Data science has been a buzz word for the last few years. Everywhere from news channels to advertisement, you hear about how great a career in data science is and how data scientist are being paid hundreds of thousands of dollars. The World Economic Forum’s Future of Work Report 2020 predicts that by 2025, the job with the highest demand and growth will be that of a Data Scientist. Naturally, a lot of people would be inclined to know what does data science mean and how can anyone make a career in data science.

So, the agenda of this post is to dissect data science as a career, right from knowing what does it actually mean, what qualifications do you need to become a data scientist and how much data scientists are paid.

What is Data Science?

What is Data Science?

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

The data used for analysis can come from many different sources and presented in various formats.

Data science’s lifecycle consists of five distinct stages, each with its own tasks:

  1. Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
  2. Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.
  3. Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.
  4. Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.
  5. Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.

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What Does a Data Scientist Do?

A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:

  • Before tackling the data collection and analysis, the data scientist determines the problem by asking the right questions and gaining understanding.
  • The data scientist then determines the correct set of variables and data sets.
  • The data scientist gathers structured and unstructured data from many disparate sources—enterprise data, public data, etc.
  • Once the data is collected, the data scientist processes the raw data and converts it into a format suitable for analysis. This involves cleaning and validating the data to guarantee uniformity, completeness, and accuracy.
  • After the data has been rendered into a usable form, it’s fed into the analytic system—ML algorithm or a statistical model. This is where the data scientists analyze and identify patterns and trends.
  • When the data has been completely rendered, the data scientist interprets the data to find opportunities and solutions.
  • The data scientists finish the task by preparing the results and insights to share with the appropriate stakeholders and communicating the results.

Now we should be aware of some machine learning algorithms which are beneficial in understanding data science clearly.

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Skills Needed to Make a Career in Data Science

Skills Needed to Make a Career in Data Science
Source: InterviewBit

Data science is a highly demanding job. You need to be well versed will all the programmes, machine learning data bases and a lot more.

Following are some the most important skills that are pre-requisite to become a data scientist:

1. Machine Learning

Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics.

2. Modeling

Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.

3. Statistics

Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.

4. Programming

Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.

5. Databases

A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them. Even if you don’t know some or all of these crucial skills, you can take up some online courses to acquire the necessary skills.

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How to Make a Career in Data Science ?

Now we come to perhaps the most important part of our discussion i.e. how to make a career in data science? Well there certain obvious things which you need to do.

First things first: get a degree in data science. While it is not at all necessary to hold a degree to become a data scientist, employers usually prefer people who have some kind of formal training just as any other career. You may go for a full time degree course from some college or for an online program.

Next, you need to work on your skills. As mentioned earlier, you need to sharpen your programming skills, databases, machine learning and data visualization. Keep in mind, career in data science is highly lucrative but only if you are on the top of your game.

Once you are done with the above two things, seek positions that work heavily with data, such as data analyst, business intelligence analyst, statistician, or data engineer. From there, it’s possible to work your way up to becoming a scientist as you expand your knowledge and skills. Though there are many paths to becoming a data scientist, starting in a related entry-level job can be a good first step.

The final step is to keep developing your skills as you progress.

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Salary of a Data Scientist

Salary of a Data Scientist
Source: InterviewBit

The average salary for a data scientist is Rs.6,98,412 per year. With less than a year of experience, an entry-level data scientist can make approximately 5,00,000 per year. Data scientists with 1 to 4 years of experience may expect to earn about 610,811 per year.

Mu Sigma, Accenture, and Tata Consultancy Services Limited are the top respondents for the job title Data Scientist in India. Inc has the highest reported salaries. Accenture and HCL Technologies Ltd. are two more firms that pay well for this position. Mu Sigma pays the least. Tata Consultancy Services Limited and IBM India Private Limited are likewise on the low end of the range.

In US, the average salary for a data scientist is around $113,000 according to Glassdoor.

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Despite the difficulties, data scientists are the most sought-after experts in the industry. With the data world developing at such a rapid rate, being a successful data scientist requires not only technical capabilities but also a thorough understanding of business requirements, collaboration with various stakeholders, and persuasion of business executives to act on the information offered.

Hope this post would have been of some value to you!

If you liked this or want to add some of your learning feel free to comment down.

Career in Data Science: Everything You Need To Know

Prashant Tiwari

Hi Peeps!! I am Prashant, a blogger by passion and an entrepreneurial enthusiast. I have written more than hundreds of articles and on a variety of issues and have fallen in love with helping people by means of meaningful blogging and articles. My aim is to add value to all my reader's life. If you feel that this post has benefitted you in any way then do share it with others to help them. Happy Reading!!

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