comprehensive training that prepares graduates for professional roles.

There is a lot to learn. Based on subject offerings for online data analytics courses, you may be required to study: Introduction to data science and analysis, mathematics for data science, data management and big data, data analytics and business management, predictive analytics and machine learning, data handling and visualization, programming for data analysis, and advanced data analysis.

In addition to formal education, it’s beneficial to supplement your learning with cutting-edge projects that reflect the demands of the industry. Employers often look for candidates with specific experience related to the platforms, languages, types of data, and analyses relevant to their particular company.

Is it possible to become a data analyst in just three months? The answer is no, at least not a competent one. In three months, you may potentially acquire some of the skills necessary for a data analyst role, but you’re likely to encounter challenges when working on real projects. In essence, you would still be a novice, learning as you go.