The field of data science can be described as an interdisciplinary one that uses scientific methods, processes, algorithms and systems to extract knowledge and insight from structured and unstructured data. A wide range of applications can be used to derive and apply actionable insights from data. There is a connection between data mining, machine learning, and big data in data science. The data science field is a concept that combines statistics, data analysis, informatics, and related methods in order to comprehend and analyze actual phenomena based on actual data. Embedding techniques and theories from many disciplines, such as mathematics, statistics, computer science, and information science, are used to conduct this research. A data scientist is different from a computer scientist or an information scientist, however.
Professionals with skills in data science are currently in high demand as one of the most lucrative career paths available. Those who are successful in today’s marketplace know that they must move beyond data analysis, data mining, and programming skills.
A data scientist must possess the following skills:
- Coding languages include Python, SQL, Scala, Java, R, and MATLAB,
- Machine Learning: Natural Language Processing, Classification, Clustering,
- Data Visualization: Tableau,R libraries, SAS, D3.js, Python, Java,
- Big data platforms: Amazon AWS, Microsoft Azure, Cloudera,Google Cloud, MongoDB, Oracle.
Additionally, they must possess the Essential key technical skills and tools, including:
R, Python, Apache Hadoop, MapReduce, Apache Spark, NoSQL databases, Cloud computing, D3, Apache Pig, Tableau, Python notebooks, GitHub
What Does a Data Scientist Do?
Almost all organizations now employ data scientists, who have become valuable assets during the past decade. Strategizing and answering questions involves synthesizing large amounts of data into complex algorithms to synthesize and organize a large amount of information. These professionals possess well-rounded, data-driven skills and possess superior technical or conceptual abilities. Along with this, multiple stakeholders in an organization or business need to be communicated to in a straightforward manner and to receive tangible results.
In addition to having outstanding industry-specific knowledge and communication skills, data scientists must prove they are curious and result-oriented. Data warehousing, mining, and modeling skills are required along with strong quantitative backgrounds in statistics and linear algebra.
Why Become a Data Scientist?
Data scientist ranked as #1 Best Job in America in 2018 for the third time in the history of Glassdoor. Increasing amounts of data have led to the need for data scientists to expand beyond large tech companies. The shortage of qualified candidates in the open positions is hampered by the growing demand for data scientists across industries, large and small.
Data scientists are in high demand. This trend is not expected to slow down any time soon. Companies cite data scientist as one of the most in-demand skills in addition to data-science-related skills as data scientist is one of LinkedIn’s most promising jobs in 2017 and 2018.
Data scientists are in high demand, as indicated below by the statistics.
- By 2021, there will be a 28% increase in demand
- 5,364 Number of Job Openings
- $120,931 Average Base Salary
- #1 Best Job in USA 2019-2021
Where can I start my career as a data scientist?
- You need to decide what role you should play. …
- Consider taking a course and completing it. …
- Make sure to stick to the same tool or language. …
- Make contact with your peers. …
- Don’t just focus on theory, but also on practical applications. …
- Make sure you follow the right resources. …
- Communication skills need to be improved.
- Take advantage of networking, but do not spend too much time on it!
- A basic understanding of databases and SQL is required
- Staying ahead of the game when it comes to resumes
- The importance of guidance cannot be overstated
Let us move on to the final and perhaps most important point – finding the right guidance. The fields of data science, machine learning, and data engineering are relatively new, and so are the alumni. This history has only been decrypted by a handful of people.
The most obvious way is to buy a recognized certification and spend thousands of rupees to become a certified data scientist, but this is a short-term fix. You can even follow a YouTube playlist for some time, but still, you’re not ready.
Employers are prioritizing Data Scientists due to the huge demand for their services. By taking the right steps, you can exponentially grow. Tips are provided here that can help you avoid costly blunders and enable you to get started.