How to Become Data Scientist
This article provides in-depth information into What is Data Scientist? What Data Scientist do? Degrees for Data Scientist, Steps to become Data Scientist and much more.
What does a data Scientist do?
Data Scientists perform data analysis. They perform data analysis within the realm of building and deploying predictive models which often incorporate machine learning and deep learning protocols.
Of which models are the best fit for the data being analyzed. These models require a lot of fine tuning since all models are approximations of current and future approximations. This fine tuning of models totally depends on the data scientists expertise in mathematics. Data scientists know how to pull data from an organization's preferred database management system.
At the most basic level, data scientists analyze massive datasets for insights that can change how companies operate and strategize. Generally, a data scientist is someone who knows how to extract meaning from and interpret data. This requires both tools and methods from statistics and machine learning, as well as being human. A data scientist spends a lot of time in the process of collecting, cleaning and munging data.
Steps to become a Data Scientist
Prepare to become a data Scientist
Students who want to pursue data science in the future can start prepping for it even before they enter college or university. Students must become proficient with the most widely used programming languages in data science such as python, java and R and use them in applied math and statistics. An early exposure to data science is helpful in determining whether a career in data science is the right fit for a student.
Complete your bachelor’s Degree
Students who want to pursue a career in data science may major in statistics, computer science, information technology or mathematics. Students must continue to learn programming languages, database architecture and SQL. students must start building networks in the college campus and look out for internships. iT typically takes 4 years to complete an undergraduate studies.
Obtain an entry level Job
Most companies have positions open for entry level jobs in data science. Positions like junior data scientist and junior data analysts are open for students with a bachelor’s degree.
Earn a Certification
Earning a certification in data related fields can improve your skills and make you a more marketable candidate. Certifications will help you climb the ladder of success at a much faster rate.
Earn a master’s degree
Those with advanced and higher degrees tend to have better job prospects in the field of data science. Students should pursue their major in computer science, statistics, mathematics or data science (if available).
Candidates who have had prior work experience in data science after completing their bachelor’s and have a master’s degree as well have better chances at getting promoted in thier respective field of work. Coupling strong technical skills with a master’s degree and leadership experience will chart a course towards more significant and better opportunities.
Data science is an ever evolving field and staying relevant is a must. In this age of constant technological innovation continuing education is a must. In order to be a successful data scientist, one must always keep on learning and evolving with the industry. A data scientist must continue networking and always be on the lookout for educational and professional development opportunities through boot camps and conferences.
Salary of a Data Scientist
A data scientist’s salary depends on the numbers of years of experience, skill set, education and location. Data scientists with specialized skills such as artificial intelligence or natural language processing. The average annual salary of a data scientist is $ 120,000 approximately. A senior data scientist earns upto $200,000 per year
Job Growth of a Data Scientist
As per the BLS, jobs in the field of data science is expected to grow 19% between 2016 to 2020. Around 5,400 new jobs in the field of data science are projected over the decade.
Types of degrees for a Data Scientist
Following are the degrees in order to be a data scientist:
An associate degree is typically a two years coursework and helps build the foundation for data science. Although there is no specific academic program for data science at the associate level, there are a number of degree programs and individual courses available including computer science, economics, statistics, mathematics etc that can help prepare a student for future studies in data science.
Bachelor’s degree in data science is an interdisciplinary program of study. It provides students with the foundational training in the principles of statistical and mathematical analysis, computer science components, data structures, algorithms and information visualization. A bachelor’s degree in data science typically lasts for up to four years ( 128 credit hours) and includes summer internships and a graduation project at the end of the course. Upon graduation students will have developed skills in data mining , computer programming and data analysis and visualization.
Master’s in data science focuses on the study of the latest developments in the field. Students pursuing a master's degree in data science develop strong statistical, mathematical, computational and programming skills. Most students that pursue a master’s in data science have work experience of a few years after completion of their undergraduate degrees. Students also gain knowledge in quantitative analysis. A master;s degree in data science is a two years course program and students are required to complete a minimum of 30 credit hours of study. Some courses require a capstone project which requires students to conduct original research in data science. After completing a master's degree, students can either enroll in a doctoral program or choose to work in higher positions in organizations.
a doctoral degree in data science emphasizes advanced research on concentrations including computer science, focussing on a cross section of advanced topics in data mining and high performance computing. It is designed to train students to manage large, unstructured and complex data sets into information that can be used to make decisions. Students learn to develop mathematical models and learn quantitative analysis techniques to solve problems using data. Although the minimum credit hours to graduate varies, a doctoral degree curriculum is typically spread across core instruction, electives and a dissertation.
It typically takes a minimum of 3 years and may take upto six to seven years to graduate. A doctoral program in data science prepares students to pursue a career in academia as a prof in a college or a university or as higher management personnel in an organization.
Concentrations to consider for data science
Following are the coursework concentrations to consider while pursuing academics in data science:
Concentrations in bachelor’s degree:
Basic Statistical Modelling: this concentration is an introduction to statistical modelling and systems modeling. In statistical modeling, students learn about data analysis within big data sets by using linear regression to develop appropriate data models. Systems modeling teaches students covering, implementing and analyzing statistical models
Software design: students learn about the components of software and design. Software components teach students the foundational instruction in the design principles of computer software. In software design class, students explore technical knowledge in software design including language processing linked data structures and component interface design.
Programming language: students are taught programming languages like C++ and Java. in C++ classes students are given an overview of the language and the study of computing data. Java introduces students to computer java programming and teaches how computer programming can aid in solving problems
Concentrations in Master’s Degree:
Research design and Questionnaire formulation: the main topics covered are data analysis applications and data analysis design. Data analysis application classes cover the decision making concepts and related role of big data. Students learn about gathering data, interpreting results and presenting relevant findings. In data analysis design classes, students learn about quantitative research methods and associated statistical techniques that are used in data analysis.
Data storage and retrieval: two basic courses for data storage and retrieval are database design and management and database systems engineering. In database design and management students explore the theoretical foundations and practical applications of database systems, including design, use, creation and management. Students also learn about database languages like MYSQL and PHP. In database systems engineering students study the core concepts of database systems including relational data model, query languages and distributed data systems.
Computational Techniques: students learn about machine learning and statistical processing under this concentration. In machine learn, students are taught its fundamentals including algorithm development, clustering and development of machine learning programs. In statistical processing, students study statistical processing algorithms and programming structures. Students are also taught to use data analysis software packages to manipulate large data sets.
Concentrations in Doctoral Degree
Statistical computing and Simulation: Students are provided classes on SAS programming and production level modelling. In SAS programming, students develop skills in SAS programming through simulated data analysis of real world data sets. Production level modelling classes include practical study and use of data and statistical mining models that are utilized to analyze massive data sets.
Advanced data mining techniques: these concentration classes in Data mining- data mining I and data mining II. In data mining I students study data extraction techniques including how to select and clean data and how to apply machine learning and data visualization techniques. Data mining II course covers advanced concepts in working with larger data sets using multivariate regression and graphing data.
Data Warehousing: students study applied warehousing and Relational Database Systems. In applied warehousing students learn the relationship between data warehousing and business intelligence applications including major data warehousing and mining techniques, analytical processing and cluster classification. In relational database systems students study how to integrate, store and manipulate large datasets, major data systems in data science and database languages, including SQL.
Following are the Job concentrations for a Data Scientist:
Data Scientist: As a data scientist, they have more programming knowledge than a statistician and more statistics than a software engineer. The primary job of a data scientist is to fine-tune the statistical and mathematical models that are applied to that data. Data scientists run data science projects from beginning to end including identifying insights, building predictive models and weaving a story around the findings. Data scientists bridge the gap between programming and implementation of data science, the theory of data science and the business implications of data.
Data Engineers: data engineers handle large amounts of data and often lay the groundwork and plumbing for data scientists to do their jobs effectively. Their day to day job includes managing database systems, scaling the data architecture to multiple servers and writing complex queries to sift through the data. Data engineers usually know some hadoop based technologies and database technologies like MYSQL, Cassandra and MongoDB.
Business / Business Analysts: the job of a data analyst is to sift through data and provide reports and visualizations to explain what insights the data is hiding. Business analysts are more concerned with the business implications of the data and the actions that should result. Business analysts will leverage the work of data science teams to communicate an answer.
Machine Learning Engineers: Machine learning engineers are highly sought after. They are responsible for building, deploying and managing machine learning projects. Most machine learning engineers use python and C++ for their work. The easiest path to becoming a machine learning engineer is to start off with a career in software engineering and then gain statistics and machine learning needed to take on the role.
Stand out skills for a data scientist
Statistics: candidates should have a good understanding of statistics. You should be familiar with statistical tests, distributions, maximum likelihood estimators etc. statistics allows you to slice and dice through data, extracting insights that are needed to make the most reasonable conclusions. Statistics is the only way to infer insights from small data sets onto larger populations which is the fundamental law of data science
Machine Learning: it is the set of algos used to make predictions based on a set of known information. Machine learning is a group of algorithms that will use machine power to unearth insights. Machine learning is used to extend thinking in order to deal with massive data sets.
Data Analysis: data analysis is the process of turning numbers into insights. A data analyst focuses on exploring large sets of data and connecting that data with actions that can drive business impact.
Data Visualization: data analysis is only half the battle. In order to drive impact, others have to be convinced to believe and adopt your insights. Data visualization comes into play here. Humans are visual creatures and it is easier to process information by examining a chart or a graph over spreadsheet.
Mathematics: for massive data sets, mathematics is used to process and structure the data. Data scientists must be familiar with statistics, linear algebra and calculus.
Algorithms: a well defined set of steps to solve a specific problem is called an algorithm. Data scientists use algorithms to make computers follow a certain set of rules or patterns. Data scientists must understand how to use machines to do their work. It is essential in processing and analyzing data sets that are too large for the human minds to process.
Deep learning: deep learning refers to the set of machine learning algorithms that extends a basic neural network to much higher levels of complexity making them capable of learning on a much larger data set and performing many more operations than standard models.
Business acumen: in most companies data scientists have to communicate the results of data mining to their stakeholders and present recommendations that can be acted upon. Data scientists have to be able to work with large, complex data sets and understand the intricacies of the business and the organization that they work for. Data scientists should have business knowledge as it allows them to ask the right questions and come up with insightful solutions and recommendations that are actually feasible given any constraints that the business might impose.