Learning Objectives: At the end of this Module, you should be able to:

• Understand various data types
• Learn Various variable types
• List the uses of variable types
• Explain Population and Sample
• Discuss sampling techniques
• Understand Data representation

Topics:

• Introduction to Data Types
• Numerical parameters to represent data
  • Mean
  • Mode
  • Median
  • Sensitivity
  • Information Gain
  • Entropy
• Statistical parameters to represent data

Learning Objectives: At the end of this Module, you should be able to:

• Understand rules of probability
• Learn about dependent and independent events
• Implement conditional, marginal and joint probability using Bayes Theorem
• Discuss probability distribution
• Explain Central Limit Theorem

Topics:

• Uses of probability
• Need of probability
• Bayesian Inference
• Density Concepts
• Normal Distribution Curve

Learning Objectives: At the end of this Module, you should be able to:

• Understand concept of point estimation using confidence margin
• Draw meaningful inferences using margin of error
• Explore hypothesis testing and its different levels

Topics:

• Point Estimation
• Confidence Margin
• Hypothesis Testing
• Levels of Hypothesis Testing

Learning Objectives: At the end of this module, you should be able to:

• Understand concept of association and dependence
• Explain causation and correlation
• Learn the concept of covariance
• Discuss Simpson’s paradox
• Illustrate Clustering Techniques

Topics:

• Association and Dependence
• Causation and Correlation
• Covariance
• Simpson’s Paradox
• Clustering Techniques

Learning Objectives: At the end of this module, you should be able to:

• Understand Parametric and Non-parametric Testing
• Learn various types of parametric testing
• Discuss experimental designing
• Explain A/B testing

Topics:

• Parametric Test
• Parametric Test Types
• Non- Parametric Test
• Experimental Designing
• A/B testing

Learning Objectives: At the end of this module, you should be able to:

• Understand the concept of Linear Regression
• Explain Logistic Regression
• Implement WOE
• Differentiate between heteroscedasticity and homoscedasticity
• Learn concept of residual analysis

Topics:

• Logistic and Regression Techniques
• Problem of Collinearity
• WOE and IV
• Residual Analysis
• Heteroscedasticity
• Homoscedasticity

The self-paced Statistics Essentials for Analytics Course has been designed in such a manner that it is easy for a future Data Scientist to get a solid foundation on the concepts. The complete mechanism of Data Science is explained in detail in terms of Statistics and Probability. Data and its types are discussed along with different kind of sampling procedures. Other essential concepts of Statistics (statistical inference, testing, clustering) are emphasized here as well since that’s a very important part of being a Data Scientist. In addition, you will be introduced to primary machine learning algorithms in this Course.

After the completion of this course, you should be able to:

• Analyze different types of data
• Master different sampling techniques
• Illustrate Descriptive statistics
• Apply probabilistic approach to solve real life complex problems
• Explain and derive Bayesian inference
• Understand Clustering techniques
• Understand Regression modelling
• Master Hypothesis
• Illustrate Testing the data

The course is designed for all those who want to learn essential statistics required for Data Science and Data analytics. The curated statistics course will help you form a strong foundation for the Data Science and predictive modelling (nowadays Machine Learning) field. The following professionals can go for this course:

• Developers aspiring to be a ‘Data Scientist’
• Analytics Managers who are leading a team of analysts
• Business Analysts who want to understand Machine Learning (ML) Techniques
• Information Architects who want to gain expertise in Predictive Analytics
• ‘R’ professionals who want to captivate and analyze Big Data
• Analysts wanting to understand Data Science methodologies

No prerequisites are required for this course.

The practicals are shown in ‘R’ which is a open-source analytics tool. The step-wise set-up guide for R will be provided to you.

Statistics and its methods are the backend of Data Science to “understand, analyze and predict actual phenomena”. Machine learning employs different techniques and theories drawn from statistical & probabilistic fields. This Statistics Essentials for Analytics Course enables you to gain knowledge of the essential statistics required for analytics and Data Science, understand the mechanism of popular Machine Learning Algorithms like K-Means Clustering, Regression. The course also takes you through the glimpse of hypothesis testing and its methods enabling you perform test on alternative hypothesis.

To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the “create your resume” option.

All the instructors at Edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by Edureka for providing an awesome learning experience.

Just give us a CALL at +91 8178510474 / +91 9967920486 OR email at admin@certadda.com/