Introduction
Data Science has been the job in demand with every companies expanding their analytics capabilities and building teams to start make use years and years of data collected in the organization. People with varied experience are looking into a possibility for them to transition their career into a data scientist. But the role ‘Data Scientist’ has lately been used as a attractive offering to people looking for a change and many organization under the role data scientist and still performing tasks at the very basic level of data maturity like manual data aggregation and reporting or tasks which could be easily automated with the rightly skilled person in place. So, these are the skills required to become a successful data scientist:
Statistics
At the core of all the data analysis is the fundamental statistics. The result of any data science project needs to generalizable to the entire population of data in that specific context, and when the all the steps involved in data journey right from data collection, wrangling, pre-processing, feature engineering, model development, evaluation adheres to the underlying statistics to conclude the outcome to be statical significance and management decision can be based upon it.
In recent years, we have surge in the automated libraries making it far easier to build machine learning and deep learning model with just couple of lines codes but without the understanding of the statistics the possibility of model being unusable is very high.
Programming Proficiency
Getting into the field of data science is a dream for many aspiring students and working professionals across the world, the most logical first step in this journey is to become proficient in several programming languages because a single language can’t solve problems in all areas.
Programming skillset would be incomplete without mastering the most frequently used languages in data science. Over the years, the demand for these languages, like Python have surged like forest fire and it doesn’t seem to be dying down in any near future.
These demands have a direct association with a set of thriving technologies that are now gaining mainstream adoption. The momentum from the cloud, augmented reality (AR), virtual reality (VR), artificial intelligence (AI), machine learning (ML), and deep learning (DL) is driving the demand for certain languages. Interestingly, languages complement the different job roles in data science, for instance business analyst can complete his workload through R whereas expertise in Python can help a machine learning (ML) engineer strive in their specific role.
The market is flooded with languages that supplement growth in the field of data science, some widely used languages include Python, R, SQL, Julia etc. with the theme of success for them being their open-source code availability, they strive to win the hearts of data-scientist because of their communities spread across the world-wide-web.
Visualization Techniques
Perhaps the choice of career path or nature of business doesn’t change with the fact that data visualization is the most efficient way of delivering data. As one of the essential steps in the data science project, it becomes crucial to look for obvious pointers in data, like nulls, distinct values, basic sensibility, trends, frequencies etc.
In advanced analytics, data scientists are creating machine learning algorithms to better compile essential data into visualizations that are easier to understand and interpret.
Data visualization is and always will be important because the human brain is not well equipped to devour so much raw, unorganized information and turn it into something usable and understandable. The need for graphs and charts to communicate data findings and identify patterns or trends to gain insight and make better decisions accurate and fast is the need of the hour.
Data Engineering
Data engineering deals with setting up the right infrastructure and process in place to maintain the high quality of data and also to make the data available for analysis at ease with SQL to extract the data. Data engineering is itself a separate team with people highly skilled in Big data technologies like Hadoop, hive, spark and also in cloud services like AWS, Azure and GCP. In smaller organization we see sometimes the responsibilities of both data science and data engineering is handed to the same team due to lack of budget and in that case, it become imperative for data scientist to have good understanding of the entire data pipeline and be able to handle model deployment to productions. Even in large organization with separate team the collaboration is much more ironed out.
Business Acumen
The entire purpose of data science is to help the business make better decisions. As data science finds its application in every field now, it has become more important for the data scientist to get the domain knowledge and business context of the data and how the result is going to be used, only then can the transition of a business problem into a analytics problem can be made and also the technical results needs to be converted into a form in which the business leaders can absorb it.
Here is when the soft skill of the data scientist come to play to put across the statistical results in the best format and communication to make it convincible and ingestible to the business. A lot of research is already going to model expandability to declutter the black box of data science.
Conclusion
Data science is a constantly evolving field and it grows into leaps and frogs in just few years and the high investment of tech companies like Google and Facebook makes the industry grow at faster pace than ever before, so a constant learning process is the only way to keep abreast with the advancement. A successful data scientist would be the one with a T shaped skill set, which means the he/she is capable and can work on all the skills of data science and is a expert in at least in one of them