Certified Data Analytics: Beyond the Basics

Introduction

Data analytics refers to techniques used to analyze raw data and extract insights to support fact-based decision making. It encompasses a range of quantitative methods and processes such as data mining, statistical analysis, predictive modeling, and machine learning algorithms. Data analytics helps organizations in areas such as optimizing operations, identifying new business opportunities, improving customer experiences, and minimizing risks.

Data analytics certifications validate the knowledge and skills of professionals to perform various data analytics functions. They demonstrate proficiency in using cutting-edge analytics tools and techniques to derive value from data. Certifications are available at different levels for job roles such as data analyst, data engineer, data architect, and data scientist. Globally recognized certifications in data analytics include:

  • Cloudera Certified Associate Data Analyst
  • SAS Certified Data Scientist
  • IBM Certified Data Engineer
  • Microsoft Certified Azure Data Scientist Associate

Let’s explore these popular data analytics certifications in more detail.

Understanding Data Analytics Certifications

Why Get Certified in Data Analytics?

Here are some key reasons to consider getting certified in data analytics:

Reasons to Get CertifiedDetails
Validate your skillsCertifications prove your expertise in data analytics to employers. They demonstrate mastery of data tools, statistical methods, analytics techniques and industry best practices.
Enhance your credibilityCertified professionals are valued for their specialized data analytics skills. Certifications give credibility and set you apart from uncertified candidates.
Increase job opportunitiesData analytics certifications open up more career opportunities and help land lucrative job roles like data analyst, data engineer, data modeler, business intelligence developer etc. They are great for advancing your career.
Higher salariesCertified data analytics professionals earn higher salaries than those without certifications. On average, certified professionals earn 20-30% higher salaries.
Gain industry recognitionCertifications from leading tech vendors like Cloudera, SAS, IBM etc. are recognized globally and exhibit your commitment to the field.
Expand your skillsGaining certifications is a great way to build and expand your data analytics skills. Preparing for exams gives exposure to latest tools and techniques.

Top Data Analytics Certifications

Now let’s look at some of the most popular and in-demand data analytics certifications for IT professionals.

Cloudera Certified Associate Data Analyst

The Cloudera Certified Associate (CCA) Data Analyst certification validates skills in exploratory data analysis using Cloudera platforms. It demonstrates proficiency in analyzing large-scale datasets, constructing analytics workflows and building machine learning models with Cloudera tools like Hadoop, Impala, Hive, Spark etc.

SAS Certified Data Scientist

Offered by the SAS Institute, this certification is designed for professionals who can apply statistical modeling and predictive analytics techniques using SAS software. The exam tests skills in data mining, machine learning, deep learning and AI.

IBM Certified Data Engineer

IBM offers various data analytics certifications including the Certified Data Engineer credential. It validates skills in developing and maintaining data architecture solutions on the IBM platform using tools like SQL, Spark, Python etc.

Microsoft Certified Azure Data Scientist Associate

This certification from Microsoft assesses skills in applying data science and machine learning techniques on the Azure cloud platform. It involves using Azure services like data preparation, experimentation, modeling and deployment.

Choosing the Right Certification for You

When deciding which data analytics certification to pursue, consider the following factors:

Assess Your Skill Level

Determine your current skill level and experience in data analytics. This will help narrow down to certifications that align with your competencies and background. Beginner level certifications require basic data proficiency while expert certifications demand advanced skills.

Consider Job Requirements

Look at the data analytics skills and certifications sought after for your target job roles and choose accordingly. For data engineering roles, the IBM certification is very applicable whereas for data scientists, the SAS certification is recommended.

Research Exam Details

Carefully research the exam details – number of questions, duration, syllabus, sample papers etc. Assess your readiness to take the exam and preparation time required. Exams with more questions and technical syllabi require more thorough prep.

Compare Program Prices

Data analytics certifications vary in costs based on vendor and level. While beginner certifications range from $100-$250, advanced certifications can cost over $500. Compare prices and choose a certification that matches your budget.

Cloudera CCA Data Analyst Certification

Overview of Cloudera CCA Data Analyst Certification

The Cloudera Certified Associate (CCA) Data Analyst certification validates foundational skills in exploratory data analysis using Cloudera platforms. It requires clearing the CCA Data Analyst Exam (CCA159) which tests competency in:

  • Performing basic data analysis using Hive and Impala
  • Creating data subsets using HBase filters and Thrift Server
  • Using Spark SQL and Spark Dataframes for faster querying
  • Building machine learning models using Spark MLlib

Data professionals who earn the CCA credential exhibit proficiency in analyzing large-scale datasets, manipulating and transforming data, constructing analytics workflows and building models using the Cloudera Enterprise Data Hub.

Exam Details and Format

Here are key details about the Cloudera CCA Data Analyst certification exam:

  • 90 multiple choice and multiple response questions
  • 120 minute duration
  • Online proctored exam
  • Costs $295 per exam attempt
  • Offered in English, Japanese, Korean and Simplified Chinese
  • Results delivered immediately upon completion
  • Certification valid for 3 years

The CCA data analyst exam assesses skills across various objective domains like:

  • The basics of Hadoop, HDFS and MapReduce
  • Using Hive for data warehousing
  • Analytics with Impala and Spark SQL
  • Ingesting data using Sqoop and Flume
  • Machine Learning with Spark MLlib
  • Security aspects related to enterprise data hubs

Benefits of Cloudera CCA Certification

Professionals certified as Cloudera CCA Data Analysts can enjoy several career benefits including:

  • Validation of foundational big data and analytics skills
  • Opportunity to work on Cloudera enterprise data hub projects
  • Ability to advance to higher certifications like CCP Data Engineer
  • Gain credibility as Cloudera certified professionals
  • Improved job prospects and salary growth in data analytics roles
  • Deeper knowledge of Cloudera’s CDH and Hadoop ecosystem

Preparing for the CCA Data Analyst Exam

To clear the Cloudera CCA data analyst exam, thorough preparation is required:

  • Gain experience with CDH using trial editions to work on sample projects
  • Take instructor-led training in Cloudera essentials to build Hadoop expertise
  • Go through the exam syllabus and blueprint to understand domains tested
  • Read Cloudera documentation and reference architectures
  • Take the CCA practice exam to evaluate your current knowledge levels
  • Identify weak areas and spend more time strengthening concepts
  • Review Hive, Impala, Spark and HBase components in-depth
  • Solve sample questions to become familiar with exam format and patterns

With disciplined preparation over 2-3 months, data professionals can clear the Cloudera CCA data analyst exam successfully.

SAS Certified Data Scientist

Overview of SAS Data Scientist Certification

Offered by the SAS Institute, the SAS Certified Data Scientist credential validates proficiency in applying statistical modeling and predictive analytics techniques using SAS software. The certification exam covers:

  • Statistical concepts like hypothesis testing, regression, experimental design etc.
  • Machine learning methods like supervised and unsupervised learning
  • SAS programming for data extraction, manipulation, visualization and modeling
  • Predictive modeling using neural networks, decision trees, gradient boosting etc.
  • Model implementation, validation strategies and A/B testing

Data science professionals with the SAS certification possess deep expertise in advanced analytics, data mining, machine learning and AI. The certification is globally recognized and highly valued.

Exam Details and Format

Here are the key details about the SAS Certified Data Scientist exam:

  • 100 multiple choice questions
  • 130 minutes duration
  • Proctored online exam
  • Offered in English, Japanese, Korean, French, German, Spanish
  • Results in 2-5 days, certification valid for 3 years
  • $180 fee per exam attempt
  • Annual recertification by exam or completing 12 CE credits

The SAS data scientist certification exam covers the following major topic areas:

  • Data preprocessing and exploration techniques
  • Model building methods like regression, decision trees, random forests etc.
  • Unsupervised learning techniques including cluster analysis, dimensionality reduction etc.
  • Deep learning models like neural networks, natural language processing etc.
  • Model evaluation strategies and techniques
  • Experimental design and A/B testing

Benefits of SAS Data Scientist Certification

Here are some of the top advantages of obtaining the SAS data scientist certification:

  • Confirms mastery of statistical modeling and machine learning using SAS
  • Enhances credibility and employability for data science roles
  • Opportunity to work on advanced analytics and AI projects
  • Exposure to latest data science best practices and techniques
  • Chance to take higher certifications like SAS Certified AI Professional
  • Recognized globally by IT industry professionals and employers
  • Applicable across domains in manufacturing, finance, healthcare etc.

Preparing for the SAS Data Scientist Exam

To successfully clear the SAS data scientist certification exam, candidates should undertake thorough preparation:

  • Complete the recommended SAS training for statistical analysis and machine learning
  • Gain at least 6-12 months of hands-on experience with SAS programming
  • Work on SAS enterprise mining projects to apply predictive modeling skills
  • Review the exam syllabus and blueprint to understand coverage areas
  • Study SAS documentation and training materials in-depth
  • Take the practice exam to gauge exam readiness
  • Focus more on advanced statistical and ML algorithms
  • Join the SAS global certification community to connect with experts
  • Allocate sufficient time for revision of concepts and techniques

With extensive preparation over 3-4 months, data professionals can achieve the coveted SAS data scientist certification.

IBM Certified Data Engineer

Overview of IBM Certified Data Engineer

Offered by IBM, the Certified Data Engineer credential validates skills in developing and maintaining data architecture solutions on the IBM platform. Key responsibilities include:

  • Designing data infrastructure blueprints and pipeline workflows
  • Building scalable data architecture using SQL and NoSQL databases
  • Developing, testing, deploying and maintaining data pipelines
  • Creating monitoring and support tools for data engineering tasks
  • Performing root cause analysis on critical data issues

Data engineers with this certification exhibit expertise in data engineering tools like SQL, Spark, Hadoop, Python etc. They demonstrate strong technical skills in architecture design, pipeline development and production deployment.

Exam Details and Format

Here are the key details about the IBM Certified Data Engineer certification exam:

  • 60 multiple choice questions
  • 90 minute duration
  • Online proctored exam in English
  • Results delivered instantly upon completion
  • $200 fee per exam attempt
  • Certification valid for 2 years
  • Offered as a standalone credential or as part of concentration exams

The domains covered in the exam include:

  • Data engineering concepts and methodologies
  • Planning and designing data architecture solutions and pipelines
  • Developing data pipelines and integration flows
  • Testing, deploying and maintaining data pipelines
  • Security, availability, resilience factors in data engineering
  • Using tools like SQL, Spark, Python, pandas, scikit-learn etc.

Benefits of IBM Data Engineer Certification

Data professionals certified as IBM Data Engineers can avail the following key advantages:

  • Validates skills in planning, designing and building enterprise data pipelines
  • Opportunity to work on complex data projects on IBM Cloud and Watson platforms
  • Global recognition and trust as a certified IBM data professional
  • Adoption of data engineering best practices recommended by IBM
  • Networking and growth opportunities within the IBM partner ecosystem
  • Scope to upgrade skills further through IBM badges and mastery courses
  • Lucrative career progression prospects in data engineering roles

Preparing for the IBM Data Engineer Exam

Here are some tips to prepare for the IBM Certified Data Engineer certification exam:

  • Complete IBM data engineering courses to learn in-depth concepts
  • Get familiar with data architecture designs and hands-on labs
  • Practice creating data models, pipelines using IBM tooling
  • Work on data projects to apply skills in a practical environment
  • Review exam syllabus and mock tests to understand coverage areas
  • Identify knowledge gaps and improve on weak domains
  • Study IBM design guides and documentation for data engineers
  • Join IBM Developer community forums to clarify doubts
  • Allocate 2-3 months preparation time before attempting exam
  • Revise technical concepts, definitions thoroughly pre-exam

With extensive hands-on practice and mock tests, data professionals can aim to clear the exam in the first try.

Microsoft Certified Azure Data Scientist Associate

Overview of Microsoft Azure Data Scientist Certification

The Microsoft Certified Azure Data Scientist Associate certification is designed for professionals using the Azure cloud platform to build and deploy data science solutions. Key responsibilities include:

  • Identifying data science problems and requirements
  • Preparing data for model building using Azure services
  • Applying data science and machine learning techniques on Azure
  • Developing and optimizing data models on Azure
  • Deploying and maintaining data solutions on Azure cloud

Data scientists with this certification possess the ability to apply data science workflows end-to-end on Microsoft Azure – from data acquisition to model deployment.

Exam Details and Format

Here are the essential details about the Microsoft Azure Data Scientist certification exam:

  • Multiple choice questions
  • 2 hours duration
  • Proctored online exam in English
  • Cost of $165 per exam attempt
  • Results in 1-2 working days
  • Certification valid for 2 years
  • Tests skills on SQL, Python, Azure ML, experimentation
  • Associate level, leads to Expert certification

The domains covered in the exam include:

  • Setting up the data science environment on Azure
  • Data exploration and preprocessing techniques on Azure
  • Building models using regression, classification, deep learning etc.
  • Training, optimizing and evaluating machine learning models on Azure
  • Operationalizing data science solutions on Azure
  • Consuming data science models in applications
  • Maintaining, monitoring and retraining models

Benefits of Microsoft Azure Data Scientist Certification

The Microsoft Azure Data Scientist certification offers several benefits:

  • Validates skills in data science workflows on Microsoft Azure
  • Opportunity to work on Azure enterprise data science projects
  • Global recognition from Microsoft and partners
  • Hands-on expertise in Azure Machine Learning, SQL, HDInsight etc.
  • Career growth prospects in data science roles involving Azure
  • Scope to advance to higher certifications like AI Engineer Associate
  • Exposure to real-world data challenges and their solutions on cloud

Preparing for the Azure Data Scientist Exam

To successfully pass the Azure data scientist exam, use these preparation tips:

  • Hands-on experience with data science projects on Azure for 6-12 months
  • Completing Microsoft Azure training sessions and workshops
  • Working through Azure architecture guides and documents
  • Practicing end-to-end data workflows using Azure ML and data services
  • Solving sample questions and mock tests
  • Identifying knowledge gaps and strengthening weak areas
  • Joining the Microsoft certification community to connect with experts
  • Allocating sufficient time for studying ML algorithms and APIs
  • Revising Azure data science concepts & services before exam

With rigorous practice for 2-3 months, data science professionals can aim to pass the exam in the first attempt.

Career Paths for Certified Data Analytics Professionals

Let’s look at some top career opportunities and growth paths for certified data analytics professionals:

Data Analyst

Data analysts turn data into insights. They analyze patterns in data sets to derive actionable intelligence for organizations. Data certifications can help analysts specialize in industry domains.

Data Engineer

Data engineers design, build and manage data pipelines. They develop architectures to collect, store, process and serve data to meet business needs. Certifications help build expertise on data engineering tools and platforms.

Data Scientist

Data scientists solve complex quantitative problems using ML and AI. They apply advanced analytics to extract insights and make predictions. Certifications validate specialized data science skills mastered.

Data Architecture

Data architects develop blueprints for enterprise data management systems. They plan robust and scalable data platforms to meet business goals. Certifications exhibit deep expertise in data architectural patterns and practices.

Conclusion

Data analytics certifications help professionals stand out in today’s highly competitive job market. They enable you to gain specialized skills as data analysts, scientists, engineers and architects. Certifications also empower you to work efficiently on analytics platforms like SAS, Azure, IBM and Cloudera. This article presented an extensive guide to trending data analytics certifications, their exam details, benefits, preparation tips and career prospects. Earning reputed certifications will accelerate your career growth in the thriving field of data analytics.