Careers in Data Science and Analytics


 Data Science and Analytics

"Data is the new fuel."  Ever heard of this statement? Even if not, you must have come across the word "Data" somewhere. Are you curious about what data can do and why data is so hyped these days? We have covered all your curiosities about data in this article, be with it and learn why data is so important to learn. 

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Data Science and Analytics are two interrelated fields that are gaining importance every other day. The diverse applications and increasing demand make them great career choices to pursue. Let's take an overview of what the Data Science and Analytics field is and the career opportunities related to it.

Are Data Science and Data Analytics the Same?

Data is a set of pieces of information, that are collected and stored for analysis. Data Science and Data Analytics are correlated fields but they have different meanings and scope. Data analytics refers to the analysis of collected data to decide what happened and why. Data science is a broader subject and it includes data analytics as a part of it. Data scientists try to analyze past data to predict possibilities related to the past data. Data Science is often more complex as it tries to predict similarities and trends of data for predictions. Data science includes machine learning and artificial intelligence for the advancement of technology. 

Career Opportunities in Data Science and Analytics:

  • Data Analyst: Data analysts work on data for analyzing and making decisions. They analyze large complex raw data from various resources to reveal patterns, trends, and relationships.
  • Skills: A good data analyst should have a good command over manipulative programming languages like SQL, R, Python, etc. Apart from that they should know how to work on spreadsheets like Excel. Some tools like Tableau, PowerBI, etc are also very important skills for visualization to be a data analyst. 
  • Business Intelligence Analyst:  Business analysts help businesses grow and decide risk factors. They work on previous data related to the business to predict future steps that will be relevant to the business growth. They analyze the trends of business, collaborate with stakeholders, and design and manage dashboards.  
  • Skills: The required skillset for business analysts include statistics, SQL, spreadsheets, and presentation skills. 
  • Database Administrator: The database administrators develop, maintain, and update databases. They are responsible for data security, updations, and storage. 
  • Skills: To become a database administrator, one should have a solid understanding of relational databases like MySQL and non-relational databases like MongoDB. Knowledge of data warehousing is also a very important skill to have. 
  • Data Scientist: Data scientists try to capture the analyzed data to design algorithms and predictive models and use machine learning techniques for gathering insights from the collected dataset.   
  • Skills: Data scientists require expertise in statistics, machine learning, and data mining knowledge. Some common coding platforms are TensorFlow, PyTorch, and similar IDEs. 
  • Marketing Analyst: Marketing data analysts analyze different data related to marketing campaigns and customer engagements to ensure marketing goals are meeting expectations. They are responsible for testing marketing campaigns to determine the effectiveness and returns of marketing experiments. 
  • Skills: Marketing analysts use different analytics tools like Google Analytics, SQL, Excel, and different CRM platforms and marketing automation tools. 

The above roles are some of the rising career opportunities in the field of data science and analytics but are not limited to only these fields. There are many more interesting career choices to choose from in this field. 

FAQs (Frequently Asked Questions)

Q1: What are the different types of data science?

Ans: The scope of data science is very versatile and immersing day by day. It may include machine learning, data analysis, marketing analysis, etc but are not limited to. 

Q2: What is data science used for?

Ans: Data science can be used for multiple fields to analyze and predict trends and relationships of data.  

Q3: What is the difference between data science and data analytics?

Ans: Data science and data analytics are correlated but they have differences in scope. Data analytics is related to the analysis of trends and characteristics of data. However, data science is a broader field that includes data analysis and prediction of future insights from different datasets. 

Q4: Is a graduate degree necessary to become a data scientist?

Ans: A graduate degree is not always necessary for becoming a data scientist,  excelling in the relevant skills and a good portfolio is much more important. 

Q5: How to start learning data science?

Ans: A basic understanding of statistics and programming languages like Python or R is very important as a prerequisite for becoming a data scientist.

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