Introduction
Data analytics and machine learning are two of the hottest fields in technology today. Both involve making sense of large datasets, uncovering patterns and insights, and using those learnings to guide decision making. However, while the two areas have some overlap, they are fundamentally different disciplines with distinct purposes, processes, and practitioner skillsets. This article will explore the key differences between certified data analytics and machine learning, when to use each approach, and how to know which career path is right for you.
What is Data Analytics?
Data analytics refers to the science of examining raw data to draw conclusions about information. It involves applying an analytical process and statistical models to derive meaningful patterns and trends. The core goal is to extract actionable intelligence from data sets, which can drive strategic business decisions across an organization.
Some key aspects of data analytics include:
- Collecting and combining data from multiple sources
- Organizing data in standard formats and structures
- Performing calculations and statistical modeling to analyze trends
- Summarizing findings through reports, visualizations and dashboards
- Identifying opportunities for improvement and change based on insights
There are four main types of data analytics:
| Type | Description |
|---|---|
| Descriptive analytics | Using historical data to understand and describe what has happened. |
| Diagnostic analytics | Looking at data to understand why something happened. |
| Predictive analytics | Using statistical models and forecasting to understand what could happen in the future. |
| Prescriptive analytics | Recommending one or more courses of action based on data insights. |
While definitions vary, data analysts typically focus on descriptive and diagnostic work, while data scientists frequently apply predictive and prescriptive techniques as well.
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) focused on building algorithms that can learn from data and improve their accuracy over time without being explicitly programmed. The key difference from traditional data analytics is that machine learning emphasizes algorithms that iteratively learn from data to make decisions and predictions, rather than following static program instructions.
Some key aspects of machine learning include:
- Developing self-improving models and algorithms
- Training models on sample data sets
- Automating complex processes to refine accuracy over time
- Enabling computers to solve problems independently
- Creating more personalized, predictive user experiences
Machine learning powers many aspects of modern technology, including:
- Image recognition
- Predictive text and autocorrect
- Fraud detection
- Product recommendations
- Automated customer service
While data analytics focuses on gaining insights from historical data, machine learning concentrates on building algorithms that continuously refine themselves as they process new data. The two fields complement each other, with data analytics identifying patterns that can inform machine learning models.
Key Differences Between the Fields
Though data analytics and machine learning are connected, there are several key differences between these disciplines:
| Data Analytics | Machine Learning | |
|---|---|---|
| Goals | Extract insights from data | Create self-improving algorithms |
| Methods | Study past data | Train predictive models on past data |
| Outputs | Reports, visualizations | Trained algorithms and models |
| Tools | SQL, spreadsheets, data visualization | Python, TensorFlow, PyTorch |
| Practitioners | Data analysts | Machine learning engineers |
| Problems Solved | Identify insights | Automate complex tasks |
| Leveraging Insights | Findings inform ML models | Outputs analyzed through analytics |
In short, data analytics focuses on deriving insights from data, while machine learning teaches computers to draw insights and decisions independently based on data patterns. The two approaches are highly complementary.
When Should You Use Data Analytics vs Machine Learning?
There are certain business situations where data analytics or machine learning will be more applicable:
Use Data Analytics To:
- Understand sales trends and segments
- Analyze website traffic data
- Optimize marketing performance
- Identify high value customers
- Uncover bottlenecks in operations
- Guide decision making with data-driven insights
Apply Machine Learning For:
- Personalized product recommendations
- Predictive text and autocorrect
- Fraud monitoring and risk assessment
- Automated customer service chatbots
- Self-driving vehicles
- Optical character recognition
- Automated language translation
In general, data analytics is the right approach when you need human analysis and interpretation to guide strategy. Use machine learning when the solution requires predicting future outcomes or automating complex tasks that change over time. Many organizations leverage both techniques to maximize the value of data.
Should You Pursue Data Analytics or Machine Learning?
Should you focus your education and career path on data analytics or machine learning? Here are a few key considerations:
| Considerations | Data Analytics | Machine Learning |
|---|---|---|
| Skills | Critical thinking, statistics, SQL, Excel | Python, R, math, physics, algorithms |
| Education | STEM bachelor’s degree | Master’s, PhD in AI and computer science |
| Salary | Avg $72k/year | Avg $114k/year |
| Demand | 25% growth forecast | 40% growth forecast |
| Interests | Solving problems with data | Coding, teaching machines intelligence |
Analyzing your specific skills and interests can help determine which field is the better focus, or whether learning both disciplines is advantageous for your career goals. Many organizations need professionals proficient in data analytics and machine learning.
Key Certifications for Data Analytics Careers
There are many data analytics certification programs that can prepare you for roles in this high-demand field. Here are some of the most popular certifications:
- SAS Certified Data Scientist: This globally recognized certification covers data manipulation, business analytics, machine learning and statistical analysis using SAS software.
- IBM Data Science Professional Certificate: Earned through an intensive online course, this certificate develops core data science skills with Python, databases, data visualization and machine learning.
- Microsoft Certified: Azure Data Scientist Associate: This certification demonstrates expertise in leveraging Microsoft Azure cloud services for advanced analytics.
- Google Analytics Individual Qualification: This certification proves competency in using Google Analytics for online data collection, reporting, and analysis.
- Tableau Desktop Certified Associate: Offered by top business intelligence and analytics firm Tableau, this introductory certification establishes proficiency in data visualization.
- Cloudera Certified Associate Data Analyst: Validates skills in Querying datasets with SQL and Hive, transforming data, and creating data visualizations.
- Oracle Data Science Certification: Demonstrates expertise in Oracle’s advanced analytics capabilities, including Oracle Database for data science.
These certifications all incorporate elements of data mining, statistical modeling, data cleansing, and analytics tools. They can help data professionals stand out in the competitive job market while advancing their careers. The certified skills align with core data analytics competencies that employers are seeking to support data-driven decision making.
Certified Data Analyst vs. Data Scientist Roles
Data analyst and data scientist are related but distinct roles that both leverage data analytics skills. Here are some of the key differences:
Data Analyst
- Focuses on descriptive and diagnostic analytics
- Heavy use of business intelligence and visualization tools
- Statistical modeling and analysis
- Data gathering, cleaning and processing
- Answering specific business questions with data
Data Scientist
- Applies predictive and prescriptive analytics
- Develops machine learning algorithms
- Data mining experience
- Statistical modeling and programming
- Identifying trends and building models to forecast outcomes
Data analysts are more likely to derive insights from past data, while data scientists use data to predict future trends. Analysts visualize and present findings to stakeholders, while scientists build the automated models. Both roles require analytics skills, but data scientists have more advanced statistical, math and coding abilities. Those with a combined skillset have the most career opportunities.
Should You Get Certified in Data Analytics?
Pursuing data analytics certifications can benefit your career, but also has some drawbacks to consider:
Benefits:
- Validates your skills and shows commitment to the field
- Distinguishes your resume from other candidates
- Enhances credibility and earning potential
- Keeps skills sharp and shows ongoing learning
Drawbacks:
- Requires time and money to earn certifications
- Must renew certifications regularly
- Less emphasis on practical experience application
- Credentials alone don’t guarantee jobs or advancement
Ultimately, certifications are worthwhile for aspiring and current data professionals seeking to fast-track their career growth. Combining credentials with hands-on experience and a degree maximizes opportunities. It depends on your career stage, current skill level, and professional goals.
Tips for Choosing the Right Data Analytics Certification
With so many options, how do you select the certification(s) that are right for you? Here are some helpful tips:
- Assess your skills: Know your strengths to choose programs that best align with your competencies.
- Research exam details: Understand the exam structure and content to pick one you can pass.
- Learn software used: Prioritize certs for tools you actively work with.
- Target job role needs: Seek out credentials valued for the positions you want.
- Consider provider reputation: Established certification bodies tend to be more recognized by employers.
- Compare time and cost: Factor in the investment required across promising certifications.
- Start with foundational certs: Entry-level ones establish core knowledge to build upon.
- Add stackable certifications: Earning certs in succession demonstrates expanded expertise.
By considering these factors, you can determine which credentials will offer the best return on investment for your data analytics career aspirations.
Preparing for Data Analytics Certification Exams
Once you’ve selected the right certification, you’ll need to prepare for the exam:
- Review the syllabus: Know the exam objectives you’ll be tested on. Create a study plan that covers each required domain.
- Take practice tests: Many exam providers offer practice tests to diagnose weaknesses and give you hands-on experience with question formats.
- Study recommended materials: Certification bodies often publish lists of suggested textbooks, online courses, tutorials and other study resources.
- Learn exam strategies: Understand how questions will be structured and scoring processes to maximize performance.
- Join online study groups: Connect with people preparing for the same exam to share tips and knowledge.
- Attend exam prep courses: Take a bootcamp or prep course if you need help covering the material efficiently.
- Get familiar with the testing software: Exams often use specialized testing interfaces you should practice using beforehand.
- Time the exam strategically: Schedule your exam after thoroughly preparing all content, but also before knowledge starts fading.
With diligent preparation using these tactics, you can pass your chosen analytics certification exam confidently.
Day-to-Day Work of a Certified Data Analyst
What does an average day look like for a certified data analyst? Here are some of the most common activities and tasks:
- Retrieve data from sources like databases, CRM systems, web analytics, etc.
- Inspect, clean, and transform data to prepare it for analysis
- Import data into analytics platforms
- Analyze data trends using statistical techniques
- Develop compelling data visualizations and reports
- Present insights from data analysis to stakeholders
- Monitor key business metrics and analyze changes over time
- Create data models to undercover relationships and patterns
- Identify data errors, inconsistencies and areas for improvement
- Document processes and findings to share with others
- Stay up-to-date with latest analytics methods and tools
Data analysts work closely with teams across IT, operations, finance and marketing to drive better decision making through data analytics. They balance technical data tasks with strategic analytical thinking. Continuing education and refinement of data skills are vital to excel in this rapidly evolving field.
Career Paths for Certified Data Analysts
What are some possible career paths and progression opportunities for certified data analysts?
Entry-Level:
- Data Analyst
- Business Intelligence Analyst
- Operations Analyst
- Marketing Analyst
Mid-Career:
- Senior Data Analyst
- Analytics Manager
- Data Analytics Consultant
- Product Analyst
Advanced:
- Director of Analytics
- Data Analytics Architect
- Chief Data Officer
- Head of Business Intelligence
Management:
- VP of Analytics
- Chief Analytics Officer
- Analytics Director
- Data Governance Program Manager
There are abundant opportunities to take on leadership roles and advance to senior positions as your experience grows. Some data analysts transition into specialized disciplines like marketing analytics, financial analysis or software engineering. Overall strong growth in digital data ensures high demand for certified professionals.
Salary and Job Outlook for Certified Data Analysts
What is the earning potential and job growth forecast for certified data analytics professionals?
The average salary for data analysts is $72,000 per year, but rises significantly with certifications and experience:
- SAS Certified Data Scientists earn $113,00 annually on average.
- IBM Certified Data Analysts make approximately $89,000 per year.
- Microsoft Certified Data Analysts receive an average salary of $120,000.
Higher pay is prevalent in major technology hubs and certain industries:
- Finance data analysts earn $94,000 typically.
- IT data analysts average around $80,000 in annual salary.
- Media and entertainment data analysts make approximately $75,000.
According to the Bureau of Labor Statistics, employment for data analysts will grow 25% from 2020 to 2030, adding over 90,000 new jobs. Strong demand and competitive compensation await certified data analytics professionals.
Alternatives to Data Analytics Certification
While certifications can boost data analytics careers, there are some alternative credentialing options as well:
- Relevant Bachelor’s Degree: Programs in information systems, computer science and statistics develop core skills.
- Data Analytics Bootcamps: Intensive workshops like those from General Assembly or DataCamp can impart beginner to intermediate skills.
- Massive Open Online Courses (MOOCs): Free online learning like courses from Coursera build foundational analytics abilities.
- Self-Study: Using textbooks, online resources and practicing analytical tools can teach skills without formal programs.
- On-the-Job Training: Some employers provide analytics training and mentoring for employees moving into data roles.
A combination of learning paths often produces the strongest credentials. Degree programs lay theoretical and mathematical groundwork while certifications, bootcamps and on-the-job training offer practical applications. There are many routes to launching or advancing a data analytics career.
Conclusion
Data analytics and machine learning are two prominent, yet distinct fields driving competitive advantage for organizations today. While machine learning focuses on developing algorithms that iteratively learn over time, data analytics concentrates on revealing insights from existing data to guide strategic decisions.
Leading data analytics certifications from SAS, IBM, Microsoft and others can jumpstart and advance your analytics career by validating your skills. Combining credentials with hands-on experience, related degree programs, and effective interviewing skills can unlock lucrative opportunities. With demand for data professionals projected to grow exponentially in coming years, it is an ideal time to invest in a data analytics career education.