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How to work in Data Science as an Engineer?

by Team Goseeko

Getting started in the Data Science field can be overwhelming for many. What to learn? Which tools to learn? What are the education requirements? Do I need to learn to code? These questions might arrive in your mind before starting your career in Data Science. In this article, we will discuss how to work in the Data Science field as an engineer.

Before jumping right into it, let’s see what this industry is all about.

What is Data Science?

Data Science is a field that can be defined as a combination of mathematics, statistics, algorithms and machine learning techniques to extract meaningful insights from a large amount of data. Such insights are crucial for making important business decisions.

Data Science is a concept to unite statistics, data analysis, informatics, computer science, programming skills, big data and domain knowledge, to understand and analyse data. Those who are experts in the Data Science industry are often called Data Scientists.

data science

Education background for a career in Data Science

Any student seeking to build their career in Data Science can:

  • Can opt for Science stream in higher secondary after 10th
  • After 12th in Science stream, they should apply for a bachelor’s degree in Computer Science (for example – Computer Engineering, Information Technology Engineering, etc). Some colleges provide bachelor’s degrees in Data Science and Analytics, so students can choose that.
  • Candidates should have a degree in one of these fields in Science, Technology, Engineering, and Mathematics.
  • Those candidates with BE / B Tech or M Tech / ME degrees are preferred.
  • Business professionals having higher degrees such as BBA or MBA are also eligible for higher studies in the Data Science field.

Skills required in Data Science

Following are the skills required to work in the Data Science industry.

1. Programming Language

Statistical programming languages such as Python or R and database languages such as SQL is a must. Python and R are the most widely used languages in the Data Science industry. Other languages like Hadoop and MATLAB are also important.

2. Statistics

A good understanding of statistics is crucial in this industry. The candidates have to study, collect, analyse, present and organize data, and for this purpose knowledge of statistics is a must. Probability, skewness, percentiles and fundamentals of statistics are important concepts that help to make better business decisions from data.

data analytics

3. Machine Learning

As the name suggests, it’s a process of making machines understand the tasks which can be automated. In this, machines can be made in such a way to make them think, analyse and make decisions. By making machine learning models, companies can have a better understanding of profitable opportunities with the minimization of risks. Machine Learning experts should have good hands-on knowledge of how algorithms work.

4. Data Extraction, Data Transformation, Data Loading

There can be multiple sources of data such as Google Analytics, MongoDB, MySQL, and others. That’s why proper Data Extraction from such sources is an essential task. Then such data needs to be transformed (like applying some calculations, concatenation, etc.) into a structure or format which can be used for querying and analysis. Finally, such transformed data needs to be loaded and stored in a Data Warehouse from where it can be used for analysis purposes.

This process is called the ETL process. ETL stands for Extract, Transform and Load.

5. Data Wrangling

Many-a-times, the data which needs to be analysed is inconsistent. For example, some data are missing values or inconsistent string format like ‘Madhya Pradesh’ vs ‘MP’, and date formatting ‘7/5/19′ vs ’07/05/2019’, unix time vs timestamps, etc. So it becomes very important to know how to deal with inconsistencies and imperfections in data.

6. Data Visualization

As it’s said in Wikipedia, Data Visualization deals with the graphical representation of data. It becomes a better way to communicate with others when the data is mapped using graphical elements such as lines, point or bar charts, graphs, time series, data table, histogram, etc.

Tableau is one of the most popular data visualization tools. There are other tools too such as matplotlib, ggplot, d3.js, qlikview, fusioncharts, and many others. It’s not just important to get familiar with such tools, it’s more important to visualize the data and use the principles behind visually encoding the information.

Interested in knowing how to pursue a career in Artificial Intelligence? Have a look at here-


7. Data Intuition

The Data Intuition technique is to find the best and the most relevant data with which the best decisions can be made. The crucial skill in Data Science is to develop problem-solving skills from a data perspective. It can also be called a data-driven problem-solving approach. As it’s key to think about important things, it’s also key to know which aren’t important. In a data-driven way, there is a systematic collection, analysis, management, interpretation and application of data.

There are several other skills which include:

  • Communication Skills
  • Problem-solving attitude
  • Curiosity
  • Adaptability (Especially while working with unstructured data, because in this industry people have to work with a huge amount of unstructured data)
  • Team player
  • Storytelling skills

How to work in the Data Science industry?

Here we will discuss how to build a career and work in the Data Science industry.

1 . Decide the right role

Data Science is a vast field and opportunities with a lot of varied job roles. Various roles are –

  • Data Scientist,
  • Data Engineer,
  • Machine Learning Expert,
  • Data Visualization Expert,
  • Big Data Analyst, and many others.

Depending on your interest, educational background and work experience, knowing about your desired role is essential. Once it’s decided, it becomes easier to get into that role. For example, people working as a Software Developer can easily switch their career to become a Data Engineer.

So the first step is to get clear about what you want to become, and then build skills focusing on that path. You can talk to people in the industry to know more about different roles, understand what those roles offer and prepare for them.

big data

2. Take courses

Once the role is decided, the next step is to understand in-depth about the role and its requirements. There are various courses both online and offline available to improve your skills. There are free as well as paid courses, but it shouldn’t bother you much as your main aim should be whether that course helps you bring basic understanding and which you can push on further.

Follow the course material and complete all the assignments provided in the course. Only doing the course won’t help much, maintain the notes, take part in discussions, and revise it as many times till all the concepts are clearly understood.

There are several courses in Data Science. Few of those are as shown below:

  • Data Science with Python Specialization
  • Data Science with R Programming
  • Business and Data Analytics
  • Data Science with Machine Learning Specialization
  • Statistics and Data Science

Check this out to know the best jobs in Data Science industry.


3. Learn Industry Tools / Languages

It’s important to have more practical experience than theory in this industry. Having hands-on experience with popular Data Science tools and programming languages will surely provide an extra edge.

Here are the best tools used in Data Science by experts:

  • SAS software – SAS is used for statistical analysis and data visualization.
  • MATLAB – MATLAB software is used for data analysis and is popular with Machine Learning applications.
  • Apache Hadoop – Apache Hadoop is used for storing and processing big data.
  • Tableau – Tableau software is a popular data visualization tool.
  • Mozenda – Mozenda tool is a data extraction tool.
  • Amazon Redshift – It is a data warehouse product that is cloud-based and is designed for large scale storage and analysis of data.
  • Alteryx – Alteryx is a tool used for advanced data analytics accessible to any data worker.
  • And many others.

Best programming languages to learn are:

  • R programming
  • Python
  • SQL (Structured Query Language)

4. Database Knowledge

Developing Database knowledge like SQL (Structured Query Language) is the most important skill in the Data Science industry. Data Scientists need SQL to handle structured data. Such structured data is stored in relational databases. Thus, to query such databases, good knowledge of SQL is a must.

While dealing with various Big Data tools, SQL is a must to carry out data wrangling and data preparation. As long as there is data involved in the Data Science field, there is a huge importance of database knowledge, especially SQL. Therefore, those are more favourable with SQL skills and knowledge of data storage and handling techniques.

cloud storage


As said earlier, Data Science is a huge field and proper guidance is essential to know how to approach this field. There are many ways to become a Data Scientist, Machine Learning Engineer, Data Engineer Data Analyst, and many other designations. Therefore, one can get lost in deciding what to do and which path to take.

The main problem is everyone doesn’t has access to expert mentors. That’s where Goseeko comes into the picture. Goseeko is an online platform dedicated to providing the right study material for students seeking higher education in the field like Engineering. As the Engineering field is one of the preferred requirements to enter into the Data Science industry, we provide study materials for BE and B Tech guidance.

What makes Goseeko unique is that we provide specific topic-wise notes as per your university syllabus. There are subject matter experts creating video lectures to help students with exam preparations. We provide Study Notes, Video Lectures made by expert college professors, MCQ’s, Question Banks and more.

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