Data Scientist Master's Program
 24 weeks
 144 hours
Our Data Scientist master's Program objective is to fasttrack your Data Science profession by making you skillful in this realm. In this blended master's Program Our intention is to make you capable in this arena by serving you study both basic and advanced notions of Data Science, along with getting introduction to programming languages and technologies including Python, R, SQL, Data visualization tools. Also, in these courses, you will gain handson involvement working on instantaneous exercises and projects that will validate your learning.
Course Overview
Data science is one of the hotcake among all the careers of the decade, and the call for data scientists who can analyse data and transfer it into results to inform data driven decision. This Professional Certificate from Handson will help anyone attracted in pursuing a profession in data science and Machine learning develop careerrelevant skills and experience. In this comprehensive program you will get to learn most demanding programming languages and tools that are used in Data Science field.
Advantage
Become a data science professional. Obtain valuable capabilities for your additional journey in data science and improve your profession. In this Program When you complete the program, you'll earn a Certificate, offered by Handson School of Data Science to share with your specialized network that help you kickstart your new career.
What You'll Learn

Python

Machine Learning

R programming

SQL

PowerBI

Artificial Intelligence(AI)
 What is Data Science?
 comparative study between Data Science and Big Data Analytics.
 Types of Data.
 The Data Science Lifecycle
 Data Acquisition and Preparation
 Data Modeling and Visualization
 Data Science Roles
 Benefits of Data Science
 Challenges of Data Science
 Business Use Cases for Data Science
 Concept of Analytics and Statistics
 Categories of Analytics
 Properties of Measurement
 Scales of Measurement
 Concept of Data visualization
 Measures of Central Tendency
 Measures of Dispersion
 Moments, Skewness and Kurtosis
 Concept of Correlation and Covariance
 Introduction to Probability Theory
 Probability Distributions
 Sampling and Estimation
 Testing of Hypothesis
 Introduction to python
 History of Python
 Internal & External IDLE
 Installation of Python &Anaconda
 Compiler & Interpreter
 Write your first program
 Data types, Input and output function
 Types of Operators
 Conditional Statement: ifelse, ifelifelse, Nested if else
 Loop: While loop, For loop
 Nested while loop, Nested for loop Break, Continue and Pass
 Basic Data Types Numeric & String
 Tuple and it’s operation
 List and it’soperation
 Dictionary and it’soperation
 Sets and It’soperation
 Basics Defining function
 Function call Return statement
 Function with parameter and without parameter
 Local and global variable
 Recursion, Anonymous (lambda) function
 User defined functions
 OOPS concepts Defining
 Class Creating object, Constructor
 Method vs function Calling methods
 Method Overriding, List of objects Inheritance
 Defining a file, Types offile and its operations
 Python read Files
 Python Write/Create Files
 Pickle Module
 Introduction to Numpy, Pandas, Matplotlib
 Array, Array indexing, Array operation
 Data frame, series, Groupby
 Missing values
 Box plot, Scatter plot, Chart styling
 Histogram, Bar chart etc.
 Group by plotting
 Class Creating object, Constructor
 Method vs function Calling methods
 Method Overriding, List of objects Inheritance
 Concept of Supervised learning
 Concept of Unsupervised learning
 Concept of Reinforcement learning
 Simple Linear Regression
 Multiple Linear Regression
 Implementation of Linear Regression
 Advanced Topics: Normal Equation, Polynomial Regression, Rsq. Score
 Python Implementation
 Concept and Theory
 Sigmoid function
 Mathematical Concepts of Logistic Regression
 Binary and Multivariate Classification Problems
 Implementation of Logistic Regression
 KNearest NeighborsConcept and Theory
 Implementation of KNearest Neighbors
 Support Vector Machine(SVM)Concept and Theory
 Implementation of Support Vector Machine
 Naïve Bayes Classifier Concept
 Implementation of Naïve Bayes Classifiert
 Decision Tree ClassifierConcept
 Implementation of Decision Tree Classifier
 Random Forest ClassifierConcept
 Implementation of Random Forest Classifier
 Dimensionality Reduction Problem Curse of Dimensionality
 Principal Component Analysis(PCA)
 Implementation of PCA
 KMeans Clustering Concept
 Implementation of KMeans Clustering
 Hierarchical Clustering Concept
 Implementation of Hierarchical Clustering
 DBSCAN ClusteringConcept
 Implementation of DBSCAN Clustering
 Introduction of Deep Learning and Neural Network
 Types and Applications of Neural Network
 Skills required for Neural network
 Why Python is best for Neural Network
 Anaconda Installation: Spyder & Jupyter Notebook
 Introduction to Keras & Tensor Flow
 Installation of Keras & Tensor Flow
 ANN and Neuron Structure
 How does Neural Network Works?
 Practical Implementation of ANN
 TrainTest Splitting
 ANN model Training
 Activation Function
 Fit all the Layers
 Backpropagation
 Fitting to the training Dataset and finding Accuracy
 Image Reading and CNN Process
 Steps of CNN
 Conclusion of CNN Process
 Importing Required libraries
 Reading Cat & Dog Dataset
 Applying CNN layers
 Fitting the Dataset in Model
 Visualization of Accuracy and Loss
 Prediction with single image
 Introduction and Application
 Process of RNN, Types of RNN, Gradient Problem
 LSTM & GRU Explanation
 Steps of LSTM
 Creation of Data Structure with Time Steps
 LSTM layers
 Google Stock market prediction
 History of Rlanguage
 Why to learn Rlanguage
 Importance of Rlanguage
 . Installation and setup Environment
 Packages interfaces and library
 Expressions and Operations
 Data Types and Data Structures Vectors, Factors,Matrix, Dataframes,Lists
 Vector Basics
 Vector Operations
 Vector Indexing and Slicing
 Matrix Operations
 Data Frame Indexing and Selection
 Operations on Data Frame
 CSV Files with R
 Operators
 Conditional Statements
 Loops & Functions
 Builtin R Features & Apply
 Dates and Timestamps
 Understanding & Working with Graph Libraries
 Overview of ggplot2
 Histograms
 Scatterplots
 Bar Plot
 Boxplots
 2 Variable Plotting
 Sorting, Concatenation of Datasets
 Concept of Hypothesis
 Null Hypothesis
 Alternative Hypothesis
 TypeI error
 TypeII error
 Level of Significance
 Confidence Intervals
 Parametric Tests and Non Parametric Tests
 One Sample T test
 Paired Sample T test
 Chisquare Test for Independence of Attributes
 Principal Component Analysis
 Concept of Communalities
 Eigen Values and Eigen Vectors
 Correlation Matrix check and KMOMSA check
 Factor loading Matrix
 Diagrammatic Representation of Factors
 Problems of Factor Loadings and Solutions
 Introduction to Machine Learning
 Data Munging in R
 Cyclical vs Seasonal Analysis
 One Way Anova
 Two Way Anova
 Concept of Linear Regression
 Important features of a Straight line
 Method of least Square
 Assumptions of Classical Linear Regression Model
 Understandig the Goodness of Fit
 Test of Significance of the Estimated parameters
 Concept of multicollinearity
 Concept of VIF
 Concept of AutoCorrelation
 Practical Application of Linear Regression using R
 Concept of Logistic Regression
 Differences between Linear Regression and Logistic Regression
 Logistic Regression Model
 ODDS AND ODDS RATIOMathematical Concepts
 Concept of Concordant Pairs, Discordant Pairs, Tied Pairs
 Confusion Matrix and its Measures
 Determining the CutPoint Probability Level
 Receiver Operating Characteristic Curves
 Practical Application of Logistic Regression using R.
 Concept of Time Series and its Applications
 Assumptions of Time Series Analysis
 Components of Time series
 Smoothening techniques
 Stationarity
 Random Walk
 ARIMA Forecasting
 Box Jenkins Technology
 Merits and Demerits of BJ TechnologyMerits and Demerits of BJ Technology
 Concept of Decision Tree
 Decision Tree Application using R
 Concept of KMeans Clustering
 Types of Cluster Analysis
 Concept of Linkage
 Ward’s Minimum Variance Criteria
 Clustering related StatisticsSemiPartial RSquare,R Square
 Graphical Representation of Cluster Analysis
 Practical Application of Clustering using R
 Concept of Text Mining and Sentiment Analysis
 Concept of Stopwords
 Practical Application of Text Mining and Sentiment Analysis
 Concept of Market Basket Analysis
 Measures of Market Basket AnalysisSupport,lift,Confidence
 Advantages of Market Basket Analysis
 Practical Application of Market Basket Analysis
 Introduction to MS Excel, Quick review on MS Excel Options, Ribbon, Sheets and
 Difference between Excel 2003, 2007, 2010 and 2013
 Saving Excel File as PDF, CSV and Older versions
 Using Excel Shortcuts with Full List of Shortcuts
 Copy, Cut, Paste, Hide, Unhide, and Link the Data in Rows, Columns and Sheet
 Using Paste Special Options
 Formatting Cells, Rows, Columns and Sheets
 Protecting & Un protecting Cells, Rows, Columns and Sheets with or without Password
 Page Layout and Printer Properties
 Inserting Pictures and other objects in Worksheets
 Lookup and Reference Functions: VLOOKUP, HLOOKUP, INDEX, ADDRESS, MATCH, OFFSET,TRANSPOSE etc
 Logical Function: IF / ELSE, AND, OR, NOT, TRUE, NESTED IF/ELSE etc
 Database Functions: DGET, DMAX, DMIN, DPRODUCT, DSTDEV, DSTDEVP, DSUM, DVAR,DVARP etc
 Date and Time Functions: DATE, DATEVALUE, DAY, DAY360, SECOND, MINUTES, HOURS,NOW, TODAY, MONTH, YEAR, YEARFRAC, TIME, WEEKDAY, WORKDAY etc
 Information Functions: CELL, ERROR.TYPE, INFO, ISBLANK, ISERR, ISERROR, ISEVEN,ISLOGICAL, ISNA, ISNONTEXT, ISNUMBER, ISREF, ISTEXT, TYPE etc
 Math and Trigonometry Functions: RAND, ROUND, CEILING, FLOOR, INT, LCM, MOD,EVEN, SUMIF, SUMIFS etc
 Statistical Functions: AVEDEV, AVERAGE, AVERAGEA, AVERAGEIF, COUNT, COUNTA,COUNTBLANK, COUNTIF, FORECAST, MAX, MAXA, MIN, MINA, STDEVA etc.
 Text Functions: LEFT, RIGHT, TEXT, TRIM, MID, LOWER, UPPER, PROPER, REPLACE, REPT, FIND, SEARCH, SUBSTITUTE, TRIM, TRUNC, CONVERT, CONCATENATE, DOLLAR etc
 Using Conditional Formatting
 Using Conditional Formatting with Multiple Cell Rules
 Using Color Scales and Icon Sets in Conditional Formatting
 Creating New Rules and Managing Existing Rules
 Sorting Data AZ and ZA
 Using Filters to Sort Data
 Advance Filtering Options Pivot Tables
 Creating Pivot Tables
 Using Pivot Table Options
 Changing and Updating Data Range
 Formatting Pivot Table and Making Dynamic Pivot Tables
 Creating Pivot Charts
 Types of Pivot Charts and Their Usage
 Formatting Pivot Charts and Making Dynamic Pivot Charts
 Introduction to VBA Macros
 Editing, Writing VBA Code and Saving as Macro or AddIn
 Adding AddIns in Exce
 Introduction of VBA Macros
 Recordings Macro
 Working with VBA Editor
 VBA Programming Concept
 VBA Syntax and Semantics
 Working with VBA Editor
 Variable Type and Declaration
 Repeating Actions with a Loops
 Procedures and Events
 Functions
 User Forms and GUI
 Sort and Filter, PivotTables and Pivot charts with VBA Macros
 ODBC Connectivity to SQL Database and Query Handling
 File Handling
 Introduction To Microsoft Power BI  Learning Outcomes
 Introduction To Power BI
 Getting Started With Power BI Pro
 Working With Various Data Sources
 Improve Power BI Reports
 Optimize Power BI Reports
 Enriching Visualization
 Introduction To Microsoft Power BI  Lesson Summary
 Working With Power BI  Learning Outcomes
 Working With Data  Advanced Techniques
 Power BI Service  The Online Version Of Power BI
 Publish Reports
 The Various Power BI Components And How To Install Power BI
 Working With Power BI  Lesson Summary
Admission Process
Please call to admission counselor for course fees, registration fees, EMI fecilities,registration form and other formalities. Contact to admission counselor
+919830247087
Who Can Join?
Any graduate with knowledge of basic computing.
Requirment
1. Personal computer/laptop with webcam and microphone
2. Stable internet connections
Payment Details
Bank Details:
KLMS HANDSON SYSTEMS PRIVET LIMITED
Account Number: 19700200000420
IFSC Code: BARB0SALTLA (5th letter is numeric zero)
UPI Payment: 9432257052@okbizaxis