The digital world includes so many different perspectives and jobs. So much that we don’t exactly know what everything is.
I often forget, but my own brother is a data scientist and today I’ve decided to interview him about his job.
What’s a data scientist?
Generally attached to the information systems department of a company, the Data Scientist’s overall job is to analyze and exploit all the data from customers, prospects or employees that the company collects via different channels. The goal is to create predictive models and build algorithms to help make decisions.
If the Data Analyst’s mission is also to exploit and interpret the data, the Data Scientist has more of a global vision. They are responsible for translating business problems into mathematical and statistical problems, in order to provide reports to guide management decisions but also to help improve marketing strategies performances. They are often required to interact with business teams such as marketing, finance or even sales. Their work has a direct impact on the improvement of the company’s overall activity.
The Data Scientist’s missions are various:
- Identification of analysis tools
- Definition of data storage solutions
- Collection and analysis of relevant data for the company
- Building algorithms to improve search and targeting results
- Building predictive models to anticipate changes in data and trends
- Creation of adapted dashboards in order to make the results readable and exploitable by all the businesses
- Technology watch (data collection, processing platforms, experimentation)
Although this is a recent profession, Data Scientists can be found in many sectors of activity: finance, IT, insurance, e-commerce or even mass distribution.
What’s a Data Scientist in 2022?
Data Scientists are in high demand. The number of specialists is growing and competition for the best positions is increasing. It is now necessary to master a broad portfolio of skills.
First of all, the professional must know how to handle the main tools of Data Science. In particular, he or she must know the Python programming language and its various libraries such as Pandas, NumPy, Matplotlib and Seaborn. The R and Julia languages are also widely used in Data Science.
The various data mining techniques hold no secrets for him, just like statistics. This expert also knows Big Data tools such as Spark, Hadoop, MongoDB or BigQuery. He also knows how to use SQL, the database manipulation language. The Data Scientist also handles different tools and techniques of Data Vizualization such as Tableau and ToucanToco, Looker or Matplotlib.
Machine Learning is another important component of this job, especially for the analysis of unstructured data. The expert knows the main algorithms and knows when and how to use them to complete his mission. Frameworks such as TensorFlow, PyTorch and Keras are increasingly used. They also master Feature Engineering and know how to work with text and image data.
Data Scientists are now often required to use artificial intelligence technologies such as Deep Learning and Computer Vision, or Natural Language Processing. These techniques must therefore be mastered at the tip of their fingers.
Finally, developing Data Science models is no longer enough. The Data Scientist must be able to deploy these models and put them into production.
The job of Data Scientist evolves over the years. What are the changes to be expected, the trends to come?
We are witnessing the rise of the « Citizen Data Scientist ». Data science tools have become easier to access, and algorithms are ready to use for a wide variety of specific applications. Turnkey solutions are becoming more common, and automation is also gaining ground.
As a result, Data Science is no longer reserved: only for the most technical profiles and Machine Learning experts. It is being democratized, and is gradually opening up to the various roles in the company. Predictive data analysis is accessible to everyone and Data Scientists can focus on more complex tasks.
We can also expect a specialization of Data Scientists. In order to be successful, professionals may choose to focus their efforts on a particular field of activity or technique. For example, the fields of cybersecurity or healthcare.
Data Scientists will also get closer to their companies and industries. This will allow them to ask more relevant questions to the data and to better understand the issues and objectives.
Finally, we can anticipate a multiplication of data sources. Digital technologies continue to develop and generate ever larger volumes of data. The Data Scientist will be able to draw on these resources but will also have to learn to better select the data to be exploited.
That’s it for today folks! I hope you enjoyed this conversation turned into a whole informative article. I also hope that was helpful and that you learned something today!