What to Learn to Become a Data Scientist in 2021
As data becomes the new ‘Gold’ for businesses, data scientists are set to find their value in this gold. This write-up clearly defines the job requirements and company expectations that this phenomenally evolving role entails.
By Andrea Laura, Freelance Writer
The work post and profile of a ‘data-scientist’ has been evolving every year. So are its salaries, and interestingly both have been witnessing a constant upward surge.
With an average data scientist salary crossing the $125,000 dollar mark and 650% growth in the numbers of job openings, it is a position whose requirement has shown buoyancy within the overall business markets, despite an overall slowdown.
There is an inordinate amount of data collected from all nooks and corners of a user's online movements. This data needs to be well stored, maintained and analyzed and systems need to be developed, in order to manage it well. Data Scientists are basically data and technology specialists that tend to do this job well. Job titles like data analysts, data engineers and business intelligence analysts come under the same purview.
This job description includes:
- Framing the right questions that would help in sieving through data
- Clean the data, once you receive it
- Find and organize means to store and integrate this data
- Process the stored data
- Initial data analysis to categorize data
- Find and enumerate data algorithms that shall help to get the job done
- Utilization of the apt app development frameworks like machine learning, statistical modeling, and artificial intelligence to develop predictive models for further data processing
- Improve results with reiterations
- Deliver results on time
- Receive feedback and rework on data as per the feedback
- Deliver end-products in data visualizations based upon user requirements
The above stated job description requires an array of expertise in a number of fields. Also, before taking steps towards a career in data sciences, you also need to understand that data mostly includes numbers. So, if you do not enjoy working with numbers, being a data scientist might not be a very good option for you.
A data scientist’s requisite skill-set includes:
1. First and foremost, you need to understand programming: The above stated job description clearly indicates that a data scientist tends to develop algorithms and systems to sieve through scores of data for business development. Only those with a solid understanding of computer programming can develop such solutions. Software specializations herein include:
- Python is the most famous and prefered language for data scientists. It is an object oriented programming language with several data libraries like Pandas, NumPy, Matplotlib, SciPy, Seaborn, TensorFlow, etc. that help developers to simply code in with already existing code bases, instead of having to fully rewrite functionality explicitly. This tends to make their job of data application development easier. Moreover, it comes free of cost. With an active user and developer community, Python continues to be a sure shot winner in this field.
- R is another programming language with similar functionalities. It may not enjoy as widespread support, but is often preferred for purely statistical programming.
- If you end up working within a big corporate organization, chances are you might also get your hands on SAS, an expensive software suite with in-built GUI options, with the benefit of being easier to use for non=programmers.
2. A love of mathematics: As a data scientist you would time and again require your high school mathematics skill set including probability and statistics, along with basic algebra and calculus concepts. So, if you intend to become a data scientist; brush up these skills ASAP.
3. Specialization in data analysis: Storage and assimilation of scores of data has been referred to as big data. As stated in the job description earlier, a data scientist needs to develop models that shall help in acquiring and analysis of acquired data to develop meaningful models and solutions. This kind of big data application development requires expertise in SQL (sequential query language that allows algorithms to call for and acquire data in particular formats using queries) or Hadoop (a software library that ends up distributing big data amongst a cluster of computational devices, for better analysis). Spark can be used in conjunction with Hadoop to work with large unstructured datasets.
4. Storytelling skills: Collecting and analyzing data is just not enough. A data scientist needs to process out meaningful outputs from data sets and present them in a manner that is understandable and usable for the stakeholders. Thus, they need to include various storytelling techniques including data visualizations, to ensure the output is presented well. Various data visualization tools like Matplotlib, Ggplot and D3.js, and others can be used for this purpose. To be an able data scientist, you should be well versed with at least one of them.
5. Understanding and deploying machine learning expertly is a must: As a data scientist, you would have to handle large amounts of data in various formats, including structured and unstructured formats. Machine learning will help you develop algorithms that will effectively sieve through and make predictions with this data. Thus, to become a better data scientist you must grasp machine learning concepts.
6. Thorough understanding of business: As a data scientist, you tend to develop solutions to business problems, through user data. But, to develop these solutions effectively, you would first need to have a form hold on business requirements and the issues you tend to resolve using big data solutions. Only then would you be able to develop and present an effective solution.
Data science is one of the most promising careers of modern times. So, if you wish to see yourself as a data scientist, try acquiring the above stated skills to some level. There are various online tutorials that can help you with python, SQL and other requisite concepts. Try going through them, for a well defined introduction to the world of data science.
Bio: Andrea Laura is a very creative writer and active contributor who love to share informative news or updates on various topics and brings great information to her readers. Being writing as her hobby, Andrea has come out with many interesting topics and information that attracts readers to unravel her write-up. Her content is featured on many mainstream sites & blogs.
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