- Who is the Data Scientist?
Data scientists are analytical data specialists with the technical know-how to solve challenging issues. They gather, examine, and understand enormous amounts of data while utilizing a variety of principles from computer science, mathematics, and statistics. They owe it to the audience to present viewpoints that go beyond statistical analysis. Both the public and private sectors, including finance, consulting, manufacturing, healthcare, eCommerce, government, and education, are hiring data scientists.
- Data Scientist Roles and Responsibilities:
To understand corporate goals and find data-driven approaches to accomplishing them, data scientists work closely with business executives and other key actors. A data scientist’s task is to collect a large volume of data, analyze the data, and extract the most important information before using tools like R programming, Python, Julia, etc. To derive insights that could be applied to boost the company’s production and efficiency. Data scientists have a broad range of tasks and duties, depending on the needs of a business. Some of the jobs and duties of data scientists are listed below:
- Understanding the problem statement
- Source identification, Data collection & Store in the database
- Analyze enormous volumes of organized and unstructured data
- Cleansing, processing, and validating the integrity of data to be used for smart analysis
- Present data using a variety of tools and data visualization techniques.
- Combining several algorithms and modules can help you find trends and patterns.
- Develop all-encompassing analytical solutions from data collection to display, aid in building data engineering pipelines
- Assisting the team of data scientists, BI developers, and data analysts with their projects as needed
- Working on cost reduction, effort estimation, and cost optimization with the sales and pre-sales team
- Keep up with the most recent tools, trends, and technology to increase general effectiveness and performance.
- Collaborating with partners and the product team to deliver data-driven solutions built on creative ideas
- Select a model or a few potential algorithms.
- Utilize data science methods like artificial intelligence, statistical modeling, and machine learning.
- Analyze and enhance outcomes
- Inform stakeholders of the outcome.
- Adjust the problems based on feedback
- To address a different issue, repeat the procedure.
- Data Scientist Skills:
- Good presentation & communication skills it is incredibly important to describe findings to a technical and non-technical audience.
- Must have very good knowledge of Python. You will benefit from your knowledge of R, Scala, Java, or C++.
- A Data Scientist needs SQL to handle structured data. This structured data is stored in relational databases. Therefore, to query these databases, a data scientist must have a sound knowledge of SQL (Query Language).
- Regression, maximum likelihood estimators, statistical tests, and other statistical techniques are all part of good applied statistical skills. For businesses that rely on data, statistical expertise is crucial.
- Since they serve as the foundation for many algorithms optimization or predictive performance strategies, it is crucial to understand the basics of linear algebra and multivariable calculus.
- A data scientist is a good data analyst. So, Experience with data visualization tools & libraries like Excel, PowerBi / Tableau / Google Data Studio, SAS, Pandas, Seaborn, and Matplotlib that help to visualize the information from data.
- In traditional databases, it is exceedingly challenging to process a lot of data. Because of this, using this program to manage our data is simple. Experience working with Big Data Tools like Hadoop, Hive, Spark, Atlas, etc.
- Understanding of website scripts & Understanding of ETL framework and ETL tools.
- Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. So must have very good knowledge of classification, regression, clustering algorithms, etc. (Sklearn Library).
- It is extremely beneficial to data scientists who are tasked with collecting, analyzing, and interpreting large amounts of data, Deep Learning makes this process faster and easier (PyTorch, Tensorflow, Keras, FastAi).
- An excellent communicator with strong skills in dispute resolution and stakeholder management.
- Join our data science community: Study Mart | Learn Data Science Smartly
- Visit YouTube Channel: STUDY MART
- How to learn the skills from Study Mart & aiQuest in Bangladesh:
aiQuest believes in delivering a quality curriculum with a highly qualified data science professional. Class conducted in Live & Bangla language.
- Step 01: Start Learning with (Zero to Intermediate)
- Learn SQL from – SQL for Data Science
- Learn Python from – Python with Problem Solving
- Learn Data Analysis Skills from – Become a Data Analyst
- Learn Python & Machine Learning from – Data Science & Machine Learning with Python
- Step 02: Learn in advance
- Learn big data tools from – Become a Big Data Engineer
- Learn Deep Learning from – Deep Learning & NLP with Python
Regards,
Founder of aiQuest Intelligence
M. Sc. in Data Science at FAU Erlangen-Nuermberg, Germany
