Data Analytics – At a time when data collection and storage are more prevalent than ever, understanding how to effectively analyze and extract insights from that data is a key enabler for many businesses and organizations. Enter Data Analysis: the field emerged in the 1960s. With the advent of big data, cloud computing, machine learning, and various other software and hardware, data analysis has developed significantly, becoming an important part of modern business decisions.
Because of the tremendeous growth in the industry, data analysis has become a popular field for those looking to change careers. But the uninitiated may have many questions about the field, such as:
What exactly is Data Collection or Gathering, anyway?
And: how to become a Data Analyst?
We’ll try to find answer to these and other questions in this beginner’s guide to data analysis.
Are you ready to dive into data analysis for beginners? Let’s get started!
What is Data Analytics?
Every time we open an app, make a purchase at a supermarket, perform a survey, or fill out a CAPTCHA to log into our email, we create data that is collected by companies and organizations.
As you can only imagine from these programs, a lot of data is collected every day! So what happens to this data? Well, for a large part of them, up to 99.5%, the answer is nothing. But the remaining 0.5%? This is used for data analysis.
In the context of data analysis, data analysts seek to extract valuable insights from the filtering of raw data presented to them. By doing this, companies and organizations can unleash their predictive power, which empowers them to make smart business decisions.
Through data collection, companies are able to answer the following questions:
What happened in the past? What is happening now? What can happen in the future? What skills do I need to become a data analyst?
While there is no clear career path to becoming a data analyst, there are a few technical principles and soft skills that any data analyst entering the field will need. This list is not exhaustive, but consider it a starting point if you are considering a career change.
Skills needed to become a Data Analyst
- Demonstrated knowledge of programming and query languages, such as R, Python and SQL
- Proficient in business intelligence and data analysis software, which may include RapidMiner, Tableau, and SAS
- Strong understanding of each step of the data analysis process
- Strong numerical and statistical skills
- Hard skills are, as you may have understood, technical skills that are required to meet the requirements of a job. They are usually measured in terms of skill, ranging from basic skills to advanced skills.
General skills required to become a Data Analyst
- Good collaboration and communication skills
- Attention to detail
- Methodology and common sense
- A problem-solving mindset
- Soft skills, compared to hard skills, are not considered. Think of soft skills as characteristics that are part of your current personality, although you may have acquired or developed these skills through work or other experiences you have had.
How do you become a Data Analyst?
Now that we have covered the basics of data analysis for beginners: what is data analysis, types of data analysis, data analysis techniques, and the skills that data analysts have, you may be wondering, “Okay! So, how How do you become a data analyst?”
It is possible to get a job as a data analyst without any training. For example, if you want to become a medical examiner and you work in the medical field and have the soft skills required, your employer may be interested in offering a company to upgrade you with the hard skills required. However, this should be considered a non-traditional input method.
For an organized field trip, here are some good steps to follow:
Attend a Boot Camp or Data Analysis program
Especially if you plan to enter the field with little or no experience, having a dedicated data collection boot camp is the best way to cover all the skills and knowledge necessary to become a researcher data.
It’s worth looking for courses with project-based courses – as you can use these projects in your future portfolio – as well as individual courses and certificates of completion. Other attractive benefits would include a focus on job readiness, networking opportunities, and job guarantees.
We’ll talk more about boot camps and data analysis courses later in this article, so read on!
In this article, we review some of the best data analysis libraries on the market.
Write a dedicated CV
The job market can be tough in any industry, so having a strong resume is key to standing out to employers and potential future employers. If you’re looking to change careers in data analysis, you’ll need to rewrite your resume to highlight any new skills you’ve learned during your data analysis program or any other skills acquired in previous jobs. past. which can be useful!
Build a strong Data Analytics portfolio
You might think that if you’ve figured out a startup, you’re good to go, right? Bad! Recruiters and employers want to see your skills and experience demonstrated in previous jobs, so many career changers will also build a data analysis portfolio alongside their CV. It’s a good idea to keep this portfolio online, so you can update it regularly.
You should include a variety of projects that demonstrate different aspects of your data analysis skills. Consider including work you’ve done alone and work you’ve done as a group; projects using different programming languages; projects carried out according to different research methods; The project uses visualizations and clearly written descriptions of your results.
Data Analysis portfolios
Learn more about data analysis portfolios (with examples!) in this article.
The field of data analysis is vast, and not all services you find online will have the same name. We describe many job titles you can find online and job descriptions that can be included in this guide. However, we recommend that you do your own research to find out which other areas, and other specific companies, are the best for your needs.
Once you’ve narrowed down the list of companies and groups you may be interested in, networking is key. This can be done by attending job fairs, contacting recruiters, or reaching out to people on LinkedIn. It’s a great way to get information about upcoming jobs without posting all the time on the job page.
Finally, run away and start applying for jobs! Make sure you tailor your cover letter to any job that interests you. Yes, it’s extra work, but it’s worth it – recruiters can spot a cover letter a mile away. Going above and beyond shows that you are genuinely interested in the job. It may take a little time for your efforts to pay off, but it will be worth it in the end!
You may enjoy this webinar recording we have prepared for you to become a data analyst. We often host live conferences and webinars related to data analysis. You can see our upcoming events here.
What are the requirements for studying data analysis?
This free data analysis process can be done without any knowledge of statistics or programming. However, a clear understanding of basic mathematics is recommended before starting this program.
How do beginners learn data analysis?
Beginners start with data analysis by learning different types of data and how to organize that information to draw meaningful conclusions. This free data analysis course can give you a complete overview of data analysis and how to use it.
How long does it take to learn data analysis?
Going through all three hours of introduction to data analysis gives you a good start on your data analysis journey. Depending on the person’s learning curve, it may take you about six months to learn data analysis.
What should I learn first in data collection?
For the field of data analysis, a good place to start is to learn about the different types of analysis and the different industry sectors where data analysis is accepted.
Is data analysis easy to learn?
To learn data analysis, one must have a strong interest in the data processing. With our free data analysis system, you can get started in this field almost effortlessly.
What are the basics of free data collection for introductory courses?
Professionals beginning to study data analysis usually have a basic understanding of math, MS Excel, SQL, and common terms used in data analysis.