Possess top five data science skills to be a professional data science candidate

Statistics

A candidate needs to have statistical ideas and procedures to pass data science examinations, thus it should come as no surprise that they should have a strong grasp of statistics. Data science candidates can gather, organize, analyze, interpret, and present data more effectively if they are knowledgeable with statistical analysis, distribution curves, probability, standard deviation, variance, and other statistical concepts.

Linear algebra and multivariable calculus

An applicant must have a thorough understanding of mathematical principles. Calculus and algebra abilities are also required for passing the data science examinations. A candidate should be familiar with the use of dimensionality reduction to simplify difficult data analysis issues.

Coding and Programming

To pass data science examinations, a candidate must have a solid understanding of programming and coding. Python is by far the most popular programming language among data science applicants. R is another widely used language, including statistical computation and graphics. C and C++, Java, and Julia are among the other programming languages that data science candidates frequently use.

Predictive modeling

Being able to use data to make predictions and model different scenarios and outcomes is a central part of data science. Predictive analytics looks for patterns in existing or new data sets to forecast future events, behavior and results; it can be applied to various use cases in different industries, such as customer analytics, equipment maintenance and medical diagnosis. The potential uses and benefits make predictive modeling a highly valued skill for data scientists.

Data wrangling and preparation

Data scientists often say that more than 80% of the time they spend on data science projects is devoted to wrangling and preparing data for analysis. While most of the data preparation tasks fall on data engineers, data scientists can benefit from being able to do basic data profiling, cleansing and modeling tasks. That enables them to deal with data quality problems and imperfections in data sets, such as missing or mislabeled fields and formatting issues. Data wrangling skills also involve collecting data from multiple sources and massaging different data formats, as well as doing data manipulation work to filter, transform and augment data for analytics applications. To aid in those efforts, data scientists should be familiar with using common data warehouse and data lake environments, including both relational and NoSQL databases and big data platforms such as Apache Spark and Hadoop.