������Ƶ

So, what is big data? People and companies across the world will create an estimated of data every day by 2025. For context, humans created three exabytes of data per day in 2012. These data points range from the number of calories you log on your food tracking app to the customer survey you fill out after a purchase.

Where data scientists come in   

Someone has to do something with all that big data, and that is the job of data scientists. They are responsible for collecting, organizing and reporting on relevant data for organizations. Employers collect thousands of internal and external data points through a specific algorithm to drive their business decisions. 

Data science is part of the information technology sector, but it’s significantly different from other areas of specialization, such as computer science. Some data scientists might play a bigger role in business operations and eventually step into leadership positions.

What is the difference between a data engineer and a data scientist?

What does a data scientist do?

Data analysts and scientists perform a complex range of overlapping data science tasks. Data scientists typically handle more complex and advanced techniques for predictive modeling and work with unstructured data. Data analysts focus on structured data and use statistical analysis and visualization tools to solve tangible business problems. Here’s a list of routine data science tasks:

• Collecting and cleaning data: Collect data from various sources, including primary and secondary sources, and clean and organize the data to prepare it for analysis.
• Exploratory data analysis (EDA): Conduct EDA to understand the data set’s characteristics before building models. This involves creating visualizations such as histograms for continuous variables, scatter plots to check correlations, and bar charts for class imbalances.
• Designing predictive models: Develop predictive models and machine learning algorithms to mine large data sets. Use advanced statistical techniques and machine learning algorithms to predict future trends.
• Model evaluation and validation: , such as confusion matrices and learning curves, to assess model performance during training. Validate model assumptions using techniques like residual plots and histogram analysis.
• Data visualization for communication: Communicate findings effectively to stakeholders who may need to gain in-depth knowledge of the subject domain. Use informative data visualizations, such as distribution plots, box plots and violin plots, to summarize and present results clearly.
• Business intelligence: Develop business intelligence tools, dashboards and reports that allow stakeholders to interact with and gain insights from the data sets. Interactive plots are often used to facilitate comparisons and highlight specific aspects of the data.
• Automation and programming: Write programs to automate data collection, processing and model deployment. Use programming languages like Python and R to perform data manipulation tasks.
• Time series analysis: Employ time and seasonal plots for time series analysis to identify trends and patterns.

Data science professionals who are seeking a higher salary may benefit from earning a data science bachelor’s or master’s degree or professional certifications in their specific field.

How to become a data scientist  

According to the (BLS), most employers require data scientists to have at least a bachelor’s degree to be qualified for a position. The degree can be in computer science, data science, statistics, mathematics or a similar field. Some employers prefer that employees have a master’s degree.

These professionals often benefit from the knowledge they can attain with information technology degrees that help them set up programs to collect and analyze data. Students can specialize in data with a bachelor’s degree in data science to learn skills such as data analytics, data management, data visualization and business intelligence. Technical know-how helps collect data and develop programs that sort through thousands of data points and report them clearly.

However, many in data science also need business acumen. Knowledge of an industry gives insights as to why certain data points are important and how they can be used to make decisions. A business internship or job shadowing program can be helpful to grow this knowledge base. 

Data scientists also have to contextualize the information they collect for the people who review it. They have to explain what it means and why it’s relevant. As a result, like logical thinking, clear communication and problem-solving are often in demand for these professionals. Developing these skills early in a career can make it easier to slide into this role.���� 

When entering a specific industry, employers may also prefer or require industry-related experience or education as part of the qualifications. For example, those working in the business or marketing sector may need business administration or management experience. Likewise, those working in finance or accounting may need a background in those disciplines.

Additional certifications for data scientists

Learn skills to become a data scientist