Data Science Career Path: Roles, Responsibilities & Skills

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Data science is regarded as one of the most lucrative careers.

It tops the list of the best careers in America for the third consecutive year.

Demand for data scientists continues to grow at a rate of 30%, and a job in this segment offers six-figure salaries.

Getting a job in the field of data science is challenging due to its very analytical nature.

As a data scientist, you need to have an aptitude for analysis, maths, and statistics and a knack for problem-solving.

Your ultimate goal is to help a company analyse trends to help them make better decisions.

In this article, I will tell you how to start data science career and understand data science roles and responsibilities.

‍Data Science Career Path – What should be the first step?

While preparing for your next data science interview, you must first cover your basics.

Let us start by seeing the most common data science job roles and what they mean.

Most Common Data Science Job Titles

You need to understand the various job roles available in this field before you begin your interview preparation.

In some companies, the boundaries between data analysts, engineers, and scientists are loose and can overlap.

Hence, it is important to check the job descriptions to understand the exact expectations of the company.

Below are the top data science roles you need to know:

🔶 Data Scientist:

This is the most general role and allows you to deal with all aspects of a project starting from the business, logic understanding, data collection, and analytics.

🔶 Data Analyst:

It is the second most in-demand role.

It involves data visualisation, transforming, and manipulating data to derive impactful insights.

🔶 Data Engineer:

Data engineers are responsible for setting up the infrastructure for others to work on, primarily being responsible for data storage and data transportation.

🔶 Machine Learning Engineer:

The responsibilities of an ML engineer include designing workable ML algorithms.

Understanding the Job Responsibilities of a Data Scientist‍

A data scientist’s primary job is to gather a large amount of data, analyse it and dig out the essential information.

It also includes transforming it into actionable insights and integrating them into existing systems to increase the productivity and efficiency of the business.

Thus, a data scientist requires a combination of technical, analytical, and communication skills.

The job responsibilities of a data scientist can be classified into six categories.


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‍1. Data Fetching:

Identifying valuable data sources and automating the collection process is important for any organisation.

Big data is worth a lot in today's data-driven world.

Data is often gathered from publicly available sources, like websites, through web scraping.

Data Scientists can either use web scraping tools, or design unique code to scrape websites quickly and effectively. 

It is important to note that almost 95% of the data generated is unstructured.

It needs to be labelled to let machines understand what they are, known as data annotation.

Data scientists also use ETL tools to fetch or scrape data depending on the business needs.

It helps to extract and transform data from several resources.

2. Data Storage

Another topic of importance is how to store the data collected.

There are magnitude requirements and expectations from a database.

It needs to be well organised, and easy to retrieve and process at the same time.

So, knowledge of SQL becomes an important skill which helps create, read, update, and delete data.

Moreover, an understanding of an open-source document-oriented database like NoSQL, MongoDB, and knowing Bigdata frameworks like Hadoop will provide you with an advantage. 

3. Data Processing

This includes the conversion of raw data into machine-readable form.

Data scientists analyse and investigate data sets, summarising their main characteristics with the help of Exploratory Data Analysis (EDA) and NumPy.

They must be able to use open-source data analysis and manipulation tools like Pandas, NLTK, and OpenCV depending on the business requirements.

‍4. Data Modeling

It is the process of creating a visual representation of the system to build connections for easy communication between structures and data points.

While creating a database structure, you need to start with a diagram of how data will flow.

You can do this with the help of various ML and DL frameworks, including Scikit-learn, TensorFlow, Keras, PyTorch, etc.

Moreover, data modelling also involves the application of artificial neural networks like:

  • Multilayer Perceptrons (MLPs)
    • Conventional Neural Networks (CNNs)
      • Recurrent Neural Networks (RNNs)

        5. Deployment:‍

        This refers to preparing the application of a model for making predictions with the help of new data.

        Designing a model is not the end of a project, but it needs to be served and presented for the organisations to use it constructively.

        There are four common methods of deploying the models in data science:

        • Data science tools (or Cloud)
          • Programming language (Java, C, VB)
            • Database and SQL script (TSQL, PL-SQL)
              • PMML (Predictive Model Markup Language)

                6. Reporting:‍

                Reporting is the process of presenting a series of results after research and analysis.

                Data reports answer basic questions about the state of a business.

                Data reporting is key to a company's business intelligence.

                The layout data may be outdated if not presented on time.

                Using a BI platform will help as it can handle multiple inputs and visualise complex and dynamic data.

                Also, Tableau Business Science knowledge will help you bring data science capabilities to various business domain experts.

                Real-Time Data Science Applications‍

                There is no shortage of data science usage and its implementations in this modern era.

                It is a fabulous field that covers various spectrums offering many real-life applications.

                Let us look into some of the practical real-time applications of data science.

                1. Email extraction

                We receive millions of emails, often containing many unwanted emails.

                You can use ML methods to figure out spam emails as a data scientist.

                Also, we can extract useful information from emails and generate actionable tasks.

                The ML model, trained on the dataset, can make predictions if the email is spam or not.

                2. Measure the quality of movie showtimes:‍

                Data scientists are specialists who can even analyse the market when the film is released and predict the right time to release it.

                For example, checking the quality of the information published about the movie such as the cast, release date, etc.

                3. Cleaning/filling the retail data:‍

                Data science is a useful tool for the retail market. It provides insights into profitable margins by developing data-driven plans.

                Marketers get a chance to improve the customer experience and thus increase their sales and profit margins.

                For e.g., finding missing data in the retail catalogues using the image/description to extract it.

                4. Visualising agriculture data:

                Experts of data scientists know how to use tools to identify patterns and relationships to track crops, pests, and water usage.

                It also provides them with local insights with the help of historical data for past land usage and various local weather conditions.

                5. Fixing the image quality issues from web scraping:‍

                Data scientists can fix image quality issues while web scraping, leveraging their business by delivering better and more convenient customer services.

                Moreover, the ability to recognise human faces on your social media sites and tag them while uploading a picture with them is a wonderful endeavour for a data scientist.

                For e.g., rejecting multiple faces, and scenery images from portrait images.

                6. Understanding user journey on windows:‍

                Customer journey analytics is a critical measure that scales customer interaction.

                Data science is the single most effective way to increase the time spent by a customer on a web page.

                Example of Different Roles and Responsibilities: Building Commerce Pipeline


                This was one of the early commerce pipelines of Bing Shopping that we needed to work on under my ownership. 

                ▶️ Data Fetching: Initially, we had to understand the data of different merchants kept in different formats and fetch them.

                ▶️ Data Cleaning & Aggregation: Different data will have different schemas. For example, dresses would be labelled as gowns and dresses both. 

                We were required to understand different categories.

                We also needed to develop a unified category or schema that would fit the different variations of the same products.

                Data cleaning will be used to clean all types of data such as images, texts, pricing, etc.

                ▶️ Product Classification: Once the products were classified under one umbrella category like the clothing segment or electronic segment, they were further classified at different levels. 

                For example, the clothing segment will be the higher level and lower levels would include, a dress segment or a pant segment, bottom wear, top wear etc.

                ▶️ Product Deduplication: Now, there will be a lot of duplicate products coming from different sources.

                A similar dress from 3 different merchants will not be shown to the customer 3 times, rather they will be listed through aggregate offer prices, the product remaining the same. 

                The deduplication is done either from the metadata or the image of the product.

                ▶️ Attribute Enrichment: Sometimes, the product will have missing attributes or minimum information. In this case, we needed to figure out the missing attributes and enrich them after collecting information from other similar products or through the web.

                ▶️ Data Publish: Finally, publishing the enriched data.

                You can also watch the "Choosing the Right Career in Data Science" below.

                What is the Process of a Data Scientist Interview?

                A data scientist interview is conducted in several rounds and can be broken into technical and non-technical.

                The candidates are qualified for the next level once they establish their knowledge in various domains.

                Technical rounds are usually focused on the hard skills required for the job role.

                Non-technical rounds check your soft skills, such as communication, leadership, and team fit.

                Research what the company is looking for and then prepare accordingly.

                Below is a brief description of what to expect during your data science interview.

                👉 Round 1

                This round includes many questions on applied data extraction, manipulation problems, computer science fundamentals, project-based queries, and machine learning algorithms.

                Depending on the job role offered, the questions can differ, but Knowledge of SQL and or DSA is required for almost all roles.

                💡 Tip: Before you start your coding journey, I would recommend that you walk through the intended solution of the problem with the interviewer first to validate it.

                Moreover, you must be well aware of all the basics but need not memorise everything.

                👉 Round 2

                This round comprises data challenges where you will have to analyse sample data and come up with suggestions on how to solve a business problem.

                It has various modelling exercises like prediction or segmentation.

                It can include statistics theory and concepts.

                💡 Tip: You also need to clearly understand the business problems in advance to help you come out with the right solutions.

                👉 Round 3

                Here, you will face practical questions, including various case studies. Apart from conceptual questions, there can be case studies/ practical questions.

                You will also be required to discuss your approach to a problem under a given business scenario and make necessary suggestions.

                You will need to share your knowledge on diagnostic metric shifts, measuring success, and evaluating a feature with tradeoffs.

                💡 Tip: Take your time to write your answers and structure your communication before answering.

                It will also help to share multiple approaches before freezing your answers.

                ‍👉 Round 4

                Here you will get questions related to domain knowledge, often comprising ML fundamentals.

                💡 Tip: You can answer probability questions by drawing out the permutations and then adding up the probabilities.

                Also, practise more on solving problems using common techniques with ML.

                You can check this in-depth guide for a comprehensive look into data science interview process and preparation.

                Tips for your Data Science interview preparation

                • Choose a coding language such as Python or R and get better at it.
                  • Take up a cloud technology like Azure or AWS if you are preparing for a Data Engineer role.
                    • Create a solid profile on LinkedIn and Github highlighting your skills and projects. 
                      • Build an online presence so that you can catch the eye of recruiters. 
                        • Focus on creating an ATS-compliant resume by including all the relevant information.

                          📍You can connect with me for tailored data science interivew guidance.

                          Let's prepare for your dream data science job together.

                          How to Start a Data Science Career (for beginners)

                          Starting a career in data science can be an exciting and rewarding journey. 

                          If you’re a beginner, wondering how to get into data science, here are 9 basic yet helpful tips to get you started.

                          1. Develop a strong foundation

                          To excel in data science, you need a solid foundation in the following areas:

                          Mathematics and statistics: Brush up on your math skills, especially linear algebra, calculus, and statistics. 

                          These are crucial for understanding algorithms and models.

                          Programming: Learn a programming language such as Python or R. 

                          Python is highly recommended due to its versatility and a wide range of libraries available for data science.

                          2. Learn data manipulation and analysis tools

                          Become proficient in using tools and libraries for data manipulation and analysis:

                          Pandas: It's a Python library for data manipulation and analysis.

                          NumPy: Essential for numerical computing in Python.

                          Matplotlib and Seaborn: Used for data visualisation.

                          3. Dive into machine learning

                          Machine learning is a significant part of data science.

                          Start with the basics.

                          Scikit-learn: A user-friendly Python library for machine learning.

                          Online courses: Select relevant online courses based on your requirements and learning needs and get feedback from a professional on your progress.

                          4. Practice projects

                          Hands-on experience is key.

                          Work on personal projects that interest you. 

                          Start with simple ones and gradually tackle more complex problems. 

                          Kaggle is a great platform for finding datasets and competing in data science competitions.

                          5. Build a portfolio

                          Create a portfolio showcasing your projects. 

                          Include detailed explanations of your approach, code, and the results you achieved.

                          A well-organised portfolio will impress potential employers.

                          6. Network

                          Attend data science meetups, conferences, and online forums like LinkedIn and GitHub

                          Networking can lead to job opportunities and collaborations with others in the field.

                          7. Apply for jobs or internships

                          Start applying for entry-level data science positions or internships. 

                          Be prepared for technical interviews and coding challenges. 

                          Your portfolio will be a valuable asset during the application process.

                          8. Continue learning

                          Data science is a rapidly evolving field. 

                          Stay updated by reading books, research papers, and blogs. 

                          Consider pursuing advanced degrees or certifications if you're looking to specialise further.

                          9. Stay Persistent and curious

                          Data science can be challenging, but persistence pays off. 

                          Stay curious, keep learning, and don't be discouraged by setbacks. 

                          Each problem you solve is an opportunity for growth.

                          Remember, everyone's journey is unique, and it's okay to take your time to learn and grow in this field. 

                          Embrace the learning process, and you'll be well on your way to a fulfilling data science career.

                          ⭐Let an experienced mentor guide you

                          Data scientist interview preparation is exhaustive and needs to be started months in advance.

                          Data science is a field that requires true passion.

                          You must have an extreme interest in this subject to achieve better results.

                          Preparing for the right data scientist post without the proper guidance can be tough, and you may waste more time.

                          If looking for a data scientist role, an experienced data scientist as a mentor can be a good help.

                          They can guide you systematically as they understand this field and have already completed the process.

                          I have been mentoring on Preplaced for a while and have conducted 50+ successful sessions.

                          I believe in the philosophy of empowerment, my goal is to not hand you a number of questions to prepare but rather go deep into the skill sets and make you confident enough to ace any interview.

                          Connect with me so that I can understand your pain points and guide you in your preparation journey.

                          Also read:

                          Tips on the Technical Skills Required for Data Science Interviews

                          Meet the Top 5 Data Science Mentors at Preplaced