Before I started writing this article, I chatted with several folks working as data scientists for quite some time. The discussion was intriguing due to the diversity of career paths shared by the participants. I’m going to break down what they shared with me.
Table of Content
- Who is a Data Scientist?
- Is it worth learning Data Science?
- Can AI replace Data Science jobs?
- Different Job Roles under Data Science and Average Salary
- What core skills do you need to become a data scientist?
- A complete Road Map to become a Data Scientist
Who is a Data Scientist?
A Data Scientist analyzes and interprets data, transforming it into valuable information through the identification of patterns and trends. This process aids companies in making more informed decisions, contributing to their success and enhancing customer support. Typically, a Data Scientist requires a robust background in mathematics, statistics, and computer science.
Is it worth learning Data Science?
The US Bureau of Statistics predicts the demand for Data Scientists will grow 36% from 2021 to 2031. Data Analyst jobs are predicted to grow by 23%. Due to the high demand, Data Science jobs will pay well. Data Science will be incorporated in every industry like:
- Technology
- Finance
- Healthcare
- Retail and E-commerce
- Manufacturing
- Transportation and Logistics
- Education
- Government and Public Sector
- Energy
- Agriculture
- Social Sciences and Nonprofits
If you’re still deciding whether to enter Data Science, here’s something that might tip the scales in your favor: In the last two years, 400k jobs have been cut, but Data Science jobs were among the least affected.
Despite a reduction of jobs in recent years, Data Science roles have remained largely unaffected, showcasing the field’s stability.
Can AI replace Data Science jobs?
If you’re questioning the future of Data Science jobs in the face of AI, rest assured, you’re not alone in this thought. However, experts suggest that the scenario of AI replacing Data Science roles is unlikely to materialize in the foreseeable future. The assumption that simply interacting with a chatbot could replicate the complex outcomes achieved by Data Scientists is misplaced. Rather than replacing them, AI is expected to enhance the capabilities of Data Scientists.
Tools like ChatGPT are designed to assist Data Scientists in performing routine tasks, such as debugging code, generating foundational code, cleaning data, and managing other repetitive tasks that previously consumed considerable time. This shift will enable Data Scientists to devote more attention to critical aspects of their work, including formulating pertinent questions, analyzing results, and exploring potential solutions.
Exploring the Diverse Roles within Data Science and Average Salary
This is a brief overview of various job roles within the Data Science field and the average salaries in the USA, according to Glassdoor.
| Job Role | Average Salary (USD) |
| Data Scientist | $157,000 |
| Data Engineer | $126,000 |
| Data Architect | $166,000 |
| Data Modeler | $106,000 |
| Big Data Engineer | $155,000 |
| Machine Learning Engineer | $153,000 |
| AI Engineer | $118,000 |
| Enterprise Architect | $164,000 |
| Business Intelligence Analyst | $87,000 |
| NLP Engineer | $88,000 |
| Database Manager | $76,000 |
| Database Developer | $97,000 |
| Data Analyst | $74,000 |
What are the skills you need to become a data scientist?
- Statistics: Fundamentals of statistics and mathematics. This includes descriptive statistics, inferential statistics, probability distribution, hypothesis testing, time series analysis, and more.
- A/B Testing is a statistical method that compares two variable versions within a controlled setting to identify the superior variant.
- Domain Knowledge: Specialization in a specific sector, such as retail, based on extensive experience.
- Excel: Utilized for managing smaller datasets.
- Data Visualization: is essential for data analysts, who must employ tools like Tableau to communicate data narratives effectively to stakeholders.
- Programming: Proficiency in programming languages, including R and Python, is crucial for data analysts.
- SQL: A database language preferred for its ability to manage large datasets, surpassing Excel’s capabilities.
- Reporting: Data analysts convey data insights, necessitating exceptional communication and presentation abilities.
Complete Raod Map to become a Data Scientist:
- Research the data science job family: There are many different roles within the field. It is essential to research and identify the specific role that interests you. This will help you tailor your learning and experience to the particular job requirements you are targeting. For instance, if you are interested in natural language processing, you should focus on learning techniques such as sentiment analysis and text classification.
- Learn the fundamentals of statistics and machine learning: Some core skills are required for all data science roles, starting with descriptive and inferential statistics and then moving on to supervised and unsupervised machine learning. Many resources are available online and in libraries to help you learn these topics. Some popular resources include textbooks and online courses.
- Learn to code: Python, R, and SQL are the most commonly used programming languages in data science. Python is used for data analysis and machine learning, while SQL is used for querying databases. Many Data Scientists recommend starting with Python and then learning SQL. Many online tutorials and courses are available to help you learn these languages.
- Learn data science tools: Once you have a foundation in statistics, machine learning, and coding, you can start learning the tools data scientists use. These tools include Jupyter Notebook, Google Colab, Git, and version control systems. Jupyter Notebook is a web-based application that allows you to create and share documents that contain code, equations, and visualizations. Google Colab is a free Jupyter Notebook environment that runs in the cloud. Git is a version control system that allows you to track changes to your code over time.
- Learn business and product fundamentals: It is crucial for data scientists to have a good understanding of the business and product that they are working on. This will help you ask the right questions, develop solutions aligned with business goals, and communicate your findings effectively to stakeholders. For instance, if you are working on a project to improve the customer experience for an e-commerce website, you would need to understand the website’s business goals, such as increasing sales or conversion rates.
- Develop your communication skills: Data scientists need to be able to communicate their findings to both technical and non-technical audiences. This includes giving presentations, writing reports, and explaining complex concepts clearly and concisely. You can develop your communication skills by taking public speaking courses, practicing giving presentations to your peers, and writing technical reports.
- Build a portfolio of projects: Once you have learned the necessary skills, you can start building a portfolio of data science projects. This will help you to showcase your skills and experience to potential employers. You can find data sets to work with on websites such as Kaggle and Google Data Search. Choosing projects relevant to your interests and career goals is essential when building your portfolio. You should also focus on projects demonstrating your ability to solve real-world problems.
- Prepare for data science interviews: Data science interviews typically consist of behavioral questions, coding questions, and questions about statistics and machine learning. Practice common data science interview questions to improve your chances of being hired. Many resources, such as mock interview websites and online courses, are available online to help you prepare for data science interviews.
