One of the biggest misconceptions about becoming a data scientist is — you need to know about the program! It is definitely one of the key skills for data scientists, but it is not mandatory. Many data scientists come from a non- IT background and have climbed up the career ladder steadily. Lack of programming knowledge isn’t a criterion for most roles in data science, so it shouldn’t discourage anyone from learning data science, there is other more important stuff that counts toward becoming a data scientist.
Some approach data science certification courses, while others feel special about workshops, online short-term courses, and even in-class programs to learn data science certification. This post is dedicated to all the people from non-programming backgrounds who wish to learn data science.
Busting the myth
Lack of programming competencies shouldn’t hold you back from pursuing a career in data science. There’s been a lot of anxiety that people aiming to work in data science must have programming skills; it is an essential skill no doubt, but it’s not all. Saying that programming is necessary for data science is like saying being fish is important to swim, which is not correct as we all know. Creatures other than fish can swim too. Companies looking for data scientists require them to have a diverse set of skills, not just programming.
There’s no doubt that programming is an essential skill for a career in data science. But there’s a lot more to learn in data science than programming. Industry experts believe that someone who has a strong base in programming concepts like what if, loop, arrays, etc., and logic do well in data science. What this means is — programming skills are highly preferred, but not it’s mandatory.
Skills for Data Scientist
Programming is a part of a diverse set of skills. To become a data scientist, you need the following skills —
- Statistics — Concepts like measures of central tendencies, dispersion, hypothesis testing, probability distribution, etc. are crucial to understand data and derive insights from the generated data.
- Mathematics and data structures — Probability and linear algebra are essential concepts to understand machine learning algorithms.
- Machine learning algorithms — KNN, SVM, Naïve Byes, Regression, and other algorithms are frequently used to build models.
- Data visualization — Visualization tools like Tableau, Microsoft BI, etc. are also required to present data to stakeholders.
- Tools — Rapid Miner, Hadoop, Spark, Hive, BigML, MLBase etc. are essentially big data analytics tools that allow data scientists to carry out analytics operations.
Data science certifications offer a convenient way to learn data science. For people from a non-technical background, certifications offer a comprehensive way to learn data science while sitting in the comfort of your home. The following are a few globally –recognized certifications that allow aspirants to learn data science from end-to-end.
IBM offers this certification program in collaboration with Coursera. As a non-technical data science aspirant, you will get the opportunity to learn to write code using Python. You will learn to clean data, analyze, and visualize data. Finally, you will learn to build a machine learning model using Python.
This certification is offered by the Data Science Council of America (DASCA). ABDA™ is a high-value, globally-recognized, vendor-neutral data science certification that equips you with all the necessary skills –programming, statistics, and various tools that accelerates your entry into a data science career. You learn R, Python, Java, and SQL as part of the certification. If you’re a beginner looking to jump-start a career in data science, ABDA™ is the best data science certification out there!
Harvard University offers this data science certification course in collaboration with edX. The highlight of this certification is R. You will learn R, statistics, data visualization, and machine learning. Lastly, you will be implementing everything you have learned using R and its related application R studio.