Becoming a data scientist requires a gamut of skills. These skills can be categorized as hard and soft skills. Generally, to learn data science, hard skills are stressed upon and there’s less emphasis on soft skills. However, soft skills are equally for data scientists to excel at their work. If you’re looking to become a data scientist or master data science, you will need the following skills.

Hard Skills

Mathematics and statistics skills: Mathematics and statistics are the foundation of data science. The core techniques of data analysis and machine learning are built on fundamental concepts of mathematics and statistics. Probability, functions, matrices are a few essential concepts

Programming skills

Data science is an extension of statistics, which requires programming to perform tedious statistical and mathematical techniques like data mining. Python and R are the two most widely used programming languages in data science.

Data wrangling and pre-processing skills

Data is a key component of the entire data science process. All forms of analysis- inferential, prescriptive, predictive, require a huge amount of data. The output of the analysis depends on the quality of the data. For instance, a predictive model will perform better if it has been trained with good data. Rarely does the data come in a useable format. Usually, the data is available in word files, web pages, spreadsheets, PDF, etc. Data scientists mine data from these sources and render it to use in the analysis. Further, data can be in the form of text, voice, video, or picture.

Data wrangling – Data scientists often get their data in a clumsy format, which can’t be used for analysis or at least for effective analysis. Data scientists collect data and clean it to turn into use for analysis. This data cleansing process reveals hidden insights which further makes data analysis easier.

Data pre-processing – This involves the following techniques:

  • Missing data – Often data can be incomplete, which interferes with analysis and other data operations.
  • Data imputation
  • Handling categorical data
  • Encoding class labels for classification problems

Techniques of feature transformation and dimensionality reduction such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

Data visualization

Data scientists work in collaboration with business leaders. This requires data science professionals to convey their findings to the leadership in a non-technical format. Data visualization skills play a crucial role here. Knowledge of packages like Matplotlib, Seaborn, and GGPLOTS, etc. is required.

Machine learning skills

The end goal of a data scientist is to build a predictive model. This requires extensive knowledge of machine learning algorithms etc.

Hands-on experience

A qualified data scientist not only knows but demonstrates skills by working on real-world problems. What data scientists do in real life is more complex than what looks on paper. Thus, hands-on experience is essential to break into a data science role. Each part of the data scientist’s role – data acquisition, analysis, building machine learning model, training machine learning model, and evaluating machine learning model requires experience.

Platforms like Kaggle, Driven Data, are good places to look for data science projects and work on a few projects.

In addition to the technical skills, data scientists are expected to be well –versed in soft skills too.

Soft skills

1. Communication skills- Data scientists work near business leaders and other data science professionals including data engineers, data analysts, machine learning engineers, software engineers, and more. This requires them to communicate their ideas. Good communication skills also mean you are confident about your ideas and can articulate your thoughts well.

2. Business Acumen skills – Data scientists are strategic additions in the leadership team, who can assist leaders in making growth-oriented business decisions. For data science professionals, acquiring business acumen skills allows them to think in a way that will help the business reduce cost, increase efficiency and productivity, and maximize return.

3.Passion – A good data scientist isn’t merely due to their skills, but due to a passion for learning. Data science in itself is a dynamic field, which requires professionals to work on new problems. Lack of passion for data science can result in extreme stress as the job is extremely demanding in terms of skills.

4. Be a team player- Data science teams require a high level of collaboration. Thus, data scientists need to be active listeners to attend to the requirements of team members. You will need to rely on other team members to provide good insights before starting a project. Maintaining a good relationship with team members is, thus, essential to building a successful career in data science.

Get a data science certification

Taking a globally –recognized data science certification will validate your skills, knowledge, and competency. Data science is a high stakes role and requires high-level responsibility, dedication, and knowledge, which is incredibly difficult to measure in candidates for data science roles. IBM, Dell, DASCA, and Microsoft offer some of the best data science certifications.

Data science certifications like DASCA (Data Science Council of America)’s ABDA (Associate Big Data Analyst) for entry-level data science professionals prove that the holder is well –versed in necessary skills required to stand out in the data analyst role; SBDA (Senior Big Data Analyst) proves hands-on experience in extensive data analysis and competence in all areas of data science and the ability to deliver business results.