Data Analyst Masters Program
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COURSE DATES AND LOCATIONS
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INTRODUCTION
This Data Analyst course will give you expertise in this booming data analytics stream. The Data Analyst training helps you become a pro in descriptive and inferential statistics, regression analysis, hypothesis testing, data blending, data extracts, and forecasting. You will also have insights on data visualization techniques by using Tableaus and Power BI and learning ways to organize data and design dashboards.
On the other hand, the program also focuses on non tech workforce to give them an idea about the technicalities. In this Data Analyst training, you will understand basic concepts of Mathematics and how to apply them.
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COURSE OBJECTIVES
By the end of the course, you‘ll be able to:
- Gain an outstanding understanding of the practices and processes used by a junior or associate data analyst
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Acquire a big picture understanding of the data analyst role
- Learn key analytical skills (data cleaning, analysis, & visualization) and tools (spreadsheets, SQL, R programming, Tableau)
- Understand how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and R programming
- Learn how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms
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COURSE AUDIENCE
This course is made for :
- Data analysts
- Data architects
- IT specialists
- Sales and marketing staff
- Data science engineers
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COURSE OUTLINE
Day One
Data Understanding
Introduction to Data Types
- Numerical parameters to represent data ( Mean, Mod, Median, Sensitivity, Information Gain and Entropy )
- Statistical parameters to represent data
Day Two
Data Usage
- Uses of probability
- Need of probability
- Bayesian Inference
- Density Concepts
- Normal Distribution Curve
Day Three
Statistical Inference
- Understand concept of point estimation using confidence margin
- Draw meaningful inferences using margin of error
- Explore hypothesis testing and its different levels
Day Four
Data Clustering
- Understand concept of association and dependence
- Explain causation and correlation
- Learn the concept of covariance
- Discuss Simpson’s paradox
- Illustrate Clustering Techniques
Day Five
Data Testing
- Types of parametric testing
- Discuss experimental designing
- Explain a/b testing
Regression Modelling
- Logistic and Regression Techniques
- Problem of Collinearity
- WOE and IV
- Residual Analysis
- Heteroscedasticity
- Homoscedasticity
