Data analysis has become an essential tool in many industries in recent years. With the growing amount of data being generated daily, the ability to analyse and interpret this data has become a valuable skill for businesses of all sizes.
However, many people are intimidated by the idea of learning data analysis, thinking that it requires extensive knowledge of statistics, programming, and advanced mathematics.
The analysis of data is not difficult. Having said that, it does take some time to become proficient in its talents.
You will need to have excellent analytical and problem-solving abilities, a solid understanding of fundamental mathematics and statistics, as well as the ability to develop code.
Studying data analytics takes only six months, and you may get work in the field even if you have no background in data-related fields.
In this article, we will explore whether it is hard to learn data analysis and provide tips for those who want to get started.
Quick Links To Online Data Science Courses
James Cook University
Graduate Diploma of Data Science Online
- 16 months, Part-time
- 8 Subjects (One subject per each 7-week study period)
- $3,700 per subject, FEE-HELP is available
University Of New South Wales Sydney
Graduate Diploma In Data Science (Online)
- Duration: As little as 16 months
- 8 courses
- Study Intakes: January, March, May, July, September and October
University Of Technology Sydney
Applied Data Science for Innovation (Microcredential)
- 6 weeks
- Avg 14 hrs/wk
Graduate Certificate In Data Science
- 8 months intensive, part-time
- 4 Courses (7 weeks each)
- $3,840 per course, FEE-HELP available
What Is Data Analytics?
The practice of analysing massive volumes of data in order to discover patterns and insights on trends, customer habits, and other topics is referred to as data analytics.
It is used to understand the demands of customers, forecast future trends, and enhance processes by revealing insights that were previously concealed.
Data analytics is a wide phrase that defines the different methods used to analyse raw, unprocessed data in order to derive meaningful insights and patterns.
This analysis is done in order to improve business operations. Afterwards, organisations utilise this knowledge to create decisions that are more informed and driven by data.
The process of data analytics begins with the collection of data, continues with their organisation, and then concludes with statistical analysis of the figures.
When the research has been completed, the Data Analyst will provide projections that the organisation may use to guide its subsequent actions.
It is estimated that 2.5 quintillion bytes worth of data are produced every single day. It is anticipated that this figure will increase as a greater number of firms embrace technology developments and extend their presence online.
Yet, until these data are evaluated, all they are is a bunch of numbers. Interpreting these numbers and transforming them into actionable insights is part of a Data Analyst's work, which requires them to combine computer programming, mathematics, and statistics.
These insights may then be shared with a variety of stakeholders within a company.
Predictive Data Analytics
Forecasting future events and the behaviour of customers is an important part of predictive data analytics. In addition to other applications, it is utilised for predictive maintenance, product suggestions, market segmentation, customer churn analysis, and the calculation of customer lifetime value.
The most well-known use of this is probably the suggestions that you get on Spotify or the Explore tab that you may access on Instagram.
They utilise algorithms to establish your interests based on the previous music and posts that you have looked at on their site. These preferences are then used to recommend content to you.
Prescriptive Data Analytics
The application of prescriptive data analytics allows for the recommendation of the most effective solutions. It is utilised for the purpose of decision support, optimisation of supply chains, and resource allocation.
Diagnostic Data Analytics
The application of diagnostic data analytics allows one to get to the bottom of an issue and find its source. It is utilised for the improvement of processes, as well as quality assurance and customer service. Customer segmentation, mapping of customer journeys, A/B testing, and other similar practices are all examples of diagnostic data analytics.
Descriptive Data Analytics
The purpose of using descriptive data analytics is to offer a summary of events that have occurred in the past. It has a variety of applications, including customer journey mapping, customer segmentation, and more. Analysis of trends, market sizing and forecasting, and other similar activities are all examples of descriptive data analytics.
Discovering new insights and improving decision-making processes via the use of those new insights are the primary goals of data analytics.
What Can You Do with Data Analytics?
The discipline of data analytics has a wide range of practical applications that may be found in a variety of fields and professions. The purpose of utilising data in order to arrive at better conclusions is at the heart of the data analytics process.
Businesses rely on the insights provided by the data in order to make more educated business decisions, which may assist them in expanding their organisation, increasing their income, or providing a better experience for both their staff and their consumers.
The majority of the guessing that is involved in marketing campaign planning, content creation, and product development may be eliminated with the use of data analytics.
This can be a huge benefit. In addition to this, it offers an overview of consumers, which enables businesses to satisfy the requirements of their clients better. If you have a deeper grasp of your audience and what it is that they want, it will be much simpler to target that demographic with more targeted marketing efforts and campaigns.
One other common application of data analytics is to enhance the quality of customer service. As data is evaluated, valuable insights about clients are revealed, which makes it possible to provide more tailored service to those customers.
Data may offer information on how consumers prefer to interact, what interests they have, what worries they have, and which goods they look at the most frequently. When an organisation's data is housed in a centralised place, it is easier for the customer service team to communicate effectively with the marketing and sales departments.
One further use for data analytics is to increase the effectiveness of processes already in place inside a business. The analysis of data can bring to light inefficiencies in operations as well as chances to simplify a variety of procedures.
This not only helps the company operate more effectively, but it also has the potential to save money over time. You won't need to waste time and money producing advertisements or writing content that doesn't directly target the interests of your target audience, for instance, if you have a better concept of what your target audience is searching for in the first place.
This results in improved campaign outcomes while also reducing the amount of money that is squandered.
How Long Does It Take To Learn Data Analytics?
Learning data analytics might take anywhere from six to eight months of full-time study. This is accomplished by ongoing training and practice in order to get an understanding of the fundamentals of data analysis and visualisations. These fundamentals include things like accessing databases, cleansing data, creating graphics, and constructing fundamental machine-learning models.
If you want to move into more advanced areas of data analytics, it is estimated that it will take you anywhere from six months to a year or even longer, depending on how much practice you are willing to put in and how quickly you are able to learn. If this is something that interests you, keep reading.
Excel was my starting point, and after about a month and a half, I was ready to perform some fundamental data analysis. After that, I switched to Tableau, which is a tool for the visualisation of data. It took me about two to three months to get to the point where I could do it effectively.
Because I learnt this while I was still in school, I was able to take advantage of a complimentary licence for Tableau Desktop, which I put to use by developing visuals that I then uploaded to my Tableau Public account. I strongly suggest that you do the same thing! This might be useful in the construction of your digital portfolio.
After that, I taught myself R using free online classes available on YouTube, but I reinforced that education by enrolling in a live session that taught me how to create good code comments and how to format code correctly. I am using RStudio, which has recently been rebranded as Posit, as my preferred R-integrated development environment (IDE). I also downloaded my own data science integrated development environment.
After that, in order to augment what I had learned, I participated in a few internships, and I also worked on several personal projects.
Following that, I switched to a programming language that is more widely used, such as Python, so that I could automate data analysis chores and construct my own machine-learning models.
Machine learning libraries written in Python, such as PyTorch and TensorFlow, are among the programming language's most notable contributions. Deep learning is an unsupervised kind of machine learning, and it will assist you in beginning your journey into this field.
In addition, despite the fact that I made far heavier use of R in the course of my real employment as a data analyst, I felt Python was a lot more amazing than R.
I would guess that it took me around eight months of constant study and practice before I was able to perform sophisticated data analytics.
What Are the Most Challenging Parts of Learning Data Analytics?
Like acquiring any new ability, mastering data analytics has certain obstacles and takes time and dedication.
Big data may be challenging to learn, especially for individuals without a technical background, who haven't worked with programming languages before, or who haven't used data visualisation software before.
The following lists some of the most significant difficulties in learning data analytics and makes some recommendations for how to make the process simpler:
Finding the data you'll utilise for data analytics is the first stage in the process. Data must be relevant in order to be useful for an organisation's decision-making process; not just any data will do.
Data comes from a variety of sources, making it more difficult for data analysts to choose which is ideal for the requirements of their firm. Since they can contain data from several sources in a single area, data warehouses are quite popular.
The procedure may be greatly aided by maintaining an ordered inventory of the data assets in data repositories.
Another problem that Data Analysts frequently run into is understanding the data that has been accessed. This method frequently calls for cataloguing data assets by keeping track of details like each column definition in a data warehouse's tables.
Automated analytic features, for example, can significantly speed up this procedure without compromising accuracy.
Data must be cleaned after it has been collected since it is frequently dirty. For Data Analysts, pre-processing data can take a lot of time because it entails laborious activities like encoding variables and removing outliers.
Although this step in the analytics process is sometimes seen as the most difficult, it is crucial since it guarantees that the models utilised are built using high-quality, clean data. Pre-processing may be greatly accelerated by employing strategies like enhanced analytics, which uses AI and machine learning.
How Does Learning Data Analytics Compare to Other Fields?
Data analytics and the area of data science are closely connected. Data analytics often entails asking questions about data and then using statistical analysis to look for the answers.
After these questions have been addressed, the data analyst can provide actionable insights that will enhance operations, boost sales, and make the company function more efficiently. Working with computer programming languages, using data visualisation tools, and doing various analyses are all common parts of this process.
But, data science is more interested in the questions that may be asked about the data than it is in the answers. Data scientists frequently carry out open-ended research and data modelling.
Data science, in a way, lays the groundwork for the many kinds of analyses the business may wish to conduct on the data. To examine massive datasets, this method frequently calls for data manipulation, statistical modelling, and computer programming.
Data scientists frequently work with the marketing and sales teams, as well as the product development and financial departments, inside their businesses. Data Scientists frequently drive the decision-making process, in contrast to Data Analysts, who typically respond to decision-makers demands.
Studying data science is similar to learning data analytics in that both need instruction in computer programming languages like Python, R, and SQL, in addition to having a strong background in Microsoft Excel.
Data analytics, on the other hand, places more of an emphasis on data visualisation using tools like Tableau, and data science has more of an emphasis on machine learning.
How to Become a Data Analyst?
Learn the fundamentals of data before attempting to become a data analyst. Discover the essentials of data analysis and visualisation, including how to use databases, prepare data sets for analysis, create graphics, and create simple machine learning models.
Online classes are a terrific method to master the fundamentals quickly. On YouTube, you may find a tonne of free resources that introduce data analytics.
After mastering the fundamentals, try getting more technical by exploring more advanced subjects like deep learning and natural language processing.
As there is no one way to become a data analyst, it's critical to follow business trends and remain up to date on emerging technology.
Speaking of cutting-edge technology, keep in mind that getting a nice laptop that functions well for programming and data science is important since it may be quite helpful in processing bigger datasets.
Furthermore, helpful for staying in touch with data experts are local gatherings and internet forums. This is a fantastic approach to acquiring suggestions and keeping informed about the most recent advancements in the industry.
Finally, it's critical to compile a strong portfolio of your completed data analytics projects and work samples. Candidates with a broad range of experiences and the ability to demonstrate their talents are more likely to be hired by employers.
Because every data analyst job is unique, be sure to read the various job descriptions for any positions that may be of interest to you. They assist you in developing the talents you are lacking.
Are Data Analytics Courses Worth It?
The answer is yes; taking classes in data analytics is an investment that is becoming increasingly useful and can help you grasp important programming languages like Python. These accelerated courses provide greater opportunities for hands-on learning and the development of skills that are specific to the job, which are two of the numerous benefits that make them superior to four-year degrees.
In addition, there has never been a time when the need for data experts was stronger, and this trend is only anticipated to continue.
Employers not only reward up-to-date data training in their existing employees, which ensures that they are able to keep up with the pace of change, but the number of new positions being created in data analytics is in the millions.
Even if you are currently employed in the data industry, expanding your skill set and developing other areas of expertise may enable you to earn a higher wage.
This is true even if you compare salaries for data positions to those of other occupations in the technology industry.
The data certificate courses offered by BrainStation were developed with the intention of assisting professionals in taking advantage of these opportunities by providing them with the opportunity to gain hands-on experience in generating striking data visualisation, making data-driven predictions, and gaining new insights from data sets.
It is important to emphasise, however, that even though Data Analysts can come into the industry with a wide variety of educational and professional experiences, the positions themselves do require a specific level of technical expertise as well as a good working knowledge of a variety of programming languages.
Even though it's a specialist sector, the level of technical difficulty is still rather high. In other words, you should be prepared to commit to studying for the rest of your life because the sector is always evolving.
Data analytics is the practice of analysing massive volumes of data to discover patterns and insights on trends, customer habits, and other topics, and is used to understand the demands of customers, forecast future trends, and enhance processes.
Data analytics is an important part of forecasting future events and the behaviour of customers and can be used for predictive maintenance, product suggestions, market segmentation, customer churn analysis, and customer lifetime value.
Data analytics can provide an overview of consumers, enhance customer service, and increase the effectiveness of processes. It can take up to six months to learn, depending on how much practice you put in and how quickly you learn.
Learning data analytics takes time and dedication, and is challenging for those without a technical background or who haven't used data visualisation software before.
Data analytics involves finding relevant data, understanding the data that has been accessed, and pre-processing data. Automated analytic features can speed up this process without compromising accuracy.
Data science is similar to data analytics in that both require instruction in computer programming languages and a strong background in Microsoft Excel. To become a data analyst, it is important to learn the fundamentals of data analysis and visualisation, follow business trends, and stay up to date on emerging technology.
Data analytics courses are becoming increasingly useful and provide hands-on learning and skills specific to the job, making them superior to four-year degrees. However, they require technical expertise and a good working knowledge of programming languages.
- However, many people are intimidated by the idea of learning data analysis, thinking that it requires extensive knowledge of statistics, programming, and advanced mathematics.
- The analysis of data is not difficult.
- In this article, we will explore whether it is hard to learn data analysis and provide tips for those who want to get started.
- These insights may then be shared with a variety of stakeholders within a company.
- Forecasting future events and the behaviour of customers is an important part of predictive data analytics.
- One other common application of data analytics is to enhance the quality of customer service.
- One further use for data analytics is to increase the effectiveness of processes already in place inside a business.
- Learning data analytics might take anywhere from six to eight months of full-time study.
- Deep learning is an unsupervised kind of machine learning, and it will assist you in beginning your journey into this field.
- The following lists some of the most significant difficulties in learning data analytics and makes some recommendations for how to make the process simpler: Finding the data you'll utilise for data analytics is the first stage in the process.
- Data analytics and the area of data science are closely connected.
- Learn the fundamentals of data before attempting to become a data analyst.
- Because every data analyst job is unique, be sure to read the various job descriptions for any positions that may be of interest to you.
- Are Data Analytics Courses Worth It? The answer is yes; taking classes in data analytics is an investment that is becoming increasingly useful and can help you grasp important programming languages like Python.
- Even if you are currently employed in the data industry, expanding your skill set and developing other areas of expertise may enable you to earn a higher wage.
- It is important to emphasise, however, that even though Data Analysts can come into the industry with a wide variety of educational and professional experiences, the positions themselves do require a specific level of technical expertise as well as a good working knowledge of a variety of programming languages.
Frequently Asked Questions
Like any acquired skill, learning data analytics poses unique challenges and requires time and commitment to master. Learning to work with big data can be difficult, especially for those without a technical background or who don't have prior experience with programming languages or data visualisation software.
Data analysis is neither a “hard” nor “soft” skill but is instead a process that involves a combination of both. Some of the technical skills that a data analyst must know include programming languages like Python, database tools like Excel, and data visualisation tools like Tableau.
As with any scientific career, data analysts require a strong grounding in mathematics to succeed. It may be necessary to review and, if necessary, improve your math skills before learning how to become a data analyst.
Several data professionals have defined data analytics as a stressful career. So, if you are someone planning on taking up data analytics and science as a career, it is high time that you rethink and makes an informed decision.
It can take anywhere from several months to several years to become a data analyst. The amount of time it takes you will depend on your current skill set, what type of educational path you choose, and how much time you spend each week developing your data analytics skills.