Key Features
Completion Certificate
Internship
Internship Certificate
7 Days Refund Policy
Expert Instructors
One-to-One Session
What Will You Learn?
Accelerate your learning journey with our comprehensive course designed to equip you with essential skills and practical knowledge in Master Data Analytics.
- Introduction to Data Analytics
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
- Statistical Analysis and Hypothesis Testing
- Data Visualization with Tableau, Power BI, and Python
- Time Series Analysis
- SQL for Data Analytics
- Advanced Excel for Data Analysis
- Big Data and Cloud Analytics
- Data-Driven Decision Making
- Reporting and Dashboards
- Data Ethics and Governance
Requirements
Before getting started with this course, it's beneficial to have the following:
- Laptop with internet access
- Basic understanding of programming (preferably Python)
- Familiarity with mathematics (basic statistics)
- Curiosity to explore data-driven decision-making
- Willingness to work with real-world datasets and solve business problems
Course Completion
Yes
Curriculum
- What is Data & Data Analytics?
- Global Scope of Data Analysis
- Data Analytics in Different Domains
- Understanding the vision of stakeholders and asking effective questions
- Road Map to be a Data Analyst
- Role Of Data Analyst?
- Data Analyst vs Business Analyst
- What is BI and Its Importance?
- BI Tools and Techniques
- Data Analysis Methods
- Types of Data Analysis
- Data Analysis Process
- System Functions in SQL
- User-Defined Functions
- Predefined Functions
- Arithmetic Functions in SQL
- Logical and Error Functions
- Date Functions in SQL
- Mathematical Functions
- Text Functions
- Statistical Functions
- Database Functions and Arrays
- Introduction to Functions
- Built-In Functions
- User-Defined Functions (Revisited)
- Anonymous Functions (Lambda Functions)
- Recursive Functions
- Types of Statistics
- Descriptive Statistics
- Inferential Statistics
- Population and Sample
- Parameter and Statistics (Mean, Median, Mode, Std, Variance)
- Uses of Variables
- Dependent Variables
- Independent Variables
- Types of Variables
- Continuous Variables
- Categorical Variables
- Distribution Types and Skewness
- Hypothesis Testing
- Type 1 Error
- Type 2 Error
- T-test (One Sample and Sample Comparison)
- ANOVA & Chi-Square
- Covariance and Correlation