Data science is currently the most exciting field and is in serious demand by data scientists. For good reason – data scientists do everything from building self-driving cars to automatically captioning photos. Considering all the interesting applications, data science is meant to be the most demanding job.
In this post I will share some steps that will help you in your journey to become a Data Scientist. The journey is not easy, but it is more inspiring than following the conventional wisdom.
Becoming a Data Scientist — FAQs :
- Data scientist qualifications
- Data scientist educational requirements
- What skills are needed to become a data scientist?
- Is it hard to become a data scientist?
- How many years does it take to become a data scientist?
- Is data science a good career choice?
- What is the data scientist career path?
- What salaries do data scientists make?
Question Everything
The appeal of data science is that you have to answer interesting questions with real data and code. These questions are "Can I predict that a flight will arrive on time?" "How Much Does America Spend on Education Per Student?". You need to develop an analytical mindset to ask and answer these questions.
The best way to cultivate this mindset is to start it with news articles. Find articles like these that make you better and find out if sugar is really bad for you. Think :
- How to draw their conclusions based on the data they discuss
- How to design a study to explore further
- Questions you would like to ask if access to basic data is possible
Do you want to become a Data Science Expert join the Live Training Program on Ekascloud?
Learn The Basics
Once you know how to ask questions, you are ready to use your technical skills to answer them. I would start learning data science by learning the basics of programming in Python.
Python is a programming language with a standard syntax that is recommended for beginners. Fortunately, it provides the skills to perform more complex data science and machine learning-related tasks, such as deep learning.
Many people worry about choosing a language, but the important things to keep in mind are :
- Data science is not about tools, it is about answering questions and running business value.
- Learning ideas is more important than learning syntax.
- You create projects and share them in a real data science role, learning this way will give you a start.
Build Projects
As you learn the basics of coding, you should begin developing programs that answer interesting questions and showcase your data science skills. Plans don't have to be complicated.
For example, you can analyze Super Bowl winners to find patterns. It's important to find interesting datasets, ask questions about the data, and then code those questions with answers. If you need help finding databases, check out this post for a list of good places to find them.
When making plans, keep this in mind :
- Data cleaning is the mainstay of most data science jobs.
- Linear regression is the most common machine learning technique.
- It all starts somewhere. Even if you think what you are doing is not enjoyable, you need to do it.
Share Your Work
Once you've created some projects, you need to share them with others! It's a good idea to upload them to GitHub for others to see. You can read a good post here about uploading projects to GitHub, and learn more about linking to a portfolio. Uploading Project:
- Motivate yourself to think about how to best present them, which you will do in a data science role
- Allow your colleagues to see and comment on your plans
- Allow employers to see your plans
Once you start building an online presence, it's a good idea to start a conversation with other data scientists. You can do this in person or in online communities. Some good online communities:
- Data science
- Data science slack
- Kaggle
- Quora
Personally, I was very active in Kaggle and Quora when I was studying, which helped me a lot. The best way to do this is by joining online communities:
- Find others to learn from
- Improve your profile and find opportunities
- Strengthen your knowledge by learning from others
- With meetups, you can interact with people in person. Individual interventions will help you meet and study experienced data scientists in your area.
Become a Data Scientist – Frequently Asked Questions :
Data scientist qualifications
- Working data scientists must have a strong command of related technical expertise, including Python or R programming, writing queries in SQL, building and improving machine learning models in their language, and some "workflow" skills such as kit and command line Huh.
- Data scientists need to have strong problem solving, data visualization and communication skills. When a data analyst is often asked a question to answer, a data scientist is expected to explore the data and find relevant questions and business opportunities that others may miss out on.
- While it is possible to get a job as a data scientist without any prior experience, working as a data analyst prior to becoming a data scientist is not a relevant professional experience for interested data scientists.
Data Scientist Educational Requirement
- Most data scientist roles require at least a bachelor's degree. Degrees in technical fields may be preferred, as are advanced degrees such as PhDs and Masters, but advanced degrees are usually not strictly required (even when they claim to be job postings).
- What employers pay most attention to is your competence. Applicants with a minimum advanced or technical-related degree can fill this gap with an excellent project portfolio, which demonstrates their advanced skills and experience in performing related data science tasks.
What skills are required to become a data scientist ?
Specific requirements vary slightly from job to job, and more specific roles develop as the industry matures. However, in general, the following capabilities are expected for any data science role :
- SQL
- Data Visualization
- Communication
- Programming in Python or R
- Building and Optimizing Machine Learning Models
- Solid Understanding of Probability and Statistics
Most of the time, machine learning will focus on a role in a specific subdomain. Every data scientist is expected to be familiar with the basics, but one role may require more in-depth experience with natural language processing (NLP), while another may focus on developing product-ready prediction algorithms. is.
How many years does it take to become a data scientist ?
- This will be different for each person. In DataQuest, most of our students report that they have achieved their learning goals within a year or less. How long the learning process will take depends on how much time you allow.
- Likewise, the length of the job search process will vary depending on the projects you have built, your other qualifications, your professional background and more.
What salaries do data scientists make?
- Salary varies greatly depending on the applicant’s location and experience. However, on average, data scientists pay very comfortable salaries.
- The average data scientist salary in the United States will exceed $ 120,000 per year by 2020.
