Companies worldwide have always gathered and analyzed data about their customers to provide better service and improve their bottom lines. In today’s digital world, we are able to gather tremendous amounts of data, which require non-traditional data processing methods and software.
What is a Data Scientist?
A data scientist is a professional who specializes in analyzing and interpreting data. They use their data science skills to help organizations make better decisions and improve their operations. Data scientists typically have a strong background in mathematics, statistics, and computer science. They use this knowledge to analyze large data sets and find trends or patterns. Additionally, data scientists may develop new ways to collect and store data.
Qualifications and Eligibility Required
To become a data scientist, you will need to have strong analytical and mathematical skills. You should be able to understand and work with complex data sets. Additionally, you should be able to use statistical software packages and be familiar with programming languages such as Python or R. Data scientists also typically have a certification from an accredited program.
Prerequisites or Pre-experience
Becoming a data scientist generally requires a very strong background in mathematics and computer science, as well as experience working with large amounts of data. In addition, it is often helpful to have experience with machine learning and statistical modeling.
While there is no one specific path to becoming a data scientist, here are some helpful prerequisites or experiences that can improve your chances of success:
- A strong background in mathematics and computer science: As a data scientist, you will be working with large amounts of data on a daily basis. Therefore, it is essential that you have a strong foundation in mathematics and computer science. In particular, you should be comfortable with statistical methods and algorithms.
- Experience working with large amounts of data: Data scientists must be able to effectively manipulate and analyze large data sets. Therefore, it is important to have some experience working with large data sets before becoming a data scientist.
- Experience with machine learning and statistical modeling: Machine learning and statistical modeling are powerful tools that data scientists use to make predictions from data. Therefore, experience with these techniques is essential for anyone interested in becoming a data scientist.
- Strong communication and visualization skills: Data scientists must be able to effectively communicate their findings to others. Therefore, strong communication and visualization skills are essential for anyone interested in becoming a data scientist.
- A willingness to learn: The field of data science is constantly evolving, which means that data scientists must be willing to continuously learn new methods and techniques. Therefore, a willingness to learn is essential for anyone interested in becoming a data scientist. One of the best ways to learn how to become a data scientist or brush up on your current skills is to enroll in a top data science education program such as the Data Science Bootcamp.
Read More: Switching to data science was one of the best decisions Ekta Saraogi took for her career. After a varied career in the IT field, our Data Scientist Master's Program offered her the variety she craved with a more stable environment for her career. Read all about Saraogi’s career from IT nomad to Data Science master in her Simplilearn Data Science Course Review.
How to Become a Data Scientist?
Data science is the area of study that involves extracting knowledge from all of the data gathered. There is a great demand for professionals who can turn data analysis into a competitive advantage for their organizations. In a career as a data scientist, you’ll create data-driven business solutions and analytics.
Step 1: Earn a Bachelor’s Degree
A great way to get started in Data Science is to get a bachelor’s degree in a relevant field such as data science, statistics, or computer science. It is one of the most common criteria companies look at for hiring data scientists.
Step 2: Learn Relevant Programming Languages
While a Bachelor’s degree might give you a theoretical understanding of the subject, it is essential to brush up on relevant programming languages such as Python, R, SQL, and SAS. These are essential languages when it comes to working with large datasets.
Step 3: Learn Related Skills
In addition to different languages, a Data Scientist should also have knowledge of working with a few tools for Data Visualization, Machine Learning, and Big Data. When working with big datasets, it is crucial to know how to handle large datasets and clean, sort, and analyze them.
Step 4: Earn Certifications
Tool and skill-specific certifications are a great way to show your knowledge and expertise about your skills. Here are a few great certifications to help you pave the path:
These two are the most popular tools used by Data Scientist experts and would be a perfect addition to start your career journey.
Step 5: Internships
Internships are a great way to get your foot in the door to companies hiring data scientists. Seek jobs that include keywords such as data analyst, business intelligence analyst, statistician, or data engineer. Internships are also a great way to learn hands-on what exactly the job with entail.
Step 6: Data Science Entry-Level Jobs
Once your internship period is over, you can either join in the same company (if they are hiring), or you can start looking for entry-level positions for data scientists, data analysts, data engineers. From there you can gain experience and work up the ladder as you expand your knowledge and skills.
Looking forward to becoming a Data Scientist? Check out the Data Science Certification Program and get certified today.
Data Science at Work
Did you know that media services provider Netflix uses data science extensively? The company measures user engagement and retention, including:
- When you pause, rewind or fast-forward
- What day of the week and what time of day you watch content
- When and why you leave content
- Where in the world you’re watching from
- Your browsing and scrolling behavior
- What device you watch on
Netflix has over 120 million users worldwide! To process all of that information, Netflix uses advanced data science metrics. This allows it to present a better movie and show recommendations to its users and also create better shows for them. The Netflix hit series House of Cards was developed using data science and big data. Netflix collected user data from the show, West Wing, another drama taking place in the White House. The company took into consideration where people stopped when they fast-forwarded and where they stopped watching the show. Analyzing this data allowed Netflix to create what it believed was a perfectly engrossing show.
Now let us explore some of the important data scientist skills that an individual should possess.
7 Skills to Become A Data Scientist
To become a data scientist, you’ll need to master skills in the following areas:
- Skill 1: Gain database knowledge which is required to store and analyze data using tools such as Oracle® Database, MySQL®, Microsoft® SQL Server and Teradata®.
- Skill 2: Learn statistics, probability and mathematical analysis. Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Probability is the measure of the likelihood that an event will occur.
Mathematical analysis is the branch of mathematics dealing with limits and related theories, such as differentiation, integration, measure, infinite series, and analytic functions. - Skill 3: Master at least one programming language. Programming tools such as R, Python, and SAS are very important when performing analytics in data.
R is a free software environment for statistical computing and graphics, which supports most Machine Learning algorithms for Data Analytics such as regression, association, and clustering.
Python is an open-source general-purpose programming language. Python libraries like NumPy and SciPy are used in Data Science.
SAS can mine, alter, manage and retrieve data from a variety of sources as well as perform statistical analysis on the data. - Skill 4: Learn Data Wrangling which involves cleaning, manipulating, and organizing data. Popular tools for data wrangling include R, Python, Flume, and Scoop.
- Skill 5: Master the concepts of Machine Learning. Providing systems with the ability to automatically learn and improve from experience without being explicitly programmed to. Machine Learning can be achieved through various algorithms such as Regressions, Naive Bayes, SVM, K Means Clustering, KNN, and Decision Tree algorithms to name a few.
- Skill 6: Having a working knowledge of Big Data tools such as Apache Spark, Hadoop, Talend, and Tableau, which are used to deal with large and complex data which can’t be dealt with using traditional data processing software.
- Skill 7: Develop the ability to visualize results. Data visualization integrating different data sets and creating a visual display of the results using diagrams, chart, and graphs
Careers in Data Science
Once you’ve mastered these skills, you’ll have a range of career opportunities available. Prepare for a job interview with our data science interview questions.
Data Scientist
Average salary: $120,931
Data scientists create data-driven business solutions and analytics by driving optimization and improvement of product development. They use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and more. Data scientists also coordinate with different functional teams to implement models and monitor outcomes.
Data Engineer
Average salary: $137,776
Data engineers assemble large, complex data sets. They identify, design, and implement internal process improvements and then build the infrastructure required for optimal data extraction, transformation, and loading. They also build analytics tools that utilize the data pipeline.
Data Architect
Average salary: $112,764
Data architects analyze the structural requirements for new software and applications and develop database solutions. They install and configure information systems and migrate data from legacy systems to new ones.
Data Analyst
Average salary: $65,470
Data analysts acquires data from primary or secondary sources and maintain databases. They interpret that data, analyze results using statistical techniques, and develop data collections systems and other solutions that help management prioritize business and information needs.
Business Analyst
Average salary: $70,170
Business analysts assist a company with planning and monitoring by eliciting and organizing requirements. They validate resource requirements and develop cost-estimate models by creating informative, actionable and repeatable reporting.
Data Administrator
Average salary: $54,364
Data administrators assist in database design and update existing databases. They are responsible for setting up and testing new database and data handling systems, sustaining the security and integrity of databases and creating complex query definitions that allow data to be extracted.
FAQs
1. What skills do data scientists need?
In order to be successful in their role, data scientists need to have strong problem-solving skills. They must be able to think critically and identify patterns in data sets. Additionally, they need to be proficient in programming languages and statistical software in order to manipulate data.
2. What are some common tasks that data scientists perform?
Some common tasks that data scientists perform include cleaning and organizing data sets, running statistical analyses, and creating data visualizations. Additionally, they may also be responsible for building predictive models and conducting research.
3. What are the career prospects for data scientists?
The demand for data scientists is growing rapidly, and the career prospects are very good. Data scientists with the necessary skills and experience can find jobs in a variety of industries, including healthcare, finance, retail, and manufacturing.
4. What are some common challenges that data scientists face?
Some common challenges that data scientists face include dealing with big data sets, working with complex algorithms, and finding ways to visualize data. Additionally, they may also need to communicate their findings to non-technical audiences.
Preparing for a Career in Data Science
Simplilearn’s Artificial Intelligence and Data Science course is an integrated program in AI and data science that includes the following intense courses to prepare you for an exciting career in data science:
- Data Science with Python
- Machine Learning
- Deep Learning
- Tableau
- Computer Vision
Mastering the field of data science begins with understanding and working with the core technology frameworks used for analyzing big data. You’ll learn the developmental and programming frameworks Hadoop and Spark used to process massive amounts of data in a distributed computing environment, and develop expertise in complex data science algorithms and their implementation using R, the preferred language for statistical processing. The insights you will glean from the data are presented as consumable reports using data visualization platforms such as Tableau.
Once you have mastered data management and predictive analytic techniques, you will gain exposure to state-of-the-art machine learning technologies. This expansive data science learning path will help you excel across the entire spectrum of big data and data science technologies and techniques.
Simplilearn’s Data Science course is exhaustive, and earning a certificate is proof that you have taken a big leap in mastering the domain. The knowledge and skills you’ll gain working on projects and simulations and examining case studies will set you ahead of the competition.
