A day’s tour into the life of a data scientist
Harvard Business Review calls Data Scientist “the hottest job of the 21st century.” (Source)
Data-related occupations and prospects have exploded worldwide as a result of this. Many people are asking how they, too, may get into the data science field. So, what is a typical day in the life of a data scientist?
Let’s dive in.
Defining the Problem
Identifying and defining the company problem at the start is one of the most fundamental and crucial roles every data scientist has to begin with. It’s vital to have the right questions in place since data is only as valuable as the questions you ask it. Unless a data scientist asks the right questions, they will not provide valuable insights for business decision-making.
Data scientists often work with a team. It involves comprehending business needs, scoping an efficient solution, and planning data analysis. Data scientists analyse the stakeholders’ pain points and frame a data science issue from their viewpoint. They acquire domain expertise from stakeholders and use it with data, technical knowledge, and business knowledge to produce a data product that is more useful for the company’s bottom line.
Gather Data for Analysis of the Defined Problem
Finding all of the data needed to solve the business problem is the duty of a data scientist. The process continues with a data scientist selecting and cleansing all relevant information from different sources. If the needed information is already available with the firm, all is well. Otherwise, if a data scientist believes that current data isn’t enough to address the business issue, they acquire new data in various ways: via consumer feedback, questionnaires, or by creating a common auto data collection strategy like cookies for a website. After collecting the data, a data scientist cleans and organises it (Almost 70% of a data scientist’s time goes on this) to exclude any mistakes, detect duplicate records, and remove missing values.
Provide an Approach to Solve the Data Science Problem
A data scientist examines the most effective and efficient methods to answer questions once they’ve gathered all the information and figured out what issues they want to address. The ideal and quickest solutions may not always be synonymous, so finding an answer is the domain of a data scientist. Applying k-means clustering to specific questions might be more efficient than a more complex technique. A data scientist must choose the best method to tackle a data science problem. Many different algorithmic solutions are used to solve a data science issue, some of which are listed below –
The Two-Class Classification Approach: This is the most effective method for receiving answers to inquiries that have two only possible options.
The Multi-Class Classification Approach: This method effectively resolves issues with various options.
Reinforcement Learning Algorithms: These are used to figure out the best course of action for a problem that isn’t predictive.
Regression: This is the most effective option for questions that must be answered with a real-valued solution rather than a class or a category.
Clustering: This is a method for organising data points into distinct groups, allowing you to answer questions about how data is distributed.
Analyse data with great depth: Uses statistical modelling, algorithms, and machine learning.
A data scientist examines mobile devices, computers, laptops, or tablets to obtain valuable insights. A data scientist is in charge of developing automated machine learning pipelines and bespoke data products for profitable business decision making. Having examined the data and determined a method, a data scientist extracts valuable information from it.
Several free, open-source data science tools and libraries in Python and R may be utilised to analyse data and reveal high-value insights for better business decision making. There’s a chance that a method decided on in Step 3 might not work when a data scientist begins to analyse the data.
A data scientist uses the following 5-step technique to discover which machine learning methods are most effective for their data science problem, starting with trying other machine learning approaches and then comparing them against one another.
- Create a machine learning model to address the questions.
- Ensure that the model is validated against the data obtained.
- Apply appropriate algorithms and conduct statistical analysis
- Present the findings utilising several data visualisation technologies.
- Compare the outcomes with other methods.
Before determining the most acceptable answer for a data science issue, a data scientist tries various methods and techniques.
Communicate Insights to the Stakeholders
The next most crucial activity for a data scientist is to effectively communicate the findings so that different stakeholders can grasp the insights and take further action based on them. A picture is worth a million rows of data. Data scientists use various data visualisation software like Tableau, QlikView, Matplotlib, ggplot, and others to illustrate real-world situations in which the model functions besides designing presentations that have an appropriate structure tell a narrative about the data so that stakeholders can understand it easily and find it interesting.
Juggle between desk-work, giving presentations and working in groups
The bulk of a data scientist’s time goes on researching, writing algorithms and code to address the issues raised by the data sets. The process of performing group work in data science is essential – obtaining the data, comprehending it, and understanding and analysing what you want from it. The number and type of roles vary based on your team’s structure, but as the data scientist, you’ll generally have someone to collaborate with who can provide you with more insights into the data, answer essential queries, and clear up any confusion. A data scientist may be asked to explain their methods and compare them to those used by other professionals in their field. In these situations, a data scientist must give some lectures to answer such questions.
Do you have any experience with data analysis? Are you a data scientist? If that’s the case, we’d be interested in learning more about your morning routine. Please leave a comment below with as much information as possible.