What will you learn in Data Analyst Certification Course
Master core data analysis concepts: statistics, hypothesis testing, and EDA
Gain proficiency in SQL for querying relational databases
Analyze and visualize data using Python (Pandas, Matplotlib) and Excel
Build interactive dashboards with Power BI and Tableau
Apply basic machine learning techniques for prediction and clustering
Prepare for roles like Data Analyst, BI Analyst, and Reporting Specialist
Program Overview
Module 1: Statistics & Exploratory Data Analysis
⏳ 2 weeks
Topics: Descriptive statistics, distributions, outliers, summary metrics
Hands-on: Perform EDA on real datasets to uncover trends and anomalies
Module 2: SQL for Data Analytics
⏳ 2 weeks
Topics: SELECT, JOINs, subqueries, window functions, CTEs
Hands-on: Query a sales database to generate customer and product insights
Module 3: Python for Data Analysis
⏳ 2 weeks
Topics: Pandas dataframes, data cleaning, groupbys, merging datasets
Hands-on: Clean and transform survey data, then visualize key findings
Module 4: Data Visualization with Power BI
⏳ 1 week
Topics: Data modeling, DAX basics, report design, custom visuals
Hands-on: Build a sales performance dashboard with filters and slicers
Module 5: Data Visualization with Tableau
⏳ 1 week
Topics: Tableau interface, calculated fields, parameters, story points
Hands-on: Create an interactive executive dashboard with drill-downs
Module 6: Advanced Analytics & Machine Learning
⏳ 2 weeks
Topics: Regression, classification basics, clustering (K-means), model evaluation
Hands-on: Develop and compare predictive models on a marketing dataset
Module 7: Excel for Data Analysis
⏳ 1 week
Topics: PivotTables, Power Query, advanced formulas, data validation
Hands-on: Automate monthly reporting using Power Query and macros
Module 8: Capstone Project & Presentation
⏳ 2 weeks
Topics: End-to-end analysis, storytelling, business recommendations
Hands-on: Conduct a full data analysis project and present insights with dashboards
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Job Outlook
Data Analysts are in high demand across finance, healthcare, retail, and tech
Typical roles: Data Analyst, BI Analyst, Reporting Specialist, Analytics Consultant
Salaries range from $70,000 to $110,000+ depending on region and skills
Strong growth in remote and contract opportunities for freelance analysts
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Specification: Data Analyst Certification Course
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FAQs
- No prior coding or analytics experience is required; the course is beginner-friendly.
- Basic math or spreadsheet familiarity helps but isn’t mandatory.
- Step-by-step guidance in SQL, Python, and Excel ensures gradual learning.
- Hands-on exercises with real datasets reinforce theoretical concepts.
- By course completion, learners gain confidence in performing full data analyses.
- Hands-on exercises cover SQL queries, Python data manipulation, and Excel analysis.
- Students build interactive dashboards in Power BI and Tableau.
- Capstone project simulates end-to-end data analysis and business recommendations.
- Real-world datasets enhance practical understanding.
- Exercises prepare learners for professional analytics workflows.
- Capstone and module projects form strong portfolio pieces.
- Skills in SQL, Python, Excel, and BI tools are in high demand across industries.
- Students learn to present insights through dashboards and reports.
- Portfolio projects showcase practical abilities to employers or freelance clients.
- Knowledge gained supports roles in finance, healthcare, retail, and tech sectors.
- Introduces basic regression, classification, and K-means clustering.
- Focuses on practical applications in business scenarios.
- Model evaluation and interpretation of results are taught for insight-driven analysis.
- Advanced ML topics like deep learning are not covered.
- Learners can pursue specialized courses for deeper machine learning skills.
- Allocate 5–8 hours weekly for modules and hands-on exercises.
- Focus on one topic or tool per session to retain concepts better.
- Document workflows and code for reference and portfolio building.
- Complete capstone and mini-projects consistently to track progress.
- Engage with online forums or peer communities for guidance and feedback.
