Data Analytics Certifications

Guide to Data Analytics

IABAC provides Data Analytics Certification programs that follow the innovative EDISON® initiative by the European Commission. These certifications aim to establish a standardized and comprehensive body of knowledge in Analytics and Data Science. By enrolling in these programs, you not only invest in your professional growth but also stay up-to-date with the latest industry trends.

Our courses leading to the Data Analytics Certification cover both fundamental concepts and advanced techniques. Aligned with the EDISON® initiative, these programs ensure that you possess the most relevant knowledge in the dynamic field of data analytics. Stay ahead in your career by becoming a skilled professional who can confidently navigate the complexities of modern analytics.

Throughout the certification journey, you will delve into crucial Analytics and Data Science aspects. Each module, ranging from fundamental principles to advanced methodologies, is meticulously designed to provide you with a comprehensive understanding of the field. By the end of the program, you will be well-prepared to tackle real-world challenges in analytics and data science, making you a valuable asset in this competitive job market.

IABAC's commitment to excellence is evident in the certification process aligned with the EDISON® initiative. These programs not only validate your expertise but also demonstrate your dedication to staying updated in a field where innovation is constantly occurring. Start on this educational journey with us, and let IABAC's Data Analytics Certification pave the way for you to be at the forefront of industry knowledge and advancements.

image

Not sure about your success rate?

Data Analytics Certifications

Certified Data Analyst (CDA)

The demand for skilled data analytics experts is growing like never before.

Data Analytics Foundation (DAF)

The IABAC Data Analytics Foundation certification is a valuable validation of essential skills in the dynamic field of data analytics.

Data Analytics for Managers (DAFM)

Get ready to boost your managerial skills by achieving the Data Analytics for Managers Certification.

Certified HR Analytics Professional (CHRAP)

Are you ready to start your journey towards earning the prestigious Certified HR Analytics Professional Certification? This DETAILS

Certified Finance Analytics Professional (CFINAP)

Become a finance analytics expert with our Certified Finance Analytics Professional Certificate, you can take your expertise in finance analytics to the next level.

Certified Healthcare Analytics Professional (CHCAP)

The importance of data analytics cannot be overstated.

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.

Specialization

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.

Get Training from Authorized Training Provider (ATP)

What is Data Analytics?

Data Analytics is the systematic process of collecting, organizing, and analyzing large amounts of data to extract valuable insights. It involves using statistical techniques, algorithms, and tools to transform raw data into actionable information. This includes data collection from various sources, organizing and cleaning the data, exploring and visualizing the data, applying statistical techniques to uncover patterns, and communicating the findings to stakeholders. Data analytics is used in various industries for informed decision-making and gaining a competitive advantage.

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?

Data analytics involves extracting valuable insights from large datasets to inform decision-making across industries. It spans areas such as business intelligence, healthcare, and finance, playing a pivotal role in optimizing processes and fostering innovation through applications like predictive analytics and machine learning. Its dynamic scope evolves with technological advancements and the increasing importance of data-driven decision-making.

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 analyze 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.