Data Scientist Job Description: Role, Responsibilities, and More

The Data Scientist profession is much needed by companies in various fields such as health, transportation, education, etc. The career paths and salaries offered are also promising.

If you want to become a Data Scientist, then you have to master the skills needed. Especially for beginners, it might take more time to learn and master these skills. How to become a Data Scientist? We will discuss this in this article. Come on, see together until the end!

Data Scientist Is Currently on The Rise!

Data Scientist is a data profession that is currently on the rise. Not only attract people with IT backgrounds but also from non-IT. This shows that anyone can become a Data Scientist if they want to keep practicing. A Data Scientist has several main tasks:

  • Ensuring good data infrastructure so that data is stored neatly and efficiently accessible when needed.
  • Conducting extensive data analysis, model experiments for data analysis.
  • Building machine learning.

Recently, the profession of a Data Scientist has been on the rise and is quite sought after by companies. It’s no wonder many people still need to fully understand how Data Scientist works. For an easier understanding, Data Scientist collects, process, and analyzes data.

Before the Big Data era, similar professions existed, such as data analysis and business intelligence. However, the difference between the two professions is that they only focus on research. At the same time, Data Scientists must be able to provide appropriate advice and input to organizations/companies according to their findings.

Data Scientist Tasks

Like a pilot, a data scientist is a person behind the control of data processing technology. His responsibilities range from analysis to getting insights or recommendations for business development.

Data scientists are tasked with analyzing various kinds of large amounts of data (big data) accumulated in the company. This profession relies on data analysis skills such as:

1. combining data from multiple sources and ensuring dataset consistency (data preparation)

  • Increase the efficiency of the data collection process.
  • Set up data infrastructure.
  • Assessing data quality and cleaning data.
  • Assess the effectiveness and accuracy of new data sources and data collection techniques.

2. choosing factors or algorithms that influence prediction results (data exploration)

  • Generate valid information from available data sets.
  • Develop processes and tools to monitor and analyze performance and data accuracy.
  • Develop custom data models and algorithms.
  • Identify relevant resources for business needs.
  • Organize data into a usable format.
  • Develop, implement and maintain databases.

3. Creating infographics to make it easier for decision-makers to understand data (data visualization).

  • Identify opportunities by leveraging enterprise data to drive business solutions.
  • Analyze data to see trends and find answers to specific questions.
  • Create data visualizations.
  • Improving and optimizing product development, marketing techniques, and business strategies.

Data Scientist Job Description 

A Data Scientist is responsible for analyzing and interpreting complex data, identifying patterns and trends, and using that information to make strategic business decisions. Some specific responsibilities of a Data Scientist include:

1. Collecting and analyzing large sets of data from various sources, including structured and unstructured data.

2. Cleaning and preprocessing data to ensure accuracy and completeness.

3. Building and implementing predictive models using machine learning algorithms.

4. Identifying patterns and trends in data to inform business decisions and strategies.

5. Communicating findings and insights to stakeholders through data visualizations and reports.

6. Continuously monitoring performance and accuracy of models, and updating them as necessary.

7. Collaborating with other teams such as product development, engineering, and marketing, to help them make data-driven decisions.

8. Keeping up-to-date with the latest technologies and trends in data science and machine learning.

9. Staying informed about industry and business-specific trends that could affect the data and analysis.

10. Continuously seeking new and creative ways to analyze data and uncover insights.

Data Scientist Basic Skills

In order to be able to work on the data science job above, an Indonesian data scientist needs to have certain skills. Here are the skills a data scientist must have.

1. Probability and Statistics

The first data scientist skills are probability and statistics. In data science, the chance is a measure of how likely something is to happen. So, data scientists can identify possible dependencies between two variables.

While statistics are algorithms used to translate data patterns to be followed up. The use of statistics for data scientists is to collect, review, and analyze data. In addition, statistics also serves to apply mathematical models to the appropriate variables.

By having these two skills, of course, you will understand all available data information and uncover anomalies. Not only that, but you can also predict future trends based on data from previous movements.

2. Multivariate Calculus and Linear Algebra

Most real data is multivariate, namely, various variables that play a role in predicting something. This makes a data scientist must have multivariate calculus and linear algebra skills.

Multivariate calculus is also used in optimizing some algorithms in machine learning. So as a data scientist, you need to have basic knowledge of these two skills. That way, you can see how the different variables play their role in predicting the output.

So, several topics of multivariate calculus and linear algebra must be mastered by data scientists, namely:

  • Derivatives and gradients.
  • Step function, Sigmoid function, Logit function, and Rectified Linear Unit function.
  • Cost function.
  • Plotting function.
  • The minimum and maximum values of a function.
  • Scalar, vector, matrix, and tensor functions.

3. Programming

As a data scientist, you must have programming skills like a programmer.

The role of programming as a data scientist is data extraction, cleaning data, and data visualization. So, these skills are needed to turn raw data into actionable knowledge.

Although many programming languages can be learned. However, you can start by learning the following programming languages.

  • Python language. Supports data collection, analysis, modeling, and visualization of big data.
  • JavaScript is a language used for interactive web development and big data visualization.
  • Java. Perfect for building full-fledged mobile or desktop applications.
  • R. An ideal choice for data science, big data, and machine learning because it is used during statistical operations.
  • SQL. Provides access to structured data and statistics for data science.

4. Data Management

Besides mathematics, statistics, and programming, database management skills are also necessary. Because 80% of a data scientist’s work is related to preparing data.

Database management is software designed to define, manipulate, retrieve and manage data in a database. A data scientist with these skills can define rules to validate and use data.

So, here are the things a data scientist with database management skills needs to do.

  • Defines retrieve, and manage data in a database.
  • Manipulate the data itself. Starting from the data format to the file structure.
  • Define rules for validating and testing data.
  • Supports multi-user environments to access and manipulate data in parallel.

5. Data Visualization

After analyzing the findings of the data, the data scientist must also be able to visualize the data. Visualization is an effective way to communicate and explore the final data results.

Data visualization skills will allow you to better understand the various data sets. That way, identifying patterns and trends in data can be done more effectively.

Also, data visualization makes it easier for data scientists to make decisions based on existing data. In addition, you can also understand business insights and set relevant business strategy plans.

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