Data Analytics Certifications

Guide to Data Analytics

If you're looking to pursue a data analytics career and enhance your data analyst skills, consider enrolling in IABAC's comprehensive data analyst course. Renowned in the industry, IABAC's certification programs offer a strategic approach to the world of data analysis and business intelligence. Aligned with the innovative EDISON® - European Commission initiative, these programs aim to standardize the Analytics and Data Science Body of Knowledge, ensuring you stay up-to-date with industry developments.

IABAC's data analyst course caters to individuals at various stages of their data analytics journey. Whether you're a curious beginner seeking to understand data visualization and statistical analysis or a seasoned professional aiming for advanced roles and deeper insights through machine learning and predictive modeling, our certification scheme accommodates all levels of expertise. The globally recognized certifications provided by IABAC open doors to an exciting future in the field of data science.

To maintain the integrity of our data science certification programs, we have a meticulous pre-examination process that verifies candidate identity proofs and prerequisites. The online exams, which are integral to the certification, are either directly overseen by IABAC or conducted by our trusted partners worldwide.

By enrolling in IABAC's data analyst course, you will gain the necessary skills to succeed in the industry. Our programs incorporate cutting-edge data analytics tools, ensuring you acquire the ability to analyze big data effectively and proficiently. Elevate your career potential by enrolling in our trusted certification programs today. Discover how to translate complex data into actionable business intelligence and step into the future with IABAC.


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Data Analytics Certifications

Certified Data Analyst (CDA)

In the rapidly expanding world of big data, the demand for professionals skilled in data analytics is skyrocketing.

Data Analytics Foundation (DAF)

IABAC Data Analytics Foundation certification is provide essentials skills validation in the field of data anlalytics.

Data Analytics for Managers (DAFM)

Elevate your managerial prowess with the Data Analytics for Managers certification, a testament to your indispensable skills in data analytics.

Certified HR Analytics Professional (CHRAP)

In today's data-driven business landscape, organizations are realizing the crucial role that analytics plays in driving strategic decisions, and human resources (HR) is no exception.

Certified Finance Analytics Professional (CFINAP)

Become a finance analytics expert with our Certified Finance Analytics Professional Certification.

Certified Healthcare Analytics Professional (CHCAP)

With healthcare data analytics becoming increasingly essential in driving informed decision-making, the CHAP certification equips you with the expertise to excel in this field.

The Benefits

International Credential

IABAC is a widely recognized credentialing framework based on European commission funded EDISON Data Science body of knowledge. This credential provides distinction as high potential certified Data Science Professionals enabling better career prospects.

Global Opportunities

IABAC certification provides global recognition of the relevant skills, thereby opening opportunities across the world.


IABAC Certification designed to cater to the job requirements of all experience levels and specializations, which suits roles aligned with the industry standards.

Relevant and updated

IABAC CPD (Continuing Professional Development) program enables credential holders to update their skills and stay relevant to the industry requirements.

Higher Salaries

On an average, a certified professional earns 30-40% more than their non-certified as per recent study by Forbes.

Summits & Webinars

In addition, IABAC members will have exclusive access to seminars and Data Science summits organised by IABAC partners across the globe.

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What is Data Analytics?

Data Analytics is the practice of examining, analysing, and interpreting large datasets to uncover meaningful patterns, insights, and trends. It involves collecting and cleaning data, applying statistical methods and machine learning algorithms, and visualizing the results to facilitate data-driven decision-making. By leveraging various tools, techniques, and technologies, Data Analytics enables organizations to extract actionable insights from their data, leading to improved efficiency, effectiveness, and strategic decision-making.

What are the Tools used for Data Analytics?

Data Analytics utilizes a range of tools to extract insights from data. Some commonly used tools in the field include programming languages like Python and R, which provide extensive libraries for data manipulation, analysis, and visualization. SQL (Structured Query Language) is essential for querying and managing databases. Data visualization tools like Tableau, Power BI, and matplotlib help in presenting data visually. Additionally, tools like Hadoop and Apache Spark are used for processing and analyzing large-scale datasets. Proficiency in these tools, along with a solid understanding of statistical concepts, is crucial for effective Data Analytics.

What is the Scope of Data Analytics?

The Scope of Data Analytics is vast and continues to expand with the riven decision-making. Organizations across industries, including finance, healthcare, e-commerce, marketing, and manufacturing, are actively investing in Data Analytics to gain valuable insights and stay competitive. This demand for data-driven professionals is expected to continue growing in the future, presenting abundant career opportunities. As technologies like artificial intelligence and machine learning advance, the scope of Data Analytics will likely expand further, offering new avenues for innovation and impact.

What are the Roles and Responsibilities for Data Analytics?

Roles and responsibilities in Data Analytics can vary depending on the organization and the specific job title. However, common responsibilities include data collection, cleaning, and preprocessing, performing statistical analysis and data modeling, creating data visualizations and reports, and interpreting and presenting insights to stakeholders. Data Analysts are also involved in identifying trends, patterns, and correlations in data, developing predictive models, and providing recommendations based on the analysis. They work closely with cross-functional teams to drive data-informed strategies and contribute to organizational goals.

What are the Applications used for Data Analytics?

Data Analytics finds applications in various domains. It can be used in marketing to analyse customer behaviour, optimize campaigns, and identify target audiences. In finance, it helps in fraud detection, risk analysis, and portfolio management. In healthcare, data analytics aids in disease prediction, patient outcomes analysis, and personalized medicine. Other applications include supply chain optimization, social media analysis, cyber security, and more. Essentially, Data Analytics enables organizations to extract actionable insights from large volumes of data, leading to improved decision-making and better business outcomes.