Organizations are dependent on data-driven strategies to create a successful marketing campaign, make an intelligent business decision, and witness growth. In this effort, they derive a voluminous amount of data from varied resources like websites, mobile apps, social media, and connected devices.
How to capture this big data? How to process this structured or unstructured data? How to derive conclusions from these humongous data? Only, a certified data scientist can do this. A data scientist professional derives insights from the data and focuses on using data by bridging the gap between the number and real-world actions.
Data scientists have knowledge from interdisciplinary fields like science, maths, computer science, business, and communication. They use various sets of business intelligence (BI) tools to integrate, transform, analyze, visualize and report data.
To work with the data and thrive in the data science industry, a data scientist must have certain essential skills. We have compiled a list of essential skills needed for a professional working with data here. Let’s take a look.
Skills Required to Become a Data Scientist
Education
Most of the data scientists have a Master’s degree and nearly 46% have a doctorate degree. To become a data scientist, one should have a bachelor’s degree in Computer Science, Social sciences, Physical sciences, and Statistics.
Technical Skills
R Programming:
Data scientists use R programming to solve statistical problems. In-depth knowledge in R is preferred to solve any problems you encounter in data science.
Python:
It is the most common coding language required in data science. In addition, working knowledge in Java, Perl, C, C++ is appreciated. As Python takes various data formats, you can easily import SQL tables into the code or create datasets.
Tableau:
Tableau serves as a complement for R or Python. It explores and visualizes data in dynamic dashboards and provides a high-level interface. It is preferred for data exploration and interactive analysis.
Hadoop:
With Hadoop, you can explore data, filter them, conduct data sampling and summarization. When the volume of data exceeds the memory of your system, you can quickly convey data to different points on a system using Hadoop.
SQL database:
SQL database helps you to carry on with certain operations like add, delete, and extract data from the database. The programming language enables you to access, communicate, and work on data. You can easily work with analytical functions and transform database structures.
Apache Spark:
It runs a complicated algorithm faster, helps to disseminate data processing, handles unstructured data sets, and prevents loss of data.
Machine Learning and Advanced Machine Learning:
You should be familiar with different types of machine learning like supervised/unsupervised learning, and reinforcement learning.
Data Visualization:
You should have a working knowledge of using tools like Tableau, Kibana, Google Charts, and Data Wrapper.
Big Data:
Big data analytics is crucial to improve business decisions and gain a competitive edge.
Data Ingestion and Munging/wrangling:
You should be able to process, transfer, load, and process data for storage or later using data ingestion tools like Apache Sqoop and Flume. Besides, you should be able to differentiate and remove data features as required and identify variables.
Data-driven problem solving:
You should productively approach a problem by understanding features, frame questions, decide approximation and make sense of data.
Non-Technical Skills
Apart from technical skills, other skills like intellectual curiosity, business acumen, and communication are essential. You should have an understanding of the business domain you are working with so that it is possible to derive business insights. You must have the fire in you to learn, dissect, and use data for business benefits.
A cross-disciplinary knowledge and motivation helps you move a long way in the chosen career successfully.
