CONTACT
ALL COURSES
CATEGORIES

Python Statistics for Data Science Certification Training in San Jose, CA

4.6 (566 ratings) 14412 learners [Rate this training]

  • Comprehensive Understanding: The course aims to provide a complete grasp of statistical analysis.

  • Practical Application: It focuses on how to perform statistical analysis and make data-driven decisions.

  • Interactive Learning: The course uses interactive lessons and hands-on exercises for a practical learning experience.

  • Key Skills Taught: It covers specific techniques like hypothesis testing and regression analysis.

  • Target Audience: The course is designed for anyone who wants to enhance their data science skills and gain a deeper understanding of statistics.

  • Career Focus: It is intended to provide knowledge to succeed in the field of data science.

Python Statistics

Python Statistics for Data Science Course Overview

Python Scripting allows programmers to build applications easily and rapidly. This course is an introduction to Python scripting, which focuses on the concepts of Python, it will help you to perform operations on variable types using Pycharm. You will learn the importance of Python in real time environment and will be able to develop applications based on Object Oriented Programming concept. At the end of this course, you will be able to develop networking applications with suitable GUI.

 

In this course you will:

  • Be introduced to data and its types and accordingly sample data and derive meaningful information from the data in terms different statistical parameters. 
  • Learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference.
  • Draw inferences from present data and construct predictive models using different inferential parameters (as a constraint). 
  • Learn the different methods of testing the alternative hypothesis. 
  • Get an introduction to Clustering as part of this Module which forms the basis for machine learning. 
  • Learn the roots of Regression Modelling using statistics. 
Show Classes
Want to check out the Python Statistics classes?
Query
Do you have any query for us?

Python Statistics for Data Science Course Topics

Goal: In this module, you will be introduced to data and its types and accordingly sample data and derive meaningful information from the data in terms different statistical parameters. 

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
Hands-On/Demo
  • Estimating mean, median and mode using python
  • Calculating Information Gain and Entropy
  • Goal: In this module, you should learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference. 

    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

  • Hands-On/Demo:
  • Calculating probability using python
  • Conditional, Joint and Marginal Probability using Python
  • Plotting a Normal distribution curve
Goal: Draw inferences from present data and construct predictive models using different inferential parameters (as a constraint). 

Objectives: At the end of this Module, you should be able to:
  • Understand the 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
Hands-On/Demo:
  • Calculating and generalizing point estimates using python
  • Estimation of Confidence Intervals and Margin of Error
Goal: In this module, you should learn the different methods of testing the alternative hypothesis. 

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
Hands-On/Demo:
  • Perform p test and t tests in python
  • A/B testing in python
Goal: Get an introduction to Clustering as part of this Module which forms the basis for machine learning. 

Objectives: At the end of this module, you should be able to:
  • Understand the 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
Hands-On/Demo:
  • Correlation and Covariance in python
  • Hierarchical clustering in python
  • K means clustering in python
Goal: Learn the roots of Regression Modelling using statistics. 

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 the concept of residual analysis
Topics:
  • Logistic and Regression Techniques
  • Problem of Collinearity
  • WOE and IV
  • Residual Analysis
  • Heteroscedasticity
  • Homoscedasticity
Hands-On/Demo:
  • Perform Linear and Logistic Regression in python
  • Analyze the residuals using python

Why choose our Python Statistics for Data Science in San Jose, CA?

Customer Satisfaction

We’re here 24/7 to ensure you’re heard and supported, no matter what questions or doubts you may have. Our team is committed to delivering exceptional customer service to every individual and organization we serve.

Python Statistics for Data Science Frequently Asked Questions

Yes, we do offer additional discounts to group and corporate training customers. Please email us at support@encertify.com to find out more about our group discount offerings.

Use the "Submit your query"section in this page or check "Contact Us" section. Alternatively, please send an email to support@encertify.com to find out more about our course offerings.

If you're unemployed right now, or you're a student taking this course for career growth, we do provide additional discounts for you on selected courses. Please email support@encertify.com to avail this benefit and discount coupon.

Note: These discounts are available on selected courses only.

We do not offer placement or placement assistance services at this time. However, our training is designed to equip you with in-demand skills, hands-on experience, and certification readiness to help you confidently pursue new career opportunities. Many of our learners have successfully transitioned into new roles or advanced in their careers based on the knowledge and certifications gained throughĀ ourĀ programs

San Jose, CA

Python Statistics for Data Science Certification Training in San Jose, CA

Explore More Certification Courses

R Statistics for Data Science

R Statistics for Data Science

4.5 (447 Ratings) 17219 Learners
Data Science using R

Data Science using R

4.3 (2185 Ratings) 18625 Learners
Data Analytics with R

Data Analytics with R

4.4 (1765 Ratings) 17288 Learners
Tableau

Tableau

4.8 (3650 Ratings) 26274 Learners
Microsoft Power BI with Gen AI

Microsoft Power BI with Gen AI

4.4 (33100 Ratings) 91397 Learners

Popular Courses

Lean Six Sigma Black Belt (LSSBB)

Lean Six Sigma Black Belt (LSSBB)

4.4 (6482 Ratings) 34739 Learners
PMI - ACPĀ®

PMI - ACPĀ®

4.1 (654 Ratings) 14785 Learners
Microsoft Power BI with Gen AI

Microsoft Power BI with Gen AI

4.4 (33100 Ratings) 91397 Learners
Tableau

Tableau

4.8 (3650 Ratings) 26274 Learners
AWS Architect

AWS Architect

4.3 (67950 Ratings) 174642 Learners
Lean Six Sigma Green Belt (LSSGB)

Lean Six Sigma Green Belt (LSSGB)

4.6 (9625 Ratings) 57145 Learners
Scrum Master Certified (SMC)

Scrum Master Certified (SMC)

4.6 (44508 Ratings) 271215 Learners
Scrum Product Owner Certified (SPOC)

Scrum Product Owner Certified (SPOC)

4.6 (30244 Ratings) 160852 Learners
Scrum Fundamentals Certified (SFC<sup>®</sup>)

Scrum Fundamentals Certified (SFC®)

4.7 (75138 Ratings) 852625 Learners
Scrum Developer Certified (SDC)

Scrum Developer Certified (SDC)

4.5 (32845 Ratings) 185308 Learners
SCRUMstudy Agile Master Certified (SAMC)

SCRUMstudy Agile Master Certified (SAMC)

4.5 (23371 Ratings) 105994 Learners

Python Statistics for Data Science Course in other cities :

Encertify Rating
4.6 out of 5 (593895 ratings)