Data Science Council of America (DASCA) – Senior Data Analyst

Course Description

The Senior Data Analyst (SDA™) certification by the Data Science Council of America (DASCA) is an internationally recognized credential tailored for experienced data professionals looking to validate and accelerate their career in data analytics, business intelligence, and data-driven decision-making.

The certification emphasizes practical mastery of advanced data analysis techniques, modern analytics tools, data visualization, and real-world business intelligence applications. It also assesses candidates on their understanding of the entire analytics lifecycle, including data preparation, exploratory analysis, statistical modeling, and data-driven storytelling.

Ideal for mid-career analysts, data managers, and business professionals transitioning into analytics leadership roles, the SDA™ proves your ability to solve complex business problems using data, across diverse industries and platforms.

Certification Overview

Credential: Senior Data Analyst (SDA™)
Issued by: Data Science Council of America (DASCA)
Level: Intermediate to Advanced
Mode: Online proctored exam
Exam Duration: 100 minutes
Question Type: Multiple choice (75 questions)
Prerequisites:

• 2–5 years of experience in data analysis, business intelligence, or related fields

• A bachelor’s degree (STEM or non-STEM with analytics exposure)

• Familiarity with Python, R, SQL, or equivalent tools preferred

Who This Certification is For

• Data Analysts and BI professionals looking to move into senior or strategic roles

• Business professionals and domain experts transitioning to analytics

• Mid-career professionals seeking global recognition of their data expertise

• Anyone aiming to formalize their skill set with a vendor-neutral, globally respected credential

What You’ll Learn / Be Assessed On

1. Data Science and Analytics Foundation

• Core concepts of data science and analytics lifecycle

• Key differences between descriptive, predictive, and prescriptive analytics

2. Data Handling and Wrangling

• Data collection, cleaning, integration, and transformation

• Working with structured and unstructured data

3. Exploratory Data Analysis (EDA)

• Using statistical tools and visual techniques to explore data

• Identifying patterns, trends, and anomalies

4. Statistical and Predictive Modeling

• Hypothesis testing, regression, classification, and clustering

• Basic machine learning model awareness (e.g., decision trees, KNN)

5. Tools and Technologies

• Overview of Python, R, SQL, and Excel for analytics

• Introduction to BI tools like Tableau or Power BI

6. Data Visualization and Storytelling

• Creating meaningful dashboards and charts

• Communicating insights effectively to stakeholders

7. Business Applications of Analytics

• Case studies in marketing, operations, HR, and finance

• ROI analysis, customer behavior, process improvement

8. Ethical and Legal Considerations

• Data privacy, governance, and responsible AI use

Skills Validated

• Advanced proficiency in data analytics and visualization

• Capability to handle real-world datasets and business cases

• Knowledge of industry tools and analytics workflows

• Analytical problem-solving, communication, and leadership potential

Course Outcomes

• Recognition as a certified Senior Data Analyst (SDA™) by DASCA

• Career advancement into senior analyst, analytics lead, or data manager roles

• Strong foundation for further learning in data science or machine learning

• Enhanced credibility with a vendor-neutral global certification