How to Become a Data Scientist
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One of the most frequently asked question that our career counselors get from Big Data professionals is – “ How to become a Data Scientist ? ”
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These are some of the basic questions when we start thinking about How to Become a Data Scientist and Big Data Professionals have regarding Data Scientist role –
- What is a data scientist ?
- What does a data scientist do ?
- What data scientist skills does it take for a big data professional to begin a career in data science ?
- How to become a Data Scientist ?
What is Data Science ?
Data Science is an evolutionary step in the field of business analysis that combines the methodologies and practices of computer science, modeling, statistics, analytics, and mathematics to drive business growth. Data Science involves leveraging automated methods to analyze a vast amount of data in order to extract insights from them.
It is the study that involves researching where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business strategies.
Who is a Data Scientist ?
Data scientists are a new generation of analytical data experts who has the traits of a technical expert and the curiosity of a scientist. They have the technical skills to solve complex problems besides possessing a research mindset to explore what problems need to be solved.
Data Scientists are a sign of the times. They are the driver of all trades in the data analysis world. They are partly mathematicians, partly computer scientist and partly explorers. Since they can straddle both the business and technology worlds, they are highly sought-after and well-paid.
Fortunately or unfortunately, the shortage of Data Scientists is a serious challenge faced by most of the business sectors. This makes Data Scientists even more valuable and the most headhunted professionals.
What Does a Data Scientist Do ?
Similar to a business/data analyst, data scientists combines knowledge of computer science and applications, modeling, statistics, analytics and math to uncover insights in data. Evolving beyond the business/data analyst, the data scientist takes those insights and combines them with strong business acumen and effective communication to change the way an organization approach challenges.
The average day of a data scientist involves extracting data from multiple sources, running it through an analytics platform and then creating visualizations of the data. They will then spend hours cleansing and analyzing the data from multiple angles, looking for trends that highlight problems or opportunities. Any insight is communicated to business and IT leaders with recommendations to adapt existing business strategies.
As an example, they might uncover a section of consumers who behave differently. After further analysis they uncover this subsection of consumers share a similar trait. They can then recommend ideas to modify the consumer’s behavior.
Data Scientist Qualifications
Before you learn about How to Become a Data Scientist, you should must know about the Data Scientist Skills or Qualifications. To become a Data Scientist you must have the following skills for understand about How to become a Data Scientist :
1) Education
Data Scientist is highly educated – 88% have at least one master’s degree and 46% are PhDs – and there are some notable exceptions, but usually a very strong educational background is required to deploy them. To become a data scientist, you can earn a graduate degree in computer science, social science, physical sciences, and statistics. The most common areas of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). In any of these courses, the degree will provide you the skills needed to process and analyze large data.
The truth is that most data scientists have a master’s degree or PHD and they also take online training to learn a special skill such as how to use Hadoop or Big Data querying. Therefore, you can enroll for a master degree program in the field of data.
2) R Programming
You must have a deep knowledge of at least one of these analytical tools, which is usually preferred for data science. R is specially designed for data science requirements. You can use R to solve any problem in data science. In fact, 43 percent of the data are using R to solve scientific statistical problems. However, there is a straightforward learning curve in R. In particular, if you have already mastered a programming language, it is difficult to learn, however, with the R programming language, there are very good resources on the internet to start Simply Learn data science training like R.
3) Python Coding
Python is the most common coding language, which is usually seen necessary in data science roles with Java, Perl, or C / C ++. Python is a great programming language for data scientists. This is the reason why 40 percent of respondents surveyed by O’Reilly use Python as their main programming language. Because
Due to its versatility, you can use Python for almost all the phases involved in data science processes. It can take different formats of data and you can easily import SQL table into your code. It allows you to create a dataset and you can literally find any type of dataset on Google.
4) Hadoop Platform
Although it is not always required, but it is very much liked in many cases. The experience with hive or pig can also be of great benefit to you. Familiar with the cloud tools like Amazon S3 can also be beneficial. In a study conducted by Krudflower, 3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill with a 49% rating for data scientists.
As a data scientist, you may have to face a situation where your data volume exceeds your system’s storage or you need to send data to different servers, this is where Hadoop arrives. You can use Hadoop to move the data instantly to the different points on the system.
5) SQL Database / Coding
Although NoSQL and Hadoop have become a major component of data science, it is expected that a candidate will be able to write and excel complex queries in SQL. SQL (structured query language) is a programming language that can help you perform operations such as editing, deleting and extracting data from a database. It can also help you perform analytical tasks and change database structures.
You need to be proficient in SQL as a data scientist. This is because the SQL is specially designed to help you access, communicate, and work on data. When you inquire from a database, it gives you insights. There are concise commands that can help you save time and reduce the amount of programming needed to make difficult questions. Learning SQL will help you the better understand relational databases and promote your profile as a data scientist.
How to Become a Data Scientist
There are three general steps of How to Become a Data Scientist. These are as follows –
- Earn a bachelor’s degree in IT, computer science, math, physics, or another related field.
- Earn a master’s degree in data or related field.
- Gain experience in the field you intend to work in (ex: healthcare, physics, business).
Steps of How to Become a Data Scientist in Detail
These are various steps for understand that How to Become a Data Scientist. Here is follows –
- Brush up your skills in applied mathematics and statistics
While it is expected that most data scientists will have backgrounds as data analysts or statisticians, many come from fields such as business or economics. If you are from a non-technical field, learn applied mathematics and develop a solid understanding of statistics before you dig your hands on Data Science. If you are from already an analyst or statistician, just brush up your skills.
- Grasp Machine Learning
Machine learning is a critical component of Data Science. It refers to a broad array of methods that deal with data modeling. Machine learning is used to make predictions and discover patterns in data by using algorithms. Becoming a Data Scientist mandates familiarity with Machine Learning tools and techniques like k-nearest neighbours, random forests, ensemble methods, etc.
- Learn to Code
No matter what type of business you are working for or what organization you are interviewing for, as a Data Scientist you are expected to know a statistical programming language, like R or Python or SAS, and a querying language like SQL.
- Understand Distributed Databases
As a professional Data Scientist, you will almost always be working with databases to store data. A solid understanding of databases such as MySQL, Postgres, MongoDB, Cassandra, etc. is a necessity to shine in your career as a Data Scientist.
- Master Multivariable Calculus and Linear Algebra
You may be wondering why a data scientist would need to understand multivariable Calculus and linear Algebra when learn or R can be used for out of the box implementations. Well, these form the basis of a lot of machine learning techniques that are used in Data Science. In interviews, you may be asked some basic multivariable calculus or linear algebra questions as they help the interviewer judge your aptitude for Data Science.
- Learn Data Munging
Data Munging is the process of the manually cleaning up a messy data sets to a convenient form prior to the data analysis. Data gathered in businesses are often messy and are difficult to work with. Therefore, a Data Scientist, especially in small companies, is often required to clean the data before they can use it to draw insights.
- Data Visualization and Reporting
Data Visualizing and reporting data comprise an incredibly important part of the role of a Data Scientist as it helps others, especially the decision makers to take data-driven decisions to drive business growth. Familiarity with data visualization tools like d3.js, Tableau, chart.js, Raw, etc. are extremely helpful for Data Scientists. However, Data Scientists should not just be familiar with data visualization tools, but also with the principles and practices behind visually encoding data and communicating information.
- Skill Up with Big Data
Skill Up with Big Data means Knowledge of Big Data technologies like Hadoop, MapReduce, Apache Spark, Hive, and Pig is a big plus to the career of a Data Scientist. Most of the Data Scientists work with large data sets that cannot be run on a single machine and require distributed data processing.
- Get Hands-On Experience
The best way to hone your skills as a Data Scientist is to get the industry exposure. Start an internship or join a bootcamp or if you already have experience as an Analyst, get started with a job.
- Think Data
They day you decide to become a Data Scientist, you should start the thinking like one. Companies seek the data-driven problem solvers. During your interview process, at some point, you may probably be given a test situation, where you will need to take data-driven decision to make a profit.
Basically, a Data Scientist is an extremely powerful and the rare combination of a varied range of traits. He or she is an amalgamation of an analyst, communicator, data hacker, and a knowledgeable adviser.
Conclusion
How to Become a Data Scientist ? Data science as a career is a great option that is interesting as well as rewarding. The demand for data scientists is just going to explode in next decade.
However, it is a challenging role too. You may be able to get into it in short term, but for longer-term success, you need to really build a strong foundation in this domain. All the best!
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