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  • Approx Completion Time 4 days
  • ValidityLifetime access
  • Course FormatRecorded
  • IncludesCertificate
    Assignments
  • Content 01:12 Hours Videos
    16 Lessons

Top Online Course in India

4.5
4.5
4.5

Our Course Stats

Students who got positive growth in their careers after course completion


1%

TeacherDada learners see an average salary hike after course completion


1%

Students who started a new career or changed job after course completion


1%

Overview

On the off chance that you are going for a profession as a Data Scientist or Business Analyst at that point looking over your statistics abilities is something you have to do.


In any case, it's only difficult to begin... Learning/re-adapting ALL of details just appears like an overwhelming undertaking.


That is precisely why we have made this course!


Here you will rapidly get the significant details learning for a Data Scientist or Analyst.


This isn't simply one more exhausting course on details.


This course is exceptionally pragmatic.


I have particularly included true models of business difficulties to demonstrate to you how you could apply this learning to help YOUR vocation.


In the meantime you will ace points, for example, dispersions, the z-test, the Central Limit Theorem, theory testing, certainty interims, measurable criticalness and some more!


So what are you sitting tight for?


Select now and enable your profession!

Who this course is for:

  • People working in any numerate field which requires data analysis
  • People carrying out observational or experimental studies
  • Any one who want to make career in Data Science

Basic Requirement

  • Interest in Learning Statistical Modelling

  • Just a basic knowledge of high school maths

  • People working in any numerate field which requires data analysis

  • People carrying out observational or experimental studies

  • Any one who want to make career in Data Science

Skills Covered

  • Understand what a Normal Distribution is

  • Apply Hypothesis Testing for Means

  • Understand standard deviations

  • Apply the Central Limit Theorem

  • Difference between continuous and discrete variables

  • Use the Z-Score and Z-Tables

  • what a sampling distribution is

Expert Review

What is Statistical Modeling?

Data science refers to the process of applying statistical analysis to datasets. It is a mathematical relationship between one or more random variables and other non-random variables. With the use of statistical modeling on raw data, data scientists are able to handle data analysis in a strategic manner, providing intuitive visualizations that aid in determining relationships between variables and making predictions.

In addition to the Internet of Things (IoT) sensors, census data, public health data, social media data, imagery data, and other public sector data that benefit from real-time prediction, statistical analysis is performed on a wide range of data sets.

Techniques for Statistical Modeling

Gathering data, which may come from spreadsheets, databases, data lakes, or the cloud, is the first step in developing a statistical model. Statistical modeling methods for analyzing this data fall into two categories: supervised learning and unsupervised learning. Examples of popular statistical models include logistic regression, time series, clustering, and decision trees. 

Techniques for supervised learning include regression models and classification models:

1) A regression model is a type of statistical model that analyzes the relationship between a dependent and an independent variable. Regression models include logistic, polynomial, and linear models. Forecasting, time series modeling, and causal effect relationships between variables are examples of applications.

2) A classification model is a type of machine learning in which an algorithm examines a set of known data points in order to better understand and classify the data; commonly used models include decision trees, Naive Bayes, nearest neighbors, random forests, and neural network models, which are typically used in Artificial Intelligence algorithms.

Clustering algorithms and association rules are examples of unsupervised learning techniques:

1) A K-means clustering method aggregates a set of data points into a set of groups based on certain similarities.

2) An area of deep learning that involves iterating over many attempts, rewarding steps that produce desired outcomes, and penalizing steps that produce unwanted outcomes, so that the algorithm learns the optimal process.

The three types of statistical models are parametric, nonparametric, and semiparametric:

1) A parametric distribution is a family of probability distributions with a finite number of parameters.

2) N‍onparametric models have parameters that are flexible and not fixed in advance.

3) A semiparametric parameter has both a finite-dimensional and an infinite-dimensional component.

Overview

On the off chance that you are going for a profession as a Data Scientist or Business Analyst at that point looking over your statistics abilities is something you have to do.


In any case, it's only difficult to begin... Learning/re-adapting ALL of details just appears like an overwhelming undertaking.


That is precisely why we have made this course!


Here you will rapidly get the significant details learning for a Data Scientist or Analyst.


This isn't simply one more exhausting course on details.


This course is exceptionally pragmatic.


I have particularly included true models of business difficulties to demonstrate to you how you could apply this learning to help YOUR vocation.


In the meantime you will ace points, for example, dispersions, the z-test, the Central Limit Theorem, theory testing, certainty interims, measurable criticalness and some more!


So what are you sitting tight for?


Select now and enable your profession!

Who this course is for:

  • People working in any numerate field which requires data analysis
  • People carrying out observational or experimental studies
  • Any one who want to make career in Data Science

  • Understand what a Normal Distribution is

  • Apply Hypothesis Testing for Means

  • Understand standard deviations

  • Apply the Central Limit Theorem

  • Difference between continuous and discrete variables

  • Use the Z-Score and Z-Tables

  • what a sampling distribution is

  • Interest in Learning Statistical Modelling

  • Just a basic knowledge of high school maths

  • People working in any numerate field which requires data analysis

  • People carrying out observational or experimental studies

  • Any one who want to make career in Data Science

What is Statistical Modeling?

Data science refers to the process of applying statistical analysis to datasets. It is a mathematical relationship between one or more random variables and other non-random variables. With the use of statistical modeling on raw data, data scientists are able to handle data analysis in a strategic manner, providing intuitive visualizations that aid in determining relationships between variables and making predictions.

In addition to the Internet of Things (IoT) sensors, census data, public health data, social media data, imagery data, and other public sector data that benefit from real-time prediction, statistical analysis is performed on a wide range of data sets.

Techniques for Statistical Modeling

Gathering data, which may come from spreadsheets, databases, data lakes, or the cloud, is the first step in developing a statistical model. Statistical modeling methods for analyzing this data fall into two categories: supervised learning and unsupervised learning. Examples of popular statistical models include logistic regression, time series, clustering, and decision trees. 

Techniques for supervised learning include regression models and classification models:

1) A regression model is a type of statistical model that analyzes the relationship between a dependent and an independent variable. Regression models include logistic, polynomial, and linear models. Forecasting, time series modeling, and causal effect relationships between variables are examples of applications.

2) A classification model is a type of machine learning in which an algorithm examines a set of known data points in order to better understand and classify the data; commonly used models include decision trees, Naive Bayes, nearest neighbors, random forests, and neural network models, which are typically used in Artificial Intelligence algorithms.

Clustering algorithms and association rules are examples of unsupervised learning techniques:

1) A K-means clustering method aggregates a set of data points into a set of groups based on certain similarities.

2) An area of deep learning that involves iterating over many attempts, rewarding steps that produce desired outcomes, and penalizing steps that produce unwanted outcomes, so that the algorithm learns the optimal process.

The three types of statistical models are parametric, nonparametric, and semiparametric:

1) A parametric distribution is a family of probability distributions with a finite number of parameters.

2) N‍onparametric models have parameters that are flexible and not fixed in advance.

3) A semiparametric parameter has both a finite-dimensional and an infinite-dimensional component.

Course Overview

On the off chance that you are going for a profession as a Data Scientist or Business Analyst at that point looking over your statistics abilities is something you have to do.


In any case, it's only difficult to begin... Learning/re-adapting ALL of details just appears like an overwhelming undertaking.


That is precisely why we have made this course!


Here you will rapidly get the significant details learning for a Data Scientist or Analyst.


This isn't simply one more exhausting course on details.


This course is exceptionally pragmatic.


I have particularly included true models of business difficulties to demonstrate to you how you could apply this learning to help YOUR vocation.


In the meantime you will ace points, for example, dispersions, the z-test, the Central Limit Theorem, theory testing, certainty interims, measurable criticalness and some more!


So what are you sitting tight for?


Select now and enable your profession!

Who this course is for:

  • People working in any numerate field which requires data analysis
  • People carrying out observational or experimental studies
  • Any one who want to make career in Data Science

Basic Requirements

  • Interest in Learning Statistical Modelling

  • Just a basic knowledge of high school maths

  • People working in any numerate field which requires data analysis

  • People carrying out observational or experimental studies

  • Any one who want to make career in Data Science

Skills Covered

  • Understand what a Normal Distribution is

  • Apply Hypothesis Testing for Means

  • Understand standard deviations

  • Apply the Central Limit Theorem

  • Difference between continuous and discrete variables

  • Use the Z-Score and Z-Tables

  • what a sampling distribution is

Expert Review

What is Statistical Modeling?

Data science refers to the process of applying statistical analysis to datasets. It is a mathematical relationship between one or more random variables and other non-random variables. With the use of statistical modeling on raw data, data scientists are able to handle data analysis in a strategic manner, providing intuitive visualizations that aid in determining relationships between variables and making predictions.

In addition to the Internet of Things (IoT) sensors, census data, public health data, social media data, imagery data, and other public sector data that benefit from real-time prediction, statistical analysis is performed on a wide range of data sets.

Techniques for Statistical Modeling

Gathering data, which may come from spreadsheets, databases, data lakes, or the cloud, is the first step in developing a statistical model. Statistical modeling methods for analyzing this data fall into two categories: supervised learning and unsupervised learning. Examples of popular statistical models include logistic regression, time series, clustering, and decision trees. 

Techniques for supervised learning include regression models and classification models:

1) A regression model is a type of statistical model that analyzes the relationship between a dependent and an independent variable. Regression models include logistic, polynomial, and linear models. Forecasting, time series modeling, and causal effect relationships between variables are examples of applications.

2) A classification model is a type of machine learning in which an algorithm examines a set of known data points in order to better understand and classify the data; commonly used models include decision trees, Naive Bayes, nearest neighbors, random forests, and neural network models, which are typically used in Artificial Intelligence algorithms.

Clustering algorithms and association rules are examples of unsupervised learning techniques:

1) A K-means clustering method aggregates a set of data points into a set of groups based on certain similarities.

2) An area of deep learning that involves iterating over many attempts, rewarding steps that produce desired outcomes, and penalizing steps that produce unwanted outcomes, so that the algorithm learns the optimal process.

The three types of statistical models are parametric, nonparametric, and semiparametric:

1) A parametric distribution is a family of probability distributions with a finite number of parameters.

2) N‍onparametric models have parameters that are flexible and not fixed in advance.

3) A semiparametric parameter has both a finite-dimensional and an infinite-dimensional component.

Get Certified

You will receive an industry-recognized Certification from TeacherDada after completing the course. You can also share your Certificate in the Certifications section of your LinkedIn profile, CVs, resumes, and other documents.

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Course creator


                                 GulabChand Tejwani

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