A complete guide to a flourishing career as a data scientist.

Posted on: 2022-05-23 13:29:58
In the last few decades, many economists and entrepreneurs have said that data is the oil of the 21st century. However, many also believe that comparing data to oil is incorrect since oil is fungible and limited, but data is infinite. Hence, it is imperative to use it wisely since it is infinite. The way you use data can make or break your business, and that is when Data Science comes into play. Data Science is one of the fastest-growing industries in our country and world. The emergence of so many social media platforms, increase in internet dependency during and post-pandemic, and cheaper data availability in the country has led to the rapid growth of this sector. Young people are attracted to this sector because of its continuous growth. There is a growing interest in becoming a data scientist among young people. The following is a complete guide on how to make a career in Data Science:
  • Learn Mathematics and Statistics:

    You will need a strong background in Statistics, and Mathematics is required if you are a Data Science aspirant. Due to the hype around the increase in data-driven decision-making, people sometimes forget the need to focus on basics. The current boom in the data science sector needs people who can execute using their statistics and math skills. It is expected of a data scientist to have a good balance between knowing how to convert data into business while also having the basics of mathematics and statistics clear.
 
  • Practice Programming:

    Studies by numerous organizations suggest that Python is the most essential and prominent programming language today. According to 75% of the respondents, it is the most powerful programming language of the modern era. Because of the prominence of Python, we suggest you spend at least the first 6-months learning the language and making databases.
After getting a good understanding of Python and coding in general, you can start learning other languages such as R, and Java, and then move on to understand machine learning.
 
  • Deep dive into Machine Learning:

    Programmers often make the mistake of learning every machine learning algorithm without knowing how it will help them solve problems. Prior to learning more complex programming algorithms, aspirants should learn well-known algorithms. It is important to understand that not all complex problems require complex algorithms in order to be solved. The goal is to solve the Machine Learning problem as efficiently as possible.
 
  • Initiate building Machine Learning Projects:

    In Machine Learning, knowledge is essential, but knowing how to put that knowledge into practice is even more crucial. Once you are familiar with writing algorithms for Machine Learning, you can start taking on projects. It will not only improve your portfolio but also refine your work. Hands-on experience is appreciated in the data science sector as well as in any other field.
 
  • Create a Portfolio:

    To showcase your skills in coding and other software to potential employers, it is necessary to create an exceptional portfolio apart from building a promising resume. A big part of data science is knowing how to code, and GitHub is one of the best places to develop your coding skills.
 

Here are some of the things that prove you have a good portfolio:

  1.  Good Follower Base: Having a large follower base on your portfolio indicates having a high-quality portfolio. Anything over 50 is considered good.
  2. Contribution Graph: The contribution graph displays how diverse and active you are within the coding community. On your graph, the amount of green indicates how good your contribution rate is.
  3. Number of Stars: Engagement in your portfolio plays a huge role in its evaluation. There should be at least 100 stars in every portfolio.
  4.  Targeting the correct audience: When you write code that targets the employer's business, you are more likely to get a response. Any level of attention is beneficial.

 
  • Focus on Soft Skills:

    Experts suggest that, beyond data science, employers also look for soft skills while hiring because employers are aware that it's not only their data science skills that will bring them business, but Soft Skills such as Management, Communication, Problem-Solving, the ability to apply data science in the real world, Prioritization and Business Acumen.
  • Know where you are applying:

    You should know everything about the company before applying for a job, as well as everything about the role you are applying for. A strong understanding of companies and their positions can help you land a job with a good work-life balance and a higher salary. Joining the workforce is a big decision, and one should always make that decision wisely by considering factors such as package, responsibilities, perks, and work culture.
  • Keep Upskilling:

    The Data Science industry is constantly evolving. A majority of Data Science companies hire people who are ready to upskill as the field changes. Rather than focusing only on one skill, companies aim to teach people a cluster of skills that are relevant to their job.
This was our guide to a successful career in Data Science. In addition to its Postgraduate Diploma in Economics, MDAE offers specialized Postgraduate Diploma in Data Science & Finance as well. Faculty members are industry experts with diverse backgrounds and we emphasize practical methods of teaching. The curriculum is based on courses taught at the London School of Economics and Political Science (LSE) in Oxford, one of the most prestigious schools in the world. In addition to a strong foundation in Mathematics and Statistics, Python, and Machine Learning, we also strive to develop soft skills in all our students.
Click on this link to know more about our PGD courses: https://forms.gle/MoDNkQbYjjgHF7GL9
Written by- Omkar Shraddha Dharmapal Manjrekar

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