Responsible for the management, analysis and exploitation of massive data within a company, Data Scientist is the evolution of the Data Analyst in the Big Data era.
Without a doubt, the profession of Data Scientist is exciting. However, there is also a high responsibility job that requires natural predisposition and a high-level education. Following are a list of 13 key skills summarized by the French magazine “Le Big Data”:
1. An analyst training
At present, 88% of Data Scientists have graduated at least a Master’s degree, and 46% of them hold a PhD degree. This school education seems necessary to develop the level of knowledge necessary to the exercise of this profession.
The majority of professionals (32%) are from training in mathematics and statistics. 19% have studied computer sciences and 16% come from engineering schools.
2. The Data Scientist must have knowledge in statistics
It is essential for a Data Scientist to have at least the concepts of statistical calculations. This knowledge will enable the Data Science to determine the right technical approach and analysis for each data.
3. The Data Scientist must master analytical tools
A thorough knowledge of at least one analytical tool such as SAS or R is required in general. Science data, preference is granted mainly to R.
4. Programming languages
Data Scientist is required fluency in at least one programming language. The most commonly used is Python, but it can be replaced by Java, Perl or C / C ++.
5. Knowledge of Machine Learning
In addition to analytical tools, understanding the methods of Machine Learning can be a real asset for the creation of a product directed by the data. This may be the decision-tree forests, k-nearest neighbors, or the ensemble methods. As these techniques can be directly implemented using R or Python libraries, it is not necessary to know how to develop these algorithms, the key is to understand how they work and know which method is the most appropriate depending on the situation.
6. The understanding of linear algebra and functions of several variables
Linear algebra and functions of several variables are the basis of many statistical calculations and Machine Learning techniques. Even if they are implemented with R or scikit-learn, some companies whose product is directed by the data may decide to develop their own implementations to improve their algorithms and their predictive performance.
7. Use of Hadoop
If some businesses do not require it, the master of the Hadoop platform is often required. Similarly, experience with Apache Hive and Pig processing tools is an additional argument for recruitment. The cloud tools such as Amazon S3 also important.
8. Programming in SQL
Hadoop and NoSQL databases are widely imposed in the area of Big Data. However, most recruiters require candidate’s proficiency in SQL programming to be able to formulate and execute queries. Moreover, the SQL tends to become the dominant language in the Big Data in 2016.
9. The management of unstructured data
To become a Data Scientist, it is essential to know how to manage unstructured data from social networks, or video or audio stream. This data is the main challenge of Big Data. It is also important to know how to process data including imperfections such as missing values or strings of inconsistent size.
10. Software engineering expertise
In a small company unaccustomed to data science, a Data Scientist must have software engineering skills. These particular enable it to support the development of a product-driven data or data logging.
11. Intellectual curiosity
Intellectual curiosity is essential to identify the most interesting data and usable in a huge volume of data. To carry out the work of the Data Scientist, it is necessary to be creative and ask their own questions rather than just respond to those involved.
12. The spirit of an entrepreneur
To successfully exploit Big Data for a business, it is necessary to understand the problems to solve and new opportunities that data can offer. This is why the Data Scientist must understand the business world in general and the industry in which he is affiliated in particular.
13. The Data Scientist must have the communication skills
Integrated within the company, the Data Scientist must always be able to communicate technical findings to other employees, marketing or trade points. Its role is to help decision makers to make the right decisions by providing the necessary information. A data science must also understand the problems of other teams and help them meet these challenges through data analysis. To do this, it is also important to control the data visualization tools such as ggplot or d3.js.
In conclusion, the skills required for a Data Scientist are numerous and specific. Before deciding to undertake training or a career in this field, it is necessary to determine whether you have or not, the profile of scientific data.