Master In-Demand Skills with dataAXI Lab
Elevate your expertise with our industry-aligned training program. Master data analytics, aptitude, and coding to confidently apply your skills in real-world scenarios.
Massive Demand: Acquire top skills in IT and analytics, highly sought after by leading companies.
Be Industry-Ready: Gain the practical and analytical skills companies expect from Day 1.
Resume-Worthy Skills: Learn tools and technologies that make you stand out in the competitive tech landscape.
Build a Strong Foundation: Develop core competencies for a successful and future-proof career in data.
Course Modules & Topics
A comprehensive breakdown of what you'll learn.
Module 1: Introduction to Data Analytics
- What is Data Analytics?
- Role of a Data Analyst
- Data Life Cycle
- Types of Data Analysis
- Tools and Technologies in Data Analytics
- Career Paths in Data Analytics
Module 2: Python for Data Analytics
- Python Environment Setup
- Python Basics (Variables, Data Types, Operators)
- Control Flow (If-Else, Loops)
- Data Structures (Lists, Tuples, Dictionaries, Sets)
- Functions and Modules
- Object-Oriented Programming (OOP) Concepts
- File I/O and Exception Handling
- Memory Management in Python
- Basic Python Project
Module 3: Data Toolkit (Pandas, NumPy, Visualization)
- NumPy for Numerical Computing (Basic to Advanced)
- Pandas for Data Manipulation (Basic to Advanced)
- Data Cleaning and Preprocessing with Pandas
- Data Visualization with Matplotlib
- Advanced Visualization with Seaborn
- Interactive Visualizations with Plotly
- Building Dashboards with Bokeh
- Practical Data Visualization Projects
Module 4: MySQL (Basic to Advanced)
- Introduction to Relational Databases
- SQL Basics and Syntax
- Database and Table Operations (CREATE, ALTER, DROP)
- Data Manipulation (INSERT, UPDATE, DELETE)
- Querying Data (SELECT, WHERE, ORDER BY, GROUP BY)
- SQL Joins (INNER, LEFT, RIGHT, FULL)
- Subqueries and CTEs
- Stored Procedures and Functions
- Views and Indexes
- Transaction Management
- SQL Interview Questions & Best Practices
Module 5: Excel for Data Analysis
- Excel Interface and Basic Operations
- Data Entry, Formatting, and Validation
- Essential Excel Functions (VLOOKUP, INDEX-MATCH, SUMIF, etc.)
- Pivot Tables and Pivot Charts
- Conditional Formatting
- Data Sorting and Filtering
- What-If Analysis and Goal Seek
- Introduction to Macros (VBA Basics)
- Data Analysis ToolPak
Module 6: Statistics for Data Analytics
- Types of Statistics (Descriptive & Inferential)
- Population vs. Sample, Parameter vs. Statistics
- Types of Variables (Continuous, Categorical, Dependent, Independent)
- Measures of Central Tendency (Mean, Median, Mode)
- Measures of Dispersion (Variance, Standard Deviation, Range)
- Probability Distributions (Normal, Binomial, Poisson)
- Hypothesis Testing (Z-test, T-test, Chi-square)
- Correlation and Regression Analysis
- A/B Testing
Module 7: Power BI for Data Analytics
- Introduction to Power BI Desktop
- Data Loading and Transformation using Power Query
- Data Modeling and Relationships
- DAX (Data Analysis Expressions) Basics to Advanced
- Creating Interactive Visualizations
- Designing Effective Reports and Dashboards
- Publishing and Sharing Reports
- Power BI Service and Gateway
- Power BI Project
Module 8: Azure for Data Analytics
- Introduction to Azure Cloud & Data Services
- Azure Data Lake Storage
- Azure Synapse Analytics (Data Warehousing)
- Azure Data Factory (Data Integration)
- Azure Databricks (Big Data Processing)
- Azure Stream Analytics (Real-time Data)
- Azure Machine Learning Basics
- Securing Data Analytics Pipelines in Azure
- Azure Data Analytics Project