How to Become a data scientist: step-by-step guide (in 2020)
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Data science is becoming the norm for decision making in many industries today. This approach to data-driven decision making has redefined the role of data and statistics in business analysis, intelligence, and decisions.
Data scientists analyze large amounts of data and help industries gain insights into their targets, analyze their performance.
Conventional business intelligence methods involved gathering a data set which was considerably smaller than the vast amount available today.
Also, most of that data was in a structured format. But today, most data is in an unstructured or semi-structured form. This has given rise to an increase in demand for data scientists who can work with any form of data.
What is data science?
Data science is a field of study where data is analyzed using some specific parameters and a decision is taken based on the pattern and results that are generated by the analysis. It is an interdisciplinary science that is about using scientific methods, algorithms, and processes to study the available data and gain knowledge from it.
It is a mixture of concepts from mathematics, statistics, information science, and computer science. It uses these concepts to unify data, machine learning and other useful technologies to derive some meaningful results from the sample data.
It is used as a synonym for many related fields like business analytics, business intelligence, predictive modeling, and plain statistics. It has many concepts taken from earlier solutions and rebranded as a part of Data Science.
It is a very sensitive field as there is a big catch- without proper utilization of resources and poor management, data science is bound to give results that are spectacular failures.
What does a data scientist do?
Data scientists use a mixture of different concepts from mathematics, statistics, information science and business intelligence to write algorithms to analyze data. The results of the analysis are used to make smarter decisions by industries.
In general, a data scientist needs to know how to code so that they can write scripts to run to process the data. While it is a good thing, the demand for data scientists has paved the way for tools that are graphical user interface based and does not need expert coding knowledge.
If the data scientists are strong with algorithms, they can use it to build the data processing models. So, even if you don’t have strong coding knowledge and a special degree for data science, you can still become a data scientist. There are online courses on data science that give you a certificate of completion which you can use to get a job as a data scientist. If you prefer attending classes on schedule, then you can look up offline courses for data science.
A data scientist should be well versed in making the data readable first. Many a time, there is a lot of data that is simply not easy to use. So the data scientist formats this data nicely into an easily usable format. All this is achieved by the data scientists with the help of special tools for data science.
Why is data science popular?
Data science was termed as the ‘The sexiest Job of the 21st century’ by Harvard Business Review way back in 2012. Now it has become one of the jobs that are in great demand and has no signs of slowing down.
Data science is a great value addition if used properly. Data scientists help businesses take better decisions in many ways. They work as a strategic partner to middle and upper management.
The data scientists track and record performance parameters and use this to improve the overall performance of the organization. The data-driven approach help managers make smarter decisions based on the data analysis results that are made available to them.
Data science helps in the recruitment process by weeding out candidates who are not suitable and saves a lot of time going through the resumes. It also helps in selecting a nearly perfect candidate with the information available on the internet on the candidates.
Data science helps organizations and each team to frame their best practices based on their recommendations. Data scientists help with a plan of action for solving issues.
These are some of the reasons why data scientists are so sought after and data science is popular.
Why should you become a data scientist?
Data science is a great job. In the current job market, there is a huge demand for a data scientist. Data scientist are well-respected nerds who are integral to the workings of an organization. It has a great learning curve and is a challenging job. It is also a very well paid job.
What are the steps you should take to become a data scientist?
There are a few basic considerations that you need to fulfill to become a data scientist. You should have an appetite for data. You should have the aptitude to work almost endlessly with the data to produce the best results. Also, most data scientists are usually having a computer science degree and have a Masters degree or Ph.D. Here are the steps you should take if you are set on becoming a data scientist.
Honing Mathematics and statistics
You need to have a strong foundation skills in mathematics and calculus. Familiarize yourself with linear algebra and the proceed to multi variable calculus. Do not ignore the basics in the light of all the hoopla about the data-driven approach. You should be able to at the minimum correlate two data. So practice hard on improving your comfort with mathematics and statistics.
Practice programming language
This is also an important aspect of data science which is being ignored in favour of using GUI based tools that do not require any coding. But learning languages offers you greater customization. Practice programming well before you proceed to the next step. Python is trending and hence recommended language to learn. Also, learn SQL to interact with databases.
Be introduced to Machine learning
Machine learning is an important aspect of data science. But, remember that it by itself is a huge field and it is easy to be sidetracked into going too deeply into the ML algorithms. Don’t get us wrong, learning everything is great. But it is of no use id you end up forgetting which ones you should use for which use case. So, if you are just getting started, keep focused and start with the standard algorithms that are used in the field of data science.
Get certified with an online course on data science.
Generally, data scientists have a good education on paper with graduation or post-graduation in the subject. If you are not one of them, then you can opt to do an online course in data science where you will get certification on completion. These courses will provide you with the required learning for honing your skills and have projects to complete. You can use this certification to become a data scientist.
Test your skills
Practice programming and algorithms that you have learned by working on projects. Learn to implement your learning in practice. Work on some live project which will count towards experience. Get some much needed hands-on experience and beef up your resume and knowledge.
Create a portfolio.
Use your sample projects and others that you have worked on to create a portfolio. You can use this to showcase your skills and competency when you are applying for a job,
Trends keep changing and more and more interesting developments are taking place in this field. New tools are released with amazing regularity and others go out of fashion. So you need to keep yourself updated on the trends and work accordingly.
Skills required to become a data scientist?
The list of skills required to become a data scientist is numerous. here, we will try to discuss a few of them in detail. To become a data scientist or to maintain the edge over the competition, you need to have the following skills.
The first is the knowledge of programming language. Learn the more popular language R and Python or some others like Matlab, TensorFlow, Julia, and Scala which are quite widely used by the data scientists. This is not an absolute must. You do not have to be an expert at any programming language as there are a lot of tools available on the internet that allows you to work with data science algorithms. But the knowledge of a programming language sets you apart from others in terms of value addition. It also gives you more room and opportunity to play around with the data and get better (maybe) results.
R,Python,Matlab,TensorFlow,Julia, Scala,SQL are some of the languages you should consider learning.
Frameworks and tools
There are a whole lot of data science tools that make the life of a data scientist easier. Many of the tools that are recently offered in the market do not even need the data scientist to know a programming language. They offer tools for data collection, data analysis, and data visualization.
The stack of technology you choose depends upon the industry requirements and purpose of the exercise. There are different tools for collecting data, data analysis, machine learning, and data visualization.
Understanding of statistics
Be familiar with advanced statistical concepts. You should have the ability to understand which methods are valid for your case and which are not. Statistics are important everywhere. It is a must for all data-driven decisions that are made and in cases where you are evaluating experiments.
This is a must for the companies whose products are itself data-driven. Even otherwise, it is a great skill to have. Though you can implement quite a few concepts using programming languages like R and Python, you should still learn various basics and algorithms for machine learning.
Linear algebra and multi-variable calculus
Linear algebra and multi-variable calculus are among the basic math skills that you need to hone to become a data scientist.
Learn to work with imperfect data. it comes across as a useful skill to have when there is no organization to data and in companies where the product is not oriented to the data-driven approach. It is also useful in companies which have simply let go of on data and you are an early hire who is there to clean up the mess.
Data visualization is important for presenting the inference to laymen for them to easily understand the result. You should be able to communicate effectively about your findings, recommendations, and the plan of action.
Other desirable skills to have are critical thinking, a software engineering background.