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Statistics Essentials for Analytics

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.

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Self Paced Online Class

Statistics Essentials for Analytics

Our Online Self-Learning Courses offer a self-paced approach, enabling participants to start learning at their preferred times. These courses provide structured training and incorporate review exercises to solidify understanding. You'll access instructional videos and PowerPoint presentations (PPTs) and engage in assignments, projects, and various activities aimed at improving learning outcomes, all tailored to your convenience.

$319  $255

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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

  World-Class Instructors

  Expert-Led Mentoring Sessions

  Instant doubt clearing

Lifetime Access

  Course Access Never Expires

  Free Access to Future Updates

  Unlimited Access to Course Content

24x7 Support

  One-On-One Learning Assistance

  Help Desk Support

  Resolve Doubts in Real-time

Hands-On Project Based Learning

  Industry-Relevant Projects

  Course Demo Dataset & Files

  Quizzes & Assignments

Industry Recognized Certification

  CertHippo Training Certificate

  Graded Performance Certificate

  Certificate of Completion

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  Preconfigured Lab Environment

  Infrastructure with Tools and Software

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Course Curriculum

  • Introduction to Data Types

  • Numerical parameters to represent data

          a. Mean  

          b. Mode 

          c. Median 

          d. Sensitivity 

          e. Information Gain 

          f.  Entropy

  • Statistical parameters to represent data

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

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

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

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

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

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Certification

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

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.

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.

You can give us a CALL at +1 302 956 2015 (US) OR email at info@certhippo.com

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?


  • 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).


    This training is open to the following professionals:

    • 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.

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.

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Projects

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

Selenium Certification

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.

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