Roles and Responsibility of a Data Scientist

Introduction

Data science is an amalgamation of machine learning, artificial intelligence, mathematics, specialized programming and statistics aimed at getting some insight from the organization's data. It greatly helps businesses to appreciate about statistics, customers and market in terms of their significance. The task of collecting, preprocessing and cleaning of data falls under the responsibility of a data scientist. Data scientists also visualize and preprocess data so as to identify patterns from the data. Data Science is one of the rapidly growing technologies today that helps extract knowledge and meaning from huge amount of data which every firm depends on in this era. On the other hand, job opportunities for Indian Data Scientists are expanding with greater use of analytics tools. Data Science Roadmap is a comprehensive guide for aspiring data scientists to understand the skills and knowledge required for a successful career in this domain.

Core Responsibilities of a Data Scientist

• Data Collection and Preprocessing: Data science starts from collecting the data and preprocessing it. Data collection includes collecting data from multiple different sources. However the collected data may have some lacking, inconsistencies and errors. Data preprocessing of data involves cleaning and improving inconsistencies and errors in the data. Pre-processing prepares the data for further analysis and it also increases the efficiency and reliability of the analysis.
• Exploratory Data Analysis (EDA): Exploratory Data Analysis is the crucial step for the data scientist which involves analyzing the data to get its understanding and it also uncover the data patterns, relationships and trends. Graphical Representations(bar charts, scatter plots, histograms), Correlation Analysis, and outlier detection are some of the methods used for Exploratory Data Analysis (EDA).
• Feature Engineering: Feature engineering is also the analysis of data to get some features from it to increase the model performance. Feature Creation, Feature Transformation, Feature Extraction, and Feature Selection are steps involved in feature engineering. Feature in data science also called an attribute or variable defines the individual measurable properties of the data which will be further used as input for the machine learning model.
• Model Development and Selection: After the data analysis and feature engineering next step is to select the model. Model selection is the process of selecting the best-suited model for your data set. Train-Test Split, Grid Search, Bayesian optimization, Random Search, and Model averaging are some of the methods used for the model selection. Model selection is in repetitive process as it involves evaluating the performance of different models and selecting the best one.
• Model Evaluation and Optimization: Model Evaluation and Optimization is the step that comes after the model selection and development, it involves evaluating the performance and accuracy of the selected model.

Additional Responsibilities of a Data Scientist

• Data Visualization and Communication: Data visualization and communication are also an important aspect to be considered for a data scientist. Data visualization is the art of communicating and presenting data insights to the stakeholders in a manner that it easy for them to make the decision for the data. Power BI, Matplotlib, Tableau and Seaborn are tools used by the data scientist for the data visualisation. Proper visualization of data makes the complex data easy to understand.
• Collaboration: Collaboration is also required for the data scientist, as the data scientist needs to collaborate with the stakeholders. They also need to communicate with the decision-makers, engineers, domain experts and business analysts to understand the business needs.
• Continuous Learning and Improvement: Nowadays data science field is continuously evolving, so to keep you updated with the growing pace of data science, continuous learning and improvement is very crucial. It includes researching, reading, taking courses, participating in conferences, etc.

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Specialized Roles in Data Science

• Data Analyst: The data analyst is responsible for interpreting and analysing the data to provide support to the business decisions. A person having strong Excel skills, SQL proficiency, and basic statistical analysis knowledge can become a data analyst.
• Data Engineer: Junior data engineers are responsible for managing and building the pipelines and infrastructure of data. They also need to maintain the accessibility and quality of data. They also participate in the discussions with the data architect and data scientists to ensure the data accessibility and quality for implementing the solution. A person having knowledge of cloud platforms, SQL, ETL tools, scripting languages and data warehousing can become a Data Junior.
• Machine Learning Engineer: The Machine Learning Engineer is responsible for designing, building and deploying the machine learning models for the solution of the real-world issues. A Machine Learning algorithms specialist with knowledge of cloud computing, software engineering and programming skills can become a Machine Learning Engineer.
• Data Scientist (Generalist): Data Science projects are led by the Senior data scientist from the initial phase to the final implementation phase. Data analysis also helps in solving real-world problems with the help of machine learning and data analysis. A person with the knowledge of machine learning and Advanced statistical skills and with the experience of big data tools can easily become a Data Scientist.

Conclusion

Data analysis is helping to reshape the field by converting enormous amounts of information into highly useful ideas and understanding. With more organizations increasingly depending on data for decision-making processes, there has been an extraordinary growth in the field of data science. As a result, India has witnessed skyrocketing job opportunities of competent data scientists. To learn more about data science and your career alternatives, LinkedIn has rich information and advice to offer.