Data Analytics

• What is Data Analytics?
• Types: Descriptive, Diagnostic, Predictive, Prescriptive
• Data lifecycle and analytics workflow
• Role of a Data Analyst

2. Excel for Data Analytics

• Data cleaning and wrangling in Excel
• Formulas, pivot tables, lookups
• Creating charts and dashboards

3. SQL for Data Extraction

• Basic to advanced SQL queries
• Joins, subqueries, and aggregations
• Data modeling and normalization
• Real-world data extraction projects

4. Data Visualization

• Principles of effective data visualization
• Tools: Tableau or Power BI
• Building dashboards and interactive reports
• Storytelling with data

5. Statistics for Data Analysis

• Descriptive statistics: Mean, median, standard deviation
• Inferential statistics: Hypothesis testing, confidence intervals
• Probability, distributions, correlation, regression

6. Python for Data Analytics

• Python basics: variables, loops, functions
• NumPy, Pandas for data handling
• Matplotlib and Seaborn for visualization
• Exploratory Data Analysis (EDA) on datasets

7. Data Cleaning and Preprocessing

• Handling missing, duplicate, and incorrect data
• Outlier detection and treatment
• Data transformation and normalization

8. Capstone Project

• Real-world dataset (e.g., sales, marketing, finance, HR)
• Full data analysis pipeline: extract, clean, analyze, visualize
• Present findings through a report/dashboard

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🛠️ Tools Covered

• Excel / Google Sheets
• SQL (MySQL/PostgreSQL)
• Python (Pandas, NumPy, Matplotlib)
• Power BI / Tableau
• Jupyter Notebook

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👨‍🎓 Who Should Take This Course?

• Fresh graduates interested in data roles
• Working professionals in marketing, finance, HR, operations
• Career switchers aiming for roles like:
  • Data Analyst
  • Business Analyst
  • Reporting Analyst
  • BI Developer

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📜 Certifications Offered

• Course completion certificate
• Optionally prepares for:
  • Microsoft PL-300: Power BI Data Analyst
  • Google Data Analytics Professional Certificate
  • Tableau Desktop Specialist

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📅 Duration

• Fast-track: 6–8 weeks (intensive)
• Regular pace: 12–16 weeks (weekend or evening classes)

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💼 Career Outcomes

• Data Analyst
• Business Intelligence Analyst
• Data Visualization Specialist
• Junior Data Scientist (with extra training)
• Marketing/Financial Analyst