Data science is predicted to grow over the next decade. Over 90% of the data in the world was generated in just two years, and it’s difficult to realize the amount of data that will be generated in the next decade. The rise in demand for data scientists will prompt educational institutes to include it in their curriculum. The data literacy will increase in the future, and a data scientist will have a specialized holding, like other important professions. According to IBM, 364,000 to 2,720,000 openings will be generated in the year 2020. This demand will only grow further to an estimated 700,000 openings.
Since the field of data science in itself is young, data scientists do not hold years of experience behind them, as compared to other IT related fields. In the next decade, data scientists will see a much greater distinction between senior data scientists and other positions. As a result, there will be a much more defined hierarchy of data scientists.
Someone with data science skills will analyze data to help the companies take meaningful decisions. The requirement for the number of data scientists is growing at an exponential rate. Therefore, it’s important to know how to become a data scientist to take advantage of demand from the job market.
Who is a Data Scientist?
Data science is a complex and often confusing field, and it involves multiple different skills that make defining the profession a constant struggle. Essentially, a data scientist is someone who gathers and analyzes information to conclude. They do this through many different techniques.
They might present the data in a visual context, which is often called “data visualization” allowing a user to look for clear patterns that would not be noticeable if the information was presented in hard numbers on a spreadsheet. They often create highly advanced algorithms that are used to determine patterns and take the data from numbers and statistics to something that can be useful for a business or organization. At its core, data science is the practice of looking for meaning in mass amounts of data.
How Do You Become a Data Scientist?
We have looked at what is needed to become a data scientist and have created the following four steps for you to follow.
Step 1 – Decide on your academic path
Data Scientist Qualifications
There are several paths that can assist you in becoming a data scientist.
You will, at the very least, need a four-year bachelor’s degree in IT, computer science, math, physics, or another related field. Optimal courses include Applied Computer Science, Data Science Specialization, Business Administration, and Business Data Analytics.
Should you continue your studies, you would need to earn a master’s degree in data, or in a related field, and look at courses such as Business Analytics or Artificial Intelligence. Information Systems, Engineering, Applied Statistics, and Programming Languages such as R or Python, are highly valued in this field.
If your goal is an advanced leadership position, you will have to earn either a master’s degree or a Doctorate specializing in data science.
Research shows that 73% of the professionals working in the industry have a graduate degree and 38% have a Ph.D.
Successful data scientists have honed their skills into one of the main areas of Mathematics, Applied Mathematics, Statistics, Computer Science, or Economics.
Step 2 – Hone Your Data Scientist Skills
What Does a Data Scientist Do?
The various tasks that data scientists do can be categorized in the following main areas. It is advised to choose one area to focus on, as one can’t be an expert at everything.
A data scientist invests a lot of time in collecting data, data cleaning, and converting the data into valuable business insights. Cleaning the data is one of the most important aspects among them and can require computer programming skills.
Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends, and correlations that might be undetectable in text-based data can be exposed and recognized easier with data visualization software.
Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) helps to understand what insights may be found from the data. At a high level, EDA is the practice of using visual and quantitative methods to understand and summarize a dataset without making any assumptions about its contents. It is an approach for summarizing, visualizing, and becoming intimately familiar with the important characteristics of a data set.
Evaluating and Interpreting EDA Results
EDA is valuable to the data scientist to make certain that the results they produce are valid, correctly interpreted, and applicable to the desired business contexts. Outside of ensuring the delivery of technically sound results, EDA also benefits business stakeholders by confirming that they are asking the right questions and not biasing the investigation with their assumptions, as well as by providing the context around the problem to ensure the potential value of the data scientist’s output can be maximized. EDA often leads to insights that the business stakeholder or data scientist wouldn’t even think to investigate, but that can be greatly informative about the business.
The model building process involves: setting up methods of collecting data, understanding and paying attention to what is important in the data to answer the questions you are asking, and finding a statistical, mathematical, or a simulation model to gain understanding and make predictions.
Model-based testing is a software testing technique where run-time behavior of the software under test is checked against predictions made by a model.
There is a good reason as to why the methods required to deploy Machine Learning into production aren’t taught in most Data Science programs: the experience and skill set for deployment are completely different than most Data Science tasks. Deployment is a software engineering discipline, not one of Data Science. Knowing to do this will give you an advantage.
This work involves taking the developed and deployed model and optimizing it in terms of performance on a computer, and in terms of performance of the results. Can the results be used more optimally?
Data Collection and Preparation
Important skills for modern data scientist include programming in languages such as R Python and SQL for database queries. Two specific platforms that are invaluable to a data scientist are the Hadoop Platform and Apache Spark.
Machine Learning and AI, unstructured data techniques, statistics, and mathematics are also invaluable skills to a data scientist.
Step 3 – Become a Certified Data Scientist
There are many certifications to choose from, including traditional university, classroom-led certifications, and online courses.
Data Scientist Certifications
Professional industry certifications are highly recognized in addition to your graduate degree. Coursea, Udacity, Code Academy, and Data Science Central are online resources offering data science courses and certifications.
Data Science Certificates that are sought after include:
- Hortonworks Certified Associate (HCA)
- Cloudera Certified Professional (CCP): Data Engineer
- Data Science Council of America (DASCA) for Big Data Engineering Professional
- HDP Certified Developer Big Data Hadoop
- SAS Certified Big Data Professional
It is wise to join relevant groups and interact with other members of the Data Science community. LinkedIn is the premier place for enterprise technology professionals to gather, connect, share ideas, and network. There are — however, many other platforms enabling collaboration.
Professional Organizations Workshops for Data Scientists
Membership to a professional organization provides you with additional resources and networking opportunities. We recommend the Data Science Association, International Institute for Analytics (IIA), International Machine Learning Society (IMLS), Institute for Operations Research and the Management Sciences (INFORMS) and the Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD)
Step 4 – Formal Employment
Depending on the academic path you choose, and your current certifications, you could select between these types of data science jobs.
A Data Engineer or Big Data Engineer will work on collecting, storing, processing, and analyzing large sets of data. The primary focus will be on choosing optimal solutions to use for these purposes, then maintaining, implementing, and monitoring them.
A Data Architect or Big Data Solutions Architect is required in any organization that wants to build a Big Data environment on-premises or in the Cloud. They are the link between the needs of the organization and the data scientists and data engineers.
A Hadoop Developer is responsible for the actual coding or programming of Hadoop applications. This role is similar to that of a Software Developer, but the Hadoop developer is part of the Big Data domain, working alongside other data scientists.
Kaggle hosts 1.5 million data scientists in the world’s largest community dedicated to the profession. Another valuable job market resource for data scientists is iCrunchData.
Data Scientist Salary
Data scientists earn well, on par with, or above software developers.
Modern data science is an interesting and challenging field to be involved in. Ever-changing and evolving as the volume of data grows each year exponentially.
Understanding the valuable role that data scientists have in an organization is key to the success of the business. Research Optimus offers comprehensive data analysis and business research services, designed to provide you with in-depth knowledge needed to make complex decisions in your business. Visit our website to see how we do this.