In other words, being a Data Scientist is an extremely important job in this data age. So much so that the article in the Harvard Business Review even called it (and this encourages becoming one of them!). “The sexiest work of the 21st century” (and this prompts you to become one of them!).
And it doesn’t hurt the fact that the work of Data Scientist pays off very well with an average salary of 1022 thousand per year. That is why this article is a complete guide to becoming a Data Scientist in 2019. This is a roadmap you can follow if you want to learn more about Data Science.
But there is still a lot of confusion between the differences in the roles of Data Analyst and Data Scientist, so we will start with this article and move on to other topics, such as education requirements and skill requirements, to become a specialist in this field.
Difference between Data Analyst and Data Scientist
Obviously, both Data Analyst and Data Scientist have a job description related to data. But what are the differences between them? This is a question that many people have about the differences between these specialties. So let's clarify this doubt here!
Data Analyst uses data to solve various problems and obtain useful data for the company. This is done using various tools on clearly defined data sets to answer corporate questions, such as “Why is a marketing campaign more effective in certain regions” or “Why product sales declined in the current quarter” and so on. For this, the main skills that a data analyst possesses are Data Mining, R, SQL, statistical analysis, data analysis, etc. In fact, many Data Analysts gain additional necessary skills and become Data Scientists.
On the other hand, Data Scientist can develop new processes and algorithms for data modeling, create predictive models and perform user data analysis in accordance with the requirements of the company. Thus, the main difference is that Data Scientist can use heavy coding to design data modeling processes, and not use existing ones to get answers from data, such as Data Analyst. To do this, the main skills that Data Scientist possesses are Data Mining, R, SQL, Machine Learning, Hadoop, Statistical Analysis, Data Analysis, OOPS, etc. Thus, the reason why Data scientists are paid more than Data analysts , lies in their high skill levels combined with high demand and low supply.
Education Requirements to Become a Data Scientist
There are many ways to achieve your goal, but keep in mind that most of these paths go through college, as a four-year bachelor's degree is a minimum requirement.
The most direct way is that you get a bachelor's degree in Data Science, as it will undoubtedly teach you the skills necessary to collect, analyze and interpret large amounts of data. You will learn all about statistics, analysis methods, programming languages, etc. , which will only help in your work as a Data Scientist.
Another workaround you can choose is to get any technical degree that will help you in the role of Data Scientist. Some of them are computer sciences, statistics, mathematics, economics. After obtaining a degree, you will have the skills of coding, data processing, and quantitative problem solving. Which can be used in Data Science. Then you can find an entry-level job or get a master's and doctorate degree for more specialized knowledge.
Skills Requirements to Become a Data Scientist
Data Scientist requires several skills that span different areas. Most of them are listed below:
1. Statistical analysis. As a data processing specialist, your main task is to collect, analyze and interpret large amounts of data and create ideas that are useful to the company. Obviously, statistical analysis is a large part of job descriptions.
This means that you should be familiar with at least the basics of statistical analysis, including statistical tests, distributions, linear regression, probability theory, maximum likelihood estimates, etc. And this is not enough! It is important to have an understanding of which statistical methods are the appropriate approach for a given data problem, and it is even more important to understand which ones are not. In addition, there are many analytical tools that are very useful in statistical analysis for Data Scientist. The most popular of them are SAS , Hadoop , Spark , Hive , Pig . Therefore, it is important that you know them well.
2. Programming skills. Programming skills are an essential tool in your arsenal. This is because it is much easier to study and understand the data in order to draw useful conclusions if you can use certain algorithms to suit your needs.
In general, Python and R are the most commonly used languages for this purpose. Python is used because of its ability to statistical analysis and its readability. Python also has various packages for machine learning, data visualization, data analysis, etc. (e.g. Scikit-learn ) that make it suitable for data science. R also makes it very easy to solve almost any problem in Data Science with packages such as e1071, rpart, and many others.
3. Machine learning. If you are in any way connected with the technology industry, most likely you have heard about machine learning . This basically allows machines to learn tasks from experience without having to program them specifically. This is done by training machines using various machine learning models using data and various algorithms.
Thus, you should be familiar with the algorithms of controlled and uncontrolled learning in machine learning, such as Linear regression, Logistic regression, Clustering K-means, Decision Tree, Nearest neighbor and more. Fortunately, most machine learning algorithms can be implemented using R or the Python Library (mentioned above), so you don't need to be an expert on them. What you need is the ability to understand which algorithm is required based on the type of data that you have and the task you are trying to automate.
4. Data management and data processing. Data plays a big role in the life of Data Scientist. Therefore, you must be experienced in data management, which includes extracting, converting, and loading data. This means that you need to extract data from various sources, then convert them to the required format for analysis and, finally, upload them to the data warehouse. There are various platforms for processing this data, such as Hadoop , Spark .
Now that you have completed the data management process, you should also be familiar with data processing. Data processing - this basically means that the data in the storage must be cleaned and unified in a consistent way before they can be analyzed to obtain any valid data.
5. Intuition of data. Do not underestimate the power of data intuition. In fact, this is the main non-technical skill that distinguishes Data Scientist from Data Analyst. Intuition of data mainly involves finding patterns in data where they are not. It's almost the same as finding a needle in a haystack, which is a real potential in a huge unexplored heap of data.
Data intuition is not a skill that you can learn so easily. Rather, it comes from experience and ongoing practice. And this, in turn, makes you much more effective and valuable in its role as a Data Scientist.
6. Communication skills. You must have good communication skills to become an expert in the field of Data Scientist. This is because, although you understand the data better than anyone else, you need to convert the data into a quantitative assessment so that the non-technical team can make a decision.
This may also include data storytelling! Therefore, you should be able to present your data in a narrative format with specific results and meanings so that other people can understand what you are saying. This is due to the fact that, in the end, data analysis becomes less important than the practical conclusions that can be obtained from the data, which, in turn, will lead to business growth.