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## The Data Science Course Complete Data Science Bootcamp

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### THE DATA SCIENCE COURSE COMPLETE DATA SCIENCE BOOTCAMP Syllabus

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THE DATA SCIENCE COURSE COMPLETE DATA SCIENCE BOOTCAMP

Data Science Professional Training

Working Professionals and Freshers

Regular Offline and Online Live Training

Week Days and Week Ends

30 to 45 days

•Learn to write Array in Data Science.

•You will learn basics of programming in Data Science

•learn How to use Test driven Development in Data Science.

•From A-Z: The Complete Beginners-Advanced Masterclass – Learn Data Science

•How to build your own apps and scripts using Data Science.

•Learn how to test your Go code with real world examples

•Go through all the steps to designing a game from start to finish.Learn Data Science Complete Course with Professionals from Scratch and Become a Pro in Data Science

•

•Get Training from Certified Professionals

• Helps you stand out in a competitive market

•Create hands-on projects at the end of the course

•Indutry oriented training with corporate casestudies

•We also provide Cost Effective and Flexible Payment Schemes

•One-on-one training, online training, team or Corporate training can be provided

•We help the students in building the resume boost their knowledge by providing useful Interview tips

•

•IOS Developer, .net c# asp.net, c c++ java, accounts finance sap fico, sap mm functional consultant

•Java Developer, Salesforce Developer, Solution Consulting, Qa Testing, Finance Executive, Full Stack Developer, Email Campaign, React.js, Ui Development

•React.js, asp.net core, .net Core, React Native, Front End Developers, .Net Developers, .Net Tech Leads

•Web Apps, ios/android/windows, Ux Designers, web/mobile developer, html5/css3/javascript/mobile code, testing, automation, manual, mobile, web, ui

•

•A Practical Example: What You Will Learn in This Course

•What Does the Course Cover

•Download All Resources and Important FAQ

•The Field of Data Science – The Various Data Science Disciplines

•Data Science and Business Buzzwords: Why are there so Many?

•What is the difference between Analysis and Analytics

•Business Analytics, Data Analytics, and Data Science: An Introduction

•Continuing with BI, ML, and AI

•A Breakdown of our Data Science Infographic

•The Field of Data Science – Connecting the Data Science Disciplines

•Applying Traditional Data, Big Data, BI, Traditional Data Science and ML

•The Field of Data Science – The Benefits of Each Discipline

•The Reason Behind These Disciplines

•The Field of Data Science – Popular Data Science Techniques

•Techniques for Working with Traditional Data

•Real Life Examples of Traditional Data

•Techniques for Working with Big Data

•Real Life Examples of Big Data

•Business Intelligence (BI) Techniques

•Real Life Examples of Business Intelligence (BI)

•Techniques for Working with Traditional Methods

•Real Life Examples of Traditional Methods

•Machine Learning (ML) Techniques

•Types of Machine Learning

•Real Life Examples of Machine Learning (ML)

•The Field of Data Science – Popular Data Science Tools

•Necessary Programming Languages and Software Used in Data Science

•The Field of Data Science – Careers in Data Science

•Finding the Job – What to Expect and What to Look for

•The Field of Data Science – Debunking Common Misconceptions

•Debunking Common Misconceptions

•: Probability

•The Basic Probability Formula

•Computing Expected Values

•Frequency

•Events and Their Complements

•Probability – Combinatorics

•Fundamentals of Combinatorics

•Permutations and How to Use Them

•Simple Operations with Factorials

•Solving Variations with Repetition

•Solving Variations without Repetition

•Solving Combinations

•Symmetry of Combinations

•Solving Combinations with Separate Sample Spaces

•Combinatorics in Real-Life: The Lottery

•A Recap of Combinatorics

•A Practical Example of Combinatorics

•Probability – Bayesian Inference

•Sets and Events

•Ways Sets Can Interact

•Intersection of Sets

•Union of Sets

•Mutually Exclusive Sets

•Dependence and Independence of Sets

•The Conditional Probability Formula

•The Law of Total Probability

•The Additive Rule

•The Multiplication Law

•Bayes’ Law

•A Practical Example of Bayesian Inference

•Probability – Distributions

•Fundamentals of Probability Distributions

•Types of Probability Distributions

•Characteristics of Discrete Distributions

•Discrete Distributions: The Uniform Distribution

•Discrete Distributions: The Bernoulli Distribution

•Discrete Distributions: The Binomial Distribution

•Discrete Distributions: The Poisson Distribution

•Characteristics of Continuous Distributions

•Continuous Distributions: The Normal Distribution

•Continuous Distributions: The Standard Normal Distribution

•Continuous Distributions: The Students’ T Distribution

•Continuous Distributions: The Chi-Squared Distribution

•Continuous Distributions: The Exponential Distribution

•Continuous Distributions: The Logistic Distribution

•A Practical Example of Probability Distributions

•Probability – Probability in Other Fields

•Probability in Finance

•Probability in Statistics

•Probability in Data Science

•: Statistics

•Population and Sample

•Statistics – Descriptive Statistics

•Types of Data

•Levels of Measurement

•Categorical Variables – Visualization Techniques

•Categorical Variables Exercise

•Numerical Variables – Frequency Distribution Table

•Numerical Variables Exercise

•The Histogram

•Histogram Exercise

•Cross Tables and Scatter Plots

•Cross Tables and Scatter Plots Exercise

•Mean, median and mode

•Mean, Median and Mode Exercise

•Skewness

•Skewness Exercise

•Variance

•Variance Exercise

•Standard Deviation and Coefficient of Variation

•Standard Deviation

•Standard Deviation and Coefficient of Variation Exercise

•Covariance

•Covariance Exercise

•Correlation Coefficient

•Correlation

•Correlation Coefficient Exercise

•Statistics – Practical Example: Descriptive Statistics

•Practical Example: Descriptive Statistics

•Practical Example: Descriptive Statistics Exercise

•Statistics – Inferential Statistics Fundamentals

•What is a Distribution

•The Normal Distribution

•The Standard Normal Distribution

•The Standard Normal Distribution Exercise

•Central Limit Theorem

•Standard error

•Estimators and Estimates

•Statistics – Inferential Statistics: Confidence Intervals

•What are Confidence Intervals?

•Confidence Intervals; Population Variance Known; Z-score

•Confidence Intervals; Population Variance Known; Z-score; Exercise

•Confidence Interval Clarifications

•Student’s T Distribution

•Confidence Intervals; Population Variance Unknown; T-score

•Confidence Intervals; Population Variance Unknown; T-score; Exercise

•Margin of Error

•Confidence intervals. Two means. Dependent samples

•Confidence intervals. Two means. Dependent samples Exercise

•Confidence intervals. Two means. Independent Samples ()

•Confidence intervals. Two means. Independent Samples (). Exercise

•Statistics – Practical Example: Inferential Statistics

•Practical Example: Inferential Statistics

•Practical Example: Inferential Statistics Exercise

•Statistics – Hypothesis Testing

•Null vs Alternative Hypothesis

•Further Reading on Null and Alternative Hypothesis

•Rejection Region and Significance Level

•Type I Error and Type II Error

•Test for the Mean. Population Variance Known

•Test for the Mean. Population Variance Known Exercise

•p-value

•Test for the Mean. Population Variance Unknown

•Test for the Mean. Population Variance Unknown Exercise

•Test for the Mean. Dependent Samples

•Test for the Mean. Dependent Samples Exercise

•Test for the mean. Independent Samples ()

•Test for the mean. Independent Samples (). Exercise

•Statistics – Practical Example: Hypothesis Testing

•Practical Example: Hypothesis Testing

•Practical Example: Hypothesis Testing Exercise

•: Introduction to Python

•Why Python?

•Why Jupyter?

•Installing Python and Jupyter

•Understanding Jupyter’s Interface – the Notebook Dashboard

•Prerequisites for Coding in the Jupyter Notebooks

•Jupyter’s Interface

•Python 2 vs Python 3

•Python – Variables and Data Types

•Variables

•Numbers and Boolean Values in Python

•Python Strings

•Python – Basic Python Syntax

•Using Arithmetic Operators in Python

•The Double Equality Sign

•How to Reassign Values

•Add Comments

•Understanding Line Continuation

•Indexing Elements

•Structuring with Indentation

•Python – Other Python Operators

•Comparison Operators

•Logical and Identity Operators

•Python – Conditional Statements

•The IF Statement

•The ELSE Statement

•The ELIF Statement

•A Note on Boolean Values

•Python – Python Functions

•Defining a Function in Python

•How to Create a Function with a Parameter

•Defining a Function in Python – Part II

•How to Use a Function within a Function

•Conditional Statements and Functions

•Functions Containing a Few Arguments

•Built-in Functions in Python

•Python Functions

•Python – Sequences

•Lists

•Using Methods

•List Slicing

•Tuples

•Dictionaries

•Python – Iterations

•For Loops

•While Loops and Incrementing

•Lists with the range() Function

•Conditional Statements and Loops

•Conditional Statements, Functions, and Loops

•How to Iterate over Dictionaries

•Python – Advanced Python Tools

•Object Oriented Programming

•Modules and Packages

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