How to become Data Scientist?
So you have finally decided that you want to become a data scientist. But where to begin? What are the skill requirements? Check out this amazing article to know everything about becoming a data scientist.
Data science is now influencing every department within the industry. Starting from product development to sales, marketing to government operations - data scientists are emerging as an integral part of businesses and organizations all around the world. Since the time Data scientist had been voted as “sexiest job of the 21st century” by Harvard Business Review, there is significant growth in demand for data scientists. Moreover, according to Glassdoor, data scientist has been chosen as the best job profile in the USA.
If you are not from IT background then at first data science might seem complex and confusing field to you as it involves dozens of different skills. But don’t get scared. If you are thinking about making your career in data science then we are here to guide you on how to become a data scientist.
Before you start your journey towards becoming data scientist – there are some questions you should ask yourself first:
- Do you love programming & statistics?
- You might be given the title of data analyst or business analyst – Are you ok with these titles?
- Do you enjoy working in a field where you need to constantly learn about latest techniques and technologies?
- Are you interested in becoming data scientist even though you will be paid average salary?
If all of your answers are yes then you are on the right path. You can start your journey towards becoming a data scientist. Before jumping into steps of becoming a data scientist you should have a clear understanding of data science.
What in the world data science is?
Data is useless without the context. Data needs a story to tell a story. As Wikipedia states, Data Science is a concept to unify statistics, data analysis, machine learning and their related methods. Fundamentally data science is a multidisciplinary blend of the data interface, algorithm development, and technology in order to solve analytically complex problems. Data science is about surfacing hidden insight that can help enable companies to make smarter business decisions.
What is a data scientist?
According to Techopedia,
"A data scientist is an individual who performs statistical analysis, data mining, and retrieval processes on a large amount of data to identify trends, figures, and other relevant information."
Data scientists are responsible for discovering insights from the massive amount of structured & unstructured data to help shape or meet specific needs and goals of the organization. A good data scientist is the one who knows what is available “outside the box” and who needs to connect with, hire or the technologies he needs to deploy to get the job done, one who can link business objectives with data and who can simply connect the dots from business gains to human behaviors.
Here is the detailed guideline on how you can become data scientist:
If you are in starting phase of your career then getting a bachelor degree in IT or computer science, statistics will be a good start. Suppose you are not from computer science background then you can always start with boot camps, online certificate programs, and self-guided learning programs. There are many popular bootcamps where you can learn data science such as - Byte Academy, DataCamp,The Data Incubator, Datasciencedojo etc. Earning a master degree will also give you more insight. As some of the prestigious companies expect candidates with master degree. Although getting a degree or online certificates is not enough. It is easy to get lost in too much theory. That is why gather maximum practical experience. Because companies are desperate for candidates with real-world skills.
What skills do you need to become a data scientist?
- Data mining
Data mining is a set of various methods that are used in the process of knowledge discovery for distinguishing the relationships and patterns that were previously unknown. Data mining is a process used by data scientists to convert the large set of data into something more useful. There are some data mining tools which can be very useful for data scientists like – regression, association rule discovery, classification and clustering. You can start learning data mining from KDnuggets.
Mathematical skill is required to be an effective data scientist. Get deep knowledge of concepts like - probability such as Bayesian probability & frequentist probability, regression, numerical analysis, linear algebra.
Once you are done with mathematics’ concepts you can move on to learning about statistics. Statistics is nothing but the branch of mathematics. You should be familiar with statistical tests, distributions, hypothesis testing, etc. You should also have knowledge of general linear model or multivariate regression model, ANOVA, MANOVA, GIS – Geographical Information System, Spatio-temporal, etc.
- Linear algebra
Linear algebra is a branch of mathematics that covers the study of vector spacing and linear mapping between these spaces. Linear algebra is used in machine learning. To have a basic understanding of linear algebra, you can learn it for free from Khan Academy.
- Machine Learning
Machine learning is a critical component of data science. Machine learning is used to make predictions and patterns in data by using algorithms. If you are working at a large company then machine learning methods will be useful for you. Start learning machine learning tools such as k-nearest neighbors, random forests, ensemble methods, etc.
Machine learning algorithm helps to detect patterns in a variable that a human could miss. It is not possible that human can detect patterns manually from large amount of data. That is where machine learning methods help to make their work easier. Commonly used machine learning methods are linear regression, logistic regression, decision tree, SVM – Support Vector Machine, Naive Bayes, Gradient Boosting algorithms, etc.
- Data visualization & reporting
Pictures communicate more effectively rather than numbers and words. There are some principles of visualizing data in an effective manner. Get familiar with those principles. There are some data visualization tools that can be very useful to data scientists such as Tableau, Infogram, ChartBlocks, Datawrapper, d3js, Google charts etc. Data visualization is a very important part of the companies depended on data-driven decisions. A data scientist must design effective data report. Data report helps the company to consume integrated data in an efficient manner so that right decision can be made.
- Learn programming
You can’t become a good data scientist if you don’t learn a language in which data communicate. Learn statistical programming languages like Python, R, Java, Jupyter, perl and C++. Also become expert in database querying language like SQL. Get familiar with NoSQL database too.
Scikit-learn is a popular machine learning library for Python. Companies like Facebook and Bank of America use Python for data science. R programming language can be used for statistical analysis, data visualization and predictive modeling. While SQL is a special-purpose programming language for managing data held in relational database management systems.
“Data is the new science. Big data holds the answers.”
- Pat Gelsinger
- Data cleaning & munging
Data munging is a process of manually cleaning up a messy data set and convert it in convenient form for data analysis. Usually, data gathered in companies are messy and require cleaning up. Therefore, data cleaning becomes an important part of data science. But if you fail to clean up your database then the accuracy of the model will be affected and it will lead to incorrect conclusions.
- Multivariable Calculus
Multivariable calculus is an essential part of a data science. It is especially applicable in machine learning. A solid understanding of multivariable calculus allows a data scientist to build in-house implementation analysis routines. In interview, an employer might ask you questions about some basic multivariable calculus since they form the basis of many techniques. Even though you have gained mastery on Python and R programming language, having knowledge of multivariable calculus can be beneficial for the company especially where the product is defined by data.
- Data engineering
The primary part of having a job as a data scientist is being able to deal with huge amount of data in efficient manner. If you have a software engineering background then getting a job in data science will be much easier for you. Because a lot of data science work is software engineering. Having software engineering background will increase the speed of your work. By using various tools you can get accurate results which will help companies to make smarter decisions. You can start your career in data science by getting a job as a data analyst.
- Communication Skill
Your communication skill defines whether you are a good data scientist or a great scientist. You should be able to discuss your techniques and discoveries to technical and non-technical audiences in a simple language. Mostly data interpretations are not easy to understand for the people from a non-technical background. With great communication skill, you can explain the data interpretations in easy language.
- Business acumen
If you are a data scientist and you don’t know the know-how of the elements to make up successful business model then remember just technical skills will not uplift your career as a data scientist. Because the vast majority of data science positions involves business interaction. You should be able to interact with individuals who are not analytically literate and make them understand your perspective discoveries. You should have the ability to conceptualize business problems and find effective and efficient solutions. Companies look for those data scientists who can help the organization to explore new business opportunities.
- Data intuition
Data intuition means perceiving patterns where none of the patterns are observable on the surface and knowing the presence of where the value lies in the unexplored pile of data bits.
- Stay up to date with data scientist community
As a data scientist, it is essential to stay connected with like-minded people. You can follow websites such as – KDNuggets, Data Science 101, Quora and DataTau to gather information regarding happenings in data science world. Stay updated with the world of data science and enrich your knowledge.
“Big data is at the foundation of all mega trends that are happening.”
- Chris Lynch
Once you have mastered technical and business techniques required – your next step should be to find a job suitable for your skills. As I mentioned earlier you can join as a data analyst in starting phase and gradually gain experience. You can also apply for an internship. In case you have experience in data science then you can directly join as a data scientist in any good company. Before you apply for a job - a good decision is to build your impressive portfolio. For example, you can solve some challenges and competitions available at Kaggle. Try to track your scripts and output using Github so recruiters can see how you approached solving the problem. According to PayScale data scientists are paid $91,000 per year. The future is definitely bright for data scientists.
Various industries are hiring data scientists nowadays. You can apply in industries like e - commerce, finance, government, science, social networking, healthcare, and telecommunication. Before you get a job in any of these industry, it is crucial to have knowledge of the concerned industry so that you can sharpen your skills accordingly.
If you are already in computer science field then being a data scientist will be an exciting career path for you. Make sure you love data. Acquire specific required skills. Keep upgrading yourself. We wish you best of luck to start your successful journey in data science world. We hope that these guidelines will help you to get started. Leave us a comment about any new information you might want to add to this article.