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How Can You Transition to Data Analytics?

No matter what field you come from and what work experience you have, you can steer your career into Data Analytics. If you are still wondering what your next steps should be, here is a comprehensive guide that you can refer to. Based on your background, what skills you need to pick up to transition to a data-driven profession, we list down what you can expect!

Are you a Fresher?

The good news is that a lot of companies tend to hire fresh college graduates and train them in-house. They need an unbiased, fresh pair of eyes to look at their business problems. As a fresher, you don’t have any baggage, and the biggest advantage is that, as a professional, you can be molded in any way.
Make yourself conceptually sound in statistics, learn relevant tools and languages to demonstrate your skill set. You can start by learning about inferential statistics, hypothesis testing, and machine learning algorithms. Then, develop a strong footing on the coding front as well by gaining expertise in R. You will have an upper hand when it comes to landing your dream job.
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Do you have Technical Work Experience?

If you have technology experience, for instance in software engineering or if you are an IT professional, then you certainly have a big advantage. This is because you already have the programming experience required and most likely some domain experience as well. All you need to do is pick up statistical knowledge to become a complete data professional in your own domain.
With this background, a data engineering role would be the easiest for you to switch to because it requires a good knowledge of data structures and programming languages.
If statistics excite you then a business analyst or a data analytics specialist role is also worth considering, because these roles require a lot of application of analytics and statistics. You should also focus on honing your soft skills as well as obtaining a mastery of tools like Excel or Tableau which will complement your presentation abilities.
The best scenario for you would be to look for data analytics roles within your current company and transition within your own organisation.

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Are you from a Marketing, Sales or Operations Background?

You already have strong domain knowledge. You can start as a data visualiser or a data analyst.
You can start by learning the applications of analytics in your domain and pick up the technical and statistical skills. You can do that through programs such as the ones offered by UpGrad and IIIT Bangalore.
You can also participate in online competitions and hackathons to test your knowledge and develop a solid foundation in data-driven problem-solving. Take up datasets from within your current company and do a small proof of concept to demonstrate your knowledge and the impact of analytics; apply it in your daily job and improve your decision-making process.
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If you have experience, let’s say in marketing, you should explore market mix modeling, which helps marketers optimise market spends across channels.
Similarly, if you have experience in sales, you can look at Lead Scoring to help understand which of the leads are more interested in your product/service. Use it to make more informed decisions and data-driven decisions within your domain. Look at it this way: Data Analytics is an extremely powerful tool in your arsenal.

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Are you a Business Consultant or a Business Analyst?

You have a good business understanding and know how to present data, but your knowledge might be lacking a bit on the technical front. Depending on your strengths and inclinations, you can choose to be either a data scientist, a data analyst, or even a Business Intelligence professional. If you are strongly inclined towards statistics, then data analytics is strongly recommended for you. 

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Data Analytics requires you to understand the business and its intricacies well and simplify business decisions through statistical tools. The field is agnostic about where you come from, it needs you to pick up new skill sets and become a jack of all trades. Someone who can make strong data-driven decisions.

Is it necessary to have some technical/engineering background to become a Data Scientist?

In the long run, you don’t need any prior experience to become a data scientist; it’s all about your curiosity and aptitude for data.

However, regardless of your job role, there are certain skills you’ll need to be adept in if you want to become a Data Scientist such as Math and Statistics, Analytics and Modeling, Machine Learning, Programming, Data Visualization, Intellectual Curiosity, Communication and Business Acumen.

What are the different careers in Data Analytics?

Some of the most common Data Analytics occupations that you should be aware of, whether you are just starting out or considering a mid-career change are

1. Data Scientist : Data scientists gather and analyse data so that meaningful insights can be presented.
2. Data Engineer : A Data Engineer is generally entrusted with enhancing the organization's infrastructure around numerous Data Analytics procedures and focuses on big data sets.
3. Data Analyst : Data analysts are needed in a variety of businesses to evaluate and represent data in various formats so that actionable insights can be derived.
4. Machine Learning Engineer : Machine Learning Engineering is a professional path in sophisticated data analytics that combines Data Science with Machine Learning/Artificial Intelligence capabilities.

Can a person with a degree in Journalism pursue a career in Data Analytics?

Data and information are important to the success of a business in journalism. If you want to pursue a career in data analytics after earning a journalism degree, Data Journalism would be a great option. Data journalism is all about gathering big amounts of data and analysing it with technology. Programming languages are required for processing large data collections.

Data Journalists must possess strong technical abilities in SQL and Python, as well as expertise in data visualisation and statistics. In order to contextualise the data for further use, the discipline also necessitates a thorough understanding of a variety of other fields.

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