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Table of contents
1.
Introduction
2.
About Samsung😁
3.
Job Description👨🏻‍🔬
4.
Salary and Perks💰
4.1.
Perks
4.1.1.
Flexible timings ⏰
4.1.2.
Promotion📈
5.
Roles and Responsibilities🤷‍♀️      
6.
Eligibility Criteria📝
6.1.
Minimum Qualifications
6.2.
Preferred Qualifications
7.
Skills Required🧠💡 
7.1.
Database Management    
7.2.
Mathematical Analysis
7.3.
Programming Language
7.4.
Data Assessment
7.5.
Machine learning
7.6.
Big Data Tools
7.7.
Data Visualization
8.
Career Path🎓   
8.1.
RoadMap for preparation
8.1.1.
A strong background in mathematics and computer science
8.1.2.
Experience working with large amounts of data
8.1.3.
Experience with machine learning and statistical modeling
8.1.4.
Strong communication and visualization skills
8.1.5.
A willingness to learn
9.
Frequently Asked Questions
9.1.
Does being an effective programmer make me a successful data scientist?
9.2.
What are some typical difficulties that data scientists encounter?
9.3.
What job opportunities exist for data scientists and analytics engineers?
9.4.
What is your biggest strength in data science and analytics engineering?
9.5.
What is data analytics in simple words?
10.
Conclusion
Last Updated: Mar 27, 2024

Data Science and Analytics engineer at Samsung India

Author Anju Jaiswal
0 upvote
Crack Google SDE interview : Essential projects
Speaker
Saurav Prateek
SDE-2 @
20 Jun, 2024 @ 01:30 PM

Introduction

Hello Ninja! You must seek proper guidance as a Samsung Data Science and Analytics engineer.

Congratulations, Your browser has taken you to the right place. After reading this article with all of your complete senses, you will have a clear vision of your path to becoming a Data Science and Analytics engineer at Samsung.

Data Science and Analytics Engineer at Samsung

Wait! Wait! But before discussing the strategy to get into Samsung. Don't you think it would be great if you knew a little about the company you were dreaming about?

About Samsung😁

The Samsung Group, also known as Samsung or simply Samsung (Korean: 삼성 [samsʌŋ] ), is a South Korean global industrial corporation with its headquarters in Samsung Town, Seoul.

In 1938, Lee Byung-Chul established Samsung as a commercial business.                     

Aspiring to rank among the most moral businesses, Samsung continues to practice fair and open corporate management while also training its staff and running monitoring systems.

Samsung's CEOs are Kyung Kye Hyun and Han Jong Hee. Samsung adheres to a straightforward business tenet: to use its expertise and technology to develop top-notch goods and services that advance a more civilized world. Samsung places high importance on its people and technologies to do this.

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Job Description👨🏻‍🔬

Organizations are increasingly often seen searching for the elusive, enigmatic  Data science and Analytics engineer in recent years. This position falls between a Data Analyst and a data engineer, as you may infer from the title.                                      

Logic is the data scientist on the more sophisticated side of data analysis and business. They frequently create machine learning (ML) models and prepare data. Helping businesses gain better insights and make predictions based on data makes up a significant portion of their work.                           

These specialists use Python because it has excellent libraries for dealing with applications for data science.

Python's ease of use and straightforward syntax are among the main factors contributing to its widespread use in the scientific and research communities,    

Job Description

makes it simple for those without an engineering background to adapt.

and SAS are also used for statistical analysis.

No idea if Harry can code in Python or not, but our Ninjas can indeed.

Keypoint of Data Science and Analytics Engineer

  1. More Business Focussed
  2. Analyze collected Data
  3. Define data Processing
  4. Prepare and clean data
  5. Build ML models that help the business get better insights and make predictions from their data, dashboards, and reports
  6. Business Focused
  7. Perform various analysis
  8. Work with business users to understand and gather requirements.
  9. Perform forecasting
  10. Python, RPandas,  BI tools like Tableau and Looker, Jupyter Notebook, SQL.


Visit our Machine Learning free content course to learn more. 

An Data analytics engineer is a member of the modern data team who is in charge of modeling data to produce accurate datasets that various company users can use. Data transformation, testing, and documentation are all part of their job.

An analytics engineer needs to be pretty tech-savvy to apply software engineering best practices to the analytics and understand data and how it will be used.

Building reports and dashboards on top of pre-processed data and extracting insights from it are the responsibility of data analysts. For their ad hoc forecasting and analyses, they use ExcelSQL code, and analytical tools.

Salary and Perks💰

Your salary depends on various factors like:

  1. Experience
  2. Location
  3. Skills
  4. Company

Samsung is known for paying decent salaries to its employees and providing a flexible work environment and good career progression chances. According to 79 percent of 

The company pays a generous wage and additional monetary advantages to Samsung employees.

According to the data received by reviews of 50 Data Science and Analytics Engineer at Samsung, we learned that the salary is ₹ 16.7 Lakhs for less than 1 to 10 years of experience. Data Science and Analytics Engineer salary at Samsung ranges from ₹ 7 Lakhs to ₹ 28 Lakhs.

 COOL!  This is how you feel as a Samsung employee!

 ** Above salary figures are subject to change                       

Perks

Some additional benefits of Data Science and Analytics engineer at Samsung:

Flexible timings ⏰

Flexible timing

Hours of work are flexible. According to many Samsung employees, the company promotes a flexible work-hours policy that allows employees to maintain a healthy work-life balance.

Promotion📈

In today's business world, earning promotions is a difficult task. According to many employees, getting promoted at Samsung is difficult.

You can check out Culture at Samsung to further know how Samsung cares for its employees.

Roles and Responsibilities🤷‍♀️      

Roles and Responsibilities

Ninja If you want to join as Data Science and Analytics engineer at Samsung, then you must fulfill the following skills:-

  1. Data Collecting and managing: Creating scripts or modules for gathering, cleaning, and storing data in a database or file system. Establishing testing and validation set for the final metric evaluation.
     
  2. Data pre-processing: Reflect the characteristics of the gathered data accurately, and create a pre-processing algorithm that reduces needless complexity.
     
  3. Data modeling: Investigate and create a suitable statistical model, deep learning algorithm, or machine learning model to address business challenges.
     
  4. Metric Reporting: Defining a metric that is related to business results. Determining and reporting the Data'sData's optimized metric results.
     
  5. Root cause analysis: Determining the cause of why specific data did not respond well to certain algorithms, analytically or intuitively. What else is needed to raise the metric above the data?
     
  6. Coding language/framework experience: Any deep learning or machine learning framework, including Keras, PyTorch, Scikit, etc. Python, R, Matlab, and other coding languages
     
  7. SW Application Integration & Deployment: Knowledge of creating and deploying AI/ML-based applications in the business domain. Knowing any application development language, including Python (Django), JavaC#C++, etc.

Eligibility Criteria📝

Below given are the minimum and preferred qualifications for a Data Scientist at google-

Minimum Qualifications

  • Master's degree in a quantitative discipline, for example, Statistics, Bioinformatics, Operations Research, Economics, Computer Science, Computational Biology, Physics, Mathematics, Electrical Engineering, Industrial Engineering, or any other equivalent practical experience. 
  • In data analysis-related fields, work experience is required of two years.
  • Experience with statistical software like R, MATLAB, Python, pandas, and database languages like SQL.

Preferred Qualifications

  • Ph.D. degree in a quantitative discipline. 
  • Relevant work experience of four years, including expertise with statistical data analysis such as multivariate analysis, linear models, sampling methods, and stochastic models.
  • Large datasets machine learning applied experience.
  • For arriving at an answer using available data, experience translating and articulating business questions, and statistical techniques 
  • Demonstrated self-direction and leadership. Willingness to both learn new techniques and teach others. 
  • For a given data analysis problem, demonstrate skills in selecting the right statistical tools. Practical written and verbal communication skills.

Skills Required🧠💡 

Extracting knowledge from the accumulated DataData is the subject of data science and analytics engineering. Professionals who can help their companies gain a competitive edge through data analysis are in high demand. You'll develop data-driven business solutions and analytics in a career as a data science and analytics engineer.                

You must become an expert in the following areas if you want to work as a data science and analytics engineer:

Skills Required

Database Management    

Learn about databases, which are necessary to store and analyze data using programs like Teradata®, Microsoft® SQL Server, Oracle® Database, and MySQL®.

Mathematical Analysis

Study probability, statistics, and mathematical analysis. The science of statistics focuses on creating and researching strategies for gathering, analyzing, interpreting, and presenting empirical data. Probability is a way to gauge how probably something is to happen.

The area of mathematics known as mathematical analysis concerns limits and theories, including measure, infinite series, differentiation, integration, and analytic functions.

Programming Language

At least one programming language to the fullest. R, Python, and SAS programming languages are crucial for data analytics. Most Machine Learning for Data Analytics algorithms, such as regression, association, and clustering, are supported by R, a free software environment for statistical graphics and computing. Python is a general-purpose, open-source programming language. Data science employs Python libraries like NumPy and SciPy. In addition to performing statistical analysis on the data, SAS can mine, modify, manage, and retrieve data from various sources.

Data Assessment

Learn how to organize, manipulate, and clean up data by practicing data wrangling. R, Python, Flume, and Scoop are well-liked data wrangling tools.

Machine learning

Become an expert in machine learning concepts. Giving systems the capacity to automatically learn from experience and get better without being specifically programmed to. Different algorithms, including Regressions, Naive Bayes, SVM, K Means Clustering, KNN, and Decision Tree algorithms, to name a few, can be used to achieve machine learning.                            

Machine Learning

Big Data Tools

Utilizing Big Data tools, such as Apache Spark, Hadoop, Talend, and Tableau, to handle large and complex data sets that cannot be handled by conventional data processing software.

Data Visualization

Become more adept to visualizing outcomes. By combining various data sets and producing a visual representation of the findings using diagrams, charts, and graphs, data visualization

Career Path🎓   

 So, You are determined to become a Data Science and analytic engineer. That is why you are here. You have already seen the perks and skills requirements in the above section.

But wondering how to gain excellence in this.

Well, Ninja! Don't panic. Coding Ninjas will help you through this.

How do I begin? Where do I begin? What subjects ought to be covered? etc. Do you need to read a book to understand every concept, should you use online tutorials, or should you learn Data Science and Analytics engineering by working on projects related to it?

Let's Investigate

A solid foundation in mathematics and computer science and prior experience working with large amounts of data are requirements for becoming a Data Science and Analytics engineer.

RoadMap for preparation

A strong background in mathematics and computer science

You will deal with a lot of data daily as a data scientist. You must therefore have a solid foundation in both computer science and mathematics. You should be exceptionally at ease using statistical techniques and algorithms.

Experience working with large amounts of data

Effective manipulation and analysis of massive data sets is a requirement for data scientists. As a result, before becoming a data scientist, you should have some experience working with large data sets.                                 

Data, Data Everywhere

Experience with machine learning and statistical modeling

Data scientists use machine learning and statistical modeling as practical tools to conclude data. Anyone interested in becoming a data scientist must have experience with these methods.

Ninja, you need to work on various projects to sharpen your relevant machine learning skills:

  • Distracted Driver Detection

Build a classification model to predict using a database of images whether a given driver is distracted, ie, texting, on a call, driving safely etc

  • Urban Sound Classification

Build a Neural network-based model to classify various sounds using their unique spectrogram into classes such as Dog Barking, Sirens, Street Music, etc.

  • Text Generation

Build a Neural Network based model to predict what the next word will be in a sequence of words/sentences

  • Neural Machine Translation

Build an advanced model for the purpose of translating phrases and symbols from one language to the other using an Artificial Neural Network.

Strong communication and visualization skills

Data scientists need to be able to explain their findings to others clearly. Therefore, anyone interested in becoming a data scientist must possess strong communication and visualization skills.

A willingness to learn

Data scientists must be willing to constantly learn new methods and techniques because the field of data science is constantly evolving. Anyone who wants to become a data scientist must be willing to learn. Enrolling in a top data science education program is one of the best ways to learn how to become a data scientist or to sharpen your existing skills.

We will help you in your practice. Go and explore the following resources:

Check out the Samsung Interview Experience to learn about Samsung’s hiring process.

You can also check out Data Analyst vs Data Scientist here.

Frequently Asked Questions

Does being an effective programmer make me a successful data scientist?

You only need to be "Good Enough" at programming; you don't need to be "great" at it.

What are some typical difficulties that data scientists encounter?

Dealing with large data sets, using complicated algorithms, and figuring out how to visualize data are some common challenges data scientists face. They might also have to explain their findings to non-technical audiences.

What job opportunities exist for data scientists and analytics engineers?

Without attractive opportunities, data science would not be regarded as the "Sexiest Job of the 21st Century." Currently valued at $38 billion, the market is anticipated to grow to $140 billion by 2025. Being a data scientist in this decade is exciting.

What is your biggest strength in data science and analytics engineering?

The best data science and analytics engineers solve the issue by not just processing the most significant amount of data or using the most sophisticated algorithm. The best candidates for the data scientist position are those who naturally desire to solve the correct problems.

What is data analytics in simple words?

Analyzing data sets to identify trends and make judgments about the information they contain is known as data analytics (DA). Data analytics is increasingly carried out with specialized hardware and software.

Conclusion

In this article, we have discussed most of the vital things you need to know about data science and analytics engineering at Samsung; a brief idea about Samsung, the most required skills, a quick roadmap, and how we will help you in this journey.

These articles will help you to explore more about technical roles at Samsung:

 

Here are a few key websites that will aid in your exploration.

  • Learn Data Science for free with Coding Ninjas
  • How Can Data Science with Python Kick-start Your Career?
  • Preparation Guide For AWS Data Science Certification

 

Refer to our Guided Path on Coding Ninjas Studio to upskill yourself in Data Structures and AlgorithmsCompetitive ProgrammingJavaScriptSystem DesignMachine learning, and many more! If you want to test your competency in coding, you may check out the mock test series and participate in the contests hosted on Coding Ninjas Studio! You can also consider our Data Analytics Course to give your career an edge over others.

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Happy Learning!!

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