How to Start a Career in Data Science?

Learning data science can be intimidating when you are competent, especially when you are just beginning your path to becoming a data scientist. You’re asking yourself a lot of questions about which tool we should start over with. R or Python, for example?  How can I learn the statistics related to my data? How should I approach new approaches and what should I concentrate on? and numerous other similar queries. Throughout your journey, you will likely receive answers to a lot of questions.

Let’s begin by learning in this article how to retrace your steps as a beginner on this journey. Simply put, Data Science is not rocket science. One needs proper direction regarding the tools and road map that must be taken into account to hide your journey’s flaws. This is the reason many people are now relying on a Data Science Bootcamp to step into this promising career field.

What is Data Science?

This branch of science deals with the transformation of raw data that has been gathered from various sources into a useful and processed form that is then simple to understand as the details are transformed into various graphical and statistical formats that aid developers in quickly understanding complex data. Many fields are benefiting over the long term from their implementations with the assistance of data science. Data professionals develop various algorithms to convert data patterns into research that is then used by the government, businesses, and organizations. Data Science is crucial because everything in this world that is connected to information technology is developing quickly, so there needs to be a way to make sense of it all.

Skill Set For Data Science

Following are some very important and practical technical skills that are needed when diving into a field like data science. They are mentioned as

  • Machine Learning
  • Derp Learning
  • Data visualisations
  • Deep knowledge and familiarity with the statistical and patterns analysis
  • Mathematics
  • Sound knowledge of programming
  • Managing raw and unstructured data
  • Big Data processes, networking, and systems
  • Knowledge of SAS, Python, R, Hadoop, Spark, Tableau, PowerBI, and other Data Analysing tools used

When working in a team with other organs, such as engineers, analysts, coders, developers, and business managers, data scientists need to have a wide range of skills, including those related to communication, critical thinking, logic building, storytelling, adaptability and flexibility, and business acumen. This synchronization between various organs requires more than just technical knowledge.

Basic Education Required

Bachelor’s Degree :

A bachelor’s degree is necessary to enter the field of data science and can open the first door for your career in this field. You can gain a base as an applicant if you have a bachelor’s degree in data science, statistics, math, business, IT, or a related field. All of these areas help you refine your analytical, data-handling, and number-sorting skills. But even if you lack a degree in this area, your capacity for critical thought and artistic creativity will still enable you to come up with novel ideas. There are numerous additional ways to help you during your startup career in data science.

Online Tutorials and Professional Courses :

 Whether or not you have a bachelor’s degree, taking an online course or earning a professional certificate can still make it easier for you to apply for jobs involving data science. You can then mention them in your resume as a beneficiary aspect or add them to job profiles that help you make a priority over a group of competitors for more credibility. These types of online courses are part-time, look great on resumes, and can be finished in a matter of months, which further aids in preparing you, at least for a position at the entry level.

We are sharing some of our top resources for learning data science in this article, both at a conceptual level and at an advanced level where you can get authorization for many different sectors. Here is a list of them:

  • Introduction to Data Science using Python (Udemy): This program is designed for beginners. It lasts for two hours and thirty minutes and contains twelve lectures, focusing on Python implementation of the fundamentals of data science.


  • Supervised Machine Learning: Regression and Classification (Coursera): This course has a duration of 33 hours of on-demand videos for 3 weeks. This is offered without charge for a seven-day trial period, which consists of foundational concepts.


  • Data Science Foundation (Codecademy): This course consists of 16 lessons,  which have a duration of approximately 15 weeks at your own pace. This is available at a rate of $ 199 after a free trial period. In this course, in-depth knowledge of practical data science is covered.


  • Programming for Data Science with Python (Udacity): This course is offered by Udacity. Python, SQL, Github, and other technologies are also covered. You become qualified to work with programming languages used for data science, particularly Python, through these programs. It takes about 3 months to complete, averaging 10 hours per week.

Job Roles Available in Data Science

As we’ve already covered in this article, it’s crucial to have the fundamental knowledge and abilities needed to understand data science quickly and easily. The three branches of data science include data scientists, data analysts, data architects, machine learning engineers, data engineers, and business analysts. These roles encompass a wide range of occupations.t.


Any of these streams can be restarted because they all fall under a single category and are productive in every way. Decide which specialization you are interested in, then move forward with it while concentrating all of your knowledge and zeal. Since everything has become digital over the past few years, there will be a huge demand for people who can manage this enormous amount of data. As a result, data science will continue to develop at an exponential rate, offering fantastic career options.

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