At the beginning of the 21st century, the world entered the era of big data. Till 2010 it was difficult for the corporate house to store the data systematically. The main focus at that time was to develop an idea to store that data. Hadoop and other frameworks solved this problem of storing the data successfully. Then the focus shifted to the processing of this data. It was the main challenge for all the big enterprises. This gave rise to the concept of Data Science.
Data Science is the combination of various tools, machine learning principles, and algorithms to discover the hidden patterns from raw data. The function of data scientist is to use their skills in math, statistics, programming, and other related subjects to organize large datasets. A data scientist looks at the data from different angles, sometimes angles not known earlier. Data Science course has become an important subject in software and has opened the door to a world of opportunities. The most important skills required to be a data scientist is listed below.
∙ Programming Skills
A data scientist is expected to have a good knowledge of the programming languages like Python, Perl, C/C++, database querying language like SQL. You should possess the ability to use the tools of the trade. Knowledge of these programming languages helps you to clean, massage and organise a large volume of unstructured data. Nowadays most of the Data Science course includes a detailed study of all the above-mentioned programming languages.
∙ Statistic
A good knowledge of statistics is very important to be a good data scientist. You should be familiar with the use of statistical tests, maximum likelihood estimators, distributions, etc. You should have a clear understanding of the different techniques and approach of statistics.
∙ Knowledge of SAS and other analytical tools
The knowledge of analytical tools is vital to the understanding of Data Science. SAS, Hadoop, Pig, Spark, and Hive are the important data analytical tools that data scientists use. Most of the data science course has modules based on these topics.
∙ Machine Learning
The Data Scientist should be familiar with the machine learning methods. You should have an idea of the working of different algorithms like k-nearest neighbours, random forests, and ensemble methods. You do not have to be a master in these techniques as most of them can be implemented using R or Python libraries. It is important for you to have a broad idea regarding them and understand the condition when it is appropriate to use such techniques.
∙ Great data intuition
Data intuition is the most significant non-technical skill required to be a data scientist. You should be able to perceive patterns which are not observable on the surface. It would make you more efficient in your work. This skill gradually develops with experience and boot camps are necessary to polis
∙ Analytical Skills
Analytical Skills or thinking skills has huge importance in data analysis. You should have the ability to view, gather and analyse all forms of information in detail. This means having an ability to view a situation from a different perspective. It helps you to take a decision in the most appropriate way.
∙ Attention to Details
The ability to pay attention to every minute detail and to infer the initially unseen links makes a great data scientist. This is especially important during the problem solving and decision-making time. You have a lower risk of making errors when you pay attention to details.
∙ Communication Skills
A good data scientist should be an excellent communicator. You should be able to facilitate meetings, be an active listener and make the right request to gather new information. Your communication proficiency must cut across all digital platforms like SMS, conference calls, etc.
The above was the different set of skills requires being a good data scientist. Most of the Data Science courses incorporate all the above skills in their different modules.
