What motivates data scientists to work

How do I become a data analyst?

In an increasingly networked world, the data analyst sits at the interface between statistics, business, IT and communication. So it's no wonder that this job profile is extremely popular on the job market. But with large data sets comes great responsibility.

For this job you have to be an analytical mastermind who can also think abstractly. In return you will receive the Unique opportunity to play a leading role in the company's product, process or company developments.

What's the best path to your first job in data analytics? What tools can you use to build your skills in the most efficient and effective way? Just take a few minutes because we have all the answers.


What exactly is a data analyst?

One of the most common definitions on the Internet is that data analysts “translate numbers into plain text”. Starting from raw or unstructured data, they create analyzes that should ultimately lead to better business decisions. This could mean figuring out how to calculate new materials for the market, how to cut transportation costs, solve problems that cost the company money, and much more. There are several types of data analysts in the world of work including business analysts, marketing analysts, financial analysts, etc. based on the area of ​​specialization.


What is the difference between a data analyst and a data scientist?

In contrast to the data analyst, the data scientist is also a professional in the interpretation of data, but also has knowledge of coding and mathematical modeling. He can do the job of a data analyst, but he can also practice machine learning, have advanced programming skills, and create new processes for data modeling. A well-trained data scientist is better than a data analyst in terms of know-how and experience and is therefore even less likely to be found on the job market.


What Makes a Great Data Analyst?

As in most professional fields, there are must-haves and nice-to-haves here too. We mainly focus on the former:

  • A high level of math skills: The analysis of data requires statistical knowledge and a good level of comfort with formulas. As a data analyst, you should have a good understanding of math and be able to solve common business problems, such as: B. Compound interest calculation, depreciation, statistical key figures (e.g. mean, median, mode), etc. It is important to be familiar with linear algebra at a high levelbecause you will be confronted with it in most data analysis.
  • Programming languages: As a data analyst, you should be able to master at least one, preferably several, programming languages. The most important are undoubtedly R, Python, C ++, Java, MATLAB and PHP.
  • Data management and processing: You should be familiar with languages ​​such as R, HIVE, SQL, etc. in order to create the desired queries. After you've analyzed the data, you need to be able to generate accurate reports. Some common tools are SAS, Oracle Visual Analyzer, Microsoft Power BI, Cognos, Tableau, etc.
  • Domain knowledge and excellent communication skills: The job of a data analyst is to provide decision makers with detailed and accurate information. Therefore, a data analyst not only needs to understand the data, but also the different requirements for his results. Good communication skills are therefore essential.
  • Microsoft Excel: Unfortunately, having a computer driver's license will not be enough here. A data analyst must be able to exploit the full potential of Excel in order to optimally organize and calculate his data. You can find a current list of the best Microsoft Excel online courses here.


What is the salary of a data analyst?

A good salary is always a good argument for a job. According to datacareer.de, the average gross annual salary for a data analyst in Austria is the equivalent of € 41,900. With the appropriate work experience and additional qualifications, it can be up to € 60,000 in this professional field. In a European comparison, we're not that bad at all. Because here the average gross annual salary is around € 35,800. But what about in the individual markets?

  • Italy: € 24,700
  • Spain: € 30,100
  • Ireland: € 36,800
  • Denmark: € 59,500

At the time this article was written, well over 500 data analyst positions were advertised in Austria - From banks to energy companies to gambling providers, the data analyst does not have to worry about his professional future. The question automatically arises ...


How do I become a data analyst?

Are you now motivated to start a career as a data analyst? Congratulation! You have chosen a lucrative, geographically flexible and extremely secure career in an up-and-coming field. We have summarized the most important points for you so that you can start the learning process directly:

Each of these books will bring you closer to your goal:

  • Data Analytics Made Accessible, by A. Maheshwar: If we had to pick one book for an absolute newbie to data science, this would be it. Data Analytics Made Accessible explains data analysis in a simple way and promotes basic understanding through: Concrete examples from practice, an intuitively organized layout and summarizing case studies at the end of each chapter.
  • Python Data Science Handbook, by Jake VanderPlas: Recent data shows that Python is still the leading language for data science and machine learning. As a data scientist, you will work on numerous tasks. Most of your time, however, will be spent on data manipulation and data cleansing. This is exactly where the Python Data Science Handbook comes into play.
  • Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by E. Siegel: We added predictive analytics to our list because, like no other, access unlocks the power of big data. From a business perspective, predictive analytics is used to analyze current data and historical facts in order to better understand customers, products and competitors and to identify potential risks and opportunities for a company.
  • Data Smart: Using Data Science to Transform Information into Insight, by J. W. Foreman: Microsoft Excel - endless tabular widths. Data Smart is useful reading for anyone with some background knowledge of applied mathematics and an installed Microsoft Excel. An absolute recommendation for all business professionals who work with data sets.
  • Creating Value with Social Media Analytics, by Gohar F. Khan: If you want to work a lot with data, you should also be familiar with the most important KPIs in the social media world. This latest text from Khan provides a clear and concise understanding of the business value created by social media data. Some prior knowledge is definitely an advantage for this reading.


Which experts should you definitely follow?

  • Dean Abbott is Co-Founder and Chief Data Scientist at SmarterHQ and Founder and President of Abbott Analytics. He is co-author of the IBM SPSS Modeler Cookbook and author of Applied Predictive Analytics. Follow his blog.
  • Kenneth Cukier is a data editor at The Economist. He is co-author of the book "Big Data: A Revolution That Will Change Our Life, Work and Thinking" and a well-booked speaker. Here you can find one of his many fascinating TED Talks.
  • Bernard Marr, Founder and CEO of the Advanced Performance Institute, regularly advises companies and government organizations on how to get better insights from their data. He contributes to the World Economic Forum and is listed by LinkedIn as one of the world's 50 leading business influencers.


Which tools are essential for data analysts?

Gone are the days when you could tame the existing amount of data with a single tool. Nowadays more and especially the following 5 are needed:

  • Microsoft Excel: The classic among data analysis tools offers a multitude of functions, from sorting and processing data to displaying this data in the form of diagrams. It can be used for all kinds of arithmetic operations, as well as statistical, technical and financial operations.
  • SAS: SAS is a software suite developed by the SAS Institute for advanced analytics, predictive modeling, business intelligence and data management. It provides numerous data management and data analysis tasks and is an excellent analysis tool for experienced users. SAS can handle pretty much any kind of statistical modeling and also large amounts of data without any problems.
  • R Programming: The direct competition for SAS is R, a programming language and software environment for statistical calculations and graphics. It is an excellent tool that can be used to perform any type of statistical analysis. It's also open source and free software to experiment with. R has a strong package ecosystem that makes working with it a lot easier.
  • SQL: SQL (Structured Query Language) is a special programming language for communication and administration of a database, especially in an RDBMS or RDSMS.
  • Python: Python is a widely used, general-purpose programming language that is easy to learn, contains easy-to-read lines of code, and is open source. It is very fast and has a large library base for statistical analysis.


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