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Statistics Fundamentals With Python Complete Skill Track




Python Professional Training


All Job Seekers


Online and Classroom Sessions


Week Days and Week Ends

Duration :

45 Days

Python Objectives

•Learn everything about {Coursetopics} in Python.
•Learn how to use advanced Python functions.
•Become a better developer by mastering Python fundamentals
•Learn about each and every major Python component.
•You will know how to design Python from scratch.
•Master the latest version of Python and create real projects
•Components states props how to pass variables between components in Python.
•Build a strong knowledge base on Python from Scratch to Advanced level
•Learn from two Python experts and take your flow skills to the next level.

statistics fundamentals with python complete skill track Training Highlights

•You Get Real Time Project to practice
•25+ projects for good Learning experience
•Highly competent and skilled IT instructors
•Regular Brush-up Sessions of the previous classes
•Fast track and Sunday Batches available on request
•Hands On Experience – will be provided during the course to practice
•Training time :  Week Day / Week End – Any Day Any Time – Students can come and study
•We do Schedule the sessions based upon your comfort by our Highly Qualified Trainers and Real time Experts

Who are eligible for Python

•c++, React.js, Java Fullstack, Core Java Data Structure, Java Micro-services, Devops, Microsoft Azure, Cloud Computing, Machine Learning, Automation Testing
•Cognos Developer, Ab initio developer, Java Developers, .net Architects, Informatica, MSBI, Tivoli Monitoring, Oracle Apps functional and technical, change
•Java/J2EE, Springs, API, REST/, MySQL, Java, Admin UI developer with HTML/JavaScript/Ember.js, Java Enterprise Integration/ESB/API Management experts with Mule
•QT Developer, STB Domain, CAS, UX DESIGNER, UI Developer, HTML5, CSS3, JAVAScript, JQUERY, FIREWORKS, Adobe Photoshop, Illustratot, Embedded C++
•Software Development, Big Data, Hadoop, Spark, Hive, Oozie, Big Data Analytics, Java, Python, R, Cloud, Data Quality, Scala, Nosql, Sql Database, Core Java


•Graphical exploratory data analysis
•Introduction to Exploratory Data Analysis
•Plotting a histogram
•Plot all of your data Bee swarm plots
•Plot all of your data ECDFs
•Onward toward the whole story
•Quantitative exploratory data analysis
•Introduction to summary statistics The sample mean and median
•Percentiles outliers and box plots
•Variance and standard deviation
•Covariance and the Pearson correlation coefficient
•Thinking probabilistically Discrete variables
•Probabilistic logic and statistical inference
•Random number generators and hacker statistics
•Probability distributions and stories The Binomial distribution
•Poisson processes and the Poisson distribution
•Thinking probabilistically Continuous variables
•Probability density functions
•Introduction to the Normal distribution
•The Normal distribution Properties and warnings
•The Exponential distribution
•Parameter estimation by optimization
•Optimal parameters
•Linear regression by least squares
•The importance of EDA Anscombes quartet
•Bootstrap confidence intervals
•Generating bootstrap replicates
•Pairs bootstrap
•Introduction to hypothesis testing
•Formulating and simulating a hypothesis
•Test statistics and pvalues
•Bootstrap hypothesis tests
•Hypothesis test examples
•AB testing
•Test of correlation
•Exploring Linear Trends
•Introduction to Modeling Data
•Visualizing Linear Relationships
•Quantifying Linear Relationships
•Building Linear Models
•What makes a model linear
•Interpreting Slope and Intercept
•Model Optimization
•LeastSquares Optimization
•Making Model Predictions
•Modeling Real Data
•The Limits of Prediction
•Standard Error
•Estimating Model Parameters
•Inferential Statistics Concepts
•Model Estimation and Likelihood
•Model Uncertainty and Sample Distributions
•Model Errors and Randomness
•Basics of randomness simulation
•Introduction to random variables
•Simulation basics
•Using simulation for decisionmaking
•Probability data generation process
•Probability basics
•More probability concepts
•Data generating process
•eCommerce Ad Simulation
•Resampling methods
•Introduction to resampling methods
•Jackknife resampling
•Permutation testing
•Advanced Applications of Simulation
•Simulation for Business Planning
•Monte Carlo Integration
•Simulation for Power Analysis
•Applications in Finance
•Wrap Up