The 'key skills' one should know to become a successful data scientist
Nowadays, big names like Google, Microsoft, Facebook, etc., are looking toward the magic wand of data science and machine learning. Data science is one of the trending and fastest growing domains.
According to Zion Market Research, by 2024, the machine learning market is expected to reach $20.83 Billion. Glassdoor stated that in India, a computer programmer’s average salary is INR 400k per year, whereas the average pay scale of a data scientist is INR 900k per year.
From sales prediction to self-driven cars and personal assistants, all the applications are power-driven by data science. This is the main reason why every firm wants to cater to data science in their business.
By 2020, there would be 2.3 million new jobs by the year in the field of machine learning and AI reported Gartner. But, there is a substantial shortage of skilled data scientists in this world full of data scientists. The scarcity of skilled data scientists remains intact with the high demand for data scientists in the market.
There is a path for budding data scientists to pursue data science careers, irrespective of their previous skills or experience. To develop essential skills to be a data scientist, you can follow the article below.
Key skills to become a Data Scientist:
You are going to be expected to be well acquainted with the use of programming tools. This means you must know how to use statistical programming languages like R, Python, SQL, etc.
It is essential to know how to deal with imperfect and messy data. Imperfect data may include missing or inconsistent values. For example, the US, United States, The US, The Unites States or 2020-09-09, 09-09-2020, etc. For data-driven companies, wrangling or sifting data is essential as their entire product depends on data.
For companies like Netflix, Google Maps, Uber, etc. that have a vast pool of data or where the product itself produces vast data, machine learning algorithms are the way to deal efficiently. It is crucial to understand which technique is to be implemented when and how the algorithms work.
Multivariable Calculus & Linear Algebra
In companies where the data define the product, it is essential to understand multivariable calculus and linear algebra. Algorithm optimisation or predictive performance improvements can lead to great success for the business. As a data scientist, you may be required to build out your implementations at some point in your career. To ace that in the future, you must be familiar with some essential multivariable calculus or linear algebra algorithms.
Having a good understanding of statistics is crucial for data scientists. A data scientist must know about statistical tests, distributions, maximum likelihood estimators, etc. Also, you must be able to identify a valid statistical approach for the respective project; the same would be implied to machine learning. Especially for data-driven companies, including all other firms, statistics is essential, as the stakeholders will depend on the statistics to make decisions and evaluate the experiments or designs.
Data Visualization & Communication
For companies that make data-driven decisions regularly, or that hire data scientists purely to influence data-driven decisions, visualising and communicating data is extremely important. If you are a budding data scientist, you must be familiar with data visualisation tools like matplotlib, ggplot, or d3.js, etc. data communication implies communicating the insights or technicalities of data to the audience in an easy to understand way. Tableau, a dashboarding tool, has also become a trendy data visualisation tool. As a data scientist, you must be familiar with data visualisation principles and communication other than just being acquainted with its tools.
To be a data scientist and get hired as one, you must possess a software engineering background. Additionally, you must know how to handle data logging and develop data-driven products for the companies.
The vital among all the skills mentioned are data intuition. To be a successful and skilled data scientist, you must know how to solve a data-driven obstacle. Develop the ability to think about what is important and what is not, how to interact with engineers, how to communicate the required to managers, which algorithms are to be followed, do the estimations make sense or not, etc., while evaluating a data-driven problem.
SRM and LEARNXT together are changing the face of the Indian education system. collaboration programs are an excellent way to learn the skills of being a data scientist. The intensive programmes involve innovative learning techniques like case-based learning, experiential learning, supplementary learning, industry learning, and mentorship-based learning.
Post the programme, students will be offered an internship that will help them gain valuable work experience, explore a career path, an edge in the job market, develop a network with professionals in the field, and allow them to test specific techniques learned in the classroom before entering the working world.
Stay tuned to LEARNXT to know more about the ‘key skills’ you should know to become a successful data scientist.
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