Data Scientists Skills: Here’s what to learn

Good remuneration and high accountability, Data Scientist is a prestigious position to hold at an organization. High accountability of these data-driven professionals stems from the work and output expected from them. Data scientists are often part of business teams involved in high-stakes business decisions, which requires them to have an extensive and critical set of skills.

The complete set of skills required to be a data scientist can be categorized into:

1.  Analytical and logical aptitude
2. Technical knowledge –

  • programming
  • statistics
  • machine learning
  • data frameworks

3. Business acumen and leadership

Let’s discuss each set of skills one after another.

  1. Analytical and logical aptitude – All data-related job roles require professionals to have a high-level of analytical and logical aptitude. Data Scientists constantly deal with a large amount of data, it is imperative to have a strong analytical and logical aptitude.  A keen analytical and logical mindset is often developed over a long period of time with continuous practice and is often a pre-requisite for people who choose to learn data science.
  2. Technical knowledge – Data Scientists work with several data tools and technologies, the knowledge of which is gained by playing around with tools. As a Data Scientist, you are expected to be proficient in following technical skills

You will use statistical techniques to describe data and manipulate it to derive business decisions.

  • Statistics – There are two parts of statistics: Inferential and Descriptive statistics. Data science professionals use descriptive statistics to describe data available to them (exploratory data analysis), which is the first toward data analysis. Data wrangling and exploration consumes the most time of data scientists and needs extensive knowledge of statistics.
  • Programming – R and Python are go-to languages for data science professionals. Moreover, these languages come with built-in packages (like Numpy, Pandas, reshape, matplotlib, reshape, etc) which facilitate data manipulation and other operations related to data analysis.
  • Machine learning: You will need models that can think, analyze, and produce results when data is fed to them. This is an essential part when organizations look to avoid unknown business risks or identify opportunities for growth. KNN, Bayes, classifications, linear and logistic regression are frequently used algorithms you are expected to know. Deep learning extends the capabilities of machine learning further. Not mandatory yet, a few employers seek data scientists to know deep learning.
  • Data frameworks – Data scientists work with a large number of platforms and tools that keep data consolidated. Hive, Pig, Hadoop, and similar tools are examples of this. As the amount of data is increasing at organizations, they choose to use these tools. Know-how of these tools is required for data science professionals.

3.Business acumen and leadership: Understanding the ins and out of business growth is important for data scientists. With this knowledge, you will be able to find the right questions to ask and gather the right data sets to solve business problems.

There you have them—all the skills you will need to become a data scientist. A data science career is fulfilling in terms of salary and challenges yet extremely demanding in terms of skills.

If you want to become a data scientist, start learning the skills today.

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