Career Shift: Data Analyst to Data Scientist

With so much data every day, skilled data professionals are in high demand. As a professional, you can find many data analysts who are ready to make a career transition to become data scientists. But they are not sure how and from where to begin. Even though Data analytics and data scientists are part of one field, both fields are distinct career paths.

To begin your career as a data scientist, data analytical skills play a pivotal role. Once you understand the depth of data analytics, it is adding more complex and technical skills to your repertoire, something you can gradually do as your career progresses. If you are a professional data analyst who is looking for a new challenge or looking to make a career shift or a new one to the data field and planning a career future, we need to understand the difference between data analysts and data scientists. Which are the data analyst course and data science courses we need to concentrate on?

Understand the difference between Data scientists and Data Analytics

Before you begin your journey into data science, you need to understand the difference between Data Science and Data Analytics. In data analysis, you will collect, analyze and derive specific information from structured data. Although data analysis is a specialist role, it will only be one of the disciplines of data science.

When it comes to data science, it is a broader, scientific discipline. It generally works with large unstructured data sets. A data scientist tends to focus on how to collect the data and the priorities of data collection. A data scientist will have a deeper knowledge about data than a data analyst. They create a data structure with a purpose with different skills to plan, design.

To become a data scientist, we need to look into the major skill sets which are associated with it.

  • Data Theory: Data theory is about creating completely new abstract algorithms. In our analogy, data scientists view algorithms as bricks and mortar. Today it is easily accepted by machine learning and it is not wrong if we say machine learning is one of the valuable tools in data science. Data theory is therefore very technical and it is a skill that not all data scientists have.
  • Data architecture: An data architecture takes algorithms and applies them in specific use-cases or specific fields such as scientific or business domains. You can equate a data architect to a traditional architect. They need to create a blueprint of specific data structures by combining algorithms.
  • Data modeling: Data modeling is nothing but a software engineer, who involves in architect’s blueprints and figuring out how to put them into practice. They are like structural engineers. They use algorithms and codes to create software structures for a particular purpose. You must be good or excellent at programming because your part of the job is to overcome unexpected obstacles and you must fix unforeseen things.
  • Data analytics: A data analyst is a person who finished the structure.

So when you want to shift your career from data analytics to data science, you need to consider all the above aspects seriously. Data science is like a tree with a wide range of various disciplines. Even though data science is one of the popular domains, it takes a lot from you. Whatever the career path you choose, you will have to get your teeth into it.

So let’s see the scope of data scientist

Data scientist is one of the highly demanding job roles in this field. Data science takes a lot of time to gain the necessary skills and not everyone can get success in this field. Hence the demand for data scientists is at a high level. There are more than 1 million data science professionals in shortage. Getting a job as a data scientist is not a problem, you can consider this one a major positive point.

  • Well pay

As I mentioned earlier, data scientist professionals are in demand and there are more than 1 million job vacancies all over the world. So the professionals tend to earn more money and they earn pretty well. As per the payscale report, Data scientists in the US earn  $67K to $134K  which is usually high compared to data analysis earning around $43K and $85K.

  • You add value to the business

A data scientist is the most trusted member of the senior team. Their inputs add more value to the business. Then they usually sit down with CEOs or board members to create strategic plans for the plan’s future.

  • Demand in every industry

Data plays an important role in the industry, be it medical or IT field, networking, finance, e-commerce, defense, and many more. The data applications are limitless. For the data scientist, learning and opportunities to practice in different industries are more. They can grow rapidly.

  • Skills

Data scientists are highly skilled, they are highly trained in the field, they have more knowledge about data structure, data mining, Data architecture, and Data analytics. If you want to be in a data science career, it is good – you have plenty of things to learn and to develop your skills. If you want to be a data scientist, you need to be experimental, you should have an understanding of statistical methods and a wide range of technical abilities. You should be skilled in data languages like advanced Python and R, databases like MySQL, PostgreSQL, Microsoft SQL Server, Oracle Database, SAP HANA. You should be thrown on machine learning algorithms such as Linear and logistic regression, random forest, SVM, KNN, and more. You should specialize in skills like Natural Language Processing(NLP), computer vision, optical character recognition(OCR), deep learning, and neural networks. You should learn API tools like IBM Watson, Microsoft Azure, OAuth. Most importantly, you should have a master’s degree or Ph.D. in computer science, statistics, or software engineering-related subjects.

Steps of the transaction from data analyst to data scientist

  • Take a qualified course, explore different tools and skills.
  • Get on GitHub
  • Learn new programming languages and technical skills.
  • Learn Machine Learning algorithms.
  • Create a data science portfolio
  • Build your network