Data science, analytics, and machine learning are growing at an astronomical rate and companies are now looking for professionals who can sift through the goldmine of data and help them drive swift business decisions efficiently. IBM predicts that by 2020, the number of jobs for all U.S. data professionals will increase by 364,000 openings to 2,720,000. We caught up with Eric Taylor, Senior Data Scientist at CircleUp, in a Simplilearn Fireside Chat to find out what makes data science, data analytics, and machine learning such an exciting field and what skills will help professionals gain a strong foothold in this fast-growing domain.
Watch the complete Fireside Chat recording to find out everything new and exciting about data science, data analytics, and machine learning.
What is Data Science?
People have tried to define data science for over a decade now, and the best way to answer the question is via a Venn diagram. Created by Hugh Conway in 2010, this Venn diagram consists of three circles: math and statistics, subject expertise (knowledge about the domain to abstract and calculate), and hacking skills. Essentially if you can do all three, you are already highly knowledgeable in the field of data science.
Data science is a concept used to tackle big data and includes data cleansing, preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They understand data from a business point of view and can provide accurate predictions and insights that can be used to power critical business decisions.
Venn Diagram
Source: Drew Conway
Skills Required to Become a Data Scientist
Anyone interested in building a strong career in this domain should gain critical skills in three departments: analytics, programming, and domain knowledge. Going one level deeper, the following skills will help you carve out a niche as a data scientist:
- Strong knowledge of Python, SAS, R, Scala
- Hands-on experience in SQL database coding
- Ability to work with unstructured data from various sources like video and social media
- Understand multiple analytical functions
- Knowledge of machine learning
Our Data Analyst Master's Program will help you learn analytics tools and techniques to become a Data Analyst expert! It's the pefect course for you to jumpstart your career. Enroll now!
What is a Data Analytics?
A data analyst is usually the person who can do basic descriptive statistics, visualize data, and communicate data points for conclusions. They must have a basic understanding of statistics, a perfect sense of databases, the ability to create new views, and the perception to visualize the data. Data analytics can be referred to as the necessary level of data science.
Also Read: How to Become a Data Analyst in 2022?
Skills Required to Become a Data Analyst
A data analyst should be able to take a specific question or topic, discuss what the data looks like, and represent that data to relevant stakeholders in the company. If you’re looking to step into the role of a data analyst, you must gain these four key skills:
- Knowledge of mathematical statistics
- Fluent understanding of R and Python
- Data wrangling
- Understand PIG/ HIVE
Data Science vs. Data Analytics
Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. A data scientist creates questions, while a data analyst finds answers to the existing set of questions.
What is Machine Learning?
Machine learning can be defined as the practice of using algorithms to extract data, learn from it, and then forecast future trends for that topic. Traditional machine learning software is statistical analysis and predictive analysis that is used to spot patterns and catch hidden insights based on perceived data.
A good example of machine learning implementation is Facebook. Facebook’s machine learning algorithms gather behavioral information for every user on the social platform. Based on one’s past behavior, the algorithm predicts interests and recommends articles and notifications on the news feed. Similarly, when Amazon recommends products, or when Netflix recommends movies based on past behaviors, machine learning is at work.
Skills Required to Become a Machine Learning Engineer
Machine learning is just a different perspective on statistics. The following are critical skills that can help you jumpstart your career in this fast-growing domain:
- Expertise in computer fundamentals
- In-depth knowledge of programming skills
- Knowledge of probability and statistics
- Data modeling and evaluation skills
Build your career in Data Analytics with our Data Analyst Master's Program! Cover core topics and important concepts to help you get started the right way!
Data Science vs. Machine Learning
Because data science is a broad term for multiple disciplines, machine learning fits within data science. Machine learning uses various techniques, such as regression and supervised clustering. On the other hand, the data’ in data science may or may not evolve from a machine or a mechanical process. The main difference between the two is that data science as a broader term not only focuses on algorithms and statistics but also takes care of the entire data processing methodology.
Source: Quora
Data science can be seen as the incorporation of multiple parental disciplines, including data analytics, software engineering, data engineering, machine learning, predictive analytics, data analytics, and more. It includes retrieval, collection, ingestion, and transformation of large amounts of data, collectively known as big data. Data science is responsible for bringing structure to big data, searching for compelling patterns, and advising decision-makers to bring in the changes effectively to suit the business needs. Data analytics and machine learning are two of the many tools and processes that data science uses.
Data science, Data Analytics, and Machine Learning are some of the most in-demand domains in the industry right now. A combination of the right skill sets and real-world experience can help you secure a strong career in these trending domains.
Enroll in Our PGP in Data Analytics, Data Science, AI and Machine Learning Today
If you’re ready to embark on your journey as a Data Scientist, Data Analyst, AI and Machine Learning Engineer, the first step is enrolling in an accredited learning program that can prepare you with a University certification from Purdue. Co-developed with IBM, our PG Program in Data Science, PG Program in Data Analytics, and AI and ML certfication courses teach students everything they need to become skilled professionals. Also, check our Caltech Data science Bootcamp.
Students in these courses learn all of the tools and techniques that are needed to succeed as a Data Scientist, Data Analyst, and Machine Learning Engineer including SQL databases, and essential programming languages, such as Python and R. Enrollment includes lifetime access to self-paced learning, the opportunity to work on more than 15 real-world projects, $1,200 worth of IBM cloud credits, and so much more.
Upon completion, students receive industry-recognized university certificates from both Simplilearn and Purdue, which can help put them one step ahead of the competition. Get started by enrolling today!
FAQs
1. Is data science or data analytics a better degree?
Both are great career options and depend on the learner on what they would like to do. Data analytics is a better career choice for people who want to start their career in analytics. Data science is a better career choice for those who want to create advanced machine learning models and algorithms.
2. Can a data analyst become a data scientist?
Yes, data analysts can definitely become data scientists by upskilling themselves. They would need to work on developing strong programming, mathematical and analytical skills.
3. What are the common skills used by data analysts and data scientists?
Skills required by data analytics include strong knowledge of Python, SAS, R, and Scala; hands-on experience in SQL database coding; ability to work with unstructured data from various sources like video and social media; understanding multiple analytical functions, and knowledge of machine learning.
In addition to the skills mentioned above, data scientists also require skills such as knowledge of mathematical statistics, fluent understanding of R and Python, data wrangling, and understanding of PIG/ HIVE.
4. What is the salary difference between a data scientist and a data analyst?
According to Glassdoor, a data analyst's salary averages around US$70,000 annually, while a data scientist's salary averages around US$100,000 annually.
5. Are Machine Learning and Data Science the same?
No, Data science focuses on serving information and insights from data, while machine learning is dedicated to building methods that utilize data to improve performance or inform predictions.
6. Which is better, Machine Learning or Data Science?
Each field is good for different types of people. People who are interested in understanding data and deriving data insights from it can choose data science, while people who prefer creating models that improve performance using the data can opt for machine learning.
7. Is Data Science required for Machine Learning?
Data Scientists must understand Machine Learning for quality predictions and estimations. Basic levels of machine learning are definitely a standard requirement for data scientists.
8. Who earns more, Data Scientist or Machine Learning Engineer?
According to PayScale, the average yearly salary of a Data Scientist in the US is $96,106. A machine learning engineer can draw an average salary of US$121,446 annually.
9. What is the Future of Data Science?
With the entry of automated data analytics platforms, data science jobs are bound to change and improve, with data scientists focusing on more complex problems while simpler problems will be solved by data science tools.
