> Data Science Introduction.
> Data Science Modules.
> Application of Data Science across multiple Industries.
> Career Opportunities in Data Science
> Scope of Data Science across different Business Domains
> How Data Science used in ERP and CRM?
> How Data Science used in HR and Operational Management?
> How Data Science Used in Supply Chain Management?
> How Data Science Used in Logistics?
> Data Collection Methods and Data Cleaning
> Data Processing Process
> EDA – Exploratory Data Analysis
> Machine Learning and Data Visualisation
> Building the automated Model
> Python Introduction
> Python IDE – Spyder, Jupiter and Notebook
> Numpy Packages
> Pandas Packages
> Matplotlib Packages
> Scipy Packages
> Sklearn Packages
> Variable Declaration
> String Declaration
> Tuple Declaration
> Python Programming
> Dictionary Declaration
> List Declaration
> Set Declaration
> Python Data Types
> Declaration of Array
> Universal Function of Numpy
> Binary Functions of Numpy
> Logical Functions of Numpy
> Statistical Functions of Numpy
> Pandas Packages
> Accessing File Processing
> Merging the Dataframe
> Joins – Inner,Outer,Left and Right
> handling the Null values
> Handling the Duplicates
> Introduction to matplolib packages
> Representation of Line Graph
> Representation of Multi Line Graph
> Including the Legends
> Representation of Histogram
> Representation of Scatter Diagram
> Representation of Box Plot
> Representation of Bar Graph
> Representation of Area Chart
> Representation of Dual Axis
> Array Shapping Using Numpy Package
> Reverse Matrix Analysis using Numpy Package
> Python Operators – Addition, Subtration and Multiplication
> Boolean Operators Execution
> String Manupulation
> Execution of IF Loop, IF-ELSE Loop
> Execution of For Loop
> Execution of While Loop
> Execution of IF-ELSE-ELSEIF Loop
> Handling Missing Values
> Handling Duplicates Values
> Handling Data Preparation Process
> Python – Funtions with Arguments
> Python – Funtions without Arguments
> Python – Functions with Arbitary Arguments
> Python – Functions with Keyword Arguments
> Python – Data Collection Methods
> Primary and Secondary Sources of Data
> File Processing using Python
> Undertand difference between Population vs Sample
> Importance of statistical concepts in data science
> Importance of statistical concepts in ML models
> Know the foundation principal in statistics – Central Limit Theorem
> Understand the importance of Mean, Medium
> Understand the importance of Mode of a variable
> Understand the importance of Variance of a variable
> Understand the importance of Standard Deviation of a variable
> Application of central tendencies for data analysis
> Application of Measures of Spread for data analysis
> Application of Central Limit Theorem for data analysis.
> Different Types of Measuring Scales
> Importance of Nominal Scales
> Importance of Ordinal Scales
> Importance of Interval Scales
> Importance of Ratio Scales
> Usage of correlation for data analysis
> Usage of regression concepts for data analysis
> Formulation of Hypothesis
> Selection of Statistical Test
> Level of Significance and Degree of Freedom
> Computing the Calculated Values
> Computing the Table Values
> Comparing Calculated and Table Values
> Hypothesis Conclusion
> Learn to perform T-test to measure the variance between the means of two samples or population
> Learn to perform Z-test to measure the variance between the means of two samples or population
> Learn to perform Chi Square-test to measure the variance between the means of two samples or population
> Wilcoxson Sign Test and Friedman Test
> MannWhitney Test and Krushkal Wallis Test
> One Sample T-Test
> 2-Sample Paired T test
> 2-Sample Independent T test
> Introduction to ANOVA
> What is One Way ANOVA?
> What is Two way ANOVA?
> What is Multi way ANOVA?
> What is ANCOVA?
> Difference between ANOVA and ANCOVA?
> Introduction to probability
> Types of events
> Marginal Probability
> Baye’s Theorem
> Introduction to Probability Distribution
> Binomial Probability
> Possion Probability Distribution
> Hypergeometric Probability Distribution
> Uniform Probability Distribution
> Normal Probability Distribution
> Exponential Probability Distribution
> Undertand difference between Population vs Sample
> Importance of statistical concepts in data science
> Importance of statistical concepts in ML models
> Know the foundation principal in statistics – Central Limit Theorem
> Understand the importance of Mean, Medium
> Understand the importance of Mode of a variable
> Understand the importance of Variance of a variable
> Understand the importance of Standard Deviation of a variable
> Introduction to Time Series Analysis
> Trend Line Analysis, Pattern Identification
> Time Series Smothening Methods
> Time Series Prediction Analysis
> Difference between AI and Machine Learning
> Difference between Machine Learning and Deep Learning
> Difference between Machine Learning and Data Science
> Difference between Machine Learning and Deep Learning
> Data Architecture Design and Data Warehousing
> Schema Design – Star Schema, Snow Flake Schema and Fact Concelltation
> Master Data Management(MDM)
> Data Science and it’s Modules
> Prediction and Classification Algorithm
> Application of Data Science
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