All business organizations collect an extensive amount of data from their users or clients. They process it to perform advanced data analysis. They study these results through processing this unstructured data and manipulate it into structured data. Then use the patterns observed to analyze future trends, patterns, possibilities, and predictions. Here, let’s know about Data Scientist Career Path.
They present the conclusive details and predictions to the stakeholders, decision-makers, and business executives for making informed decisions for their company, which will contribute to their growth and help them keep up with current trends.
The top industries where data scientists can join are our Healthcare and Pharmaceutical industry, the Telecommunication industry, Social Media companies, Energy and Automotive industry.
The data science field is vast, like the ocean. It is better not to look for perfection in acquiring all the skills required to become a data scientist. There is a possibility that you may get lost in this vast sea of data. It can be very overwhelming and very hard to achieve. The best path is to recognize the business requirements of the organization you work for, study thoroughly their needs and then make a plan on how to recognize and identify the real-world problems you have to solve.
These are some of the basic skills every entry-level data scientist should be familiar with.
Skills Required for entry-level data scientists
The languages usually used are Python, R, and SQL. We can use either of these for programming. Although the widely used Python is robust. The important thing is proficiency. It is always better to concentrate on one programming language, be more proficient in that language, and work your way through it. The data analytics part of data science, especially the extraction of data and transformation, is through SQL. A data scientist should have exceptional command in programming, especially for prototyping solutions to learn and organize unstructured data. The common languages are R, Python, SAS, SPSS, Perl, and SQL/NoSQL. There are a lot of resources such as Coursera, Code academy online to practice coding.
- Probability, Statistics Linear algebra
Mathematics is the basic skill for data scientists, especially when working on data. Building data, viewing the patterns of data, converting data to analyze or visualize the structured data requires excellent knowledge in applying concepts in Probability, Statistics, Linear Algebra, Matrix calculations, etc.
Big data is a large amount of data generated from multiple sources, which traditional database management systems such as MySQL, NoSQL cannot handle. Tools like Hadoop and Spark are open sources software frameworks used for distributed storage and processing of this data. Data wrangling is the process of transforming this data into a structured form. The patterns observed through data wrangling processed to convenience, are visually represented by statistical graphics, information graphics. This data visualization allows finding patterns in this data and consolidating the details observed into informational reports. These reports are presented to the business executives and other important decision-making personnel, which they use to gain insights, make future developments, important decisions for the company
- A data engineer should be able to put the model in production and have excellent coding knowledge. Knowledge of the codebase and the libraries used, provide architecture insights on both a system and data perspective, automate the process, and deploy the code. They are looking for someone who is a “Jack of all trades and a master of all trades”.
The soft skills required for the role are:
- Time management
One pathway to becoming a data scientist is for candidates who already have graduated and have a background in Statistics and Mathematics.
A data scientist is essentially proficient in statistics. These groups lack technical skills, which in contrast to candidates specialized in computer science, is their mode of approach to machine learning models. The companies sometimes advertise data analyst positions with skills such as extraction, wrangling, and analyzing data.
Once you get their foot in the door, they can further improve their skillset and gain more knowledge on the technical side of things.
Solving complex real problems and real projects challenges you to a deeper understanding of the subject and helps you gain experience to proceed to new projects.
Data scientists need to build a portfolio of their work and through it exhibits your ability to tell a story.
Always network with industry experts and peers. Learn from experts, other data scientists who have worked on different projects, took a different path from you, and learn from them. Expand your boundaries and go deep.
To be successful in any industry requires excellent communication skills. In data science, you must be capable enough to convey your ideas in a manner, which is very less technical. It is imperative to keep in mind; you are presenting your findings and conclusions to a bunch of people who will be less interested in technical matters.
How to become a data scientist?
- A data science degree
An academic credential is important and something which will give confidence to the employer and you as something of a basis of your experience and experience with data science. A Bachelor’s degree in data science is a good option. However, you can also have a degree in computer science or Statistics degree and pursue a master’s degree in data science, and get your foot in the door. You can also pursue a master’s through online learning websites such as Coursera, Code Academy, Google certifications, etc.
- Job search
You have to start at the entry level to start for the job. Do your research and try to get interviews with companies, which are working data, big data. The basic entry-level positions are Statistician, data engineer, data analyst, business intelligence analyst. Working in these roles will give you enough experience with the job. It will also help you to identify your interests, where you want to branch out in the vast field of data science, and expand your skillset.
When you have bagged an opportunity to interview. The first step is to research the company. Their work culture, their recent projects. You can refer to their website and do thorough research. Study the job responsibilities and prepare on how you can contribute to the role.
You can expect many technical questions for the interview. Be prepared with your work experience and academic projects to appear confident.
These are examples of some of the questions:
- An example of how you dealt with an unsolvable problem?
- What is your competitive advantage?
- Do you want to be a data scientist in any field? If so, what would you choose and why?
- What is Random Forest?
- What is selection bias?
Types of Data Scientists
- To become a data scientist, there is no predetermined ‘One path’. It depends on the company you work for, the type of project you are involved in. The expectations and responsibilities of the role embalms are dynamic. You have to be more than a statistician, a programmer, or a modeler. The role demands the application of multiple skills when called for action. The listed here are the most common types of data scientist roles and the skills required.
- Analyst Data Scientist
Analyst data scientists focus more on in-depth big data analytics rather than data modeling. They use statistical tools to interpret big data and effectively communicate patterns and predict possible outcomes. The progression of the data scientist role for this group is Senior Data scientist🡪Lead Data Analyst🡪 Head Data Analyst🡪Chief data officer.
- Hacker Data scientist:
Hacker Data scientists are required in a startup environment. They have to manage different roles along with their data scientist responsibilities. Hacker scientists are familiar with operating systems, multiple databases, the cloud, and programming.
- Machine Learning Data scientist
Machine learning engineers with a background in software engineering can pursue data science, as the focus of the role would be towards deploying and scalable machine learning features. The progression of the career path would be from Machine Learning Engineer🡪Software development engineer🡪Data Science Manager. Machine learning engineers automate the computers to learn and develop using big data. ML data scientists can make high-value predictions and make decisions in real-time. In today’s age, ML data scientists are valuable as every company relies on real-time automated systems and improves the user experience. The best examples are self-driving cars, unmanned aerial vehicles, recommendation engines, etc.
- Research Data Scientist
The other data scientist roles cater mainly to the products and their business solutions, being a research data scientist brings out the inventor in you. You can write your algorithms, publish papers with your new ideas and dive deep into the research field of data science. You can pursue a Ph.D.; do in-depth research in ML/DL algorithms. Present your ideas in conferences, expand your knowledge, and create your ideas. The career progression for a Research data scientist is senior researcher🡪research associate 🡪Research scientist🡪Head Researcher.
- Customer Data Scientist
Customer data scientists apply their skills and resources in marketing. They converse with the clients and make them understand the importance of data science. They help the clients explore how to use their products to their maximum capability for the betterment of their company’s future. A customer data scientist should be knowledgeable in Artificial Intelligence, Algorithms, etc. Customer data scientist is a relatively new career path. The career progression for the customer Data Scientist is from associate, senior positions to the Director of Sales/ Marketing position.
In Coursera, IBM offers a data scientist course with certification. The reference link to access the course:
Code academy is another online learning platform, which offers an array of courses such as Python, SQL. There is a skill path for various courses such as Python, SQL, Web development. There is a career path for Data scientists which is divided into a section where Python, SQL and Data acquisition, Data Manipulation with Pandas, and different real-time projects. They also have a data science blog with exceptional resources, an online community where you can meet and connect with other data scientists.
Data Scientist salary
As of 2021, the Bureau of Labor Statistics, an average data scientist earns $90000 to $140000 per year. In 2021, the pandemic forced everyone to work from home. There is an increased demand for remote work opportunities in the data science field. There are more than 6000 jobs created worldwide. There is a growth of 20% in the field onset of pandemics with the increase of cloud computing networks to accommodate remote workers. There is a talent gap for data scientists with excellent experience and knowledge, especially in cloud computing. The demand for data scientists will further increase which expands the opportunities for the job in the coming years.
- Entry-level data scientist earns an average base salary of $98,000 per year.
- Mid-level to executive level data scientists earn $150000.
- Experienced higher-level data scientists earn an income of $180000 annually.
The table lists all the skills acquired by the data scientists and the platforms used:
|Data analysis||Data Engineering||Data Modelling||Business Intelligence||Other Skills|
|Hacker Data Scientist||SQLMongoDBPandas||TensorFlowDytorchOpenCV||APIWeb Development such as Flask, Computer Networking|
|Analyst Data Scientist||SQLPostgresHivePySpark||Scikit-learn||Presentation skills, PowerPoint, Excel|
|Research Scientist||Should have fundamental knowledge in Statistics, Probability, CalculusFocus on Publishing papers, ConferencesML/DL Algorithm|
|Machine Learning Scientist||Working of ML/DL algorithms||Fundamental Knowledge of Data structures and algorithms|
|Customer Data Scientist||TableauPandasSQL||Soft Skills, Negotiation skills sales and Marketing skills such as Targeting, Chasing Lead and Retention|
- There is an infinite amount of information. You should not get overwhelmed with all the advice regarding which path to choose. It is always better to identify the problems and find the solutions for them.
- At the end of the day, no matter what path you choose to focus. Let the focus be in engineering, product, or business. However many skills you acquire, to excel in the field is important for success. To achieve that level of experience you have to get your hands dirty. Level up your skills with real-life practice and experience. Always push yourself. Many companies do not have data science roadmaps.
- You should be intuitive enough to apply your knowledge to a problem. You should know how to approach a problem. Persistence is always the key to success.