According to a McKinsey report, Artificial Intelligence (AI) will account for more than 30 percent of work in 60 percent of occupations. This change is inevitable. Not only, Data Scientists use their judgment, analytics, and soft skills, but they also leverage open-source technologies to grasp the capabilities that robots cannot replicate. Evidently, the future Data Scientists will increasingly focus on innovative technologies or services such as autonomous vehicles, space exploration, and chatbots. So, understand the steps to become a Data Scientist to see if this is your right career path.
Being a Data Scientist is worthwhile. According to LinkedIn, the average annual salary of Data Scientists in the US is currently US$120,000 and, in India, it is ₹700,000. Meanwhile, professionals with the relevant Data Scientist qualifications and experience can easily double or triple their salary in a year or two.
Let’s take a quick look at the topics covered in this blog:
If you want to learn how to become a Data Scientist, then here is a step-by-step guide that will help you in your journey.
So, How to become a Data Scientist?
1. Data Scientist Job: What do Data Scientists do and what do companies look for in a candidate?
Check out the roles and responsibilities of Data Scientists working at Facebook and Snapchat (Indeed) to get a clear picture of the general Data Scientist’s job descriptions.
To sum up, companies generally look to hire professionals who can:
- Identify Data Analytics issues
- Understand business problems and implement analytical solutions
- Showcase their expertise/skills on Big Data platforms and tools to gain business insights or create a business objective
- Conduct research and prototype innovation
- Collect data and requirements, scope, and architecture
- Evaluate the available data sources or propose ways to procure new data sources
- Identify or create appropriate algorithms to solve business problems
- Plan, design, and deliver high-value business solutions and in-depth analysis of the solutions
- Collaborate with technical/non-technical teams and stakeholders to communicate the findings
- Provide advice to customers and client teams on statistical and Machine Learning (ML) issues
- Provide AI and ML-based solutions for areas such as customer segmentation, recommendation systems, targeting, modeling, forecasting, pricing optimization, marketing mix optimization, etc.
- Deploy ML algorithms on cloud
- Work seamlessly on Spark, R, and PySpark
- Convey the findings in a simple story to empower the decision makers to act on
Read this insightful blog on ‘What is a Data Scientist?’
2. Assess Yourself Vis-à-vis What a Data Scientist Does
The field of Data Science is evolving and welcoming for all, mainly due to the acute shortage of Data Scientists. However, 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, 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 career in Data Science because it requires solving complex problems. You need to be able to frame the problems and solve them in an orderly manner.
3. Learn Data Scientist Requirements: Decide If You Will Pursue It Alone or with an Expert’s Help
If you decide to become a Data Scientist on your own, then you need the following to get yourself on track:
- Knowledge of statistics, mathematics, etc.
- Fundamental Machine Learning knowledge
- Experience with:
- Computer languages such as R, Python, etc.
- Data mining techniques
- A few web services: S3, Spark, Redshift, etc.
- Analyzing third-party data
- Any of the computing tools: MapReduce, Hive, Hadoop, Spark, etc.
- Data visualization
To get a quick glimpse of how the Data Scientist journey would look like, you can go through this Data Science Tutorial!
Now, if you decide to take the road frequently traveled, then you need to choose a guide or a mentor to help you on your journey. Either way, you have to learn the above-mentioned topics.
4. Data Scientist Education Requirement/Career Outlook: Discard Common Myths
Now, as you have assessed your commitment, skills, and goals, let us break some myths about becoming a Data Scientist.
To become a Data Scientist, you do not necessarily need:
- A Ph.D. or a Data Scientist degree
- To be a die-hard coder
- To aggregate a large number of computing resources to build an ML model. The company you work for will provide you with the data.
Also, Data Science is more about the interpretation of data. Evidently, to do this, you must collect, prepare, cleanse, and munge the data. Thus, it is not enough to learn just one tool.
Now, let’s take a brief look at the Data Scientist career outlook and what is currently happening in the IT world.
- Forrester Research estimates that, by 2020, the total value of AI-based platforms will reach US$1.2 trillion.
- 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. If you don’t upskill your learning of Data Science right now, then you will be left without many options in the future.
5. Data Scientist Qualifications: Technical/Non-technical Skills You Should Have
A Data Scientist sits at the trijunction of programming, statistics (algorithm), and communication. If you foresee yourself as a Data Scientist, then imagine yourself at the center of the closed curve in a Venn diagram comprising these three as the subsets. Not only you need to understand programming and statistics, but also you have to convey your findings. Thus, you need to make yourself stronger in the following technical/non-technical areas:
- Programming (Python, R, etc.)
- Data wrangling
- Inferential statistics
- Machine Learning (ML)
- Data Science at scale (Big Data)
- Data visualization and analysis
- Structured thinking approach with problem-solving skills
- Business know-how
Henceforth, let us now dissect each one of these technical/non-technical Data Scientist requirements:
A typical layman’s definition of a Data Scientist might be: ‘A person who identifies business problems, works with datasets, analyzes it, develops ML models to predict results, and creates stories to convey the inferred insights.’ However, nowhere in this definition comes programming or coding!
To use an algorithm, you have to implement it and to do that, you need programming knowledge. Moreover, down the lane, when you gain some experience in the Data Scientist role, you need to do some data cleaning, productization, and adaptation of algorithms for unique purposes. Thus, some programming experience will be an advantage.
Now, you must choose the programming language you want to learn.
- Python is considerably easy to learn and takes on the heavy mathematical and statistical computing like a hero.
- On the other hand, R is more suitable for dissecting datasets, but it is more difficult to learn.
If you have questions or concerns about Data Scientist qualifications, please post it to our Data Science Community!
Data wrangling helps perform a set of tasks to understand the data and prepare it for Machine Learning. Evidently, it consumes the maximum time of a Data Scientist. Thus, it is one of the most important and indispensable Data Scientist qualifications.
One of the caveats of learning data wrangling is that it is not a machine-driven task, so the robots will not replace Data Scientists for the foreseeable future!
With inferential statistics, you can derive a pattern or come up with a feasible solution when given a data sample. Thus, the knowledge of statistics will help you come up with the key trends and dig deeper into the dataset to create predictive models.
With Machine Learning, you can:
- Capture a set of data (preferably large)
- Train the model
- Optimize it so that the program gets the best results when faced with new data
Finally, you will be able to come up with a program that learns from the data. Essentially, Machine Learning algorithms unite the key aspects of statistics and programming to derive useful insights and predictions.
As a Data Scientist, you can make predictions, recommendations, or generalizations to classify (find) groups and categories in complex datasets.
Data Science at Scale
Merely processing a dataset or creating a model is not enough. Learning Big Data is one of the most important Data Scientist requirements to have, which will enable you to achieve economic or viable results. Moreover, it will help your employer/business reduce costs, improve quality, and find new opportunities.
Learn key Data Science concepts from this tutorial video:
Data Visualization and Analysis
As a Data Scientist, you need to grasp the extensive concepts of datasets. You also need to know how to use them to come up with interesting insights. With data visualization, you can bring together insightful techniques to communicate valuable stories.
Structured Thinking Approach with Problem-solving Skills
Currently, people around the world are still contemplating the purpose and scope of AI. However, AI is creating a series of new Data Scientist requirements every day. This requires professionals to understand and turn it into valuable insights.
Business Know-how with Apt Communication Skills
As a Data Scientist, you should have an in-depth understanding of the company you work in. Likewise, the best Data Scientists not only have the ability to process large amounts of complex data but also know the subtleties of the business or organization in which they work.
So, with the right business understanding and good communication skills, you can:
- Ask the right questions
- Find insightful solutions
- Make practical recommendations to bring a change
6. 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
7. Data Scientist Career Path
As mentioned in the blog above, Data Scientists have multiple roles. However, if you boil down to the roles and responsibilities of Data Scientists and want to become one, you need to build a solid foundation for Machine Learning algorithms with appropriate coding capabilities. In addition, this work requires a thorough understanding of the software development life cycle (SDLC) with a knack for innovation. The career as a Data Scientist is usually as follows:
Shaping the learning curve (0–2 years)
If you are pursuing an undergraduate course or are considering changing your domain, you must start with the basics. First, you have to learn data preparation—you will spend most of your time processing raw data. Second, you must learn any distributed system, for example, Spark. In other words, you must learn how to extend/develop models with Big Data, the company’s proprietary system, and so on.
Finding a niche (3–5 years)
While working in the Data Science domain for 3 to 5 years, a professional will have considerable hands-on experience with medium-to-large datasets. In short, unlike newcomers, Data Scientists have a better understanding of systems, business, and data.
At this stage of your career, you can understand the impact of your work and make better judgments.
Becoming a technology expert (6–8/10 years)
When you have over 5 years of experience, you are bound to become an expert in any Data Science technology. To sum up, you can foresee a project from start to end!
Making big bets (10 years and beyond)
Any technology, no matter which industry it belongs to, needs professionals who can make big bets. For example, if you have more than 10 years of experience, then, you have the knowledge and technical know-how to experiment and implement new ideas. Most importantly, since you are familiar with the various technologies, your professional role requires you to help stakeholders bet in new ideas and spend time on research.
The above guide on how to become a Data Scientist will help you get started. Today, many course programs exist that can put you on the right career path. Therefore, a well-designed Data Scientist course program will not only upskill you with the essentials of Data Science but will help you channelize your learning through real-world projects.
Jumpstart your Data Scientist career with an immersive Data Scientist Training Program!