We encounter many terms or buzz words related to the data science domain. There are various career paths for data science. In the previous section, we started with the Data Science domain with a basic overview. Now we will look briefly at the job profiles present in this domain concerning the work offered in different profiles.
There can be many categories of a career in this domain, including Data Scientist, Data Analyst, Business Analyst, ML Engineer, etc.
Let us look briefly about these.
Data Science is the sexiest and trending job of this decade. We must have heard this. But what work Data scientists perform?
Data Scientist is responsible for extracting useful insights from the data, and based on these insights, it predicts future trends or decides on any business.
So a Data Scientist have many tasks to do simultaneously, which are-
1. Analyzing data and extracting useful information.
2. Building Hypothesis model for predicting future
3. Based on the model and prediction, deciding for the particular set of problem.
It is one of the most common terms we have heard. But what is Data Analysis, and how is it different from Data Science?
Data Analyst mainly focuses on analyzing data statistically and finding useful insights from the raw data. It performs the Exploratory Data Analysis, which we will see in upcoming sessions.
Data Scientist and Data Analyst's main difference is that a data analyst mainly focuses on analyzing data only. At the same time, a Data Scientist examines data and predicts future trends using the Machine Learning model.
Business Analyst serves as a bridge between the company's IT department, the Analytics department, and the Stakeholders of the company. It is one of the crucial job roles. The person should have a fair understanding of Data Science, Information Technology, and Public relations.
Business Analyst communicates with the company's stakeholders and identifies the problem or the requirement for the product or services and then forward the business problem to Data Scientist to find an actionable solution. After getting the solution, the Business Analyst communicates the necessary changes in the IT department's service to make changes accordingly.
Business Intelligence Analyst
A Business Intelligence Analyst uses tools and techniques to analyze and visualize the Big Data and derives useful information that helps the enterprise grow.
So what is the difference between a BI Analyst and a Data Scientist?
The significant and fundamental difference between these is that a BI Analyst deals with the past and present trend in the data and find useful insights. At the same time, a Data Scientist makes future decisions based on past experiences.
As the profile name suggests, a Data engineer performs all the engineering related to raw data.
A data engineer extracts the data from various sources, store the Data in Database Management System and then perform multiple actions on it to clean, organize and manipulate the data using Structured Query Language (SQL).
A Data Engineer must be proficient in DBMS and SQL.
Machine Learning Engineer
Machine Learning is a trending technology that enables the machine or computer to learn from past data without being programmed. We will cover Machine learning in-depth in upcoming sessions.
ML engineer focuses on the research and development of the machine learning algorithms.
Artificial Intelligence emphasizes the development of intelligent machines programmed to think like humans and mimic their actions.
It is a vast topic of study and covers many applications, which we will cover in upcoming sessions.
So the AI engineer primarily focuses on various real-world applications and domains such as Computer Vision, NLP, Robotics, Speech-to-text, etc.
This blog was to give a brief overview of the career paths in this domain and their relation to each other.
Many of the terms used in this might be new for beginners, but everything will be cleared as we proceed forward.
In the next session we will discuss about the steps involved in End to End Data Science Project and then we will proceed in depth to all the steps.
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