How to Become a Data Scientist in 2022
Working with data necessitates a curious attitude, which is why IT experts have added science.
Data Scientists investigate issues and propose data-driven solutions. Surprisingly, Data Scientists can uncover the answer even if a robot does not learn the data. How? They identify patterns by using their judgment.
Do you have the qualities required of a Data Scientist? Or do you wish to work in this industry because of the high demand and benefits? Be clear about this. Your goals will be the same, but the speed will be different.
Second, before becoming a Data Scientist, you must develop a broad analytical thinking style to launch your Data Science profession, which entails tackling complicated issues. You must be able to formulate problems and systematically solve them.
What is Data Science?
Data science is a new, multidisciplinary discipline that combines big data, data analytics, statistics, and informatics with computer science and technology. Because it is utilized in many fields (from medical and banking to retail and scientific research), its definition must be comprehensive. Consequently, the subtleties of a data scientist’s function are found in the quirks of each position.
Ultimately, regardless of the specifics, one thing unites all data scientists. They all uncover patterns in extensive data, derive insights from them, and employ them to pose strategic ‘big picture’ questions that propel their enterprises. Where things get muddy is defining exactly how a data scientist discovers these patterns and establishing what business growth looks like in their given setting. The responses here vary substantially based on the industry and function.
What Are the Similarities and Differences Between Data Science and Data Analytics?
Data analytics, which encompasses gathering, cleaning, storing, and analyzing massive data, is a critical ability for all data scientists. But on the other hand, data scientists require more than just data analytics abilities.
They will also be specialists in a particular business subject, such as medicines or software programming, and will be required to perform jobs that data analysts would not.
These tasks could include developing complex machine learning algorithms from scratch, deploying and managing massive data warehouses, constructing deep learning infrastructures, and communicating the results of their work to various stakeholders ranging from C-suite executives to product teams.
Although the phrase “data scientist” is famous right now, the fact is that the position is young and continually changing. As new technologies ease data scientists’ most time-consuming jobs, new skills and expectations are flooding in at an alarming rate.
Given that data science is all about generating predictions, the lack of predictability regarding the field’s future is, ironically, what drives many people to it in the first place. In summary, a career in data science will not be dull.
What Is the Typical Educational Background of a Data Scientist?
Data scientists do not come from a single background and do not have a single professional growth route open to them. Nonetheless, individuals will continue to try to categorize data science. This is because, in the past, highly skilled professions typically followed the same path: attend college, earn a degree in a particular subject, and then enter the workforce.
However, this approach is becoming increasingly obsolete in our fast-changing, high-tech world. Data science is an example of a new type of position in the twenty-first century. Rather than following a predetermined career path, this view sees data science as a spectrum of abilities you may direct in whatever way you like and grow into a profession you directly influence in defining.
Data Science at Work
Did you know that the media company Netflix heavily uses data science? The firm uses the following metrics to assess user engagement and retention:
- When you press the pause, rewind, or fast-forward button.
- When and on what day of the week do you watch content?
- When and why do you delete content?
- From where are you watching?
- Your scrolling and browsing habits.
- What gadget do you use to watch TV?
Netflix has more than 120 million subscribers globally! Netflix employs robust data science metrics to help it provide better movie and television program suggestions to its consumers. For example, the Netflix smash House of Cards was created with data science and big data.
Netflix obtained user data from West Wing, another program set in the White House. The business considered where consumers stopped fast-forwarding and where they stopped viewing the show. By analyzing this data, Netflix could construct what it thought was a flawlessly compelling show.
7 Skills To Become A Data Scientist
A data scientist must be proficient in the following areas:
Skill-1: Learn how to store and analyze data using tools including Oracle Database, MySQL, Microsoft SQL Server, and Teradata.
Skill-2: Learn statistics, probability, and quantitative analysis as a second skill. Statistics studies and develops techniques for gathering, analyzing, interpreting, and presenting empirical data. Probability is a measure of the possibility of an event occurring.
Mathematical analysis studies limits and associated ideas such as grading, integration, measurement, exponentials, and analytic functions.
Skill-3: Mastery of at least one programming language. Programming tools such as R, Python, and SAS are essential when executing data analytics.
R is a free computing language and graphics environment that supports most Machine Learning methods for Data Analytics, such as regression, association, and clustering.
Python is an open-source programming language. Data Science uses Python packages such as NumPy and SciPy. SAS is capable of mining, altering, managing, and retrieving data from several sources and analyzing the data.
Skill-4: Data Wrangling is a skill that involves cleaning, modifying, and organizing data. R, Python, Flume, and Scoop are popular data wrangling tools.
Skill-5: Understand Machine Learning principles. Giving systems the capacity to learn and develop independently without being expressly designed. For example, regressions, Naive Bayes, SVM, K Means Clustering, KNN, and Decision Tree algorithms, to mention a few, can be used to achieve Machine Learning.
Skill-6: Working understanding of Big Data technologies such as Apache Spark, Hadoop, Talend, and Tableau, which deal with massive and complicated data sets that standard data processing applications cannot handle.
Skill-7: Develop your capacity to visualize outcomes. Data visualization combines several data sets and provides a visual representation of the findings using diagrams, charts, and graphs.
Careers in Data Science
Once you’ve acquired these talents, you’ll have various job options.
Data Scientist
Average Income: $120,931
Data scientists provide business solutions and analytics by driving product development optimization and enhancement. They employ predictive modeling to improve and enhance customer experiences, revenue generation, ad targeting, and other functions. Data scientists collaborate with other functional teams to implement models and track results.
Data Engineer
Average Income: $137,776
Data engineers put together massive, complicated data sets. Then, they investigate, create, and execute internal process changes before constructing the infrastructure for effective data extraction, transformation, and loading. They also provide analytics tools that make use of the data flow.
Data Architect
Average Income: $112,764
Data architects design database solutions by analyzing the structural needs for new software and applications. They set up and configure information systems and move data from old to new systems.
Data Analyst
Average Income: $65,470
Data analysts collect information from primary and secondary sources and keep databases up to date. Analyzing the data, using statistical tools to test the results, and developing data gathering systems and other solutions can help management prioritize business and information needs.
Business Analyst
Average Income: $70,170
Business analysts help a firm plan and manage by gathering and arranging needs. They validate resource requirements and establish cost-estimation models by producing valuable, actionable, and repeatable reports.
Data Administrator
Average Income: $54,364
Data administrators help to develop databases and maintain existing ones. In addition, they are in charge of installing and testing new database and data handling technologies, maintaining database security and integrity, and developing complicated query definitions that allow data extraction.
How to Become a Data Scientist?
So far, we’ve examined what data scientists do, how much they earn, and what background they often have. So how do you then become a data scientist? Assuming you’re new to data, here’s our step-by-step roadmap to becoming a true data scientist.
Step-1: Become a qualified data analyst
You’ll need to master the essential skills necessary to work in data science, whether you’re a software developer or a mathematician. But, again, we’ll assume this is your first time dabbling in data. In this situation, you should have a bachelor’s degree (but it doesn’t have to be in an area connected to data science).
It’s recommended that you enroll in a data science boot camp or another accredited school to gain the necessary skills and obtain an industry-recognized certification. Python, SQL, statistics, data cleansing, and exploratory data analysis will likely be among the skills you’ll study.
Step-2: Select an entry-level path
If you have an educational background in IT or the computer sciences, you might wish to study data science via one of these routes. If not, it could be an excellent time to look for entry-level data analytics careers. Of course, we don’t encourage taking jobs, but if your ultimate goal is to work in data science, consider looking at everything as an opportunity.
It’s advised to remain open-minded regarding the sector or profession you want to work in. Finance, healthcare, information technology, and government are common sectors requiring data analysts. Don’t be concerned if your first job isn’t your ideal. Every position is a stepping stone. Make sure to learn everything you can about the industries and specialties you’re most interested in.
Step-3: Get a degree
While a degree is not required to secure an entry-level data job, you will struggle to advance in data science without one. This might be an undergraduate degree in data science, a Master’s degree, or a Ph.D. if you wish to work as a researcher.
After a year or so of working in data analytics, you can develop an interest in IT systems. Perhaps data science in the healthcare sector will pique your interest. Whatever your interests are, utilize what you’ve learned so far to seek a specialization that builds on your current abilities and points you toward your data science ambitions. Your degree might be in a discipline such as math, statistics, or computer science, or a domain such as accounting, finance, or business management.
Step-4: Learn as many unique skills as you can
Do all you can to learn new talents wherever you are on your trip. For example, to be a data scientist, you should experiment with machine learning frameworks like PyTorch or TensorFlow. Perhaps, despite your expertise in arithmetic, you’re becoming increasingly interested in software systems.
If this is the case, seize every opportunity to work with IT or hone your coding skills with programming languages such as Python, JavaScript, and R. Even if you acquire a new ability that isn’t for you, you’ve gained knowledge. Even powers you dislike will seem reasonable on your CV.
Step-5: Make a list of your favorite companies/industries
Make a mental or physical list of businesses you’d want to work with as your career grows. Alternatively, include specific companies that interest you, such as banking, or areas of data science that interest you, such as data engineering.
After you’ve created your “dream list,” look for businesses or firms ahead of the curve in the subject that interests you the most. You’ll need to start gaining expertise in these areas right now. Keep a watch on firm job postings, send out speculative applications, and network with other data science specialists to locate new contacts.
Step-6: Make yourself irreplaceable
Data scientists are in high demand; however, not all are excellent data scientists! Therefore, make oneself indispensable to stand out from the pack.
The more specialist talents you acquire, the more you may tailor your function and make excellent recommendations or ideas. All of this will make you more indispensable. The more critical you are to a company, the more they will require your abilities and the more money you will eventually be able to make.
Conclusion
That’s all there is to it! As this chapter emphasizes, data science is more of an interdisciplinary discipline with the ability to be molded into any number of viable vocations.
As long as you have — or are willing to gain — the essential prerequisite skills (such as a degree, a data analytics certification, and expertise in your company domain), you may carve out a niche and a successful career in practically any industry. The route to data science is not predetermined. While this means it’s not always easy, the benefits of working in this field more than compensate.
