Top 10 Skills needed to become Data ScientistCategory: Data Science Posted:Jan 30, 2019 By: Robert
According to Glassdoor report, a data scientist role is ranked number one profession in America for the year 2019. Well, there is no doubt that a data scientist claimed the top spot for the second year in a row. The data scientists are getting an average salary of $123,000. The job market of data science is a long way from soaked, with an expected shortage of 190,000 experts in the US only. In the event that you are looking for a career in data science, in the upcoming year, there will be plenty of profitable opportunities. A data scientist is likely to be paid an excellent salary. Due to the growing demand and high ranging salary, most of the people want to become a data scientist now, but many questions arise in their minds, that is how to become a data scientist and what skills are required for it. In order to become a data scientist, a person should possess some essential skills such as programming skills, machine learning, data visualization, communication, data wrangling, software engineering, etc. Let’s discuss them in detail:
1. Education: Typically, a strong educational background is usually required to have a depth of knowledge necessary to become a data scientist. To become a data scientist, you should have a Bachelor’s degree in Computer science, Social science, Physical science, and Statistics. The most common fields of study are Mathematics and Statistics followed by Computer Science and Engineering. A degree in any of these courses will give you the required skills to process and examine big data. Most of the data scientist has a master’s degree or Ph.D. and also take the online training to learn a particular skill such as how to use Hadoop or Big Data. Thus, it is required enrolling for a master’s degree program in the field of Data Science, Mathematics, or any other related field. The skills which you have learned during your degree program will help you to move towards the data science field.
2. Fundamentals and Statistics knowledge: A candidate must have a basic understanding of matrices and linear algebra functions, hash functions and binary trees. They must know relational algebra, basics of the database, reporting VS BI, i.e. Business Intelligence and analytics. Along with this, a data scientist should have a good knowledge of statistics. A candidate must know descriptive Statistics, Exploratory Data Analysis, Probability Theory, random variables, Byes theorem, statistical test, etc. The statistics are essential in all types of companies’ specifically data-driven enterprises where stakeholders will depend on data scientist to make design and decisions, and also to evaluate experiments.
3. Programming Skills: It doesn’t matter which role or company you are interviewing for, the data scientists must have excellent programming skills. A data scientist should know to utilize the tools of the trade. The data scientist must have knowledge about programming languages such as SQL and statistical programming languages like Python and R.
4. Linear Algebra and Calculus: It is essential for organizations to grasp the concept of Linear algebra and calculus where the data characterize the essence of the item, and algorithmic advancement or small improvements in predictive execution can prompt the accomplishment of the organization. When you give an interview for a job in data science, the interviewer may ask you some essential linear algebra questions or multivariable analytics. Or on the other hand, you will be asked to determine a few statistics or machine learning results you execute somewhere else.
5. Communication: A data scientist must have excellent communication skills. Data scientist must have the ability to report technical findings with the ultimate objective that they are understandable to non-specific accomplices, irrespective of whether partners or corner-office executives in the marketing department. Data scientist must be capable of making your data-driven story not only possible yet rather convincing and you could urge your supervisor to give you a raise.
6. Machine Learning: A data scientist must know machine learning algorithms. If you are working in a gigantic organization with a massive amount of data or working in an organization where the data-driven product is available, for instance, Google Maps, Netflix, Uber, etc. There might be a situation, where you should be comfortable to learn machine learning techniques. This can mean things like ensemble strategies, irregular timberlands, k-nearest neighbors, and so on. Many of these methods can be executed utilizing Python and R libraries.
7. Data Visualization: Data visualization is an essential part of Data lifecycle. It is found that images often speak more proficiently than the words or numbers, thus it helps data scientist by presenting data in a visually stimulating way. This not only requires you to familiarize yourself with the principles of visualizing data proficiently, but you should also master data visualization tools. Some of the visualization tools are Tableau, Kibana, Google charts, Data wrapper, etc.
8. Data Wrangling: Data wrangling is another skill, which a data scientist must have. It is also called as data munging. Data Wrangling is a process of mapping and converting data from a single raw data form in a different format. The data to be investigated is challenging to work, and it will be chaotic. A portion of the defects in data incorporates conflicting string organizing, missing qualities, and date arranging. This will be profoundly critical at small organizations where you’re an early data appoint.
9. Knowledge of Big Data: Big data is everywhere now, and there is a crucial requirement for collecting and reserving data is being generated. There is a tremendous amount of data which is gliding around. What we do with it is the only thing that is important at present. That’s the reason Big Data Analytics is in the limit of IT. Big Data Analytics has turned out to be critical as it helps in enhancing business, decision makings and giving the most significant edge over the contenders. This applies to associations as well as experts in the Analytics space. Data Scientist should know about frameworks which can process Big Data. Hadoop and Spark are the two most well-known frameworks.
10. Data-Driven Problem Solving: A data scientist must develop a data-driven problem-solving skill, which will come with experience. A data scientist must know how to approach a problem effectively. This means recognizing scenario’s salient features, determining what estimate makes sense, and checking with right co-workers at the appropriate stage of the analytic process, etc.
Conclusion: A candidate should have effective skills to become a data scientist. Having these skills will help them to make a successful data science career. The demand for a data scientist is increasing day by day, and it is expected that the requirement of the data scientist will increase in the future; thus it is an excellent time to develop a career in this particular field.