The title ‘Data Scientist’ is already acknowledged as the sexiest job in the world. Defining the job, or a role of a data scientist is also an ever-changing task, as the list of things keeps adding on due to the technological advances. Also, the business needs and demands drive the changes in the data science and analytics industry.
A data scientist is unfortunately used as a blanket title for various other data related job which might or might not have a connection with the actual role of a data scientist.
Due to big data, huge volumes of data is being collected through various channels, and as technology evolves and new devices get connected to the internet, this phenomenon is only going to increase. With the huge volumes of data being collected, extracting value and insight from this data is also becoming a daunting task.
There are enormous amounts of insights in this data, and in many cases, the extracted logic is shaping our personal lives in ways that we are not aware or cannot predict. A data scientist is a key director on how this information must be mined and interpreted.
To explain in layman language, ’A data scientist collects, cleans, does analysis, and predict the data that we provide by using a combination of computer science, business domain knowledge, and statistical analysis’.
Traditional data jobs in past were more focused on the interpretation of data and were more concerned with the past activities and reasons on why something happened. The current breed of data scientist is more focused on mathematical interpretation with a future prediction.
Data science is a more systematic evaluation of facts, both on quantitative and qualitative variables. This method has revolutionised the way traditional data used to be analysed and thus has become very popular in most industries, giving rise to the demand, to the role of a data scientist.
There are clear differences between the traditional data analysis, and in tasks that a data scientist performs. The commonality is the SQL queries and data analytics techniques, however, a data scientist has evolved and advanced knowledge of Machine Learning Techniques, Programming, and Engineering familiarity, helps the data scientist to manipulate the data, by which they can reach and uncover deeper insights.
So to put it simply a data scientist can look at the past and understand what happened, discover current insights and thus can predict what will happen by applying Statistics and Complex Data Modelling to the data set.
To be a successful data scientist, one needs to apply the vital combination of Cleaning the data, Interpreting the data and Transforming the data.
Therefore, a data scientist should have
(A) A mathematical mindset of interpreting the data, Statistics, Data Analytics, Data Mining and Machine Learning are non-negotiables.
(B) Fluency in programming languages with the knowledge of Database querying languages like SQL, statistical programing language, R, Python, skills in data extraction and hypothesis testing are central.
(C) Lastly, they need to develop or have experience in software engineering and develop a strong computer science background.
Along with the above, to match the demands of the data science landscape, a data scientist needs to have additional understanding and proficiency in other tools like Data Warehousing and Data Visualisation. We offer Data Science Prodegree in collaboration with Genpact as Knowledge Partner. This program helps you with a deep understanding of Data Analysis and Statistics, along with business perspectives and cutting-edge practices using SAS, R, Python, Hive, Spark and Tableau.