For years now, data analytics has been one of the popular fields in IT. However, data analytics has many challenges, and this is what makes it so marketable. Some people are reluctant to venture into data analytics because they assume it is complex and complicated to master and learn. In truth, data analytics is challenging, especially for those without any background in related subjects. However, it becomes less daunting when you do it right from the word go and learn. There are different lucrative job opportunities in data analytics, This makes efforts put into learning data analytics quite rewarding.
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Data analytics involves collecting, analyzing, and interpreting data to ensure performance is optimized in different businesses. It is also done to achieve goals, mitigate risks, and solve various issues. The area is vast and touches so many industries. This technology is applicable in all sectors today. Data analytics handles all sorts of information. Data analysis has to do with the identification of trends and the calculation of metrics.
On social media, data analytics can adjust how content is presented to the viewers. Manufacturers can data collected from their factories to find out if there are any bottlenecks.
Why is data analytics tough?
The reason why most people consider data analytics hard is because of the cross-disciplinary nature it has. It is a field that may not be suitable for everyone. Some of the reasons why many people consider data analytics difficult include the following:
- Mathematics: Many people consider mathematics a complex subject at all levels of learning. Data analytics is a stem field, and it is linked with math. This means you have to be good at the subject if you want to thrive in data analytics. You may not have to do math manually in many cases because there are algorithms and software to handle that. However, you need to know all workings and how to analyze results.
- Technical skills: data analysts need to have advanced technical skills. It is not the same as some other IT sectors. Data analytics involves working with large data sets and complex systems. The practices and processes are standardized, and the analyst must be accurate with their findings.
- Research skills: As a data analyst, you do more than analyze data. Part of the job is to find the data needed. This helps in the identification of problems. In addition, with data, it becomes possible to measure performance. Therefore, a data analyst needs a keen eye and research skills.
- Learning never stops: data analytics keeps changing, and new trends are introduced now and then. New practices in the industry and tools mean you have to learn without stopping. You must keep in step with changes in the field and always equip yourself with knowledge. This ensures you remain relevant in the competitive field.
Time taken to learn.
Since we have established the things that make data analytics look tough, we can see why it is important to dedicate enough time to the learning process. Giving it enough time allows the student to grasp concepts and understand them.
There are different paths you can take in data analytics. This means that the duration of time taken to learn may also vary. It may depend on whether you have some expertise or technical knowledge in the area or not,
For most data analysts, a four-year course may be necessary. You could start with a computer science degree followed by certification courses. In some cases, a master’s degree in data analytics may be necessary. This route can take around 4 years or more to complete college and all needed certifications in data analytics. Data analytics also requires hands-on experience in the industry, and the best course includes this in the syllabus.
Today, we see a different trend where data analysts complete short certifications and courses to hasten the qualification processes. Courses can take weeks or months, depending on what they are about or cover.
As a beginner, you may need some 6 months dedicated to learning analytics through self-practice, boot camps, and courses. However, even with the courses taken, it is not the end. It is important to keep learning about any technologies as they arise in data analysis. In addition, it takes less time to fully cover the course if you have some data analytics knowledge.
Getting started
To start your data analytics journey, you don’t need a degree or to go to college. Instead, there are some approaches that you can use to learn analytics and data sets. By gaining knowledge, you set off on the right path.
Like all other careers, you need to start with the foundation. The base needs to be strong. Data analytics relies on technical skills, databases, and mathematics. Before using any tools and data sets, you must cover the basics.
Some core concepts and foundational skills to focus on include:
- Data visualization
- Data preparation and cleaning
- SQL
- Data structures and types
- Python or R
- Statistics
- Study
Self-study is possible. You can achieve this by taking courses, watching videos, and reading tools on the areas that matter.
There are free online resources that can be used to build a strong foundation and learn data analytics. Video tutorials are a good match for visual learners. This is a good option when you are learning basic comets like what data analytics is and what it is used for.
If you want to study code, basic subscriptions can come in handy. In addition, you can take advantage of many data analytics free courses in different communities.
Free resources come with great benefits. However, you can better prepare yourself by choosing dedicated analytics courses. You can find a suitable course close to where you are, but you can try an online one. These are more convenient and flexible.
Taking a course means you are connected to professionals. Learning from them boosts your chances of being hired. With the proper certification, recruiters will notice and hire you. Data analytics is tough to master. You cannot do it alone. Don’t be reluctant to ask for help from a friend, mentor, or instructor already in the field. Keep polishing your skills and updating your knowledge throughout your career.
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