What is a data scientist?
Data scientists, in general, work with large amounts of data and use their machine-learning skills and knowledge to help organizations build the right and appropriate solutions as well as decisions.
These data professionals have technical abilities and expertise in delivering useful solutions across multiple industries, such as healthcare and finance.
In organizations, data scientists not only always speak technical stuff to tech-savvy professionals but are also good at communicating with non-technical team members.
Simply put, these professionals ensure that every team member understands their on-paper idea or in-hand project. To keep up with these things, they definitely need to learn new and latest changes in the industry.
Why should you become a data scientist?
If you're considering becoming a data scientist, you must first take some time to understand this career path. Data scientists, for example, are mathematicians, computer scientists, and business strategists all at once.
Having these complex skill sets means that data scientists need to be both technically proficient and business savvy. This is why becoming a data scientist is a great career choice, as this expertise is in high demand.
Now, back to the topic, a data scientist is a professional who focuses primarily on finding deep knowledge through data analysis and inference, and to succeed in this field, you should possess both statistical knowledge and computer skills to solve complex problems.
In this field, you will learn to use mathematical and algorithmic techniques to solve the most difficult and analytically complex business problems.
In addition, you will gain an understanding of the methods for analyzing raw data to uncover hidden insights.
At its core, data science is about building strong decision capabilities through precise and minutia-driven analysis.
As a data scientist, you should present your findings to your manager, colleagues and even clients who may or may need help understanding complex statistical terminology.
For this, you must also possess excellent verbal, written, and visual communication skills.
As a data scientist, you can enjoy the following benefits:
- There are many job opportunities in different industries for you to choose from.
- The salary and benefits you receive will be competitive.
- It will give you a sense of accomplishment and high job satisfaction.
- In your organization, you will contribute to important research and development projects.
How to become a data scientist?
A data scientist is an analytical professional who uses various skills and programs to collect and “clean” large databases to benefit a company’s growth and needs. These pros perform data collection, processing, and analysis on different data sets and present the results clearly and visually.
Also, data scientists help the company find valuable data sets and automate collection processes. They use data to predict future trends and risks and suggest improving the company’s performance.
If you want to become a data scientist, you can follow the steps that most data scientists have taken:
- Have a bachelor’s degree in a related field, such as computer science or statistics, as most companies require this for a data scientist role.
- Have 2-4 years of experience in related fields, which is the typical requirement for data scientist jobs.
- Start your career as a research assistant, data analyst, or intern, as these are some of the common job titles before becoming a data scientist.
- Have soft skills such as logical thinking, math skills, and detail-oriented, as hiring managers look for these in a data scientist.
- Need to undergo 6-12 months of job training to become a data scientist, as this is the average duration of training.
- Earn the data scientist certification like the Associate - Data Science Version 2.0 to boost your earnings potential.
What do data scientists do?
As we learned above, a data scientist is a certified or experienced technical professional who uses information from various sources to discover hidden patterns and insights that can give the company an edge over its competitors.
These first-line professionals also apply their skills and knowledge to different domains, such as manufacturing, healthcare, education, and finance.
As a data scientist, you will have the following responsibilities:
- Manage and update regional CRM database and records for customers, vendors, and suppliers.
- Set up and run JobScope ERP system for a design and manufacturing environment that produces custom or standard products.
- Led the analysis in SAS for combining mortality data from different sources using meta-analysis methods.
- Implement a method to fit a regularized logistic regression model in Scala using proximal stochastic gradient descent with a line search.
- Test the performance of a linear regression model of data using sci-kit-learn and cross-validation.
- Develop Python-based statistical visualization to show insights from fuzzy social media data.
- Perform data profiling and analysis, develop indicators/metrics, conduct trend analysis, and evaluate advanced analytics tools.
- Act as a key internal adviser on statistical modeling, machine learning, data validation, data visualization, and business intelligence processes.
- Implement clinical reporting programs used by clinical and data management teams to help with data visualization and reporting.
- Clean data using numpy and pandas.
- Work within the company’s MDM team.
- Assist students in learning chemistry, biology, and math.
- Have experience in maintaining the cluster on AWS EMR.
- Support stack for GPUs, used for TensorFlow models.
- Work on EMR to analyze data in S3 buckets.
Important technical and non-technical skills needed to become a data scientist
Python: Develop categorization models in Python to detect customer loss and metrics to identify customers before discontinuation for better retention.
Data Science: Provide consultation services in Data Science and statistical analysis, database development and design, and machine learning (classification, regression, etc.).
Visualization: Implement clinical reporting programs utilized by clinical and data management teams to aid in data visualization and reporting.
Java: Enhance computational efficiency of the 1-bucket-theta algorithm in Java by eliminating unnecessary input data in the filter.
Hadoop: Analyze Wikipedia traffic volume spikes using R and Hadoop to discover interesting correlations with news events.
Tableau: Design visually rich and intuitively interactive Tableau workbooks and dashboards for executive decision-making.
Other soft skills
Logical thinking: Logical thinking is a crucial soft skill for data scientists. This skill is essential because computer algorithms rely on logic to function effectively.
Math skills: Another vital soft skill for data scientists is math proficiency. This skill is highly valued because computer and information research scientists must have advanced knowledge of mathematics and other technical topics that are critical in computing.
Detail-oriented: Data scientists are also known for their attention to detail, which is critical to their duties. This skill is essential because computer and information research scientists must pay close attention to their work, as a small programming error can cause an entire project to fail.
Analytical skills: Analytical skills are often required for data scientist responsibilities. This skill is necessary because computer and information research scientists must be organized in their thinking and analyze the results of their research to formulate conclusions.
Communication skills: Communication skills are commonly found in job descriptions and are essential to what data scientists do. Data scientist responsibilities rely on this skill because computer and information research scientists must communicate well with programmers and managers and be able to clearly explain their conclusions to people who have a technical background.
What is the average salary of a data scientist?
Data scientists earn an average salary of $106,104 in the United States per year. The typical salary range for this profession is between $75,000 and $148,000 annually. On an hourly basis, data scientists earn an average of $51.01 per hour.
What are the best data scientist certifications?
Currently, the data scientist job is one of the most in-demand jobs in the IT industry. Businesses are looking for data professionals to help them understand the data they collect.
In order to break into this lucrative field or to set yourself apart from the crowd, you will need to earn the best data scientist certification.
Here are the most valuable data scientist certifications you can consider earning in 2023: ### 1. Certified Analytics Professional (CAP) The Certified Analytics Professional (CAP) certification is a vendor-neutral credential. The CAP certification demonstrates your skills and knowledge in using data to transform complex data into fruitful insights and actions,” which is the core of what data scientists do.
These professionals can analyze data, draw logical conclusions and communicate their findings and implications to the relevant clients. To take the CAP or the associate level aCAP exams, you need to apply and meet some specific criteria based on your education and experience.
For the Certified Analytics Professional (CAP) certification exam, you need at least three years of related work experience if you have a master’s degree in a related field, five years of related work experience if you have a bachelor’s degree in a related field, or seven years of related work experience if you have any degree that is not related to analytics.
For the CPA exam, you need a master’s degree and less than three years of related work experience in data or analytics.
2. Open Certified Data Scientist (Open CDS)
The Open Group Professional Certification Program for the Data Scientist Professional (Open CDS) certification is based on your experience and does not require formal training courses or exams. You’ll begin at level one as a Certified Data Scientist.
You can advance to level two, where you’ll become a Master Certified Data Scientist, and finally, you can reach the third level to become a Distinguished Certified Data Scientist. To earn the Open Certified Data Scientist (Open CDS) certification, you need to follow a three-step process, including applying for the certification, filling out the experience application form, and going through a board review.
3. IBM Data Science Professional Certificate
The IBM Data Science Professional Certificate is a series of nine online courses on data science topics, such as open source tools, data science methodology, Python, Databases and SQL, data analysis, data visualization, machine learning, and a final applied data science capstone.
The IBM Data Science Professional Certificate is offered through Coursera, and you can set your own pace and schedule. The IBM Data Science Professional Certificate takes about three months on average to finish the courses, but you can take more or less time as you wish.
The course also includes practical projects that help you build a portfolio demonstrating your data science skills to potential employers.
4. Cloudera Data Platform Generalist Certification
Cloudera has replaced its Cloudera Certified Professional (CCP) and Cloudera Certified Associate (CCA) certifications with the new Cloudera Data Platform (CDP) Generalist certification, which evaluates your skills with the platform.
The new exam covers general topics of the platform and is relevant for multiple roles, such as administrator, developer, data analyst, data engineer, data scientist, and system architect. The Cloudera Data Platform Generalist Certification exam has 60 questions, and you have 90 minutes to answer them.
5. Microsoft Certified: Azure AI Fundamentals
Microsoft’s Azure AI Fundamentals certification demonstrates your understanding of machine learning and artificial intelligence concepts and their application to Microsoft Azure services.
The Microsoft Certified: Azure AI Fundamentals certification is a fundamentals exam, so you don’t need much experience to pass it. This certification exam is a good starting point if you are new to AI or AI on Azure and want to show your skills and knowledge to employers.
6. Microsoft Certified: Azure Data Scientist Associate
The Azure Data Scientist Associate certification from Microsoft tests your ability to use machine learning to create and run machine learning workloads on Azure. You must know how to design and implement ML, AI solutions, NLP, computer vision, and predictive analytics.
You will also need to be proficient in deploying and managing resources, managing identities and governance, implementing and managing storage, and setting up and managing virtual networks.
7. Data Science Council of America (DASCA) Senior Data Scientist (SDS)
The Data Science Council of America (DASCA) Senior Data Scientist (SDS) certification program is designed for professionals with at least five years of experience in research and analytics. To take this certification exam, candidates must have skills in databases, spreadsheets, statistical analytics, SPSS/SAS, R, quantitative methods, and the basics of object-oriented programming and RDBMS.
The program has five tracks that suit different candidates —each track has different requirements for degree level, work experience, and prerequisites to apply. You’ll need a minimum of a bachelor’s degree and more than five years of experience in data science to qualify for each track. In contrast, some tracks require a master’s degree or previous certifications.
8. Data Science Council of America (DASCA) Principal Data Scientist (PDS)
If you have 10 or more years of experience in big data, you can apply for the Principal Data Scientist (PDS) certification from the Data Science Council of America (DASCA). The Data Science Council of America (DASCA) Principal Data Scientist (PDS) certification has three tracks for different data science roles.
The exam tests your knowledge of fundamental and advanced data science topics such as big data best practices, data-driven business strategies, organizational support for data, machine learning, natural language processing, scholastic modeling, and more.
The Data Science Council of America (DASCA) Principal Data Scientist (PDS) exam is ideal for experienced and successful data science leaders and practitioners.
9. SAS Certified AI and Machine Learning Professional
If you want to show proficiency in using open-source tools to extract insights from data with AI and analytics, you can earn the AI and Machine Learning Professional certification from SAS.
This certification requires you to pass several exams that test your knowledge of machine learning, natural language processing, computer vision, and model forecasting and optimization.
You’ll have to complete the SAS Certified Specialist exams in Machine Learning, Forecasting and Optimization, and Natural Language Processing and Computer Vision to obtain the professional AI and Machine Learning credential.
10. SAS Certified Advanced Analytics Professional Using SAS 9
The SAS Certified Advanced Analytics Professional Using SAS 9 credential shows your ability to analyze big data using various statistical analysis and predictive modeling techniques.
You’ll need skills in machine learning and predictive modeling techniques, including how to apply them to big, distributed, and in-memory data sets. You should also have skills in pattern detection, experimentation in business optimization techniques, and time-series forecasting.
This certification requires passing three exams: Predictive Modeling Using SAS Enterprise Miner 7, 13, or 14; SAS Advanced Predictive Modeling; and SAS Text Analytics, Time Series, Experimentation, and Optimization.
11. SAS Certified Data Scientist
To become a SAS Certified Data Scientist, you need to master two other data certifications from SAS. These certifications teach you how to program, manage, and improve data, transform, access, and manipulate data, and use popular data visualization tools.
After completing both the Big Data Professional and Advance Analytics Professional certifications, which require 18 courses and five exams, you can earn your SAS Certified Data Scientist designation.
The final say
Data is the backbone of modern business strategies. We create and consume huge amounts of data in various forms and formats daily. A recent report says that we generate more than 2.5 quintillion bytes of data every day, and this number will only grow.
That means every second, each person produces about 1.7MB of data. This shows how much data science is needed to make sense of this massive and messy data.
Data science helps us to transform complex, unstructured data into clear and useful data by using highly skilled professionals called data scientists.
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