In addition to covering how you can become a Data Scientist, we have also prepared a set of content that will help you truly understand the scope and career path that you can expect while pursuing the Data Science field.
The demand for Data Scientists has increased significantly over the years as more and more businesses are finding value in utilizing voluminous amounts of structured, unstructured, and semi-structured data generated by these enterprises and IoT.
Check out this video by Intellipaat on Data Science:
Data Science Overview
In 2017, studies by NodeGraph showed that our digital universe had accommodated 2.75 zettabytes (ZB) of data. It is predicted that this will grow to 175 ZB, a ridiculously colossal amount in the global atmosphere, by the year 2025, according to IDC. It shouldn’t even come as a surprise with the kind of online activity that exists today.
The Data Science domain deals with all these giant datasets, finding ways to make them useful and integrating them into real-world applications. Digital data is considered the oil of the 21st century with its multitude of business, research, and daily-life benefits. Everything from your social media posts to your most recent Google search is essential for Data Scientists in one way or the other at the end of the day.
The process of sifting through the wide gamut of data is a task that Data Scientists are trained specifically for. They are skilled in delivering critical insights, which in turn enable better decision-making. Most companies today boast of using some form of Data Science. The truth is that it is difficult to put everything in a box and specifically define it. In more general terms, Data Science encompasses the extraction of clean data from raw data and further goes on to the analysis of these datasets to make sense out of it or, in other words, generate valuable and actionable insights through visualization.
Searching for techniques to have a proper Data Set? check out our blog on Data Cleaning.
Although Data Science doesn’t have a clean life cycle with well-defined stages, there are, however, seven major stages in Data Science:
- Data acquisition
- Data preparation
- Data modeling
- Evaluation and interpretation
- Model deployment
- Maintenance
- Optimization
Check out the Data Science Life Cycle and Process in our comprehensive blog.
But, what does a Data Scientist do? Following is the list of the common deliverables in Data Science:
- Automation and decision-making (medical treatments, credit card approval, etc.)
- Classifications (important emails, spam, promotions, etc.)
- Recommendations (based on learned preferences about movies, restaurants, music, etc.)
- Forecasting (electricity demand, customer retention, revenue, etc.)
- Anomaly detection (fraud, malfunctioning equipment, disease, etc.)
- Pattern detection (financial market patterns, data mining, weather patterns, etc.)
- Recognition (text, voice, facial, etc.)
- Actionable insights (visualizations, reports, dashboards, etc.)
- Segmentation (e.g., demographic-based marketing)
- Scoring or ranking (e.g., FICO score)
- Optimization (e.g., risk management)
What is a Data Scientist?
Data Scientist responsibilities include extracting and analyzing huge amounts of data to identify trends and patterns, which may benefit individuals, businesses, and organizations. They use various analytics tools for advanced analytics and technologies that include predictive modeling and Machine Learning. Reporting and visualization tools are used to display the insights generated through data mining that will further enable one to make informed decisions that are customer-oriented and consider potential revenue opportunities, among many others.
The Data Scientist role is often a subset of many other traditional job profiles such as statistician, computer professional, mathematician, and scientist.
How to Become a Data Scientist?
You must know that working with data requires an investigative mindset; thus, the IT pundits have added science to it.
Data Scientists analyze problems and develop data-driven solutions. Interestingly, even if a robot does not learn the data, Data Scientists can find the answer. How? They use their own judgment to discover patterns.
Do you have what it takes to be a good Data Scientist? Or, is it because of the huge demand and rewards it offers, that you want to work in this field? It is important to have clarity on this. In both cases, your goals are the same, but the pace will be quite different.
Second, before knowing how to become a Data Scientist, you need a general analytical thinking approach to set your Data Science career because it requires solving complex problems. You need to be able to frame the problems and solve them in an orderly manner.
Learn more about this from our Data Science Tutorial blog!
Data Scientist Qualifications
Data Scientists are expected to have a strong command over programming languages, such as Python, R, SQL, and Machine Learning models, and have workflow proficiency in Git and the command line. Apart from this, these professionals also require strong communication, problem-solving, and data reporting skills.
It is not difficult to take up the role of a Data Scientist without prior experience in the domain. It is common for such aspiring individuals to transition from Data Analyst roles if they have no experience in the relevant field.
Where a Data Analyst will often explore answers for already available questions, a Data Scientist will need to explore the data in the first place to come up with relevant questions and potential business opportunities that have chances of being overlooked.
Data Scientist Educational Requirements
A Data Scientist will be expected to have a bachelor’s degree. Higher-level or advanced degrees may not be strictly mandatory to land a job (even with job descriptions that ask for such requirements). Most employers look for relevant skill sets in the field. Any applicant with less-relevant degrees can spruce up their portfolio with advanced skills and experience in relevant Data Science projects.
However, the educational requirements may typically include an advanced degree in computer science, mathematics, statistics, or Data Science. A number of certification opportunities are also available for Data Science aspirants, such as Certified Analytics Professional, Microsoft MCSE Data Management and Analytics, MCSA: Various SQL/Data Engineering options, and Dell EMC DECA-DS.
Take a look at the Data Science Courses offered by Intellipaat and enroll today.
Data Scientist Skills
The four fundamental skills required for Data Scientist are:
- Mathematics (statistics and probability)
- Computer science (engineering, software architecture, and data architecture)
- Business or the domain
- Communication (verbal and non-verbal)
The above is not in any particular order of priority. People are usually strong in one or two of these four fundamental pillars.
Data Scientists are required to be familiar with a number of Big Data tools and platforms, viz. Hadoop, MapReduce, Apache Pig, Hive, Spark, etc., programming languages, viz. Python, Scala, SQL, Perl, etc., and the statistical computing language, R. Hard skills, including data mining, Machine Learning, Deep Learning, structured and unstructured data integration, etc., are essential in Data Science. Modeling, clustering, predictive analysis, segmentation, and data visualization are all statistical research techniques that play huge roles in this domain.
Data Scientist Job Description
What do companies look for in a candidate? As a professional Data Scientist, you will be expected to be skilled in:
- All phases of the Data Science life cycle
- Data Science, computer science, statistics, mathematics, economics, operations research, or other quantitative fields
- Common data warehouse structures
- Working with a wide variety of data sources, databases, standard data formats, such as YAML, JSON, and XML, and public or private APIs
- Statistical approaches for analytical problems
- Common Machine Learning frameworks
- Public cloud platforms and services
- Qualitative and quantitative analyses and effectively sharing results with the audience
- Implementing various Machine Learning techniques in business processes for improved efficiency and effectiveness
- Designing and making use of reporting dashboards to provide actionable insights
- Visualization tools such as Tableau and Power BI
- Python, R, or Scala
- Data aggregation from disparate sources
- Machine Learning techniques: K-nearest neighbors, support vector machines (SVM), Naive Bayes, decision trees, random forests, etc.
- Designing and implementing validation tests
- Conducting ad-hoc analysis and presenting results effectively
Requirements can vary from job to job. There are more and more specialized roles emerging in the industry. However, knowledge of the following Data science skills will be expected from any Data Scientists role:
- Python or R
- SQL
- Machine Learning models
- Probability and statistics
- Data visualization
A Data Scientist has to have knowledge of the basics, but one role might require some more in-depth experience in one particular area, whereas another might be focused on different specifications.
Check out these Data Scientist Interview Questions and stay prepared for your next interview.
Data Scientist Roles And Responsibilities
- Study the accuracy and effectiveness of data sources and data gathering methods
- Mine and analyze data to enable the optimization of product development, business strategies, and marketing techniques
- Build custom data models and algorithms
- Use predictive modeling to optimize customer experiences, revenue generation, ad targeting, etc.
- Coordinate with different functional teams
- Come up with the A/B testing framework and test model quality
- Develop tools and methods to monitor and analyze the performances of models and data accuracy
- Identify opportunities with stakeholders in effectively using company data to drive business decisions and solutions
Data Scientist Career Path
As already mentioned above, many Data Scientists begin their careers first as Data Analysts, and then enter the field of Data Science via job changes or an internal promotion. Once experienced, these professionals can then look for senior roles in Data Science. More experienced Data Scientists who have management skills can go on to take up director-level and executive-level roles.
Here are a few ways one can stay active in the world of Data Science:
1. Follow Data Science groups and influencers
Joining Data Science groups is an effective way to stay updated and maintain relationships with fellow Data Science enthusiasts. Furthermore, attending meetups from time-to-time is also a great way to expand your network. Many Data Scientists and big players have turned to social media to share Data Science-related know-how. Make sure to follow these accounts for regular updates.
2. Build a public portfolio of Data Science projects
As a beginner, you can start building a simple portfolio of interesting Data Science projects you have taken up and showcase them on platforms such as GitHub. Not only will you be creating a personal brand from scratch, but it will also ensure future growth in your career path.
3. Go for an online training course and earn certification
The most effective and the least costly way to boost your skills is by enrolling in courses that develop hot skills such as Python, R, Tableau, SQL, and Machine Learning.
You can start with our video tutorial on Intellipaat’s Data Science course:
4. Target employers
Every business, big or small, generates data. Not every company might be able to afford a full-sized team of Data Science experts. They need capable professionals who can do the work for them. Your job is to define your target employers and make a list of organizations that appeal to you. Once you start following them and stay updated over social media, you will get a clear picture of what you should be doing as a Data Scientist if you want to join their team.
Learn about the difference between Data Engineer and Data Scientist in our blog on Data Engineer vs Data Scientist!
Data Scientist Career Outlook
- According to Belong’s Talent Supply Index, the demand for Data Science professionals in all industries has increased by 417 percent over the past years
- Analytics India Magazine predicts that, by 2025, India’s demand for Data Science professionals will grow sevenfold in the next seven years, reaching US$20 billion
- As per the market research firm Tractica, the global Artificial Intelligence market will reach US$118.6 billion in 2025
- As per McKinsey, Artificial Intelligence has the potential to generate US$1.4–2.6 trillion in sales and marketing worldwide and may generate US$1.2–2 trillion in supply chain management and manufacturing
This is just the tip of the iceberg. In the next few years, the role of a full-stack Data Scientist will change, reform, and innovate the world.
Data Scientist Salary
Data Scientists collaborate with people from different settings. Most of their work, however, involves working with data, i.e., finding data and writing programs to analyze the information. Therefore, the salary of a Data Scientist depends on several factors, including his/her industry experience, job function, industry, skills, location, and so on.
This is a general breakdown of the salary of Data Scientists (USA).
- Entry-level Data Scientist salary: US$95,000
- Junior Data Scientist salary: US$180,000
- Senior Data Scientist salary: US$165,000 to US$250,000
A Data Scientist’s Tools
Let’s explore the quintessential Data Science tools found in a Data Scientist’s toolbox:
Programming Languages
You don’t necessarily have to be an expert in all of the above, but learning R or Python and SQL are strongly recommended.
Mathematics, Statistics, Modeling, Algorithms, and Data Visualization
Pre-existing libraries and packages are used wherever possible. Some popular Python-based ones are:
- TensorFlow
- Scikit-Learn
- NumPy
- Matplotlib
- PyTorch
- Pandas
Big Data Tools
- Hadoop
- Kafka
- Spark
- Pig
- Hive
- Drill
- Mahout
- Presto
Research and Reporting
RDBMS, NewSQL DBMS, and NoSQL
- Hadoop
- MongoDB
- MySQL
- Redis
- PostgreSQL
- Redshift
- Snowflake
- HBase
Cloud-based Services and Cloud Computing
- Microsoft Azure
- Amazon Web Services (AWS)
- Google Cloud Compute (GCP)
Read Intellipaat’s blog to find the difference between Azure, AWS, and GCP.
DevOps and DataOps Orchestration and Deployment
- Kubernetes (K8s)
- Docker
- IaC tools such as Terraform
Conclusion
Data Scientist jobs are in extremely high demand, and the domain can have a massive impact on any business in many different aspects depending on the business’s goals. This blog ‘How to become a Data Scientist’ has tried to cover all the different paths that can lead you to a rewarding career in this domain.
Check out our Data Science Community and start a discussion.