Top Skills for a Data Scientist in 2020

Data Science is one of the most competitive fields, and people work day and night to build more skills and experience in this field. Data Science gives businesses and stakeholders the power to extract knowledge from data to answer a particular question and make informed decisions. It is impossible to churn these insights from large amounts of data without a data science expert.

Data Science is growing, and so are the scientists. McKinsey estimates that significant data initiatives in the USA’s healthcare system could account for up to US$450 billion, 17% of the total healthcare cost. Bad data costs trillions to the economy, thus, growing the need for efficient data processing and, in turn, Data scientists.

In 2020, with the rise in the number of data science jobs, the number of skills required is also surging. The job description of a data scientist has widened to that of a full-stack data science developer. The job description of a data scientist has widened to that of a full-stack data science developer. To mark your arrival in the market, you need to be extra, effective, and productive. If you want to stay in the competition, you have to combat the unicorn working methods that come with new tools and significant challenges. This article takes a look at data science-specific skills that can help you make a splash in your career. 

If you are looking for the best data science programs online that can help you in your career, then your search would end by the end of the blog.


Required Skills


It is essential to have a strong base to keep up with new technology trends. Here is the list of top skills for a Data Scientist in no particular order.


  • Math and Statistics

Data Science is a mix of algorithms, capital processes, and systems to extract informed decision-making insights. So, predicting, making inferences, and estimating are essential parts of the field. For estimation, probability and statistical methods are essential. Statistics and probability are intertwined. Data drive companies to depend on the design of data models. So knowing about machine learning techniques is integral to data science. For tuning algorithms and hyperparameter optimization, the mathematical foundation is necessary. To understand the working of algorithms, calculus, linear algebra, and probability theory are fundamental. This skill is crucial for data exploration, identifying underlying relationships and patterns, forecasting, and uncovering anomalies.



  • Data Visualisation


Data visualization is a remarkable way to present the results of a machine learning algorithm. It is a way to represent your findings of the considered data graphically and forms the foundation of data storytelling. A well-crafted visualization helps in explaining the critical results to the stakeholders with only a few non-technical words. Data visualization gives the power to learn and understand the data better. By data visualization, things are portrayed comprehensively, and the real value is established. The results of the visualization give meaningful and surprising information. Bar charts, pie charts, scatter plots, histograms, heat maps, etc. can be used for your data. Data visualization is used to display trends, determine correlation, highlight essential areas, and devise marketing strategies in data science. Popular tools are Plotly, Tableau, Google Analytics, and more.


  • Machine and Deep Learning

A company with data-centric processes demands skilled Machine Learning experts. ML is a data science subset that contributes to data modeling and getting results. For data science, ML algorithms like Random Forest, K-Nearest Neighbors, Regression, Naïve Bayes are useful. So are Keras, TensorFlow, and PyTorch. For problems involving image recognition and Natural Language Processing, Deep Learning is best suited. Also, algorithms like XGBoost are suitable for routine data science applications that involve tabular or structured data. Basic knowledge of DL and experimentation with NLP is enough, and specialization isn’t required for all job profiles. Machine learning in Data Science is used for Risk detection, fraud management, automatic spam filtering, document recognition, route planning, etc.



  • Cloud Computing

Industrialization of Machine Learning is a severe constraint for data scientists and IT in general. The cloud will be the data science and machine learning king in the coming years. A fast and remotely accessible machine learning environment can be set up by moving storage and compute resources to external vendors like Microsoft Azure, AWS, or Google Cloud. The knowledge of cloud computing wasn’t necessary but is taking center stage as a valuable skill set. Data for analyzing and visualization is generally stored in the cloud. It works hand in hand with data science because of access to frameworks, operational tools, and programming languages. Thus, the cloud is a relevant skill and has become crucial because of its use in data mining and data acquisition. Some popular services and cloud platforms are Google cloud and collab, IBM Cloud, Azure, AWS, etc.



  • GitHub

Git and GitHub are developer tools used to manage different versions of the software. Git lets you manage and keep track of source code, and GitHub is a cloud-based hosting service that manages Git repositories. This platform allows secure collaboration as multiple developers can track and make changes in real-time. Git has become a job requirement in data science, and its best practices come with time. Git has gained popularity because data science is becoming dev-heavy. It is a struggle for a newbie when it comes to working in teams.



  • SQL

Many times the data sets come from enterprise relational databases, so SQL is required for acquiring data. For maximum benefit in terms of data acquisition, SQL and R packages are a great way. A data scientist is a master of all jacks. Thus data management is quintessential to be a full-stack data scientist. Database management comprises indexing, editing, and manipulating databases. DBMS helps store and retrieve data in large systems. Knowing SQL is essential to define rules to validate and test data, operate on record-level, and support multi-user environments. MySQL and Oracle are popular management systems.


All these skills that can keep you ahead in the competition. It is recommended to take up a data science training that touches all these aspects. Data science certification course is one such training module provided by the Global Tech Council.