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  • Skill Level: Beginners
  • Duration: 4 Months
  • Class Per Week: 2 Day
  • Total Class: 32
  • Certificate: Yes
  • Provide Class Video
  • Language: Bangla & English

Opening Hours

  • Saturday               : Remote office.
  • Sunday, Moday    : Close
  • Tuesday                : 10.00 am - 6.00 pm
  • Wednesday          : 10.00 am - 6.00 pm
  • Thursday              : 10.00 am - 6.00 pm
  • Friday                    : 08.00 am - 8.00 pm
Data-Analytics-1024x683

Data Analytics

Course Fee: ৳10,000
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Overview:
What is Data Analytics? Data Analytics is the science of examining raw data to uncover patterns, draw conclusions, and support business decision-making. This course covers the complete workflow—from data collection to visualization—using industry-standard tools. What You’ll Learn
  • SQL for data extraction & manipulation
  • Python (pandas, numpy) for data cleaning & analysis
  • Data visualization with Power BI / Tableau
  • Statistical methods for insights & decision-making
  • End-to-end analytics workflow (collect → clean → analyze → visualize)
Who Should Join?
  • Fresh graduates & entry-level professionals
  • Business analysts, marketing, and operations teams
  • IT professionals transitioning to data roles
  • Managers seeking data-driven decision-making skills
  • Career changers entering the high-demand data field
Why This Course?
  • High demand – Data analyst roles growing 25%+ annually
  • Job-ready skills – Build portfolio with real-world projects
  • Tool-focused – Master SQL, Python, Power BI/Tableau
  • No degree required – Practical, hands-on learning
  • Career growth – Path to BI analyst, data scientist, or analytics manager

Trainer: MD. Rowson Al Mamun (MCT, MCSA, MCP)
Duration: 3 Months (Approx. 30 Classes)

Module 1: Foundations of Data Analytics

  • Data analytics lifecycle and workflow

  • Types of data (structured, semi-structured, unstructured)

  • Descriptive, diagnostic, predictive, prescriptive analytics

  • Data ethics and privacy basics

Module 2: SQL for Data Analysis

  • SELECT statements, filtering, sorting

  • Joins (INNER, LEFT, RIGHT, FULL)

  • Aggregations (GROUP BY, HAVING)

  • Subqueries and Common Table Expressions (CTEs)

  • Window functions (ROW_NUMBER, RANK, LAG/LEAD)

Module 3: Python for Data Analysis

  • NumPy – Numerical operations and arrays

  • Pandas – Data manipulation (DataFrames, merging, grouping)

  • Data Cleaning – Handling missing values, duplicates, outliers

  • Exploratory Data Analysis (EDA) – Summary statistics and correlations

Module 4: Data Visualization

  • Matplotlib – Customizing plots (line, bar, scatter, histogram)

  • Seaborn – Statistical visualizations (heatmaps, pairplots)

  • Interactive Dashboards – Power BI or Tableau

  • Storytelling with data – Best practices

Module 5: Statistics for Analytics

  • Descriptive statistics (mean, median, mode, standard deviation)

  • Probability distributions (normal, binomial)

  • Hypothesis testing (t-test, chi-square)

  • Correlation vs. causation

  • Regression analysis basics

Module 6: Advanced Topics (Optional)

  • Introduction to Excel for data analytics (PivotTables, Power Query)

  • Time series analysis and forecasting

  • Introduction to Big Data (Hadoop, Spark basics)

  • Introduction to Machine Learning (scikit-learn)

Module 7: Capstone Project

  • Real-world dataset analysis (e.g., sales, customer, finance)

  • End-to-end workflow: SQL extraction → Python analysis → Dashboard creation

  • Presentation of insights to stakeholders