Statistics Essentials for Analytics

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A self-paced course that teaches you the fundamentals of statistical techniques and how each approach is used to real-world data sets to evaluate and draw conclusions. Statistics and its methodologies are the backbone of Data Science to “understand, analyze and forecast actual occurrences”. Machine learning incorporates a variety of statistical and probabilistic techniques and ideas.

Why This Course

With the aid of this course, you will be able to comprehend and use numerous statistical approaches such as Sampling Techniques, Conditional Probability, Bayesian Theorem, and so on. You’ll also learn when and how to use each statistical approach.

Why Enroll In Course?

Statistics Essentials for Analytics training and certification can improve statistical knowledge and understanding of data analysis, enhancing analytical skills for effective problem-solving and decision-making. It can also give a competitive edge in the job market by differentiating you from other candidates with strong statistical knowledge and analytical skills. Overall, undertaking this training can provide the skills, knowledge, and recognition needed to advance a career in data analytics.

Training Features

Live Interactive Learning

Lifetime Access

24x7 Support

Hands-On Project Based Learning

Industry Recognized Certification

Cloud

Course Curriculum

Understanding the Data

Introduction to Data Types
Numerical parameters to represent data

Mean
Mode
Median
Sensitivity
Information Gain
Entropy

Statistical parameters to represent data

Probability and its uses

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

Statistical Inference

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

Data Clustering

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

Testing the Data

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

Regression Modeling

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 heteroskedasticity and homoscedasticity
Learn concept of residual analysis

Topics:

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

Free Career Counselling

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Certification

CertHippo Certification Process:

After completing the project successfully (as reviewed by a CertHippo expert), you will be granted the CertHippo Statistics Expert credential.

CertHippo certification is widely recognized in the industry, and we are the chosen training partner for many multinational corporations, including Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mind Tree, BNYMellon, and others. Please be confident.

Online Training FAQs

Will I Get Placement Assistance?

We have included a resume creation feature in your LMS to assist you in this attempt. You may now design a winning CV in just three simple steps. You will have unrestricted access to these templates across all roles and designations. All you have to do is sign in to your LMS and select the "make your resume" option.

Who are the Instructors at CertHippo?

All of the teachers at CertHippo are industry practitioners with at least 10-12 years of relevant IT experience. These are subject matter experts who have been trained by CertHippo to provide an excellent learning experience.

Course Description

About The Course

The self-paced Statistical Basics for Analytics Course has been developed in such a way that a potential Data Scientist may quickly have a strong understanding of the topics. The entire Data Science mechanism is presented in full in terms of Statistics and Probability. Data and its many kinds are reviewed, as well as various sampling strategies.

Other basic Statistical principles (statistical inference, testing, clustering) are also stressed here because they are a vital element of being a Data Scientist. This Course will also teach you basic machine learning algorithms.

Course Objectives

After finishing 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 modeling
Master Hypothesis
Illustrate Testing the data

Who should 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

Pre-requisites

There are no prerequisites for this course.

This training is open to the following professionals:
The course is intended for anybody interested in learning the fundamental statistics necessary for Data Science and Data Analytics. The tailored statistics course will assist you in laying a solid basis for the discipline of Data Science and predictive modeling (nowadays Machine Learning).

Why learn Statistics Essentials for Analytics?

Statistics and its methodologies are the backbone of Data Science to “understand, analyze and forecast actual occurrences”. Machine learning incorporates a variety of statistical and probabilistic techniques and ideas. This Statistical Fundamentals for Analytics Course will teach you the fundamental statistics needed for analytics and data science, as well as the mechanisms of popular Machine Learning Algorithms such as K-Means Clustering and Regression. The course also provides an overview of hypothesis testing and its methodologies, allowing you to evaluate different hypotheses.

Projects

How will I execute the Practical's?

The practical's are displayed in 'R,' an open-source analytics programmer. You will be given a step-by-step setup tutorial for R.