Table of contents-
1. Why should you pursue a data science or analytics career?
2. From analyst to data scientist : Advancing our data science career
3. What's the difference between data analysts and data scientists?
4. Which data science career should you pursue?
5. Women In data science
6. Building a data science career : The job market for data scientists
7. Data science career FAQs
Why Should You Pursue a Data Science or Analytics Career? Three Key Advantages of a Data Science Career-
The availability of data has increased dramatically in recent years, as has the demand for data science skills and data-driven decision making. Analytics and data science have been solidified as crucial navigational tools across industries and functions, thanks to the severe shift in corporate operations and consumer behaviour induced by the COVID-19 pandemic.
"Data science is a 21st-century employment skill that everyone should have," says Eric Van Dusen, the University of California (UC) Berkeley's curriculum coordinator for data science education. "Each and every field." I tell my pupils that they must all graduate with this set of talents. Whatever career you choose, you're going to be a lot more powerful."
Data science is a popular subject that pays well and provides many chances.
Also read- Top Data Structure Interview Questions And Answers - 2022
1. Get a High-Paying Job
According to Robert Half data, the median beginning wage for data scientists is $95,000, which is nearly double the national average. Even the average wage for data analysts, a more entry-level position, is far greater than the median salary in the United States, at almost $70,000.
Work experience is the most important element in data science pay, according to a survey by Burtch Works. Data science experts with at least seven years of experience may expect to make an average of $129,000 in their mid-career. Highly skilled data scientists in management positions might earn well over $250,000. When it comes to data science salary, however, education, company size, and industry are all essential considerations.
2. Solve Difficult Problems
As a data scientist, you'll never be bored if you appreciate tackling complicated, real-world problems. Your major role is to analyse and process massive amounts of raw data in order to find answers and insights. Here are a few instances of business difficulties you'll have to solve:
Finding ways to increase sales
Discovering features that distinguish a target audience segment.
Finding potential opportunities in disparate data sets.
Identifying unrecognized problems in current business operations.
Building infrastructure that helps an organization ingest and centralize all the data.
"The best thing about being a statistician is that you get to play in everyone's backyard," stated the legendary John Tukey "Philippe Rigollet, an associate professor in the MIT mathematics department and the Statistics and Data Science Center, explained the findings. "In data science, this is true: whatever your topic of interest is, I can tell you that there is data to improve it." With data being collected in many parts of life, from marketing to health, and even sports and entertainment, being able to extract knowledge from data is a very strong position to be in."
3. Avoid Job Automation
Data science roles, particularly data analysts, are at very low risk for automation for a few reasons:
1. The demand for data science roles is growing at an average rate of 50 %.
2. Very few platforms can produce sophisticated analyses.
3.
Data scientists are the ones who are doing most of the automating.
Also Check >>> Top Websites/Platforms To Prepare For Data Science Interviews
From Analyst to Data Scientist: Advancing Your Data Science Career
You can use your data science abilities to advance your data science career/ career in data science/data-centric career in one of two ways: as a data science professional (pursuing occupations like data analyst, database developer, or data scientist) or as a functional business analyst or a data-driven manager.
Both careers necessitate a foundation of data analytics, programming, data management, data mining, and data visualisation skills and knowledge.
Despite the two approaches, the relatively new field's dynamic nature implies that career paths are variable.
Data scientists, such as data analysts, can transition into data science or data system developer roles, based on their areas of competence.
A data analyst can advance to the profession of data scientist by learning more about artificial intelligence, statistics, data management, and big data analytics.
A data analyst can become a data system developer by leveraging current technical abilities in Python, relational databases, and machine learning.
Many of these abilities can be acquired through job experience or through self-paced online data science courses.
We'll concentrate on the data science careers track in this guide.
Check >> Top 10 Skills Required To Be A Data Scientist - 2022
What's the Difference Between Data Scientists and Data Analysts?
Entry-level data science roles and data analysts have a lot in common in terms of skills and work duties.
Both roles necessitate statistical knowledge and programming skills.
The focus, though, is noticeably different.
When we talk about a career in data science, this question definitely arises.
What Does a Data Scientist Do?
Data scientists answer questions about the business from the context of data.
They leverage data to create new product features and tend to do more modeling and open-ended research.
They’ll spend a lot of time cleaning data to make sure that it is usable for their models and their machine learning algorithms.
When you watch Netflix and see a personalized list of recommended shows, that’s machine learning algorithms and data science at work.
Predictive analytics is also a subfield of data science activity.
"Predictive data analysis is more complicated because, as the name implies, it predicts what will happen in the future based on data from the past, or by combining data from multiple datasets and sources," said Rafael Lopes, an Amazon Web Services Partner Solutions Architect and instructor for Getting Started with Data Analytics on AWS.
"In a nutshell, it attempts to forecast the future based on past events.
In diagnostic analysis, neural networks, regression, and decision trees are frequently used."
Core Data Science Skills
- Big data: All data is large or complex data sets that can’t be managed with traditional data processing software. That’s why data scientists must know Apache Hadoop or Apache Spark, which is an open-sourced distributed processing system.
- Data modeling: data modeling is the process of formatting specific data into a database.
- Data visualization: data visualization is the graphic representation of data used to show trends and insights.
- Machine learning: machine learning is a series of techniques used to predict and forecast data.
- Programming: knowing programming languages such as Python and R are critical if you want to automate data manipulation.
- Statistics: although you don’t have to be a statistician, you must know some form of applied statistics to interpret data.
- Teamwork: data scientists don’t work in silosーthey’re often part of larger data science teams comprised of data engineers, software developers, and others.
What Does a Data Analyst Do?
Data analysts are responsible for answering questions about data.
Unlike data scientists, data analysts are not concerned with using data to find trends or figuring out the business’s future.
Their job is to analyze historical data, create and run A/B tests in product, and even design systems.
Data analysts need to be proficient at data storing, warehousing, and utilizing tools such as Tableau.
Core Data Analyst Skills
- A/B testing: A/B testing is a statistical approach used to compare two versions of a variable in a controlled environment. A/B testing is employed to determine which variable version performs better.
- Domain knowledge: you can think of domain knowledge as specialization. For example, if you have significant experience working specifically in the retail sector, you have domain knowledge in retail.
- Excel: Microsoft Excel is often used to manage small data sets.
- Data Visualization: like data scientists, data analysts must know how to use data visualization tools such as Tableau to tell stories to stakeholders with data.
- Programming: data analysts should have competent programming skills in languages like R and Python.
- SQL: SQL is a database language used for data management and building database structures. SQL is often used instead of Excel because it’s more apt at handling large datasets.
- Reporting: as a data analyst, you need to report your data insights, which means you should also have excellent communication and presentation skills.
Which Data Science Career Should You Pursue?
Do you have a strong desire to understand technical subjects?
Are you willing to learn advanced mathematics such as linear algebra and applied statistics?
Do you appreciate using statistics to tell stories?
Are you a self-starter who enjoys coming up with new tasks to work on on your own?
Do you appreciate programming and computer science?
Are You a Data Engineer?
Data engineers are very technical. They essentially organize and give structure to raw data in order for the data scientists and data analysts to execute their work. A good data engineer enjoys building data pipelines and likes software development. They have an advanced understanding of programming languages such as Java, SQL, or SAS. Therefore, you’ll be an ideal candidate for data engineering if:
- You enjoy highly technical roles.
- You like building and managing data infrastructures.
- You enjoy software development.
Women in Data Science
Now that we know the difference between these data science career roles, let us also see the role of women in data science career field when we ta;k about a career in data science.
According to a 2020 study by the Boston Consulting Group, only 15% of data scientists are women. That lack of diversity is a serious issue, the study says: "AI algorithms are susceptible to bias, so building them requires a team that includes a wide range of views and experiences."
Begin your data science career and analytics career today.
In an unpredictable world, data is more vital than ever.
Businesses will be searching for personnel with data science and analytical abilities to assist them maximise resources and make data-driven choices as they continue to evolve.
Whether you want to learn more about data science for the first time, get valuable analytics skills that can be used in a variety of businesses, or earn a degree, there's something for everyone.
Register here to prepare for the course you are interested for.
Building a Data Science Career: The Job Market for Data Scientists
Learn more about how to build a career in data science under this area.
The post of data scientist has become the trendiest job of the decade, with millions of job openings worldwide in Big Data.
Companies are leveraging data scientists' insights to stay one step ahead of their competition while keeping overhead expenses low in today's data-driven world.
Data scientists are often hired by companies such as Oracle, Apple, Microsoft, Booz Allen Hamilton, State Farm, Walmart, and others.
With these data science interview questions, you can prepare for your data science interview and pass it on the first go!
There are 218,250 Data Scientist jobs available worldwide, according to LinkedIn.
Data scientists are still one of the fastest-growing professions, coming in second among the top 50 positions in the United States for 2021.
Despite the fact that data science jobs aren't as popular as they were a few years ago, demand remains high and steady.
There will be a demand for Data Scientists as long as organisations rely on data to make educated decisions and design effective, can't-miss strategies.
Hence, a career in data science is very promising in the future.
Data Science Career FAQs
- What is a data scientist?
A data scientist is a professional who has a multidisciplinary skill set and works with large amounts of data to find insights and answers to business problems. Data scientists typically have a postgraduate degree in a technical subject such as computer science or statistics.
- Is data science a good career?
Data science career is an excellent career choice. According to the U.S. Bureau of Labor Statistics, data science is one of the fastest growing and highest-paid fields in the country.
- What kinds of jobs can you get with data science?
You can get a data science job in virtually any field. From retail to finance and banking, almost every industry needs the help of data science professionals to collect and process insights from their datasets.
- Is it hard to get a data science job?
Getting a data science job can be hard because the data science field is very new. Because of that, the field is constantly changing, so you need to stay on top of new skills.
- How do I start a career in data science with no experience?
There are a few ways you can start a career in data science if you have no experience. One way is to incrementally build fundamental data science skills and knowledge such as applied statistics, data modeling, data management and warehousing, and deep learning. Explore edX courses and programs that can help you get started.
