But there’s one huge, looming problem…
> 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
> Linear Predictive Analysis
> Implementation of Predictive Analysis Using Python
> What is Multiple Predictive Model?
> Building the Multiple Predictive Model using Python
> Assumption of Multiple Predictive Model
> AutoCorrelation,MultiColliniearity and Hetrosadacity
> How Simple Predictive Model used in real time application
> How Multiple Predictive Model used in Real Time Application
> Simple Predictive Model used in Real Time Industry
> Multiple Predictive Model used in Real Time Industry
> Comparing Simple and Multiple Predictive Model using Python
> What is Correlation Analysis?
> Correlation Coefficient and Hypothesis Testing
> Product Movement Correlation, Partial Correlation and Non Metric Co-relation
> Introduction to Classification Model
> Framming Single and Multiple Predictor Model
> Application of Classification Model
> Introduction to Discriminant Analysis
> Two Group Discriminant Analysis
> Three Group Discriminant Analysis
> Multiple Group Discriminant Analysis
> Application of Discriminant Analysis
> Introduction to Association Rule
> What is Apriori Algorithm?
> How Apriori Algorithm used to Build to Recommendation System?
> What is MBA(Market Basket Analysis)?
> Application of MBA in Retail and Telecom Sectors
> How MBA helps to Build the Recommendation System?
> Image Processing and Image Extraction
> Image-Histogram and Contrast Measures
> Image Processing and Object Regognition
> Viola Jones Algorithm – Face Regognition
> Introduction to Time Series Analysis
> Trend Line Analysis, Pattern Identification
> Time Series Smothening Methods
> Time Series Prediction Analysis
> Differnece between AI and Machine Learning
> Differnece between Machine Learning and Deep Learning
> Differnece between Machine Learning and Data Science
> Differnece 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 Processing for Attrition Analysis Project
> Performing Descriptive Statitics and Inferential Statistics for Attrition
> Perform the Data Visualisation for Attrition Analysis Project
> Perform Predictive and Classification Model for Attrition Analysis
> Data Processing for Bank Loan Modelling Project
> Performing Descriptive Statitics and Inferential Statistics for Bank Loan Modelling
> Perform the Data Visualisation for Bank Loan Modelling Project
> Perform Predictive and Classification Model for Bank Loan Modelling
> 6.3 Merger and Acquisition Project Deep Learning
> 6.2.1 LKP Project using Python
> 6.2.2 Recommendation of New Model
> Data Science and it’s Modules
> Prediction and Classification Algorithm
> Application of Data Science
How does lifetime sound for a deal? Once you pay, you have full access to all the course content, open to study at your convenience. More importantly, you get lifetime access to our community and network of professionals that will expose you to some of the best opportunities for work and internships in India.
Professional support expert engineers are always available to address all your questions or concerns. Plus we have a close-knit community to help you through your journey.
Learners will work on one project that gets designed by working professionals and is a replica of the problems they’re currently addressing working in the industry.
A basic level of interest and keen dedication is all that you need to complete this course and successfully become a sought-after data science professional.
The program is a self-paced learning course that one can complete at the flexibility of their schedule. However, you can expect to complete the course in _____ weeks if you dedicate 2-3 hours daily.
Dr. Dinesh Babu will be your Mentor and Trainer
Data Science is currently one of the highest-paying careers in India and the average salary is about INR 10 LPA.
Although we don’t guarantee placement at this course (which is unheard of at this price point), we do connect learners with various internship opportunities and do provide some placement opportunities through our network of professionals.