Complete Data Analytics Certification Course
Data Analytics Complete Training Course equips students and professionals with industry-relevant skills in data collection, cleaning, analysis, visualization, and business reporting. Gain hands-on experience with real-world datasets and practical projects to support data-driven decision-making across modern organizations.
The course is structured into four progressive, industry-aligned learning modules.
- Learning Block 1: Foundations (Modules 1-3)
- Learning Block 2: SQL for Data Analytics (Modules 4-5)
- Learning Block 3: Python for Data Analytics (Modules 6-9)
- Learning Block 4: Data Visualization & Capstone Project (Modules 10-12)
By the end of this course, participants will confidently analyze real-world data, create professional reports and dashboards, communicate actionable insights, and build a strong foundation for careers in data analytics, business intelligence, and data science.
Course Content:
Module 1: Introduction to Data Analytics
- What is Data Analytics? Why It Matters Today
- Types of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
- Understanding Data: Qualitative vs. Quantitative Data
- Structured vs. Unstructured Data
- The Data Analytics Process: Collect, Clean, Analyze, Visualize, Report
- Real-World Examples of Data Analytics in Business
- Introduction to Tools Used in This Course (Excel, SQL, Python)
Module 2: Excel for Data Analytics
Excel:
- Excel Interface, Rows, Columns & Cells
- Data Entry, Formatting & Basic Formulas
- Sorting and Filtering Data
Excel for Analysis:
- Essential Functions: Sum, Average, Count, If, Vlookup
- Conditional Formatting for Data Insights
- PivotTables and PivotCharts
- Creating Basic Charts and Graphs
Project #1 Sales Data Analysis Using Excel
Module 3: Statistics Fundamentals for Data Analytics
Descriptive Statistics:
- Mean, Median, Mode & Range
- Variance and Standard Deviation
- Understanding Data Distribution
Working with Data:
- Identifying Outliers and Anomalies
- Correlation vs. Causation
- Introduction to Probability Concepts
Module 4: Introduction to SQL & Databases
Database:
- What is a database? Tables, Rows & Columns
- Introduction to SQL and Relational Databases
- Setting Up a SQL Environment (MySQL/SQLite)
Queries:
- Select Statements Retrieving Data
- Filtering with Where Clause
- Sorting with Order By
- Using Distinct, Limit & Aliases
Mini Exercise: Write 10 Basic SQL Queries on a Sample Dataset
Module 5: SQL for Data Analysis
Joins & Aggregation:
- Understanding Table Relationships
- InneR Join & Left Join for Combining Data
- Aggregate Functions: Count, Sum, Avg, Min, Max
- Group By and Having for Summarizing Data
Practical Analysis:
- Writing Queries to Answer Business Questions
- Subqueries for Deeper Analysis
- Exporting SQL Results for Reporting
Project #2 Customer Sales Report Using SQL
Module 6: Python Programming
Getting Started:
- Python Installation & Environment Setup
- Choosing an IDE (Jupyter Notebook / VS Code)
- Writing and Running Your First Python Script
Core Concepts:
- Variables, Data Types & Operators
- Conditional Statements & Loops
- Lists, Dictionaries & Basic Functions
Hands-On Exercises Practice Problems for Beginners
Module 7: Data Handling with Pandas & NumPy
Pandas:
- Introduction to Pandas DataFrames & Series
- Loading Data from CSV and Excel Files
- Indexing, Selecting & Filtering Data
NumPy:
- Working with NumPy Arrays
- Basic Numerical Operations
- Using NumPy with Pandas for Calculations
Mini Exercise Load and Explore a Real-World Dataset
Module 8: Data Cleaning & Preparation
Cleaning Data:
- Identifying and Handling Missing Values
- Removing Duplicates
- Fixing Data Type Issues
Preparing Data:
- Renaming and Reordering Columns
- Creating New Columns from Existing Data
- Merging and Combining Datasets
Project #3 Clean a Messy Real-World Dataset
Module 9: Exploratory Data Analysis (EDA)
Analyzing Data:
- Summary Statistics with Pandas (describe())
- Grouping and Aggregating Data (groupby)
- Identifying Trends and Patterns
Practical EDA:
- Detecting Outliers in Data
- Correlation Analysis Between Variables
- Drawing Initial Insights from Data
Project #4 Exploratory Analysis on a Business Dataset
Module 10: Data Visualization with Python
Matplotlib:
- Creating Line, Bar & Pie Charts
- Customizing Titles, Labels & Legends
- Saving and Exporting Charts
Seaborn for Better Visuals:
- Histograms and Distribution Plots
- Heatmaps for Correlation Analysis
- Box Plots for Outlier Detection
Mini Exercise: Visualize Key Trends from a Dataset
Module 11: Introduction to Dashboards & Business Intelligence
Dashboard:
- What is a dashboard? Why It Matters in Analytics
- Introduction to Power BI / Google Data Studio (Basics)
- Connecting Data Sources to a Dashboard Tool
Building Dashboards:
- Creating Visuals: Charts, Tables & Kpi Cards
- Designing a Clean, Readable Dashboard Layout
- Data Storytelling Presenting Insights Clearly
Project #5 Build a Simple Sales Dashboard
Module 12: Capstone Project & Career Guidance
Capstone Project:
- Select a Real-World Dataset (Sales, Marketing, or HR Data)
- Apply Full Analytics Process: Clean, Analyze, Visualize
- Build a Final Report or Dashboard with Key Insights
Learning Outcomes:
Core Analytics Skills
- Understanding Data Types & Data Quality
- Descriptive Statistics & Data Interpretation
- Data Cleaning & Preparation Techniques
- Exploratory Data Analysis (EDA)
- Identifying Trends, Patterns & Outliers
- Data-Driven Decision Making
- Presenting Insights & Data Storytelling
- Foundation for BI / Data Science Careers
Tools & Technical Skills
- Microsoft Excel for Data Analysis
- SQL for Querying & Joining Data
- Python (Pandas, NumPy) for Analysis
- Data Visualization (Matplotlib, Seaborn)
- Building Dashboards (Power BI Basics)
- Working with Real-World Datasets
- Building 4+ Hands-on Projects
- Capstone Data Analytics Project
International Student Fee: 500 USD
Flexible Class Options
- Corporate Group Training | Fast-Track
- Weekend Classes For Professionals SAT | SUN
- Online Classes-Live Virtual Class (L.V.C) Online Training
