Building a Career in Data Science and Analytics
The Ultimate Guide:
A career in data science is lucrative and rewarding. But the trail to starting or advancing a knowledge science or analytics career isn't always linear. Unlike more traditional jobs, you don’t necessarily need a technical bachelor’s degree or a master’s degree to become a knowledge science professional. you merely need the proper skills and knowledge . In this guide, you’ll learn the ins and outs of knowledge science and analytics career pathways and skills. Plus, deduct recommendations on the way to decide which data science career is true for you.
Why Build a Career in Data Science or Analytics? 3 Top Benefits of a Data Science Career
Over the past decade, the supply of knowledge and demand for data science skills and data-driven deciding has skyrocketed. Pushed further into the spotlight by the drastic shift in business operations and consumer behavior caused by the COVID-19 pandemic, analytics and data science are now cemented as essential navigational tools across industries and functions.
1. Earn a High Salary
According to data from Robert Half, the median starting salary for data scientists is $95,000, almost double the U.S. median salary average. At about $70,000, even the average salary for data analysts, a more entry-level role, is considerably higher than the median salary in the U.S.
According to a study by Burtch Works, work experience is that the largest think about data science salaries. Mid-career data science professionals who have a minimum of seven years of experience can expect to earn a mean of $129,000. Highly experienced data scientists who hold managerial roles can earn upwards of over $250,000. However, education, company size, and sector also are important factors when determining data science salaries.
2. Solve Complex Problems
If you enjoy solving complex, real-world problems, you’ll never be bored as a knowledge science professional. The primary responsibility of your job is to find answers and insights by analyzing and processing vast amounts of raw data. A few samples of business problems that you’ll get to unravel are:
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.
3. Avoid Job Automation
Data science roles, particularly data analysts, are at very low-risk for automation for a few reasons:
The demand for data science roles is growing at an average rate of 50%.
Very few platforms can produce sophisticated analyses.
Data scientists are the ones that are doing most of the automating.
Growing Your Data Science Career: From Analyst to Data Scientist
There are two primary ways you can use data science skills to grow data-centric careers: become a data science professional—pursuing jobs like data analyst, database developer, or data scientist—or transition into an analytics-enabled role like a functional business analyst or a data-driven manager. Both career paths require foundational skills and knowledge in data analytics, programming, data management, data mining, and data visualization.
Despite the 2 tracks, the evolving nature of the relatively new field means career paths are flexible. Data science professionals like data analysts can lean into a knowledge science or information system developer role counting on where they deepen their expertise. By expanding knowledge in artificial intelligence, statistics, data management, and big data analytics, a data analyst can transition into a data scientist role. By building on existing technical skills in Python, relational databases, and machine learning, a knowledge analyst can become a knowledge system developer. Much of those skills are often learned either from work experience or independently through online data science courses. In this guide, we focus totally on the info science jobs track.
Data Scientists vs. Data Analysts: What’s the Difference?
The skills and job responsibilities of entry-level data science roles and data analysts often overlap. Both roles require statistical knowledge and the ability to program. However, there is a clear difference in the focus.
What Does a Data Scientist Do?
Data scientists answer questions on the business from the context of knowledge. They leverage data to make new product features and have a tendency to try to to more modeling and open-ended research. They’ll spend tons of your time cleaning data to form sure that it's usable for his or her 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.
Core Data Science Skills
Big data: big 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 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 liable for answering questions on data. Unlike data scientists, data analysts aren't concerned with using data to seek out trends or deciding the business’s future. Their job is to research 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.
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 is Right For You?
Deciding whether a career in data science is right for you is more than asking if you like working with data or not. It’s about asking yourself if you like working on complex, ambiguous problems and figuring out if you have the aptitude and patience to build your skillset. To determine if a data science career is right for you, ask yourself:
Are you committed to learning technical subjects?
Are you willing to learn applied statistics and other types of advanced mathematics like linear algebra?
Do you enjoy storytelling with data?
Are you a self-starter and willing to generate new projects to work on independently?
Do you enjoy computer science and programming?
If you said yes to at least three or more of the above questions, then you may have what it takes to succeed as a data science professional—but which type of role makes sense?
Are You a Data Analyst?
Data analysts are generalists, which suggests they get to figure in several teams and roles. They enjoy working on clearly defined, structured problems. They use data to extract and produce reports that are valuable to a business. Successful data analysts generally enjoy some level of complexity, but not the maximum amount as data scientists. Here’s how you'll tell if you're fit become a knowledge analyst:
You enjoy complexity.
You enjoy ambiguity.
You like delving into a single question.
You’re okay with not finding an answer to a problem.
Are You a Data Engineer?
Data engineers are very technical. They essentially organize and provides structure to data so as for the info scientists and data analysts to execute their work. A good data engineer enjoys building data pipelines and likes software development. They have a complicated understanding of programming languages like 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.
Start Building Your Data Science and Analytics Career
Data is more important than ever in a world full of uncertainty. As businesses continue to transform, they’ll be looking for employees with data science and analytical skills to help them optimize resources and make data-driven decisions. Whether you want to explore data science for the first time, gain valuable analytics skills that can be applied to careers in many industries, or earn a degree, there’s a path at Bygrad for you.