Harvard Business Review had published an article on “Data Scientist: The Sexiest Job of the 21st Century” in their blog. This shows that most of the people are fascinated by this job and at the same time they have a lot of questions in their minds like-
What is a data scientist?
What do data scientists do exactly?
There are no one-word answers to these questions. In simple words, a data scientist is one who is an expert in software engineering than any other statistician and an expert in statistics than any other software engineer.
A search on Google for “How to become a data scientist” will result in a long list of resources where people who are not skilled in the field face difficulties with a jungle of information. By considering all the factors, we are explaining a few things that are important to become a data scientist.
Get good at Math and Statistics
In general, most of the data scientists come from a Statistics, Applied Mathematics, and Computer Science background. You need to have a better understanding of statistics and algebra if you want to be proficient in data science. People who are not from a mathematics background can be terrified to learn mathematics. Such people can start with the basic concepts and choose the remaining topics as they improve.
The data science community supports mainly Python and R programming languages to do coding in data science. Other languages such as GO, Matlab, Julia, Java can also be used, but Python and R are most popular in this area. A basic knowledge of programming will help you deal with practical problems using Python and R.
Start learning Python or R from any of the following fundamental courses:
Databases such as MongoDB, MySQL, Cassandra, Postgres etc. are particularly used to store data. As a data scientist, you should understand databases well because once you enter the industry, frequently you will be working with data.
Here are two useful courses you can enroll in-
Progress to the next level with Big Data
A data scientist should have a variety of skills and having knowledge of major Big Data frameworks like Hadoop that could be used in data science is one of them. Start with big data for better and easy understanding before going to start with Hadoop.
Get more practice and experience
Learn data science by taking up some practice problems or any projects
Participate in competitions to find out your learning level
Create a Meetup and meet data scientists
Start your pet project to improve your learning skills
Don’t be shocked or surprised by thinking that only the 5 things that are presented above are enough to become a data scientist. There are so many other things that need to be considered. It takes a lot of time and personal investment to become a data scientist. But don’t worry, different courses are available to direct and set you in the right path.
Data Science job demand, growth, and salary
“Every company collects mountains of data: some valuable, most not,” said Jay Samit, Vice Chairman at technology consulting firm Deloitte Digital. “It’s the data scientist’s job to distinguish between the two.”
Not only in retail industry, but data scientist’s role has occupied a standard position in job searches and job postings as well, according to Indeed.
IBM predicted that the demand for data scientists, data engineers, and data developers will reach approximately 700,000 by 2020.
Stratistics MRC reported that data science market is expected to grow from $19.75 billion in 2016 to $128.21 billion by 2022, at a CAGR of 36.5% during the forecast period.
Salary.com has conducted a survey in the United States and reported that as of November 28, 2017, the average annual data scientist salary is $123,226, with a range between $107,370-$138,122.
Approach to a Data Science Problem
Most of the people who are new to this role sometimes approach solutions that fail to address the problems efficiently. This is because of the lack of understanding of how to solve problems using data science tools and technologies. So, data scientists need a methodology that directs them to solve the data science problems. The methodology described here is independent of tools and technologies and provides a framework to move forward with processes and methods that will help data scientists to get adequate answers and results. This foundational methodology for data science clearly defines how to solve a data science problem from beginning to deployment, feedback, and refinement.
Every project starts with business understanding
Business understanding is crucial to start any project, which builds a strong foundation for solving the business problem successfully. This is the hardest stage among all and here the business sponsors who require analytic solutions has to define the Project objectives, problem, and solution requirements from a business approach. This helps data scientists to identify techniques that are appropriate for successful resolution. Now a data scientist can identify the analytical approach to solve the problem efficiently, after clearly explaining a business problem.
Data understanding followed by data requirements and data collection
Data requirements will be evaluated based on the analytical approach chosen. The data scientist determines and collects structured, semi-structured and unstructured data that are suitable for the problem domain. Data scientists might need to study the data requirements again and gather more data, in case of experiencing gaps in data collection.
Data collection is important for better understanding of data. Detailed visualization and statistical techniques can assist data scientists to understand the subject of data, analyse data quality and locate initial understandings into the data.
Data preparation- Helps to construct the data set
This stage contains all the tasks used to build data set that will be used in the next stage i.e modeling. The tasks include data refining, uniting data from different sources and converting data into useful variables. This is the most time-consuming stage. But, it can be reduced to 50% by managing and integrating the data sources well and automating some of the data preparation steps may lower the percentage even more.
Modeling- Highly iterative process
Data scientists along with the prepared data set use a training set-historical data where the outcome of business is known, to build descriptive or predictive models with the help of analytic approach described above.
Evaluation of descriptive or predictive models
Data scientist evaluates the quality of the models prepared and verifies whether it approaches the business problem appropriately and fully. To execute this, it requires assessing different diagnostic measures and other outputs as well such as graphs and tables.
Deployment of predictive models
The model will be deployed into the comparable test environment or production environment, once it is approved by the business sponsors. Deploying a predictive model generally requires skills, technologies and multiple groups.
Collecting the feedback and enhancing the model
By gathering results from the deployed model, the organization obtains feedback on the performance of models and monitors how it influences its development environment. Data scientist analyses the feedback and enhances the accuracy and usefulness of the model. This additional stage will offer extra advantages if considered as a part of the complete process.
The process of this methodology demonstrates the iterative form of problem solving. Instead of designing and deploying the model once and left in place unchanged, a model should undergo feedback, refinement and redeployment process to provide profit to the organization for as far as the solution is required.